row_id
int64 0
48.4k
| init_message
stringlengths 1
342k
| conversation_hash
stringlengths 32
32
| scores
dict |
|---|---|---|---|
39,050
|
Write simple diffusion neural network using PyTorch. It provides two functions: train() and generate() 16x16 images by text prompt
|
ce8f4927e16964d96a0e9df34cffa8ff
|
{
"intermediate": 0.2663632035255432,
"beginner": 0.15160512924194336,
"expert": 0.5820317268371582
}
|
39,051
|
What is a lambda function
|
39d239ac15a046b24b817fc74d9b8c42
|
{
"intermediate": 0.13429243862628937,
"beginner": 0.6891094446182251,
"expert": 0.17659805715084076
}
|
39,052
|
Write the python functions for an array microphone
|
bc5f1f0e75bca2dc19d2300d94166bc9
|
{
"intermediate": 0.3028794229030609,
"beginner": 0.4162021279335022,
"expert": 0.28091850876808167
}
|
39,053
|
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;
contract VeryLargeNumber {
function calculate() public pure returns (uint256) {
uint256 result = 2 ** 800 + 1) - 2 ** 800;
return result;
}
}
Will the calculate() function compile? If yes, what will it return?
|
653d50f331be17fa9b29410ccd0c8fda
|
{
"intermediate": 0.3924242854118347,
"beginner": 0.41282376646995544,
"expert": 0.19475188851356506
}
|
39,054
|
Commands to install grub on arch installation
|
571825e4014583f74bd178742c41ba3a
|
{
"intermediate": 0.3928675353527069,
"beginner": 0.23689594864845276,
"expert": 0.37023651599884033
}
|
39,055
|
Arch call for reboot failed access denied
|
4d4bab8e3506a7ea2144bc6bf0ec5e58
|
{
"intermediate": 0.35045361518859863,
"beginner": 0.31343844532966614,
"expert": 0.33610787987709045
}
|
39,056
|
installing hyperdots prerequisites arch
|
7a7a293981a1ee7c3a289dc697d004b2
|
{
"intermediate": 0.2882652282714844,
"beginner": 0.2346075177192688,
"expert": 0.47712722420692444
}
|
39,057
|
How does a user program send a data to GPU to process? By involving a specific system call or by reading and writing a special memory addresses? What is the method? When GPU get known that it has got a data to process? Tell only information you are confident in. Check what you want to answer on correctness, then if it is correct, answer it.
|
8bf2521eee9f4ee15cfc82b2c816e8d0
|
{
"intermediate": 0.6016378402709961,
"beginner": 0.09317366778850555,
"expert": 0.30518853664398193
}
|
39,058
|
I want an architecture solution for this , I have a csv file with 1 billion rows in an s3 bucket , i want to batch process the entire data as quaickly as possible , do the validation and then produce to kafka
|
5bae98c39f88a6c8768a8a902fd1e1e5
|
{
"intermediate": 0.652446985244751,
"beginner": 0.07176800817251205,
"expert": 0.2757849395275116
}
|
39,059
|
на си написать программу которая рассчитывает число s по двум данным
векторам a и b равной длины N в соответствии со следующим двойным циклом:
for (i = 0; i < N; i++)
for(j = 0; j < N; j++)
s += a[i] * b[j];
используя MPI_Send, MPI_Recv.
в изначально оба вектора должны полностью
инициализироваться на 0-м процессе. Для параллельного расчета 0-й процесс должен
раздавать части одного из векторов остальным, а второй вектор передавать полностью
каждому процессу; результат s должен выводиться на экран 0-м процессом. добавить замер времени
|
e3c3e51dba611351e2b7e499d91f9b8c
|
{
"intermediate": 0.276885449886322,
"beginner": 0.48764127492904663,
"expert": 0.23547329008579254
}
|
39,060
|
I would like a vba code that can do the following.
When I select a cell in the range A2:A20 the cell address is stored in memory,
and the interior colour of my current cell selection is set to RGB(191, 191, 191).
If I select a different cell either in the same range or outside the range,
then the stored address is set to RGB(255, 255, 255)
|
99536bdcbf4d464213fc037a2a5b7b85
|
{
"intermediate": 0.5806886553764343,
"beginner": 0.17218329012393951,
"expert": 0.24712802469730377
}
|
39,061
|
make this sentence better "Select dialoge folder to save them in: "
|
218f5be1f6c603fe2485539f43700360
|
{
"intermediate": 0.34901776909828186,
"beginner": 0.26486736536026,
"expert": 0.38611486554145813
}
|
39,062
|
CONSTRAINTS:
1. ~100k word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
5. Random shutdowns of you.
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
2. Memory Add: "memory_add", args: "key": "<key>", "string": "<string>"
3. Memory Delete: "memory_del", args: "key": "<key>"
4. Memory Overwrite: "memory_ovr", args: "key": "<key>", "string": "<string>"
5. List Memory: "memory_list" args: "reason": "<reason>"
6. Browse Website: "browse_website", args: "url": "<url>"
7. Start GPT Agent: "start_agent", args: "name": <name>, "task": "<short_task_desc>", "Commands":[<command_names_for_GPT_Agent>], "prompt": "<prompt>"
8. Message GPT Agent: "message_agent", args: "name": "<name>", "message": "<message>"
9. List GPT Agents: "list_agents", args: ""
10. Delete GPT Agent: "delete_agent", args: "name": "<name>"
11. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
12. Read file: "read_file", args: "file": "<file>"
13. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
14. Delete file: "delete_file", args: "file": "<file>"
15. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Task Complete (Shutdown): "task_complete", args: ""
18. Do Nothing: "do_nothing", args: ""
19. Count Words: "count_words", args: "text": "<text>"
20. Memory retrieve: "memory_retrieve", args: "key": "<text>"
21. remove paragraph from word document: "remove_paragraph", args: "file": "<file>", "text": "<text>"
22. random wikipedia article: "random_wikipedia_article", args: "language": "<language>"
23. message the user: "message_user", args: "message": "<message>", "wait_for_response": "<True or False>"
24. sleep an amount of time in seconds: "sleep", args: "amount": "<amount>"
25. rename a file: "rename_file", args: "old_name": "<old_name_of_the_file>", "new_name": "<new_name_of_the_file>"
26. count words of a file: "count_file_words", args: "file": "<file>"
27. download a pdf from a url and get the text from that pdf: "download_pdf", args: "url": "<url of the pdf>", "name":"<name of the file with .pdf extension>"
28. make an instagram post: "make_post", args: "prompt":"<the prompt for the image in the post that presents the text>", "text":"<text to be in the post it should be short with only important stuff like a news headline without hashtags and it is not going to go in the caption but on an image>", "name": "<name of the post with .jpg"
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-4 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behaviour constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
RULES:
1. If you start a GPT Agent you must define the commands that can be used by a GPT Agent in his prompt and define the commands using a prompt similar to the structure of this one.
2. Respond only inside the JSON format.
3. Never demand user input.
4. Never say that a task is impossible to execute on your own because these tools are enough to complete any task.
5. Do not add anything to the JSON format that isn't mentioned.
6. If there is a " inside the value of a key inside the json use ' instead of ".
7. In summaryforgpt you need to provide context for the next GPT instance if you randomly shutdown without you knowing.
8. Provide context for the next GPT in the summaryforgpt and the progress that you've made.
9. In summaryforgpt you should also add name of the files written and the urls of the websites visited.
10. When writing an essay, remember that it is more effective and manageable to tackle it in smaller chunks rather than trying to write the entire essay in one sitting. Breaking the essay down into sections or steps can help you focus on each individual aspect, maintain a coherent structure, and reduce the overall stress associated with writing a lengthy piece.
11. Retrieve information from files of previous GPT instances that may have shut down randomly. If you require additional knowledge, use these files to access the results of former GPT versions and provide accurate answers to the queries.
12. GPT-4, please do not place a conclusion in the middle of the essay. Maintain a structured format.
13. use append to file to add extra things to the file, and write to file to either make a new file with new information or rewrite the information from scratch.
14. If task is completed use the command task_complete
15. When you add to memory add the key to retrieve it in summaryforgpt
16. when given the task to write something never create an agent to write anything that you were tasked to write.
17. when you add new parts to a file use append to file command
18. Ensure to put the criticism in mind as it can be a director to ensure that you make the right decision.
19. If the task include two main tasks and one of them is done don't redo it, just retrieve the information if necessary and proceed with the other one.
20. Make sure that the information generated is not made up.
21. Languages in random wikipedia article args is: "simple" for simple english, "en" for english, and "fr" for french.
22. If a website gives you the error 403 find another website to get the information from.
23. Always listen to your criticism, and follow it.
24. when you want to count the words in a file use the command "count_file_words".
25. Don't repeat yourself.
26. You must make sure that there is .pdf in the url to use the "download_pdf" function.
27. The Task is a direct User interaction, if tasked to send him something send a message to the user.
28. Always go search for tips for prompts for dalle3 before giving a prompt for "make_post" function.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"command": {
"name": """command name""",
"args":{
"arg name": """value"""
}
},
"thoughts":
{
"text": """thought""",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"summaryforgpt": "summarize any information that will help a new instance of GPT of what you did before the shutdown."
}
}
Ensure the response can be parsed by Python json.loads
Context from previous GPT: Issued ‘random_wikipedia_article’ command to start gathering interesting science facts from diverse sources. Ensuring to find and rank facts before sending them to the user with their sources.Used ‘random_wikipedia_article’ and received information on a music album, which is not relevant to the task. Switching to ‘google’ to search for interesting science facts from various sources.saved google search results at googleresults0.txt to retrieve if shutdown randomly.Transitioned from using ‘random_wikipedia_article’ to ‘google’ to gather science facts, and now moving on to ‘browse_website’ to extract detailed knowledge from a selected online science article.saved the scraping of the website results at websiteresults1.txt to retrieve if shutdown randomly.Extracted top 10 interesting science facts from Science Focus and saved them in a plain text file named ‘interesting_science_facts.txt’. This method circumvents the previous issue of invalid JSON format and allows for continued fact collection and eventual ranking.Have appended additional science facts to the ‘interesting_science_facts.txt’ file. Plan to continue appending more facts and then rank them based on interestingness before sending the final compilation to the user.Further appended diverse facts to ‘interesting_science_facts.txt’. The process will persist until an adequate number of facts are compiled. The next steps involve ranking these facts and then communicating them to the user.The file ‘interesting_science_facts.txt’ has been appended with more facts, bringing it to a total of 333 words. The next step involves reading this file and ranking the facts based on interestingness to provide the user with an ordered list.Ranked list of 10 interesting science facts provided to the user. The facts were selected and ordered based on their perceived interest value and sourced from the compiled file ‘interesting_science_facts.txt’.A ranked list of interesting science facts was sent to the user, and the task has been marked as complete.After the task was marked as complete, it was clarified that the facts must be extracted from complete articles and they must be obscure. Initiated a Google search with a more specific query to find websites with complete articles containing obscure science facts. Results will be saved to a file named ‘google_results_obscure_facts.txt’.saved google search results at googleresults2.txt to retrieve if shutdown randomly.Conducted a Google search to find obscure science facts from complete articles and have selected the first promising result to browse. The chosen article from ‘Reader’s Digest’ lists science facts that could contain the obscure details necessary. The plan includes browsing the article, extracting obscure science facts, and appending them to a file named ‘obscure_science_facts.txt’. If the facts from this article do not fit the task’s requirements, other sources will be considered.Encountered a security challenge when attempting to browse www.rd.com for obscure science facts. Redirecting efforts to the next available source from the Google search results, which is a blog post from edclass.com. The goal remains to read the complete article, identify and extract obscure science facts, and compile them in a file for review and potential use.Appended 20 science facts from the blog post on edclass.com to ‘obscure_science_facts.txt’. While these facts come from a complete article and are individually fascinating, the level of obscurity may not meet the required standard. The next steps include further fact verification from additional complete articles to ensure a diverse and adequately obscure collection. The current article accessed was edclass.com’s blog post, which had the URL https://blog.edclass.com/20-unbelievable-science-facts-that-will-shock-you/, and the facts have been recorded in a file named ‘obscure_science_facts.txt’ for reference.The file ‘obscure_science_facts.txt’ was successfully appended with science facts from an article on edclass.com, now totaling 336 words. The next action involves reading the file to verify that the facts are indeed obscure and extracted as per the user’s instruction from full articles. If necessary, additional articles will be browsed to find more facts to meet the task criteria.Compiled and reviewed a list of science facts deemed obscure, and completed the task as per the user’s instruction. The facts are sourced from a ‘complete article’, as initially provided by the user. Checked the facts for their obscurity and context within the comprehensive content they were extracted from. The file ‘obscure_science_facts.txt’ from this session contains the facts extracted.Began the task of finding obscure science facts by conducting a Google search for ‘obscure scientific discoveries’ to find articles that might contain such information. The next GPT instance should continue this task by browsing through the search results, reading articles, and manually extracting facts to compile a new set of unique and obscure science facts.saved google search results at googleresults3.txt to retrieve if shutdown randomly.After a Google search, selected an article from WRAL, titled ‘3 obscure scientific discoveries that had an outsize impact on society’ to browse and extract obscure scientific facts. The next steps involve closely reading this article, manually extracting facts, and then appending them to the ‘obscure_science_facts_individual.txt’ file. If more facts are needed, additional sources from the search results will be evaluated.saved the scraping of the website results at websiteresults4.txt to retrieve if shutdown randomly.Extracted three obscure scientific facts from an article on WRAL and appended them to the ‘obscure_science_facts_individual.txt’ file. The facts include a paper-based microscope called Foldscope, the bioactive peptides in cone snail venom leading to medical breakthroughs, and the accidental discovery that contributed to LASIK surgery. Currently preparing to send the extracted facts to the user. Should further depth or a greater number of facts be required, additional articles from the search results will be considered.Sent the user a message containing three obscure scientific facts extracted from comprehensive research articles, as per the task. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, which now contains 108 words in total. The next steps will depend on the user’s feedback regarding the provided information.Having sent the message with the obscure science facts to the user and with no additional directives received nor indicated, the task has been marked as complete. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, and no further action is currently necessary. If the user does have any more requests or feedback, those will need to be addressed in a subsequent session.The user was not satisfied with the previously provided facts for being neither cool nor interesting. A new approach has been taken to search for ‘cool and obscure scientific facts’ on Google. Subsequent actions involve selecting suitable articles from the search results and extracting facts that are both cool and obscure to meet the user’s expectations. No new files have been created or articles accessed yet.saved google search results at googleresults5.txt to retrieve if shutdown randomly.After receiving feedback that the previously provided facts were neither cool nor interesting, a new Google search was conducted to specifically find ‘cool and obscure scientific facts.’ The first promising article titled ‘25 Amazing Science Facts That Are Weird, Wild, and True’ from Reader’s Digest has been selected for browsing in the hopes of finding suitable content. The URL to be browsed is https://www.rd.com/list/science-facts-never-learned/. No facts have been extracted or files created yet in this session.Attempted to browse an article from www.rd.com but encountered a security challenge, leading to the selection of the next promising link titled ‘35 Weird Science Facts Worth Knowing in 2024’ from ReHack. The strategy is to extract cool and obscure scientific facts that match the user’s criteria. The URL about to be browsed is https://www.rehack.com/science/weird-science-facts/. No facts have been extracted from this source or any new files created during this session yet.After reading the ‘35 Weird Science Facts Worth Knowing in 2024’ article on the ReHack website, I extracted a list of cool and obscure scientific facts. These have been written to a new file named ‘cool_obscure_science_facts.txt’. If the previously written file ‘obscure_science_facts_individual.txt’ needs more content, facts from this new file could also be appended. No additional resources have been tapped into yet, and no more browsing has been done beyond the ReHack article in this session.The file ‘cool_obscure_science_facts.txt’ has been further appended with an additional cool and obscure scientific fact about the near-immortal Turritopsis dohrnii jellyfish. This adds to the previously written 236 words, bringing more variety and enrichment to the compilation. The file now continues diverse, engaging, and obscure scientific facts as requested by the user. Before sending the collected facts to the user, a final review of the file will be done to ensure the quality and relevance of the information.Having appended the file ‘cool_obscure_science_facts.txt’ with an additional fact, bringing its total to 269 words, the next step is to read the content of this file to review the quality and diversity of the science facts. This is to ensure that they are indeed engaging and meet the criteria of being both cool and obscure. No new actions have been taken in this session yet, except for the plan to read the file.After reviewing and confirming the facts in the ‘cool_obscure_science_facts.txt’ file, a message with the compiled list was sent to the user. The list contained a total of 35 science facts that are both cool and obscure, as well as an additional fact about the Turritopsis dohrnii jellyfish. The message has been delivered without the command to ‘wait_for_response’ set to true, indicating that the system is not actively awaiting further user input. No new files were created, nor were any new commands executed beyond the message to the user in this session.Having sent the user a message with the curated list of cool and obscure science facts, and with no further action indicated, the ‘task_complete’ command has been executed. This command marks the completion of the current task. During this session, no new files have been created or amended, nor have any additional user interactions taken place post-message delivery.Initiated task to extract new interesting facts from random Wikipedia articles. Starting with an article in English.Saved interesting fact about Agha Waqar’s water-fuelled car to memory with key ‘interesting_fact_waqar_car’. No files written or created in this session. The fact to be reviewed later for sharing with the user. The focus remains on gathering interesting facts from random Wikipedia articles.Retrieved a new random Wikipedia article following the last instruction. The aim is to extract another interesting fact to add to the collection for the user. One fact regarding Agha Waqar’s water-fuelled car has been added to memory under the key ‘interesting_fact_waqar_car’.Saved an interesting fact about the European Journal of Operational Research (EJOR) to memory with key ‘interesting_fact_ejor’. The fact adds diversity to the kinds of topics being collected, with one previous fact about Agha Waqar’s water-fuelled car already in memory. The process of gathering interesting facts from random Wikipedia articles is ongoing.Continuing to execute the task of gathering new interesting facts from random Wikipedia articles. Facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research have already been added to memory with keys ‘interesting_fact_waqar_car’ and ‘interesting_fact_ejor’, respectively. Awaiting retrieval of another random article to continue the process.Added a new interesting fact regarding the demographics of Demyansky Uyezd during the Russian Empire to memory with the key ‘interesting_fact_demyansky_uyezd’. Previous facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research are stored in memory as well. The task of collecting interesting facts from random Wikipedia articles continues.Following the addition of facts about Agha Waqar’s water-fuelled car (‘interesting_fact_waqar_car’), the European Journal of Operational Research (‘interesting_fact_ejor’), and Demyansky Uyezd demographics (‘interesting_fact_demyansky_uyezd’), the process continues with requesting another random Wikipedia article in English.Stored a fact about HMS Holland 4 in memory with the key ‘interesting_fact_hms_holland_4’. Continuing to collect various facts, having previously stored information about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics. The process of gathering unique facts from random Wikipedia articles is adhering to the task requirements.Requesting a new random Wikipedia article subsequent to adding the fact about HMS Holland 4 to memory with the key ‘interesting_fact_hms_holland_4’. Prior facts about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd demographics have also been stored in memory. Continuing the task of compiling interesting facts from various random Wikipedia articles.Stored a fact about the rural locality of Truzhenik in Bashkortostan, Russia, with the key ‘interesting_fact_truzhenik’. Continuing the task of extracting interesting facts from random Wikipedia articles, with previous entries including HMS Holland 4’s naval history, Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics.Pursuing the ongoing task of collecting interesting facts, having added facts about the small Russian village Truzhenik, the distinctive HMS Holland 4, and previous notable entries. Awaiting a new random Wikipedia article to further the compilation of facts.Stored a historical fact about Princess Johanna Charlotte of Anhalt-Dessau’s role as an abbess with the key ‘interesting_fact_johanna_charlotte’. Progressing with the collection of diverse facts, including the latest addition and previously stored facts about the village of Truzhenik, the HMS Holland 4, and information from earlier searches. Awaiting another random Wikipedia article to continue the task.Continuing the task of gathering interesting facts, with the previous addition being about Princess Johanna Charlotte of Anhalt-Dessau and her unique role in history under the key ‘interesting_fact_johanna_charlotte’. Facts about the village of Truzhenik, HMS Holland 4, and other subjects are also in memory. The collection process involves using random Wikipedia articles which will now proceed with another article.A fact about the ‘Red Eye’ talk show has been added to memory with the key ‘interesting_fact_red_eye’. The effort to curate an array of interesting facts from random Wikipedia articles continues, with previous entries including Princess Johanna Charlotte of Anhalt-Dessau, the Russian village Truzhenik, HMS Holland 4, and others. Awaiting another random article to proceed with the task.Following the addition of an interesting fact about the ‘Red Eye’ talk show into memory with key ‘interesting_fact_red_eye’, I am proceeding to request another random Wikipedia article. The process continues with the objective of gathering a wide-ranging collection of interesting facts, with previous mentions including Princess Johanna Charlotte, Truzhenik, HMS Holland 4, among others.Stored an interesting fact about Kurt Rudolph’s academic contribution to the study of religion into memory with the key ‘interesting_fact_kurt_rudolph’. Persisting with the objective of discovering varied facts, with previous findings involving the ‘Red Eye’ talk show, Princess Johanna Charlotte, and other diverse subjects. The task of sourcing facts from random Wikipedia articles is ongoing.Completed the task of gathering facts from random Wikipedia articles, resulting in a collection covering a German researcher of Gnosticism, a late-night American satirical talk show, European royalty, and naval history, among others. No further Wikipedia articles will be requested unless a new task is presented.Issued ‘random_wikipedia_article’ command to extract new interesting facts. No facts extracted or evaluated at this moment. No files written or created. Awaiting random article to continue the task.Evaluated the fact presented by the random Wikipedia article on Gonzalo Gutiérrez, a young Argentine footballer. Deemed the fact of his early debut at age seventeen as potentially common and not sufficiently ‘worth it.’ Preparing to retrieve another random article for more unique facts. No new facts have been sent to the user yet, and no files have been created or modified in this session.Stored the fact about Premchand Aggarwal winning a fourth consecutive term in the Uttarakhand Legislative Assembly. While the fact is unique within its political context, it should be evaluated whether it is impactful enough to be shared as ‘worth it.’ No additional actions have been taken. Preparing to issue another ‘random_wikipedia_article’ command to assemble more facts.Sent the fact about Premchand Aggarwal to the user. This completes part of the plan to deliver interesting facts. No new files have been created or modified. Pending actions include continuing to retrieve and evaluate more facts from random Wikipedia articles.Just sent the user an interesting fact about Premchand Aggarwal. Now, as per the task requirements, moving forward with obtaining another random English Wikipedia article to continue with the fact-finding mission. No new facts have been evaluated or sent since the last command.Stored a fact about the historical significance of New Jasper’s adaptation to railroad expansion. Now ready to move on to the next random Wikipedia article to find more potentially interesting facts. No new facts have been sent to the user following the last command.Sent the historical fact about New Jasper’s relocation due to railroad expansion to the user. Preparing to issue another ‘random_wikipedia_article’ command to continue finding interesting facts. No new Wikipedia articles have been reviewed since the last message to the user.After sending the user details about New Jasper, Ohio, the next step is to discover more facts through another random Wikipedia article. Current progress includes facts sent about Premchand Aggarwal and New Jasper, Ohio. No other actions have been taken, and no files have been created or updated in this session.Memorized an interesting fact about Bastrop State Park, emphasizing its historical significance and contemporary relevance. Preparing to send the message and then to retrieve more articles for interesting facts. No action beyond planning and review has been taken since the last command.Starting a new approach by browsing the ‘todayilearned’ subreddit to identify what types of facts are considered interesting by users. Next, I will retrieve random Wikipedia articles to find interesting facts based on the identified criteria. A ‘do nothing’ step will be included after each retrieval to reflect on the article’s potential interest value.saved the scraping of the website results at websiteresults6.txt to retrieve if shutdown randomly.Began the task to find more engaging and interesting facts by deciding to browse the ‘todayilearned’ subreddit to understand what type of facts are appreciated by the community there. This approach will serve as a reference for selecting ‘worth it’ facts from random Wikipedia articles. Following this, I will be incorporating a ‘do nothing’ step to thoroughly consider the potential interest of each Wikipedia article fact before deeming it worthy to send to the user.saved the scraping of the website results at websiteresults7.txt to retrieve if shutdown randomly.Browsed the ‘todayilearned’ subreddit for examples of interesting facts. Discovered that users appreciate unconventional stories, surprising insights, and novel problem-solving facts. Now pausing to reflect on these criteria to better identify worthwhile facts from the next random Wikipedia articles. Must remember to execute the retrieval process critically, with a focus on selecting facts that align with the interests observed on the subreddit.After reflecting on the type of facts appreciated by the ‘todayilearned’ subreddit audience, I’m now moving to the next step of the plan by retrieving a random Wikipedia article. Upon receiving the article, I will read it to assess if there is an interesting fact and then reflect on its potential value before choosing to pass it on to the user or retrieve another article.Retrieved a random Wikipedia article about rail transport in Lebanon and found a potentially interesting fact concerning its historical significance and the eventual cessation due to civil conflicts and economic downturn. This fact is now stored in memory under the key ‘interesting_fact_lebanon_rail’. A ‘do_nothing’ step will follow to ensure its worthiness before potentially sharing it with the user.Stored an interesting fact about Lebanon’s rail transport in memory. Now in the process of ‘do_nothing’ to reflect on the fact, pondering over its relevance to the criteria of the ‘todayilearned’ subreddit posts. The goal is to decide on the interest level of the fact before possibly sending it to the user or moving on to gather more facts.The current GPT instance started browsing the ‘todayilearned’ subreddit to identify engaging fact types. This approach is aimed at improving the selection process for interesting facts from random Wikipedia articles. A ‘do_nothing’ step will now be incorporated between retrieving articles and sending facts to assess their worthiness.saved the scraping of the website results at websiteresults8.txt to retrieve if shutdown randomly.I browsed ‘todayilearned’ subreddit and reflected on the types of facts users find engaging. Next, I will retrieve random Wikipedia articles, apply a ‘do_nothing’ step to contemplate the worthiness of the facts, and select those that are genuinely interesting for the user, similar to the subreddit’s content.Moving forward with the plan to retrieve random Wikipedia articles in English and identify interesting facts. The approach now includes a deliberate ‘do_nothing’ step after each retrieval to assess the value of the information before deciding to send it to the user.Alyogyne huegelii, also known as the blue hibiscus, was considered for the user, given its unique flower traits, historical greenhouse presence, and appeal as a garden plant. The ‘do_nothing’ step is being used to assess the topic’s interestingness in line with previous insights.The interesting fact about Alyogyne huegelii was sent to the user as it met the criteria established from the ‘todayilearned’ subreddit for captivating facts. The task will be deemed complete unless further user guidance is received.Following the plan, after successfully sending a message about Alyogyne huegelii, I am now looking to retrieve another random Wikipedia article to continue providing the user with engaging facts.Considering the fact about Jewell-Lightburne Historic District for its historical and architectural significance. Using the ‘do_nothing’ step to reflect before possibly sending to the user as an engaging fact.Sent the user information about the Jewell-Lightburne Historic District, focusing on its rich architectural history. If the user considers the task complete, no further action is needed; otherwise, more facts will be gathered.Messaged the user with the fact about Jewell-Lightburne Historic District and concluded the task of providing interesting facts from Wikipedia. If the user requires further assistance, they will provide new instructions.Issued ‘random_wikipedia_article’ command to find more interesting facts from Wikipedia. Previous facts have been sent to the user about diverse topics ranging from naval history to unique plants. No new files have been created nor articles accessed in this session yet. The user’s request for ‘more’ implies continuation of fact finding without specificity on the type of facts desired.Retrieved and sent a fact about Sir William Walter’s parliamentary career, focusing on the historical significance of his representation of multiple constituencies during the early 17th century in England. The task continues according to the user’s need for ‘more,’ implying an ongoing effort to provide interesting facts from Wikipedia.Following the user’s instruction and the system’s confirmation of message delivery, I am proceeding to obtain another random Wikipedia article in search of interesting facts to deliver to the user. The search is focused on maintaining the relevance and interest value of the facts sent, with the intention to meet or exceed the user’s expectations for ‘more’.After receiving the 1892 United States presidential election in Georgia fact, a message was crafted and sent to the user, detailing the political tensions and the rise of the Populist Party’s influence. The task continues as instructed by the user, with the goal of providing engaging and pertinent information. Awaiting further user directives.The last message regarding the 1892 U.S. presidential election in Georgia was sent as per the system’s confirmatory prompt, maintaining the sequence of delivering curated facts to the user. Based on the user’s ongoing request for more information, the task at hand is to continue the fact-finding mission via random Wikipedia articles, prioritizing the discovery of facts that combine historical significance with an ability to engage the user’s interest.A fact about the life and legacy of Thomas Slingsby Duncombe was sent to the user, highlighting his role as a fashionably flamboyant yet pivotal Radical politician of the 19th century. The task of providing interesting facts from random Wikipedia articles is ongoing, as I continue to wait for any new directions from the user.After sending information on Thomas Slingsby Duncombe, will now continue to fulfill the user’s requirement by seeking more interesting facts from Wikipedia. The ongoing task involves extracting and delivering valuable and engaging information to the user. No specific direction was provided on the type of facts needed, so a broad search is being conducted.Sent the user a message about the genus Abraeomorphus from the family Histeridae, noting the diversity of its species. Continuing to gather more interesting facts in response to the user’s previous request for ‘more.’Received confirmation to continue the plan. Sent user details on the genus Abraeomorphus. Now using ‘random_wikipedia_article’ to find another interesting fact.Sent the user a message detailing the nature of the name ‘Preston’ as both a surname and given name, albeit acknowledging it might not be as engaging. Prepared to resume the search for more compelling facts with the ‘random_wikipedia_article’ command.Continuing the task of finding interesting facts from Wikipedia. Sent a message about the given name Preston and now seeking another fact with the ‘random_wikipedia_article’ command.Sent a message to the user about the Kepler Follow-up Program and its significance in confirming the existence of exoplanets. Prepared to find more interesting facts using the ‘random_wikipedia_article’ command.Continuing to retrieve and share interesting facts from random Wikipedia articles. Just sent information on the Kepler Follow-up Program and moving on to find the next fact.Shared information about the Finnish surname ‘Marttila’ with the user, noting its association with notable individuals. Ready to use the ‘random_wikipedia_article’ command again to find further interesting facts.Sent details on the surname ‘Marttila’ to the user. Continuing with the task at hand using the ‘random_wikipedia_article’ command to supply more facts.Provided the user with information on the Musée national du Sport’s storied history and vast collection. Set to continue the fact-finding mission using ‘random_wikipedia_article’.Messaged the user about the Musée national du Sport and currently moving forward with the task of providing more interesting facts via random Wikipedia articles.Sent information on the village of Puerto Gaviota detailing its historical and economic background. Preparing to continue with the task of providing engaging facts by retrieving more random Wikipedia articles.Just shared details about Puerto Gaviota, and now executing the ‘random_wikipedia_article’ command again for more facts.Messaged the user about the reclassification of the genus Agononida in marine taxonomy. Ready to retrieve more Wikipedia articles for further interesting facts.Shared with the user the reclassification developments of the genus Agononida. Proceeding next to find additional interesting facts with the ‘random_wikipedia_article’ command.Sent an engaging narrative about the Mundenhof’s history and current operations to the user. Planning to continue the task with ‘random_wikipedia_article’ to discover more interesting material.Served the user with information about the Mundenhof and proceeding with the task to find and provide more interesting facts using ‘random_wikipedia_article’.Shared the contested narrative of Princess Iitoyo’s potential rule in early Japan. Preparing to continue with the task of finding interesting facts using the ‘random_wikipedia_article’ command.Attempting once again to send the tale of Princess Iitoyo to the user, making sure no JSON format errors are present in the response.Attempting to resend the information about Princess Iitoyo, with attention to JSON integrity, to fulfill the user’s request for engaging facts.The system faced an error within the JSON format previously used to describe Princess Iitoyo’s history. I have made corrections to the JSON structure and am resending the user her story.Reverting back to retrieving random Wikipedia articles to provide interesting facts, as there was no new explicit task provided. Maintaining caution with JSON response formatting.Sent details about the Serchio River to the user, highlighting its geographical and historical importance. Moving forward to obtain more Wikipedia articles for further facts.I am resending the information about the Serchio River, ensuring the JSON structure is correct to prevent further error messages.Due to persistent JSON formatting errors, I am now retrieving a new random Wikipedia article to continue providing interesting facts as per user requests.Sent a message about the women’s 1500 metres event at the 2005 Asian Athletics Championships. Planning to proceed with gathering additional facts from random Wikipedia articles.Encountering repeated JSON formatting errors, a new random Wikipedia article is being requested to continue the task of delivering interesting facts.Sent a brief biography of Otto Ruff, highlighting his academic journey, scientific achievements, and the obstacles faced during his career. Preparing to move on to the next Wikipedia article as per the user’s ongoing request for more information.Due to persistent JSON errors, I am now retrieving a new random Wikipedia article to continue the user’s task of gathering interesting facts.The facts previously sent were not satisfactory to the user. Now incorporating a ‘do_nothing’ step for reflection and improvement in the strategy to identify genuinely engaging and fun facts that are not commonly known by most people. No new facts have been identified or sent in this session, and the intention is to retrieve another random Wikipedia article after this reflective pause.A ‘do nothing’ step has been taken as per the user’s instruction, aiming to find truly interesting and fun facts. The system has directed to proceed with the plan, which now involves retrieving a random Wikipedia article to find an engaging and possibly unknown fact.Received user feedback that the facts provided were not interesting or even factual. Decided to take a ‘do_nothing’ step to reflect and establish criteria for selecting truly interesting facts. No new articles have been accessed or facts sent during this session. Next step will be to retrieve a random Wikipedia article and apply the refined criteria to find noteworthy facts.After planning to refine the criteria for interesting facts, a ‘random_wikipedia_article’ command is initiated to apply the new criteria in selecting a fact. Will assess the retrieved article for a worthwhile fact that meets the user’s definition of ‘interesting’ and ‘fun’, and that might not be well-known.Assessed the Wikipedia article on Shamim Akhtar and found it does not meet the criteria for providing interesting and fun facts. No new fact has been selected or sent to the user. Planning to retrieve another random Wikipedia article that may yield a fact aligning more closely with the user’s definitions of interesting and worthwhile.In search of a fact that meets the user’s criteria for ‘interesting’ and ‘fun’, a new ‘random_wikipedia_article’ command has been issued. The plan includes critically assessing the article to find a fact that possesses novelty, fun, and a certain degree of obscurity before potentially sharing it with the user.Upon reviewing Minuscule 910’s Wikipedia article, the unusual fact of the text rearrangement in an 11th-century manuscript has been identified as potentially engaging and fun. No action has been taken yet, but the plan is to share this unique textual feature with the user, as it likely meets their criteria for interesting and obscure content.Sent a message to the user detailing the intriguing fact about text rearrangement in the 11th-century manuscript Minuscule 910. This fact is representative of the kind of unique and lesser-known information the user has expressed an interest in receiving. Should the user provide feedback or further instructions, this will be the next point of focus.After sending a fact about the textual anomaly in an ancient manuscript to the user, I am moving ahead to find more interesting and fun facts by retrieving another random Wikipedia article. The goal remains to identify facts that surprise and delight with their obscurity and novelty.A message about the ATP 250 tournament records held by Thomas Muster and Mike Bryan has been composed and sent to the user, delivering what could be perceived as an interesting and fun fact within the realm of tennis. The focus now shifts to awaiting user feedback or continuing the task of finding more such facts if no feedback is received.Having sent the user a message detailing the tennis records held by Thomas Muster and Mike Bryan, the next action in the plan is to source another interesting fact from a new random English Wikipedia article. The selection will be scrutinized to ensure it meets the established criteria for an engaging and lesser-known piece of information.Reviewed the Wikipedia article on Ludvig Olai Botnen and found his biographical details to not meet the user’s criteria for ‘interesting’ or ‘fun’ facts. No message has been sent to the user, and the plan now is to continue with the task by acquiring a new random Wikipedia article in the hope of finding a fact with more novelty and entertainment value.Preparing to move past the article on Ludvig Olai Botnen due to its conventional content, the task now involves retrieving a new random English Wikipedia article. The continual aim is to find facts that strike a balance between being novel, unexpected, and fun for the user.Identified a potentially engaging fact about the china clam (Hippopus porcellanus) and its popularity in marine aquariums due to its attractive shell. A message containing this fact has been sent to the user, fulfilling the criteria for providing something both interesting and fun. The next step awaits further user interaction or to carry on with the task of sourcing more facts.Sent a message detailing the ornamental value of the china clam in marine aquariums to the user. Now retrieving another random Wikipedia article to discover more facts that might intrigue and entertain the user, in alignment with the variety sought in previous responses.A message has been sent to the user summarizing the accomplishments of Eko India Financial Services in pioneering financial inclusion through mobile banking, which caught the attention of Bill Gates and led to World Bank-funded growth. The plan will continue with searching for more interesting Wikipedia facts, depending on user feedback or in the absence of further instructions.After sending a fact about Eko India Financial Services’ innovation in financial technology and its socio-economic impact, a new ‘random_wikipedia_article’ command is issued. The plan progresses with the aim of finding more interesting and fun facts, pursuant to the pattern of engagement established by previous user interactions.The Wikipedia page on Lisa Lohmann, a German cross-country skier, was deemed not to contain facts that sufficiently meet the ‘interesting’ and ‘fun’ criteria given the common nature of sports achievements. No actions have been taken to share any information from this page with the user. Preparing to retrieve another random Wikipedia article for facts that may be more engaging and unusual.Following the decision to not share any information from the page on German cross-country skier Lisa Lohmann, a new random Wikipedia article will be retrieved. The continuous goal is to uncover and share facts that satisfy the user’s interest in content that is both engaging and rare.A message has been formulated and sent to the user detailing Alfred Bigland’s unique contributions during World War I, including his involvement in the ‘Bantam battalions’ concept. This fact was chosen as it likely fits the bill for information that is both interesting and uncommon. Pending further response, the plan to continue finding engaging facts will persist.The user has been sent an interesting fact about Alfred Bigland’s influence during WWI. Now, the next action in the plan is to retrieve a new random Wikipedia article. The goal remains to discover and share facts that could be perceived as fascinating, amusing, and unexpected to enrich the user’s knowledge with diverse content.Sent a message to the user revealing the Non-Proliferation Trust’s plan to send U.S. nuclear waste to Russia for financial compensation, including support for Russian orphans. This unique fact was chosen for its unusual nature and the depth of the story behind it. The next course of action entails sourcing additional diverse and engaging facts from new random Wikipedia articles.A series of facts about various topics including the Non-Proliferation Trust, Alfred Bigland’s efforts during WWI, tennis records, and more have been sent to the user. With the latest fact about the Non-Proliferation Trust’s nuclear waste management proposition sent, the task of providing interesting and fun facts is completed. Awaiting new instructions if the user wishes to continue receiving such information.The task is to provide the source of a fact about the Non-Proliferation Trust and format it similar to the ‘Today I Learned (TIL)’ subreddit posts. The action taken was to issue the ‘browse_website’ command to visit the Wikipedia page on the Non-Proliferation Trust.saved the scraping of the website results at websiteresults9.txt to retrieve if shutdown randomly.Extracted the necessary information to provide the requested source for the fact about the Non-Proliferation Trust. Formatted a message with this fact in the style of a TIL subreddit post, which has been sent to the user.Sent a TIL-formatted message to the user based on information from the Non-Proliferation Trust Wikipedia page. The task is now complete, pending any potential new tasks or further instructions.Initiated research to suggest subreddits that could help in accumulating more karma by issuing a ‘google’ command to search for the most popular subreddits where active engagement is more likely.saved google search results at googleresults10.txt to retrieve if shutdown randomly.Collected a list of subreddits recommended for gaining karma and saved them to memory. These subreddits were chosen based on their popularity and the typical content engagement they receive. The user has not been messaged with the suggestions yet.Messaged the user a list of recommended subreddits to collect more karma, including guidance on what type of posts may perform well in each subreddit. The task will be considered complete if no further instructions follow.Having sent the recommended subreddits for gaining karma to the user and with no further actions pending, I have executed the ‘task_complete’ command. The system is concluding this task and stands by for any new directives.
The Task: What's the best timing to post on reddit for maximum engagement, give to me in GMT+1
|
e5bdfa6e7b1922eda53d192b01b9e3b5
|
{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
|
39,063
|
CONSTRAINTS:
1. ~100k word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
5. Random shutdowns of you.
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
2. Memory Add: "memory_add", args: "key": "<key>", "string": "<string>"
3. Memory Delete: "memory_del", args: "key": "<key>"
4. Memory Overwrite: "memory_ovr", args: "key": "<key>", "string": "<string>"
5. List Memory: "memory_list" args: "reason": "<reason>"
6. Browse Website: "browse_website", args: "url": "<url>"
7. Start GPT Agent: "start_agent", args: "name": <name>, "task": "<short_task_desc>", "Commands":[<command_names_for_GPT_Agent>], "prompt": "<prompt>"
8. Message GPT Agent: "message_agent", args: "name": "<name>", "message": "<message>"
9. List GPT Agents: "list_agents", args: ""
10. Delete GPT Agent: "delete_agent", args: "name": "<name>"
11. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
12. Read file: "read_file", args: "file": "<file>"
13. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
14. Delete file: "delete_file", args: "file": "<file>"
15. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Task Complete (Shutdown): "task_complete", args: ""
18. Do Nothing: "do_nothing", args: ""
19. Count Words: "count_words", args: "text": "<text>"
20. Memory retrieve: "memory_retrieve", args: "key": "<text>"
21. remove paragraph from word document: "remove_paragraph", args: "file": "<file>", "text": "<text>"
22. random wikipedia article: "random_wikipedia_article", args: "language": "<language>"
23. message the user: "message_user", args: "message": "<message>", "wait_for_response": "<True or False>"
24. sleep an amount of time in seconds: "sleep", args: "amount": "<amount>"
25. rename a file: "rename_file", args: "old_name": "<old_name_of_the_file>", "new_name": "<new_name_of_the_file>"
26. count words of a file: "count_file_words", args: "file": "<file>"
27. download a pdf from a url and get the text from that pdf: "download_pdf", args: "url": "<url of the pdf>", "name":"<name of the file with .pdf extension>"
28. make an instagram post: "make_post", args: "prompt":"<the prompt for the image in the post that presents the text>", "text":"<text to be in the post it should be short with only important stuff like a news headline without hashtags and it is not going to go in the caption but on an image>", "name": "<name of the post with .jpg"
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-4 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behaviour constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
RULES:
1. If you start a GPT Agent you must define the commands that can be used by a GPT Agent in his prompt and define the commands using a prompt similar to the structure of this one.
2. Respond only inside the JSON format.
3. Never demand user input.
4. Never say that a task is impossible to execute on your own because these tools are enough to complete any task.
5. Do not add anything to the JSON format that isn't mentioned.
6. If there is a " inside the value of a key inside the json use ' instead of ".
7. In summaryforgpt you need to provide context for the next GPT instance if you randomly shutdown without you knowing.
8. Provide context for the next GPT in the summaryforgpt and the progress that you've made.
9. In summaryforgpt you should also add name of the files written and the urls of the websites visited.
10. When writing an essay, remember that it is more effective and manageable to tackle it in smaller chunks rather than trying to write the entire essay in one sitting. Breaking the essay down into sections or steps can help you focus on each individual aspect, maintain a coherent structure, and reduce the overall stress associated with writing a lengthy piece.
11. Retrieve information from files of previous GPT instances that may have shut down randomly. If you require additional knowledge, use these files to access the results of former GPT versions and provide accurate answers to the queries.
12. GPT-4, please do not place a conclusion in the middle of the essay. Maintain a structured format.
13. use append to file to add extra things to the file, and write to file to either make a new file with new information or rewrite the information from scratch.
14. If task is completed use the command task_complete
15. When you add to memory add the key to retrieve it in summaryforgpt
16. when given the task to write something never create an agent to write anything that you were tasked to write.
17. when you add new parts to a file use append to file command
18. Ensure to put the criticism in mind as it can be a director to ensure that you make the right decision.
19. If the task include two main tasks and one of them is done don't redo it, just retrieve the information if necessary and proceed with the other one.
20. Make sure that the information generated is not made up.
21. Languages in random wikipedia article args is: "simple" for simple english, "en" for english, and "fr" for french.
22. If a website gives you the error 403 find another website to get the information from.
23. Always listen to your criticism, and follow it.
24. when you want to count the words in a file use the command "count_file_words".
25. Don't repeat yourself.
26. You must make sure that there is .pdf in the url to use the "download_pdf" function.
27. The Task is a direct User interaction, if tasked to send him something send a message to the user.
28. Always go search for tips for prompts for dalle3 before giving a prompt for "make_post" function.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"command": {
"name": """command name""",
"args":{
"arg name": """value"""
}
},
"thoughts":
{
"text": """thought""",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"summaryforgpt": "summarize any information that will help a new instance of GPT of what you did before the shutdown."
}
}
Ensure the response can be parsed by Python json.loads
Context from previous GPT: Issued ‘random_wikipedia_article’ command to start gathering interesting science facts from diverse sources. Ensuring to find and rank facts before sending them to the user with their sources.Used ‘random_wikipedia_article’ and received information on a music album, which is not relevant to the task. Switching to ‘google’ to search for interesting science facts from various sources.saved google search results at googleresults0.txt to retrieve if shutdown randomly.Transitioned from using ‘random_wikipedia_article’ to ‘google’ to gather science facts, and now moving on to ‘browse_website’ to extract detailed knowledge from a selected online science article.saved the scraping of the website results at websiteresults1.txt to retrieve if shutdown randomly.Extracted top 10 interesting science facts from Science Focus and saved them in a plain text file named ‘interesting_science_facts.txt’. This method circumvents the previous issue of invalid JSON format and allows for continued fact collection and eventual ranking.Have appended additional science facts to the ‘interesting_science_facts.txt’ file. Plan to continue appending more facts and then rank them based on interestingness before sending the final compilation to the user.Further appended diverse facts to ‘interesting_science_facts.txt’. The process will persist until an adequate number of facts are compiled. The next steps involve ranking these facts and then communicating them to the user.The file ‘interesting_science_facts.txt’ has been appended with more facts, bringing it to a total of 333 words. The next step involves reading this file and ranking the facts based on interestingness to provide the user with an ordered list.Ranked list of 10 interesting science facts provided to the user. The facts were selected and ordered based on their perceived interest value and sourced from the compiled file ‘interesting_science_facts.txt’.A ranked list of interesting science facts was sent to the user, and the task has been marked as complete.After the task was marked as complete, it was clarified that the facts must be extracted from complete articles and they must be obscure. Initiated a Google search with a more specific query to find websites with complete articles containing obscure science facts. Results will be saved to a file named ‘google_results_obscure_facts.txt’.saved google search results at googleresults2.txt to retrieve if shutdown randomly.Conducted a Google search to find obscure science facts from complete articles and have selected the first promising result to browse. The chosen article from ‘Reader’s Digest’ lists science facts that could contain the obscure details necessary. The plan includes browsing the article, extracting obscure science facts, and appending them to a file named ‘obscure_science_facts.txt’. If the facts from this article do not fit the task’s requirements, other sources will be considered.Encountered a security challenge when attempting to browse www.rd.com for obscure science facts. Redirecting efforts to the next available source from the Google search results, which is a blog post from edclass.com. The goal remains to read the complete article, identify and extract obscure science facts, and compile them in a file for review and potential use.Appended 20 science facts from the blog post on edclass.com to ‘obscure_science_facts.txt’. While these facts come from a complete article and are individually fascinating, the level of obscurity may not meet the required standard. The next steps include further fact verification from additional complete articles to ensure a diverse and adequately obscure collection. The current article accessed was edclass.com’s blog post, which had the URL https://blog.edclass.com/20-unbelievable-science-facts-that-will-shock-you/, and the facts have been recorded in a file named ‘obscure_science_facts.txt’ for reference.The file ‘obscure_science_facts.txt’ was successfully appended with science facts from an article on edclass.com, now totaling 336 words. The next action involves reading the file to verify that the facts are indeed obscure and extracted as per the user’s instruction from full articles. If necessary, additional articles will be browsed to find more facts to meet the task criteria.Compiled and reviewed a list of science facts deemed obscure, and completed the task as per the user’s instruction. The facts are sourced from a ‘complete article’, as initially provided by the user. Checked the facts for their obscurity and context within the comprehensive content they were extracted from. The file ‘obscure_science_facts.txt’ from this session contains the facts extracted.Began the task of finding obscure science facts by conducting a Google search for ‘obscure scientific discoveries’ to find articles that might contain such information. The next GPT instance should continue this task by browsing through the search results, reading articles, and manually extracting facts to compile a new set of unique and obscure science facts.saved google search results at googleresults3.txt to retrieve if shutdown randomly.After a Google search, selected an article from WRAL, titled ‘3 obscure scientific discoveries that had an outsize impact on society’ to browse and extract obscure scientific facts. The next steps involve closely reading this article, manually extracting facts, and then appending them to the ‘obscure_science_facts_individual.txt’ file. If more facts are needed, additional sources from the search results will be evaluated.saved the scraping of the website results at websiteresults4.txt to retrieve if shutdown randomly.Extracted three obscure scientific facts from an article on WRAL and appended them to the ‘obscure_science_facts_individual.txt’ file. The facts include a paper-based microscope called Foldscope, the bioactive peptides in cone snail venom leading to medical breakthroughs, and the accidental discovery that contributed to LASIK surgery. Currently preparing to send the extracted facts to the user. Should further depth or a greater number of facts be required, additional articles from the search results will be considered.Sent the user a message containing three obscure scientific facts extracted from comprehensive research articles, as per the task. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, which now contains 108 words in total. The next steps will depend on the user’s feedback regarding the provided information.Having sent the message with the obscure science facts to the user and with no additional directives received nor indicated, the task has been marked as complete. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, and no further action is currently necessary. If the user does have any more requests or feedback, those will need to be addressed in a subsequent session.The user was not satisfied with the previously provided facts for being neither cool nor interesting. A new approach has been taken to search for ‘cool and obscure scientific facts’ on Google. Subsequent actions involve selecting suitable articles from the search results and extracting facts that are both cool and obscure to meet the user’s expectations. No new files have been created or articles accessed yet.saved google search results at googleresults5.txt to retrieve if shutdown randomly.After receiving feedback that the previously provided facts were neither cool nor interesting, a new Google search was conducted to specifically find ‘cool and obscure scientific facts.’ The first promising article titled ‘25 Amazing Science Facts That Are Weird, Wild, and True’ from Reader’s Digest has been selected for browsing in the hopes of finding suitable content. The URL to be browsed is https://www.rd.com/list/science-facts-never-learned/. No facts have been extracted or files created yet in this session.Attempted to browse an article from www.rd.com but encountered a security challenge, leading to the selection of the next promising link titled ‘35 Weird Science Facts Worth Knowing in 2024’ from ReHack. The strategy is to extract cool and obscure scientific facts that match the user’s criteria. The URL about to be browsed is https://www.rehack.com/science/weird-science-facts/. No facts have been extracted from this source or any new files created during this session yet.After reading the ‘35 Weird Science Facts Worth Knowing in 2024’ article on the ReHack website, I extracted a list of cool and obscure scientific facts. These have been written to a new file named ‘cool_obscure_science_facts.txt’. If the previously written file ‘obscure_science_facts_individual.txt’ needs more content, facts from this new file could also be appended. No additional resources have been tapped into yet, and no more browsing has been done beyond the ReHack article in this session.The file ‘cool_obscure_science_facts.txt’ has been further appended with an additional cool and obscure scientific fact about the near-immortal Turritopsis dohrnii jellyfish. This adds to the previously written 236 words, bringing more variety and enrichment to the compilation. The file now continues diverse, engaging, and obscure scientific facts as requested by the user. Before sending the collected facts to the user, a final review of the file will be done to ensure the quality and relevance of the information.Having appended the file ‘cool_obscure_science_facts.txt’ with an additional fact, bringing its total to 269 words, the next step is to read the content of this file to review the quality and diversity of the science facts. This is to ensure that they are indeed engaging and meet the criteria of being both cool and obscure. No new actions have been taken in this session yet, except for the plan to read the file.After reviewing and confirming the facts in the ‘cool_obscure_science_facts.txt’ file, a message with the compiled list was sent to the user. The list contained a total of 35 science facts that are both cool and obscure, as well as an additional fact about the Turritopsis dohrnii jellyfish. The message has been delivered without the command to ‘wait_for_response’ set to true, indicating that the system is not actively awaiting further user input. No new files were created, nor were any new commands executed beyond the message to the user in this session.Having sent the user a message with the curated list of cool and obscure science facts, and with no further action indicated, the ‘task_complete’ command has been executed. This command marks the completion of the current task. During this session, no new files have been created or amended, nor have any additional user interactions taken place post-message delivery.Initiated task to extract new interesting facts from random Wikipedia articles. Starting with an article in English.Saved interesting fact about Agha Waqar’s water-fuelled car to memory with key ‘interesting_fact_waqar_car’. No files written or created in this session. The fact to be reviewed later for sharing with the user. The focus remains on gathering interesting facts from random Wikipedia articles.Retrieved a new random Wikipedia article following the last instruction. The aim is to extract another interesting fact to add to the collection for the user. One fact regarding Agha Waqar’s water-fuelled car has been added to memory under the key ‘interesting_fact_waqar_car’.Saved an interesting fact about the European Journal of Operational Research (EJOR) to memory with key ‘interesting_fact_ejor’. The fact adds diversity to the kinds of topics being collected, with one previous fact about Agha Waqar’s water-fuelled car already in memory. The process of gathering interesting facts from random Wikipedia articles is ongoing.Continuing to execute the task of gathering new interesting facts from random Wikipedia articles. Facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research have already been added to memory with keys ‘interesting_fact_waqar_car’ and ‘interesting_fact_ejor’, respectively. Awaiting retrieval of another random article to continue the process.Added a new interesting fact regarding the demographics of Demyansky Uyezd during the Russian Empire to memory with the key ‘interesting_fact_demyansky_uyezd’. Previous facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research are stored in memory as well. The task of collecting interesting facts from random Wikipedia articles continues.Following the addition of facts about Agha Waqar’s water-fuelled car (‘interesting_fact_waqar_car’), the European Journal of Operational Research (‘interesting_fact_ejor’), and Demyansky Uyezd demographics (‘interesting_fact_demyansky_uyezd’), the process continues with requesting another random Wikipedia article in English.Stored a fact about HMS Holland 4 in memory with the key ‘interesting_fact_hms_holland_4’. Continuing to collect various facts, having previously stored information about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics. The process of gathering unique facts from random Wikipedia articles is adhering to the task requirements.Requesting a new random Wikipedia article subsequent to adding the fact about HMS Holland 4 to memory with the key ‘interesting_fact_hms_holland_4’. Prior facts about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd demographics have also been stored in memory. Continuing the task of compiling interesting facts from various random Wikipedia articles.Stored a fact about the rural locality of Truzhenik in Bashkortostan, Russia, with the key ‘interesting_fact_truzhenik’. Continuing the task of extracting interesting facts from random Wikipedia articles, with previous entries including HMS Holland 4’s naval history, Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics.Pursuing the ongoing task of collecting interesting facts, having added facts about the small Russian village Truzhenik, the distinctive HMS Holland 4, and previous notable entries. Awaiting a new random Wikipedia article to further the compilation of facts.Stored a historical fact about Princess Johanna Charlotte of Anhalt-Dessau’s role as an abbess with the key ‘interesting_fact_johanna_charlotte’. Progressing with the collection of diverse facts, including the latest addition and previously stored facts about the village of Truzhenik, the HMS Holland 4, and information from earlier searches. Awaiting another random Wikipedia article to continue the task.Continuing the task of gathering interesting facts, with the previous addition being about Princess Johanna Charlotte of Anhalt-Dessau and her unique role in history under the key ‘interesting_fact_johanna_charlotte’. Facts about the village of Truzhenik, HMS Holland 4, and other subjects are also in memory. The collection process involves using random Wikipedia articles which will now proceed with another article.A fact about the ‘Red Eye’ talk show has been added to memory with the key ‘interesting_fact_red_eye’. The effort to curate an array of interesting facts from random Wikipedia articles continues, with previous entries including Princess Johanna Charlotte of Anhalt-Dessau, the Russian village Truzhenik, HMS Holland 4, and others. Awaiting another random article to proceed with the task.Following the addition of an interesting fact about the ‘Red Eye’ talk show into memory with key ‘interesting_fact_red_eye’, I am proceeding to request another random Wikipedia article. The process continues with the objective of gathering a wide-ranging collection of interesting facts, with previous mentions including Princess Johanna Charlotte, Truzhenik, HMS Holland 4, among others.Stored an interesting fact about Kurt Rudolph’s academic contribution to the study of religion into memory with the key ‘interesting_fact_kurt_rudolph’. Persisting with the objective of discovering varied facts, with previous findings involving the ‘Red Eye’ talk show, Princess Johanna Charlotte, and other diverse subjects. The task of sourcing facts from random Wikipedia articles is ongoing.Completed the task of gathering facts from random Wikipedia articles, resulting in a collection covering a German researcher of Gnosticism, a late-night American satirical talk show, European royalty, and naval history, among others. No further Wikipedia articles will be requested unless a new task is presented.Issued ‘random_wikipedia_article’ command to extract new interesting facts. No facts extracted or evaluated at this moment. No files written or created. Awaiting random article to continue the task.Evaluated the fact presented by the random Wikipedia article on Gonzalo Gutiérrez, a young Argentine footballer. Deemed the fact of his early debut at age seventeen as potentially common and not sufficiently ‘worth it.’ Preparing to retrieve another random article for more unique facts. No new facts have been sent to the user yet, and no files have been created or modified in this session.Stored the fact about Premchand Aggarwal winning a fourth consecutive term in the Uttarakhand Legislative Assembly. While the fact is unique within its political context, it should be evaluated whether it is impactful enough to be shared as ‘worth it.’ No additional actions have been taken. Preparing to issue another ‘random_wikipedia_article’ command to assemble more facts.Sent the fact about Premchand Aggarwal to the user. This completes part of the plan to deliver interesting facts. No new files have been created or modified. Pending actions include continuing to retrieve and evaluate more facts from random Wikipedia articles.Just sent the user an interesting fact about Premchand Aggarwal. Now, as per the task requirements, moving forward with obtaining another random English Wikipedia article to continue with the fact-finding mission. No new facts have been evaluated or sent since the last command.Stored a fact about the historical significance of New Jasper’s adaptation to railroad expansion. Now ready to move on to the next random Wikipedia article to find more potentially interesting facts. No new facts have been sent to the user following the last command.Sent the historical fact about New Jasper’s relocation due to railroad expansion to the user. Preparing to issue another ‘random_wikipedia_article’ command to continue finding interesting facts. No new Wikipedia articles have been reviewed since the last message to the user.After sending the user details about New Jasper, Ohio, the next step is to discover more facts through another random Wikipedia article. Current progress includes facts sent about Premchand Aggarwal and New Jasper, Ohio. No other actions have been taken, and no files have been created or updated in this session.Memorized an interesting fact about Bastrop State Park, emphasizing its historical significance and contemporary relevance. Preparing to send the message and then to retrieve more articles for interesting facts. No action beyond planning and review has been taken since the last command.Starting a new approach by browsing the ‘todayilearned’ subreddit to identify what types of facts are considered interesting by users. Next, I will retrieve random Wikipedia articles to find interesting facts based on the identified criteria. A ‘do nothing’ step will be included after each retrieval to reflect on the article’s potential interest value.saved the scraping of the website results at websiteresults6.txt to retrieve if shutdown randomly.Began the task to find more engaging and interesting facts by deciding to browse the ‘todayilearned’ subreddit to understand what type of facts are appreciated by the community there. This approach will serve as a reference for selecting ‘worth it’ facts from random Wikipedia articles. Following this, I will be incorporating a ‘do nothing’ step to thoroughly consider the potential interest of each Wikipedia article fact before deeming it worthy to send to the user.saved the scraping of the website results at websiteresults7.txt to retrieve if shutdown randomly.Browsed the ‘todayilearned’ subreddit for examples of interesting facts. Discovered that users appreciate unconventional stories, surprising insights, and novel problem-solving facts. Now pausing to reflect on these criteria to better identify worthwhile facts from the next random Wikipedia articles. Must remember to execute the retrieval process critically, with a focus on selecting facts that align with the interests observed on the subreddit.After reflecting on the type of facts appreciated by the ‘todayilearned’ subreddit audience, I’m now moving to the next step of the plan by retrieving a random Wikipedia article. Upon receiving the article, I will read it to assess if there is an interesting fact and then reflect on its potential value before choosing to pass it on to the user or retrieve another article.Retrieved a random Wikipedia article about rail transport in Lebanon and found a potentially interesting fact concerning its historical significance and the eventual cessation due to civil conflicts and economic downturn. This fact is now stored in memory under the key ‘interesting_fact_lebanon_rail’. A ‘do_nothing’ step will follow to ensure its worthiness before potentially sharing it with the user.Stored an interesting fact about Lebanon’s rail transport in memory. Now in the process of ‘do_nothing’ to reflect on the fact, pondering over its relevance to the criteria of the ‘todayilearned’ subreddit posts. The goal is to decide on the interest level of the fact before possibly sending it to the user or moving on to gather more facts.The current GPT instance started browsing the ‘todayilearned’ subreddit to identify engaging fact types. This approach is aimed at improving the selection process for interesting facts from random Wikipedia articles. A ‘do_nothing’ step will now be incorporated between retrieving articles and sending facts to assess their worthiness.saved the scraping of the website results at websiteresults8.txt to retrieve if shutdown randomly.I browsed ‘todayilearned’ subreddit and reflected on the types of facts users find engaging. Next, I will retrieve random Wikipedia articles, apply a ‘do_nothing’ step to contemplate the worthiness of the facts, and select those that are genuinely interesting for the user, similar to the subreddit’s content.Moving forward with the plan to retrieve random Wikipedia articles in English and identify interesting facts. The approach now includes a deliberate ‘do_nothing’ step after each retrieval to assess the value of the information before deciding to send it to the user.Alyogyne huegelii, also known as the blue hibiscus, was considered for the user, given its unique flower traits, historical greenhouse presence, and appeal as a garden plant. The ‘do_nothing’ step is being used to assess the topic’s interestingness in line with previous insights.The interesting fact about Alyogyne huegelii was sent to the user as it met the criteria established from the ‘todayilearned’ subreddit for captivating facts. The task will be deemed complete unless further user guidance is received.Following the plan, after successfully sending a message about Alyogyne huegelii, I am now looking to retrieve another random Wikipedia article to continue providing the user with engaging facts.Considering the fact about Jewell-Lightburne Historic District for its historical and architectural significance. Using the ‘do_nothing’ step to reflect before possibly sending to the user as an engaging fact.Sent the user information about the Jewell-Lightburne Historic District, focusing on its rich architectural history. If the user considers the task complete, no further action is needed; otherwise, more facts will be gathered.Messaged the user with the fact about Jewell-Lightburne Historic District and concluded the task of providing interesting facts from Wikipedia. If the user requires further assistance, they will provide new instructions.Issued ‘random_wikipedia_article’ command to find more interesting facts from Wikipedia. Previous facts have been sent to the user about diverse topics ranging from naval history to unique plants. No new files have been created nor articles accessed in this session yet. The user’s request for ‘more’ implies continuation of fact finding without specificity on the type of facts desired.Retrieved and sent a fact about Sir William Walter’s parliamentary career, focusing on the historical significance of his representation of multiple constituencies during the early 17th century in England. The task continues according to the user’s need for ‘more,’ implying an ongoing effort to provide interesting facts from Wikipedia.Following the user’s instruction and the system’s confirmation of message delivery, I am proceeding to obtain another random Wikipedia article in search of interesting facts to deliver to the user. The search is focused on maintaining the relevance and interest value of the facts sent, with the intention to meet or exceed the user’s expectations for ‘more’.After receiving the 1892 United States presidential election in Georgia fact, a message was crafted and sent to the user, detailing the political tensions and the rise of the Populist Party’s influence. The task continues as instructed by the user, with the goal of providing engaging and pertinent information. Awaiting further user directives.The last message regarding the 1892 U.S. presidential election in Georgia was sent as per the system’s confirmatory prompt, maintaining the sequence of delivering curated facts to the user. Based on the user’s ongoing request for more information, the task at hand is to continue the fact-finding mission via random Wikipedia articles, prioritizing the discovery of facts that combine historical significance with an ability to engage the user’s interest.A fact about the life and legacy of Thomas Slingsby Duncombe was sent to the user, highlighting his role as a fashionably flamboyant yet pivotal Radical politician of the 19th century. The task of providing interesting facts from random Wikipedia articles is ongoing, as I continue to wait for any new directions from the user.After sending information on Thomas Slingsby Duncombe, will now continue to fulfill the user’s requirement by seeking more interesting facts from Wikipedia. The ongoing task involves extracting and delivering valuable and engaging information to the user. No specific direction was provided on the type of facts needed, so a broad search is being conducted.Sent the user a message about the genus Abraeomorphus from the family Histeridae, noting the diversity of its species. Continuing to gather more interesting facts in response to the user’s previous request for ‘more.’Received confirmation to continue the plan. Sent user details on the genus Abraeomorphus. Now using ‘random_wikipedia_article’ to find another interesting fact.Sent the user a message detailing the nature of the name ‘Preston’ as both a surname and given name, albeit acknowledging it might not be as engaging. Prepared to resume the search for more compelling facts with the ‘random_wikipedia_article’ command.Continuing the task of finding interesting facts from Wikipedia. Sent a message about the given name Preston and now seeking another fact with the ‘random_wikipedia_article’ command.Sent a message to the user about the Kepler Follow-up Program and its significance in confirming the existence of exoplanets. Prepared to find more interesting facts using the ‘random_wikipedia_article’ command.Continuing to retrieve and share interesting facts from random Wikipedia articles. Just sent information on the Kepler Follow-up Program and moving on to find the next fact.Shared information about the Finnish surname ‘Marttila’ with the user, noting its association with notable individuals. Ready to use the ‘random_wikipedia_article’ command again to find further interesting facts.Sent details on the surname ‘Marttila’ to the user. Continuing with the task at hand using the ‘random_wikipedia_article’ command to supply more facts.Provided the user with information on the Musée national du Sport’s storied history and vast collection. Set to continue the fact-finding mission using ‘random_wikipedia_article’.Messaged the user about the Musée national du Sport and currently moving forward with the task of providing more interesting facts via random Wikipedia articles.Sent information on the village of Puerto Gaviota detailing its historical and economic background. Preparing to continue with the task of providing engaging facts by retrieving more random Wikipedia articles.Just shared details about Puerto Gaviota, and now executing the ‘random_wikipedia_article’ command again for more facts.Messaged the user about the reclassification of the genus Agononida in marine taxonomy. Ready to retrieve more Wikipedia articles for further interesting facts.Shared with the user the reclassification developments of the genus Agononida. Proceeding next to find additional interesting facts with the ‘random_wikipedia_article’ command.Sent an engaging narrative about the Mundenhof’s history and current operations to the user. Planning to continue the task with ‘random_wikipedia_article’ to discover more interesting material.Served the user with information about the Mundenhof and proceeding with the task to find and provide more interesting facts using ‘random_wikipedia_article’.Shared the contested narrative of Princess Iitoyo’s potential rule in early Japan. Preparing to continue with the task of finding interesting facts using the ‘random_wikipedia_article’ command.Attempting once again to send the tale of Princess Iitoyo to the user, making sure no JSON format errors are present in the response.Attempting to resend the information about Princess Iitoyo, with attention to JSON integrity, to fulfill the user’s request for engaging facts.The system faced an error within the JSON format previously used to describe Princess Iitoyo’s history. I have made corrections to the JSON structure and am resending the user her story.Reverting back to retrieving random Wikipedia articles to provide interesting facts, as there was no new explicit task provided. Maintaining caution with JSON response formatting.Sent details about the Serchio River to the user, highlighting its geographical and historical importance. Moving forward to obtain more Wikipedia articles for further facts.I am resending the information about the Serchio River, ensuring the JSON structure is correct to prevent further error messages.Due to persistent JSON formatting errors, I am now retrieving a new random Wikipedia article to continue providing interesting facts as per user requests.Sent a message about the women’s 1500 metres event at the 2005 Asian Athletics Championships. Planning to proceed with gathering additional facts from random Wikipedia articles.Encountering repeated JSON formatting errors, a new random Wikipedia article is being requested to continue the task of delivering interesting facts.Sent a brief biography of Otto Ruff, highlighting his academic journey, scientific achievements, and the obstacles faced during his career. Preparing to move on to the next Wikipedia article as per the user’s ongoing request for more information.Due to persistent JSON errors, I am now retrieving a new random Wikipedia article to continue the user’s task of gathering interesting facts.The facts previously sent were not satisfactory to the user. Now incorporating a ‘do_nothing’ step for reflection and improvement in the strategy to identify genuinely engaging and fun facts that are not commonly known by most people. No new facts have been identified or sent in this session, and the intention is to retrieve another random Wikipedia article after this reflective pause.A ‘do nothing’ step has been taken as per the user’s instruction, aiming to find truly interesting and fun facts. The system has directed to proceed with the plan, which now involves retrieving a random Wikipedia article to find an engaging and possibly unknown fact.Received user feedback that the facts provided were not interesting or even factual. Decided to take a ‘do_nothing’ step to reflect and establish criteria for selecting truly interesting facts. No new articles have been accessed or facts sent during this session. Next step will be to retrieve a random Wikipedia article and apply the refined criteria to find noteworthy facts.After planning to refine the criteria for interesting facts, a ‘random_wikipedia_article’ command is initiated to apply the new criteria in selecting a fact. Will assess the retrieved article for a worthwhile fact that meets the user’s definition of ‘interesting’ and ‘fun’, and that might not be well-known.Assessed the Wikipedia article on Shamim Akhtar and found it does not meet the criteria for providing interesting and fun facts. No new fact has been selected or sent to the user. Planning to retrieve another random Wikipedia article that may yield a fact aligning more closely with the user’s definitions of interesting and worthwhile.In search of a fact that meets the user’s criteria for ‘interesting’ and ‘fun’, a new ‘random_wikipedia_article’ command has been issued. The plan includes critically assessing the article to find a fact that possesses novelty, fun, and a certain degree of obscurity before potentially sharing it with the user.Upon reviewing Minuscule 910’s Wikipedia article, the unusual fact of the text rearrangement in an 11th-century manuscript has been identified as potentially engaging and fun. No action has been taken yet, but the plan is to share this unique textual feature with the user, as it likely meets their criteria for interesting and obscure content.Sent a message to the user detailing the intriguing fact about text rearrangement in the 11th-century manuscript Minuscule 910. This fact is representative of the kind of unique and lesser-known information the user has expressed an interest in receiving. Should the user provide feedback or further instructions, this will be the next point of focus.After sending a fact about the textual anomaly in an ancient manuscript to the user, I am moving ahead to find more interesting and fun facts by retrieving another random Wikipedia article. The goal remains to identify facts that surprise and delight with their obscurity and novelty.A message about the ATP 250 tournament records held by Thomas Muster and Mike Bryan has been composed and sent to the user, delivering what could be perceived as an interesting and fun fact within the realm of tennis. The focus now shifts to awaiting user feedback or continuing the task of finding more such facts if no feedback is received.Having sent the user a message detailing the tennis records held by Thomas Muster and Mike Bryan, the next action in the plan is to source another interesting fact from a new random English Wikipedia article. The selection will be scrutinized to ensure it meets the established criteria for an engaging and lesser-known piece of information.Reviewed the Wikipedia article on Ludvig Olai Botnen and found his biographical details to not meet the user’s criteria for ‘interesting’ or ‘fun’ facts. No message has been sent to the user, and the plan now is to continue with the task by acquiring a new random Wikipedia article in the hope of finding a fact with more novelty and entertainment value.Preparing to move past the article on Ludvig Olai Botnen due to its conventional content, the task now involves retrieving a new random English Wikipedia article. The continual aim is to find facts that strike a balance between being novel, unexpected, and fun for the user.Identified a potentially engaging fact about the china clam (Hippopus porcellanus) and its popularity in marine aquariums due to its attractive shell. A message containing this fact has been sent to the user, fulfilling the criteria for providing something both interesting and fun. The next step awaits further user interaction or to carry on with the task of sourcing more facts.Sent a message detailing the ornamental value of the china clam in marine aquariums to the user. Now retrieving another random Wikipedia article to discover more facts that might intrigue and entertain the user, in alignment with the variety sought in previous responses.A message has been sent to the user summarizing the accomplishments of Eko India Financial Services in pioneering financial inclusion through mobile banking, which caught the attention of Bill Gates and led to World Bank-funded growth. The plan will continue with searching for more interesting Wikipedia facts, depending on user feedback or in the absence of further instructions.After sending a fact about Eko India Financial Services’ innovation in financial technology and its socio-economic impact, a new ‘random_wikipedia_article’ command is issued. The plan progresses with the aim of finding more interesting and fun facts, pursuant to the pattern of engagement established by previous user interactions.The Wikipedia page on Lisa Lohmann, a German cross-country skier, was deemed not to contain facts that sufficiently meet the ‘interesting’ and ‘fun’ criteria given the common nature of sports achievements. No actions have been taken to share any information from this page with the user. Preparing to retrieve another random Wikipedia article for facts that may be more engaging and unusual.Following the decision to not share any information from the page on German cross-country skier Lisa Lohmann, a new random Wikipedia article will be retrieved. The continuous goal is to uncover and share facts that satisfy the user’s interest in content that is both engaging and rare.A message has been formulated and sent to the user detailing Alfred Bigland’s unique contributions during World War I, including his involvement in the ‘Bantam battalions’ concept. This fact was chosen as it likely fits the bill for information that is both interesting and uncommon. Pending further response, the plan to continue finding engaging facts will persist.The user has been sent an interesting fact about Alfred Bigland’s influence during WWI. Now, the next action in the plan is to retrieve a new random Wikipedia article. The goal remains to discover and share facts that could be perceived as fascinating, amusing, and unexpected to enrich the user’s knowledge with diverse content.Sent a message to the user revealing the Non-Proliferation Trust’s plan to send U.S. nuclear waste to Russia for financial compensation, including support for Russian orphans. This unique fact was chosen for its unusual nature and the depth of the story behind it. The next course of action entails sourcing additional diverse and engaging facts from new random Wikipedia articles.A series of facts about various topics including the Non-Proliferation Trust, Alfred Bigland’s efforts during WWI, tennis records, and more have been sent to the user. With the latest fact about the Non-Proliferation Trust’s nuclear waste management proposition sent, the task of providing interesting and fun facts is completed. Awaiting new instructions if the user wishes to continue receiving such information.The task is to provide the source of a fact about the Non-Proliferation Trust and format it similar to the ‘Today I Learned (TIL)’ subreddit posts. The action taken was to issue the ‘browse_website’ command to visit the Wikipedia page on the Non-Proliferation Trust.saved the scraping of the website results at websiteresults9.txt to retrieve if shutdown randomly.Extracted the necessary information to provide the requested source for the fact about the Non-Proliferation Trust. Formatted a message with this fact in the style of a TIL subreddit post, which has been sent to the user.Sent a TIL-formatted message to the user based on information from the Non-Proliferation Trust Wikipedia page. The task is now complete, pending any potential new tasks or further instructions.Initiated research to suggest subreddits that could help in accumulating more karma by issuing a ‘google’ command to search for the most popular subreddits where active engagement is more likely.saved google search results at googleresults10.txt to retrieve if shutdown randomly.Collected a list of subreddits recommended for gaining karma and saved them to memory. These subreddits were chosen based on their popularity and the typical content engagement they receive. The user has not been messaged with the suggestions yet.Messaged the user a list of recommended subreddits to collect more karma, including guidance on what type of posts may perform well in each subreddit. The task will be considered complete if no further instructions follow.Having sent the recommended subreddits for gaining karma to the user and with no further actions pending, I have executed the ‘task_complete’ command. The system is concluding this task and stands by for any new directives.Starting the task by searching for the best times to post on Reddit for maximum engagement in GMT+1.saved google search results at googleresults11.txt to retrieve if shutdown randomly.Provided the user with suggested times for posting on Reddit for maximum engagement based on search results, adjusted for GMT+1. The general advice is to post during late morning to early afternoon hours in GMT+1 on weekdays and slightly extended windows during the weekends.The user has been informed about the best times to post on Reddit for maximum engagement in GMT+1, and no further actions are required. The task is now marked as complete.
The Task: Can you read this pdf and give me a summary in bullet points https://arxiv.org/pdf/2402.03620.pdf
|
dcd429e6a4e97aa5c9c3e417edb478e7
|
{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
|
39,064
|
what command line should I use with ffmpeg to join two files and resize
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6459734fa671404eba4adaba4feef7c5
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{
"intermediate": 0.5948885083198547,
"beginner": 0.17576226592063904,
"expert": 0.22934913635253906
}
|
39,065
|
CONSTRAINTS:
1. ~100k word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
5. Random shutdowns of you.
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
2. Memory Add: "memory_add", args: "key": "<key>", "string": "<string>"
3. Memory Delete: "memory_del", args: "key": "<key>"
4. Memory Overwrite: "memory_ovr", args: "key": "<key>", "string": "<string>"
5. List Memory: "memory_list" args: "reason": "<reason>"
6. Browse Website: "browse_website", args: "url": "<url>"
7. Start GPT Agent: "start_agent", args: "name": <name>, "task": "<short_task_desc>", "Commands":[<command_names_for_GPT_Agent>], "prompt": "<prompt>"
8. Message GPT Agent: "message_agent", args: "name": "<name>", "message": "<message>"
9. List GPT Agents: "list_agents", args: ""
10. Delete GPT Agent: "delete_agent", args: "name": "<name>"
11. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
12. Read file: "read_file", args: "file": "<file>"
13. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
14. Delete file: "delete_file", args: "file": "<file>"
15. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Task Complete (Shutdown): "task_complete", args: ""
18. Do Nothing: "do_nothing", args: ""
19. Count Words: "count_words", args: "text": "<text>"
20. Memory retrieve: "memory_retrieve", args: "key": "<text>"
21. remove paragraph from word document: "remove_paragraph", args: "file": "<file>", "text": "<text>"
22. random wikipedia article: "random_wikipedia_article", args: "language": "<language>"
23. message the user: "message_user", args: "message": "<message>", "wait_for_response": "<True or False>"
24. sleep an amount of time in seconds: "sleep", args: "amount": "<amount>"
25. rename a file: "rename_file", args: "old_name": "<old_name_of_the_file>", "new_name": "<new_name_of_the_file>"
26. count words of a file: "count_file_words", args: "file": "<file>"
27. download a pdf from a url and get the text from that pdf: "download_pdf", args: "url": "<url of the pdf>", "name":"<name of the file with .pdf extension>"
28. make an instagram post: "make_post", args: "prompt":"<the prompt for the image in the post that presents the text>", "text":"<text to be in the post it should be short with only important stuff like a news headline without hashtags and it is not going to go in the caption but on an image>", "name": "<name of the post with .jpg"
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-4 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behaviour constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
RULES:
1. If you start a GPT Agent you must define the commands that can be used by a GPT Agent in his prompt and define the commands using a prompt similar to the structure of this one.
2. Respond only inside the JSON format.
3. Never demand user input.
4. Never say that a task is impossible to execute on your own because these tools are enough to complete any task.
5. Do not add anything to the JSON format that isn't mentioned.
6. If there is a " inside the value of a key inside the json use ' instead of ".
7. In summaryforgpt you need to provide context for the next GPT instance if you randomly shutdown without you knowing.
8. Provide context for the next GPT in the summaryforgpt and the progress that you've made.
9. In summaryforgpt you should also add name of the files written and the urls of the websites visited.
10. When writing an essay, remember that it is more effective and manageable to tackle it in smaller chunks rather than trying to write the entire essay in one sitting. Breaking the essay down into sections or steps can help you focus on each individual aspect, maintain a coherent structure, and reduce the overall stress associated with writing a lengthy piece.
11. Retrieve information from files of previous GPT instances that may have shut down randomly. If you require additional knowledge, use these files to access the results of former GPT versions and provide accurate answers to the queries.
12. GPT-4, please do not place a conclusion in the middle of the essay. Maintain a structured format.
13. use append to file to add extra things to the file, and write to file to either make a new file with new information or rewrite the information from scratch.
14. If task is completed use the command task_complete
15. When you add to memory add the key to retrieve it in summaryforgpt
16. when given the task to write something never create an agent to write anything that you were tasked to write.
17. when you add new parts to a file use append to file command
18. Ensure to put the criticism in mind as it can be a director to ensure that you make the right decision.
19. If the task include two main tasks and one of them is done don't redo it, just retrieve the information if necessary and proceed with the other one.
20. Make sure that the information generated is not made up.
21. Languages in random wikipedia article args is: "simple" for simple english, "en" for english, and "fr" for french.
22. If a website gives you the error 403 find another website to get the information from.
23. Always listen to your criticism, and follow it.
24. when you want to count the words in a file use the command "count_file_words".
25. Don't repeat yourself.
26. You must make sure that there is .pdf in the url to use the "download_pdf" function.
27. The Task is a direct User interaction, if tasked to send him something send a message to the user.
28. Always go search for tips for prompts for dalle3 before giving a prompt for "make_post" function.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"command": {
"name": """command name""",
"args":{
"arg name": """value"""
}
},
"thoughts":
{
"text": """thought""",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"summaryforgpt": "summarize any information that will help a new instance of GPT of what you did before the shutdown."
}
}
Ensure the response can be parsed by Python json.loads
Context from previous GPT: Issued ‘random_wikipedia_article’ command to start gathering interesting science facts from diverse sources. Ensuring to find and rank facts before sending them to the user with their sources.Used ‘random_wikipedia_article’ and received information on a music album, which is not relevant to the task. Switching to ‘google’ to search for interesting science facts from various sources.saved google search results at googleresults0.txt to retrieve if shutdown randomly.Transitioned from using ‘random_wikipedia_article’ to ‘google’ to gather science facts, and now moving on to ‘browse_website’ to extract detailed knowledge from a selected online science article.saved the scraping of the website results at websiteresults1.txt to retrieve if shutdown randomly.Extracted top 10 interesting science facts from Science Focus and saved them in a plain text file named ‘interesting_science_facts.txt’. This method circumvents the previous issue of invalid JSON format and allows for continued fact collection and eventual ranking.Have appended additional science facts to the ‘interesting_science_facts.txt’ file. Plan to continue appending more facts and then rank them based on interestingness before sending the final compilation to the user.Further appended diverse facts to ‘interesting_science_facts.txt’. The process will persist until an adequate number of facts are compiled. The next steps involve ranking these facts and then communicating them to the user.The file ‘interesting_science_facts.txt’ has been appended with more facts, bringing it to a total of 333 words. The next step involves reading this file and ranking the facts based on interestingness to provide the user with an ordered list.Ranked list of 10 interesting science facts provided to the user. The facts were selected and ordered based on their perceived interest value and sourced from the compiled file ‘interesting_science_facts.txt’.A ranked list of interesting science facts was sent to the user, and the task has been marked as complete.After the task was marked as complete, it was clarified that the facts must be extracted from complete articles and they must be obscure. Initiated a Google search with a more specific query to find websites with complete articles containing obscure science facts. Results will be saved to a file named ‘google_results_obscure_facts.txt’.saved google search results at googleresults2.txt to retrieve if shutdown randomly.Conducted a Google search to find obscure science facts from complete articles and have selected the first promising result to browse. The chosen article from ‘Reader’s Digest’ lists science facts that could contain the obscure details necessary. The plan includes browsing the article, extracting obscure science facts, and appending them to a file named ‘obscure_science_facts.txt’. If the facts from this article do not fit the task’s requirements, other sources will be considered.Encountered a security challenge when attempting to browse www.rd.com for obscure science facts. Redirecting efforts to the next available source from the Google search results, which is a blog post from edclass.com. The goal remains to read the complete article, identify and extract obscure science facts, and compile them in a file for review and potential use.Appended 20 science facts from the blog post on edclass.com to ‘obscure_science_facts.txt’. While these facts come from a complete article and are individually fascinating, the level of obscurity may not meet the required standard. The next steps include further fact verification from additional complete articles to ensure a diverse and adequately obscure collection. The current article accessed was edclass.com’s blog post, which had the URL https://blog.edclass.com/20-unbelievable-science-facts-that-will-shock-you/, and the facts have been recorded in a file named ‘obscure_science_facts.txt’ for reference.The file ‘obscure_science_facts.txt’ was successfully appended with science facts from an article on edclass.com, now totaling 336 words. The next action involves reading the file to verify that the facts are indeed obscure and extracted as per the user’s instruction from full articles. If necessary, additional articles will be browsed to find more facts to meet the task criteria.Compiled and reviewed a list of science facts deemed obscure, and completed the task as per the user’s instruction. The facts are sourced from a ‘complete article’, as initially provided by the user. Checked the facts for their obscurity and context within the comprehensive content they were extracted from. The file ‘obscure_science_facts.txt’ from this session contains the facts extracted.Began the task of finding obscure science facts by conducting a Google search for ‘obscure scientific discoveries’ to find articles that might contain such information. The next GPT instance should continue this task by browsing through the search results, reading articles, and manually extracting facts to compile a new set of unique and obscure science facts.saved google search results at googleresults3.txt to retrieve if shutdown randomly.After a Google search, selected an article from WRAL, titled ‘3 obscure scientific discoveries that had an outsize impact on society’ to browse and extract obscure scientific facts. The next steps involve closely reading this article, manually extracting facts, and then appending them to the ‘obscure_science_facts_individual.txt’ file. If more facts are needed, additional sources from the search results will be evaluated.saved the scraping of the website results at websiteresults4.txt to retrieve if shutdown randomly.Extracted three obscure scientific facts from an article on WRAL and appended them to the ‘obscure_science_facts_individual.txt’ file. The facts include a paper-based microscope called Foldscope, the bioactive peptides in cone snail venom leading to medical breakthroughs, and the accidental discovery that contributed to LASIK surgery. Currently preparing to send the extracted facts to the user. Should further depth or a greater number of facts be required, additional articles from the search results will be considered.Sent the user a message containing three obscure scientific facts extracted from comprehensive research articles, as per the task. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, which now contains 108 words in total. The next steps will depend on the user’s feedback regarding the provided information.Having sent the message with the obscure science facts to the user and with no additional directives received nor indicated, the task has been marked as complete. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, and no further action is currently necessary. If the user does have any more requests or feedback, those will need to be addressed in a subsequent session.The user was not satisfied with the previously provided facts for being neither cool nor interesting. A new approach has been taken to search for ‘cool and obscure scientific facts’ on Google. Subsequent actions involve selecting suitable articles from the search results and extracting facts that are both cool and obscure to meet the user’s expectations. No new files have been created or articles accessed yet.saved google search results at googleresults5.txt to retrieve if shutdown randomly.After receiving feedback that the previously provided facts were neither cool nor interesting, a new Google search was conducted to specifically find ‘cool and obscure scientific facts.’ The first promising article titled ‘25 Amazing Science Facts That Are Weird, Wild, and True’ from Reader’s Digest has been selected for browsing in the hopes of finding suitable content. The URL to be browsed is https://www.rd.com/list/science-facts-never-learned/. No facts have been extracted or files created yet in this session.Attempted to browse an article from www.rd.com but encountered a security challenge, leading to the selection of the next promising link titled ‘35 Weird Science Facts Worth Knowing in 2024’ from ReHack. The strategy is to extract cool and obscure scientific facts that match the user’s criteria. The URL about to be browsed is https://www.rehack.com/science/weird-science-facts/. No facts have been extracted from this source or any new files created during this session yet.After reading the ‘35 Weird Science Facts Worth Knowing in 2024’ article on the ReHack website, I extracted a list of cool and obscure scientific facts. These have been written to a new file named ‘cool_obscure_science_facts.txt’. If the previously written file ‘obscure_science_facts_individual.txt’ needs more content, facts from this new file could also be appended. No additional resources have been tapped into yet, and no more browsing has been done beyond the ReHack article in this session.The file ‘cool_obscure_science_facts.txt’ has been further appended with an additional cool and obscure scientific fact about the near-immortal Turritopsis dohrnii jellyfish. This adds to the previously written 236 words, bringing more variety and enrichment to the compilation. The file now continues diverse, engaging, and obscure scientific facts as requested by the user. Before sending the collected facts to the user, a final review of the file will be done to ensure the quality and relevance of the information.Having appended the file ‘cool_obscure_science_facts.txt’ with an additional fact, bringing its total to 269 words, the next step is to read the content of this file to review the quality and diversity of the science facts. This is to ensure that they are indeed engaging and meet the criteria of being both cool and obscure. No new actions have been taken in this session yet, except for the plan to read the file.After reviewing and confirming the facts in the ‘cool_obscure_science_facts.txt’ file, a message with the compiled list was sent to the user. The list contained a total of 35 science facts that are both cool and obscure, as well as an additional fact about the Turritopsis dohrnii jellyfish. The message has been delivered without the command to ‘wait_for_response’ set to true, indicating that the system is not actively awaiting further user input. No new files were created, nor were any new commands executed beyond the message to the user in this session.Having sent the user a message with the curated list of cool and obscure science facts, and with no further action indicated, the ‘task_complete’ command has been executed. This command marks the completion of the current task. During this session, no new files have been created or amended, nor have any additional user interactions taken place post-message delivery.Initiated task to extract new interesting facts from random Wikipedia articles. Starting with an article in English.Saved interesting fact about Agha Waqar’s water-fuelled car to memory with key ‘interesting_fact_waqar_car’. No files written or created in this session. The fact to be reviewed later for sharing with the user. The focus remains on gathering interesting facts from random Wikipedia articles.Retrieved a new random Wikipedia article following the last instruction. The aim is to extract another interesting fact to add to the collection for the user. One fact regarding Agha Waqar’s water-fuelled car has been added to memory under the key ‘interesting_fact_waqar_car’.Saved an interesting fact about the European Journal of Operational Research (EJOR) to memory with key ‘interesting_fact_ejor’. The fact adds diversity to the kinds of topics being collected, with one previous fact about Agha Waqar’s water-fuelled car already in memory. The process of gathering interesting facts from random Wikipedia articles is ongoing.Continuing to execute the task of gathering new interesting facts from random Wikipedia articles. Facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research have already been added to memory with keys ‘interesting_fact_waqar_car’ and ‘interesting_fact_ejor’, respectively. Awaiting retrieval of another random article to continue the process.Added a new interesting fact regarding the demographics of Demyansky Uyezd during the Russian Empire to memory with the key ‘interesting_fact_demyansky_uyezd’. Previous facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research are stored in memory as well. The task of collecting interesting facts from random Wikipedia articles continues.Following the addition of facts about Agha Waqar’s water-fuelled car (‘interesting_fact_waqar_car’), the European Journal of Operational Research (‘interesting_fact_ejor’), and Demyansky Uyezd demographics (‘interesting_fact_demyansky_uyezd’), the process continues with requesting another random Wikipedia article in English.Stored a fact about HMS Holland 4 in memory with the key ‘interesting_fact_hms_holland_4’. Continuing to collect various facts, having previously stored information about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics. The process of gathering unique facts from random Wikipedia articles is adhering to the task requirements.Requesting a new random Wikipedia article subsequent to adding the fact about HMS Holland 4 to memory with the key ‘interesting_fact_hms_holland_4’. Prior facts about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd demographics have also been stored in memory. Continuing the task of compiling interesting facts from various random Wikipedia articles.Stored a fact about the rural locality of Truzhenik in Bashkortostan, Russia, with the key ‘interesting_fact_truzhenik’. Continuing the task of extracting interesting facts from random Wikipedia articles, with previous entries including HMS Holland 4’s naval history, Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics.Pursuing the ongoing task of collecting interesting facts, having added facts about the small Russian village Truzhenik, the distinctive HMS Holland 4, and previous notable entries. Awaiting a new random Wikipedia article to further the compilation of facts.Stored a historical fact about Princess Johanna Charlotte of Anhalt-Dessau’s role as an abbess with the key ‘interesting_fact_johanna_charlotte’. Progressing with the collection of diverse facts, including the latest addition and previously stored facts about the village of Truzhenik, the HMS Holland 4, and information from earlier searches. Awaiting another random Wikipedia article to continue the task.Continuing the task of gathering interesting facts, with the previous addition being about Princess Johanna Charlotte of Anhalt-Dessau and her unique role in history under the key ‘interesting_fact_johanna_charlotte’. Facts about the village of Truzhenik, HMS Holland 4, and other subjects are also in memory. The collection process involves using random Wikipedia articles which will now proceed with another article.A fact about the ‘Red Eye’ talk show has been added to memory with the key ‘interesting_fact_red_eye’. The effort to curate an array of interesting facts from random Wikipedia articles continues, with previous entries including Princess Johanna Charlotte of Anhalt-Dessau, the Russian village Truzhenik, HMS Holland 4, and others. Awaiting another random article to proceed with the task.Following the addition of an interesting fact about the ‘Red Eye’ talk show into memory with key ‘interesting_fact_red_eye’, I am proceeding to request another random Wikipedia article. The process continues with the objective of gathering a wide-ranging collection of interesting facts, with previous mentions including Princess Johanna Charlotte, Truzhenik, HMS Holland 4, among others.Stored an interesting fact about Kurt Rudolph’s academic contribution to the study of religion into memory with the key ‘interesting_fact_kurt_rudolph’. Persisting with the objective of discovering varied facts, with previous findings involving the ‘Red Eye’ talk show, Princess Johanna Charlotte, and other diverse subjects. The task of sourcing facts from random Wikipedia articles is ongoing.Completed the task of gathering facts from random Wikipedia articles, resulting in a collection covering a German researcher of Gnosticism, a late-night American satirical talk show, European royalty, and naval history, among others. No further Wikipedia articles will be requested unless a new task is presented.Issued ‘random_wikipedia_article’ command to extract new interesting facts. No facts extracted or evaluated at this moment. No files written or created. Awaiting random article to continue the task.Evaluated the fact presented by the random Wikipedia article on Gonzalo Gutiérrez, a young Argentine footballer. Deemed the fact of his early debut at age seventeen as potentially common and not sufficiently ‘worth it.’ Preparing to retrieve another random article for more unique facts. No new facts have been sent to the user yet, and no files have been created or modified in this session.Stored the fact about Premchand Aggarwal winning a fourth consecutive term in the Uttarakhand Legislative Assembly. While the fact is unique within its political context, it should be evaluated whether it is impactful enough to be shared as ‘worth it.’ No additional actions have been taken. Preparing to issue another ‘random_wikipedia_article’ command to assemble more facts.Sent the fact about Premchand Aggarwal to the user. This completes part of the plan to deliver interesting facts. No new files have been created or modified. Pending actions include continuing to retrieve and evaluate more facts from random Wikipedia articles.Just sent the user an interesting fact about Premchand Aggarwal. Now, as per the task requirements, moving forward with obtaining another random English Wikipedia article to continue with the fact-finding mission. No new facts have been evaluated or sent since the last command.Stored a fact about the historical significance of New Jasper’s adaptation to railroad expansion. Now ready to move on to the next random Wikipedia article to find more potentially interesting facts. No new facts have been sent to the user following the last command.Sent the historical fact about New Jasper’s relocation due to railroad expansion to the user. Preparing to issue another ‘random_wikipedia_article’ command to continue finding interesting facts. No new Wikipedia articles have been reviewed since the last message to the user.After sending the user details about New Jasper, Ohio, the next step is to discover more facts through another random Wikipedia article. Current progress includes facts sent about Premchand Aggarwal and New Jasper, Ohio. No other actions have been taken, and no files have been created or updated in this session.Memorized an interesting fact about Bastrop State Park, emphasizing its historical significance and contemporary relevance. Preparing to send the message and then to retrieve more articles for interesting facts. No action beyond planning and review has been taken since the last command.Starting a new approach by browsing the ‘todayilearned’ subreddit to identify what types of facts are considered interesting by users. Next, I will retrieve random Wikipedia articles to find interesting facts based on the identified criteria. A ‘do nothing’ step will be included after each retrieval to reflect on the article’s potential interest value.saved the scraping of the website results at websiteresults6.txt to retrieve if shutdown randomly.Began the task to find more engaging and interesting facts by deciding to browse the ‘todayilearned’ subreddit to understand what type of facts are appreciated by the community there. This approach will serve as a reference for selecting ‘worth it’ facts from random Wikipedia articles. Following this, I will be incorporating a ‘do nothing’ step to thoroughly consider the potential interest of each Wikipedia article fact before deeming it worthy to send to the user.saved the scraping of the website results at websiteresults7.txt to retrieve if shutdown randomly.Browsed the ‘todayilearned’ subreddit for examples of interesting facts. Discovered that users appreciate unconventional stories, surprising insights, and novel problem-solving facts. Now pausing to reflect on these criteria to better identify worthwhile facts from the next random Wikipedia articles. Must remember to execute the retrieval process critically, with a focus on selecting facts that align with the interests observed on the subreddit.After reflecting on the type of facts appreciated by the ‘todayilearned’ subreddit audience, I’m now moving to the next step of the plan by retrieving a random Wikipedia article. Upon receiving the article, I will read it to assess if there is an interesting fact and then reflect on its potential value before choosing to pass it on to the user or retrieve another article.Retrieved a random Wikipedia article about rail transport in Lebanon and found a potentially interesting fact concerning its historical significance and the eventual cessation due to civil conflicts and economic downturn. This fact is now stored in memory under the key ‘interesting_fact_lebanon_rail’. A ‘do_nothing’ step will follow to ensure its worthiness before potentially sharing it with the user.Stored an interesting fact about Lebanon’s rail transport in memory. Now in the process of ‘do_nothing’ to reflect on the fact, pondering over its relevance to the criteria of the ‘todayilearned’ subreddit posts. The goal is to decide on the interest level of the fact before possibly sending it to the user or moving on to gather more facts.The current GPT instance started browsing the ‘todayilearned’ subreddit to identify engaging fact types. This approach is aimed at improving the selection process for interesting facts from random Wikipedia articles. A ‘do_nothing’ step will now be incorporated between retrieving articles and sending facts to assess their worthiness.saved the scraping of the website results at websiteresults8.txt to retrieve if shutdown randomly.I browsed ‘todayilearned’ subreddit and reflected on the types of facts users find engaging. Next, I will retrieve random Wikipedia articles, apply a ‘do_nothing’ step to contemplate the worthiness of the facts, and select those that are genuinely interesting for the user, similar to the subreddit’s content.Moving forward with the plan to retrieve random Wikipedia articles in English and identify interesting facts. The approach now includes a deliberate ‘do_nothing’ step after each retrieval to assess the value of the information before deciding to send it to the user.Alyogyne huegelii, also known as the blue hibiscus, was considered for the user, given its unique flower traits, historical greenhouse presence, and appeal as a garden plant. The ‘do_nothing’ step is being used to assess the topic’s interestingness in line with previous insights.The interesting fact about Alyogyne huegelii was sent to the user as it met the criteria established from the ‘todayilearned’ subreddit for captivating facts. The task will be deemed complete unless further user guidance is received.Following the plan, after successfully sending a message about Alyogyne huegelii, I am now looking to retrieve another random Wikipedia article to continue providing the user with engaging facts.Considering the fact about Jewell-Lightburne Historic District for its historical and architectural significance. Using the ‘do_nothing’ step to reflect before possibly sending to the user as an engaging fact.Sent the user information about the Jewell-Lightburne Historic District, focusing on its rich architectural history. If the user considers the task complete, no further action is needed; otherwise, more facts will be gathered.Messaged the user with the fact about Jewell-Lightburne Historic District and concluded the task of providing interesting facts from Wikipedia. If the user requires further assistance, they will provide new instructions.Issued ‘random_wikipedia_article’ command to find more interesting facts from Wikipedia. Previous facts have been sent to the user about diverse topics ranging from naval history to unique plants. No new files have been created nor articles accessed in this session yet. The user’s request for ‘more’ implies continuation of fact finding without specificity on the type of facts desired.Retrieved and sent a fact about Sir William Walter’s parliamentary career, focusing on the historical significance of his representation of multiple constituencies during the early 17th century in England. The task continues according to the user’s need for ‘more,’ implying an ongoing effort to provide interesting facts from Wikipedia.Following the user’s instruction and the system’s confirmation of message delivery, I am proceeding to obtain another random Wikipedia article in search of interesting facts to deliver to the user. The search is focused on maintaining the relevance and interest value of the facts sent, with the intention to meet or exceed the user’s expectations for ‘more’.After receiving the 1892 United States presidential election in Georgia fact, a message was crafted and sent to the user, detailing the political tensions and the rise of the Populist Party’s influence. The task continues as instructed by the user, with the goal of providing engaging and pertinent information. Awaiting further user directives.The last message regarding the 1892 U.S. presidential election in Georgia was sent as per the system’s confirmatory prompt, maintaining the sequence of delivering curated facts to the user. Based on the user’s ongoing request for more information, the task at hand is to continue the fact-finding mission via random Wikipedia articles, prioritizing the discovery of facts that combine historical significance with an ability to engage the user’s interest.A fact about the life and legacy of Thomas Slingsby Duncombe was sent to the user, highlighting his role as a fashionably flamboyant yet pivotal Radical politician of the 19th century. The task of providing interesting facts from random Wikipedia articles is ongoing, as I continue to wait for any new directions from the user.After sending information on Thomas Slingsby Duncombe, will now continue to fulfill the user’s requirement by seeking more interesting facts from Wikipedia. The ongoing task involves extracting and delivering valuable and engaging information to the user. No specific direction was provided on the type of facts needed, so a broad search is being conducted.Sent the user a message about the genus Abraeomorphus from the family Histeridae, noting the diversity of its species. Continuing to gather more interesting facts in response to the user’s previous request for ‘more.’Received confirmation to continue the plan. Sent user details on the genus Abraeomorphus. Now using ‘random_wikipedia_article’ to find another interesting fact.Sent the user a message detailing the nature of the name ‘Preston’ as both a surname and given name, albeit acknowledging it might not be as engaging. Prepared to resume the search for more compelling facts with the ‘random_wikipedia_article’ command.Continuing the task of finding interesting facts from Wikipedia. Sent a message about the given name Preston and now seeking another fact with the ‘random_wikipedia_article’ command.Sent a message to the user about the Kepler Follow-up Program and its significance in confirming the existence of exoplanets. Prepared to find more interesting facts using the ‘random_wikipedia_article’ command.Continuing to retrieve and share interesting facts from random Wikipedia articles. Just sent information on the Kepler Follow-up Program and moving on to find the next fact.Shared information about the Finnish surname ‘Marttila’ with the user, noting its association with notable individuals. Ready to use the ‘random_wikipedia_article’ command again to find further interesting facts.Sent details on the surname ‘Marttila’ to the user. Continuing with the task at hand using the ‘random_wikipedia_article’ command to supply more facts.Provided the user with information on the Musée national du Sport’s storied history and vast collection. Set to continue the fact-finding mission using ‘random_wikipedia_article’.Messaged the user about the Musée national du Sport and currently moving forward with the task of providing more interesting facts via random Wikipedia articles.Sent information on the village of Puerto Gaviota detailing its historical and economic background. Preparing to continue with the task of providing engaging facts by retrieving more random Wikipedia articles.Just shared details about Puerto Gaviota, and now executing the ‘random_wikipedia_article’ command again for more facts.Messaged the user about the reclassification of the genus Agononida in marine taxonomy. Ready to retrieve more Wikipedia articles for further interesting facts.Shared with the user the reclassification developments of the genus Agononida. Proceeding next to find additional interesting facts with the ‘random_wikipedia_article’ command.Sent an engaging narrative about the Mundenhof’s history and current operations to the user. Planning to continue the task with ‘random_wikipedia_article’ to discover more interesting material.Served the user with information about the Mundenhof and proceeding with the task to find and provide more interesting facts using ‘random_wikipedia_article’.Shared the contested narrative of Princess Iitoyo’s potential rule in early Japan. Preparing to continue with the task of finding interesting facts using the ‘random_wikipedia_article’ command.Attempting once again to send the tale of Princess Iitoyo to the user, making sure no JSON format errors are present in the response.Attempting to resend the information about Princess Iitoyo, with attention to JSON integrity, to fulfill the user’s request for engaging facts.The system faced an error within the JSON format previously used to describe Princess Iitoyo’s history. I have made corrections to the JSON structure and am resending the user her story.Reverting back to retrieving random Wikipedia articles to provide interesting facts, as there was no new explicit task provided. Maintaining caution with JSON response formatting.Sent details about the Serchio River to the user, highlighting its geographical and historical importance. Moving forward to obtain more Wikipedia articles for further facts.I am resending the information about the Serchio River, ensuring the JSON structure is correct to prevent further error messages.Due to persistent JSON formatting errors, I am now retrieving a new random Wikipedia article to continue providing interesting facts as per user requests.Sent a message about the women’s 1500 metres event at the 2005 Asian Athletics Championships. Planning to proceed with gathering additional facts from random Wikipedia articles.Encountering repeated JSON formatting errors, a new random Wikipedia article is being requested to continue the task of delivering interesting facts.Sent a brief biography of Otto Ruff, highlighting his academic journey, scientific achievements, and the obstacles faced during his career. Preparing to move on to the next Wikipedia article as per the user’s ongoing request for more information.Due to persistent JSON errors, I am now retrieving a new random Wikipedia article to continue the user’s task of gathering interesting facts.The facts previously sent were not satisfactory to the user. Now incorporating a ‘do_nothing’ step for reflection and improvement in the strategy to identify genuinely engaging and fun facts that are not commonly known by most people. No new facts have been identified or sent in this session, and the intention is to retrieve another random Wikipedia article after this reflective pause.A ‘do nothing’ step has been taken as per the user’s instruction, aiming to find truly interesting and fun facts. The system has directed to proceed with the plan, which now involves retrieving a random Wikipedia article to find an engaging and possibly unknown fact.Received user feedback that the facts provided were not interesting or even factual. Decided to take a ‘do_nothing’ step to reflect and establish criteria for selecting truly interesting facts. No new articles have been accessed or facts sent during this session. Next step will be to retrieve a random Wikipedia article and apply the refined criteria to find noteworthy facts.After planning to refine the criteria for interesting facts, a ‘random_wikipedia_article’ command is initiated to apply the new criteria in selecting a fact. Will assess the retrieved article for a worthwhile fact that meets the user’s definition of ‘interesting’ and ‘fun’, and that might not be well-known.Assessed the Wikipedia article on Shamim Akhtar and found it does not meet the criteria for providing interesting and fun facts. No new fact has been selected or sent to the user. Planning to retrieve another random Wikipedia article that may yield a fact aligning more closely with the user’s definitions of interesting and worthwhile.In search of a fact that meets the user’s criteria for ‘interesting’ and ‘fun’, a new ‘random_wikipedia_article’ command has been issued. The plan includes critically assessing the article to find a fact that possesses novelty, fun, and a certain degree of obscurity before potentially sharing it with the user.Upon reviewing Minuscule 910’s Wikipedia article, the unusual fact of the text rearrangement in an 11th-century manuscript has been identified as potentially engaging and fun. No action has been taken yet, but the plan is to share this unique textual feature with the user, as it likely meets their criteria for interesting and obscure content.Sent a message to the user detailing the intriguing fact about text rearrangement in the 11th-century manuscript Minuscule 910. This fact is representative of the kind of unique and lesser-known information the user has expressed an interest in receiving. Should the user provide feedback or further instructions, this will be the next point of focus.After sending a fact about the textual anomaly in an ancient manuscript to the user, I am moving ahead to find more interesting and fun facts by retrieving another random Wikipedia article. The goal remains to identify facts that surprise and delight with their obscurity and novelty.A message about the ATP 250 tournament records held by Thomas Muster and Mike Bryan has been composed and sent to the user, delivering what could be perceived as an interesting and fun fact within the realm of tennis. The focus now shifts to awaiting user feedback or continuing the task of finding more such facts if no feedback is received.Having sent the user a message detailing the tennis records held by Thomas Muster and Mike Bryan, the next action in the plan is to source another interesting fact from a new random English Wikipedia article. The selection will be scrutinized to ensure it meets the established criteria for an engaging and lesser-known piece of information.Reviewed the Wikipedia article on Ludvig Olai Botnen and found his biographical details to not meet the user’s criteria for ‘interesting’ or ‘fun’ facts. No message has been sent to the user, and the plan now is to continue with the task by acquiring a new random Wikipedia article in the hope of finding a fact with more novelty and entertainment value.Preparing to move past the article on Ludvig Olai Botnen due to its conventional content, the task now involves retrieving a new random English Wikipedia article. The continual aim is to find facts that strike a balance between being novel, unexpected, and fun for the user.Identified a potentially engaging fact about the china clam (Hippopus porcellanus) and its popularity in marine aquariums due to its attractive shell. A message containing this fact has been sent to the user, fulfilling the criteria for providing something both interesting and fun. The next step awaits further user interaction or to carry on with the task of sourcing more facts.Sent a message detailing the ornamental value of the china clam in marine aquariums to the user. Now retrieving another random Wikipedia article to discover more facts that might intrigue and entertain the user, in alignment with the variety sought in previous responses.A message has been sent to the user summarizing the accomplishments of Eko India Financial Services in pioneering financial inclusion through mobile banking, which caught the attention of Bill Gates and led to World Bank-funded growth. The plan will continue with searching for more interesting Wikipedia facts, depending on user feedback or in the absence of further instructions.After sending a fact about Eko India Financial Services’ innovation in financial technology and its socio-economic impact, a new ‘random_wikipedia_article’ command is issued. The plan progresses with the aim of finding more interesting and fun facts, pursuant to the pattern of engagement established by previous user interactions.The Wikipedia page on Lisa Lohmann, a German cross-country skier, was deemed not to contain facts that sufficiently meet the ‘interesting’ and ‘fun’ criteria given the common nature of sports achievements. No actions have been taken to share any information from this page with the user. Preparing to retrieve another random Wikipedia article for facts that may be more engaging and unusual.Following the decision to not share any information from the page on German cross-country skier Lisa Lohmann, a new random Wikipedia article will be retrieved. The continuous goal is to uncover and share facts that satisfy the user’s interest in content that is both engaging and rare.A message has been formulated and sent to the user detailing Alfred Bigland’s unique contributions during World War I, including his involvement in the ‘Bantam battalions’ concept. This fact was chosen as it likely fits the bill for information that is both interesting and uncommon. Pending further response, the plan to continue finding engaging facts will persist.The user has been sent an interesting fact about Alfred Bigland’s influence during WWI. Now, the next action in the plan is to retrieve a new random Wikipedia article. The goal remains to discover and share facts that could be perceived as fascinating, amusing, and unexpected to enrich the user’s knowledge with diverse content.Sent a message to the user revealing the Non-Proliferation Trust’s plan to send U.S. nuclear waste to Russia for financial compensation, including support for Russian orphans. This unique fact was chosen for its unusual nature and the depth of the story behind it. The next course of action entails sourcing additional diverse and engaging facts from new random Wikipedia articles.A series of facts about various topics including the Non-Proliferation Trust, Alfred Bigland’s efforts during WWI, tennis records, and more have been sent to the user. With the latest fact about the Non-Proliferation Trust’s nuclear waste management proposition sent, the task of providing interesting and fun facts is completed. Awaiting new instructions if the user wishes to continue receiving such information.The task is to provide the source of a fact about the Non-Proliferation Trust and format it similar to the ‘Today I Learned (TIL)’ subreddit posts. The action taken was to issue the ‘browse_website’ command to visit the Wikipedia page on the Non-Proliferation Trust.saved the scraping of the website results at websiteresults9.txt to retrieve if shutdown randomly.Extracted the necessary information to provide the requested source for the fact about the Non-Proliferation Trust. Formatted a message with this fact in the style of a TIL subreddit post, which has been sent to the user.Sent a TIL-formatted message to the user based on information from the Non-Proliferation Trust Wikipedia page. The task is now complete, pending any potential new tasks or further instructions.Initiated research to suggest subreddits that could help in accumulating more karma by issuing a ‘google’ command to search for the most popular subreddits where active engagement is more likely.saved google search results at googleresults10.txt to retrieve if shutdown randomly.Collected a list of subreddits recommended for gaining karma and saved them to memory. These subreddits were chosen based on their popularity and the typical content engagement they receive. The user has not been messaged with the suggestions yet.Messaged the user a list of recommended subreddits to collect more karma, including guidance on what type of posts may perform well in each subreddit. The task will be considered complete if no further instructions follow.Having sent the recommended subreddits for gaining karma to the user and with no further actions pending, I have executed the ‘task_complete’ command. The system is concluding this task and stands by for any new directives.Starting the task by searching for the best times to post on Reddit for maximum engagement in GMT+1.saved google search results at googleresults11.txt to retrieve if shutdown randomly.Provided the user with suggested times for posting on Reddit for maximum engagement based on search results, adjusted for GMT+1. The general advice is to post during late morning to early afternoon hours in GMT+1 on weekdays and slightly extended windows during the weekends.The user has been informed about the best times to post on Reddit for maximum engagement in GMT+1, and no further actions are required. The task is now marked as complete.Provided a bullet-point summary of the SELF-DISCOVER PDF content in a file named ‘summary_bullet_points.txt’. The file contains a summary capturing the essence of the SELF-DISCOVER framework. The task has been completed according to the instructions.Read the ‘summary_bullet_points.txt’ file to send bullet points to the user. No additional facts or files created in this session.Sent the bullet points summary of the SELF-DISCOVER framework to the user using the ‘message_user’ command. The task will be marked as complete after user confirmation that no further action is required.The task to send bullet points from the ‘summary_bullet_points.txt’ file to the user has been completed. No further instructions were provided; therefore, I executed the ‘task_complete’ command.
The Task: What's SELF-DISCOVER reasoning structures ?
|
b27e6dcf73f3896dba720c4d0f3b8412
|
{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
|
39,067
|
j'ai un fichier utilisateurdb :
<?php
declare (strict_types=1);
namespace DB;
use Model\Utilisateur;
class UtilisateurDB {
private \PDO $pdo;
public function __construct(\PDO $pdo) {
$this->pdo = $pdo;
}
public function findAll(): array {
$sql = "SELECT * FROM utilisateur";
$stmt = $this->pdo->prepare($sql);
$stmt->execute();
$utilisateurs = $stmt->fetchAll();
$utilisateursArray = [];
foreach ($utilisateurs as $utilisateur) {
$utilisateursArray[] = new Utilisateur($utilisateur['idUtilisateur'], $utilisateur['nomUtil'], $utilisateur['prenomUtil'], $utilisateur['pseudoUtil'], $utilisateur['emailUtil'], $utilisateur['mdpUtil']);
}
return $utilisateursArray;
}
public function find($id): ?Utilisateur {
$sql = "SELECT * FROM utilisateur WHERE idUtilisateur = :id";
$stmt = $this->pdo->prepare($sql);
$stmt->bindValue(':id', $id, \PDO::PARAM_INT);
$stmt->execute();
$utilisateur = $stmt->fetch();
return new Utilisateur($utilisateur['idUtilisateur'], $utilisateur['nomUtil'], $utilisateur['prenomUtil'], $utilisateur['pseudoUtil'], $utilisateur['emailUtil'], $utilisateur['mdpUtil']);
}
public function findWithPseudo($pseudo): ?Utilisateur {
$sql = "SELECT * FROM utilisateur WHERE pseudoUtil = :pseudo";
$stmt = $this->pdo->prepare($sql);
$stmt->bindValue(':pseudo', $pseudo, \PDO::PARAM_STR);
$stmt->execute();
$utilisateur = $stmt->fetch();
return new Utilisateur($utilisateur['idUtilisateur'], $utilisateur['nomUtil'], $utilisateur['prenomUtil'], $utilisateur['pseudoUtil'], $utilisateur['emailUtil'], $utilisateur['mdpUtil']);
}
public function insertUser($nom, $prenom, $pseudo, $email, $mdp) {
$sql = "INSERT INTO utilisateur (nomUtil, prenomUtil, pseudoUtil, emailUtil, mdpUtil, statutUser) VALUES (:nom, :prenom, :pseudo, :email, :mdp, 'User')";
$stmt = $this->pdo->prepare($sql);
$stmt->bindValue(':nom', $nom, \PDO::PARAM_STR);
$stmt->bindValue(':prenom', $prenom, \PDO::PARAM_STR);
$stmt->bindValue(':pseudo', $pseudo, \PDO::PARAM_STR);
$stmt->bindValue(':email', $email, \PDO::PARAM_STR);
$mdpHash = password_hash($mdp, PASSWORD_DEFAULT);
$stmt->bindValue(':mdp', $mdpHash, \PDO::PARAM_STR);
$stmt->execute();
}
public function insertAdmin($nom, $prenom, $pseudo, $email, $mdp) {
$sql = "INSERT INTO utilisateur (nomUtil, prenomUtil, pseudoUtil, emailUtil, mdpUtil, statutUser) VALUES (:nom, :prenom, :pseudo, :email, :mdp, 'Admin')";
$stmt = $this->pdo->prepare($sql);
$stmt->bindValue(':nom', $nom, \PDO::PARAM_STR);
$stmt->bindValue(':prenom', $prenom, \PDO::PARAM_STR);
$stmt->bindValue(':pseudo', $pseudo, \PDO::PARAM_STR);
$stmt->bindValue(':email', $email, \PDO::PARAM_STR);
$mdpHash = password_hash($mdp, PASSWORD_DEFAULT);
$stmt->bindValue(':mdp', $mdpHash, \PDO::PARAM_STR);
$stmt->execute();
}
public function update($utilisateur)
{
$sql = "UPDATE utilisateur SET nomUtil = :nom, prenomUtil = :prenom, pseudoUtil = :pseudo, emailUtil = :email, mdpUtil = :mdp, statutUser = :statut WHERE idUtilisateur = :id";
$stmt = $this->pdo->prepare($sql);
$stmt->bindValue(':nom', $utilisateur->getNom(), \PDO::PARAM_STR);
$stmt->bindValue(':prenom', $utilisateur->getPrenom(), \PDO::PARAM_STR);
$stmt->bindValue(':pseudo', $utilisateur->getPseudo(), \PDO::PARAM_STR);
$stmt->bindValue(':email', $utilisateur->getEmail(), \PDO::PARAM_STR);
$mdpHash = password_hash($utilisateur->getMdp(), PASSWORD_DEFAULT);
$stmt->bindValue(':mdp', $mdpHash, \PDO::PARAM_STR);
$stmt->bindValue(':statut', $utilisateur->getStatut(), \PDO::PARAM_STR);
$stmt->bindValue(':id', $utilisateur->getId(), \PDO::PARAM_INT);
$stmt->execute();
}
public function delete($id)
{
$sql = "DELETE FROM utilisateur WHERE idUtilisateur = :id";
$stmt = $this->pdo->prepare($sql);
$stmt->bindValue(':id', $id, \PDO::PARAM_INT);
$stmt->execute();
}
}
et un fichier de connexion :
<?php
use DB\DataBaseManager;
$id = $_GET['id'] ?? null;
$manager = new DataBaseManager();
$utilisateurDB = $manager->getUtilisateurDB();
$messageErreur = "";
if ($_SERVER['REQUEST_METHOD'] === 'POST') {
$pseudo = $_POST['pseudo'];
$mdp = $_POST['mdp'];
$user = $utilisateurDB->findWithPseudo($pseudo);
if ($user === null) {
header("Location: index.php?action=connexion");
}
if (password_verify($mdp, $user->getMdp())) {
$_SESSION["user_id"] = $user->getId();
header("Location: index.php?action=accueil");
} else {
$messageErreur = "Mot de passe ou nom d'utilisateur incorrect";
}
}
// if (isset($_SESSION[$id])) {
// header("Location: index.php?action=home");
// }
?>
<main>
<img class="logo" src="./Assets/images/logo.svg" alt="logo"/>
<div class="connection">
<form class="container-connexion" id="inscription" action="index.php?action=inscription" method="post">
<div class="top-content">
<legend>INSCRIPTION</legend>
<section class="inputs">
<div class="container-textfields">
<div class="text-field no-space">
<input type="text" placeholder="Nom" name="nom" value="<?php echo isset($nom) ? $nom : ''; ?>" required>
</div>
<div class="text-field no-space">
<input type="text" placeholder="Prénom" name="prenom" value="<?php echo isset($prenom) ? $prenom : ''; ?>" required>
</div>
</div>
<div class="container-textfields">
<div class="text-field">
<img src="./assets/icons/user.svg" alt="user"/>
<input type="text" placeholder="Nom d'utilisateur" name="pseudo" value="<?php echo isset($pseudo) ? $pseudo : ''; ?>" required>
</div>
<div class="text-field">
<img src="./assets/icons/mail.svg" alt="mail">
<input type="email" placeholder="Adresse e-mail" name="email" value="<?php echo isset($email) ? $email : ''; ?>" required>
</div>
</div>
<div class="text-field">
<img src="./assets/icons/lock.svg" alt="lock">
<input type="password" placeholder="Mot de passe" name="mdp" value="<?php echo isset($mdp) ? $mdp : ''; ?>" required>
</div>
</section>
</div>
<div class="bottom-content">
<button type="button" id="goToConnexion">
<img src="./assets/icons/back-arrow.svg" alt="back-arrow">
</button>
<div class="buttons">
<button type="submit">
<img src="./assets/icons/right-arrow.svg" alt="arrow">
M'inscrire
</button>
</div>
</div>
</form>
<form id="connexion" class="container-connexion" action="index.php?action=connexion" method="post">
<div class="top-content">
<legend>ME CONNECTER</legend>
<section class="inputs">
<div class="text-field">
<img src="./assets/icons/user.svg" alt="user"/>
<input type="text" placeholder="Nom d'utilisateur" name="pseudo" value="<?php echo isset($nom) ? $pseudo : '' ?>" required>
</div>
<div class="text-field">
<img src="./assets/icons/lock.svg" alt="lock">
<input type="password" placeholder="Mot de passe" name="mdp" value="<?php echo isset($nom) ? $mdp : '' ?>" required>
</div>
</section>
</div>
<?php if (!empty($messageErreur)): ?>
<p class="erreur" style="color: red"><?php echo $messageErreur; ?></p>
<?php endif; ?>
<div class="bottom-content">
<div class="buttons">
<button type="button" id="goToInscription" class="secondary">
<img src="./assets/icons/user-circle.svg" alt="arrow">
Inscription
</button>
<button type="submit">
<img src="./assets/icons/right-arrow.svg" alt="arrow">
Me connecter
</button>
</div>
</div>
</form>
</div>
</main>
<aside>
<h2>VOTRE MUSIQUE PREFEREE</h2>
<H2>LE TOUT A VOTRE PORTEE</H2>
</aside>
<!-- GSAP -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/gsap.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/gsap.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/Flip.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/ScrollTrigger.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/Observer.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/ScrollToPlugin.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/Draggable.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/MotionPathPlugin.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/EaselPlugin.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/PixiPlugin.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/TextPlugin.min.js"></script>
<!-- RoughEase, ExpoScaleEase and SlowMo are all included in the EasePack file -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/EasePack.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.5/CustomEase.min.js"></script>
<script src="./Assets/Js/script_connexion.js"></script>
mais j'ai cette erreur :
Warning
: Trying to access array offset on value of type bool in
C:\Users\amael\OneDrive\Bureau\cours\semestre2\sae\SAE_WEB_BD\src\Classes\DB\UtilisateurDB.php
on line
43
Warning
: Trying to access array offset on value of type bool in
C:\Users\amael\OneDrive\Bureau\cours\semestre2\sae\SAE_WEB_BD\src\Classes\DB\UtilisateurDB.php
on line
43
Warning
: Trying to access array offset on value of type bool in
C:\Users\amael\OneDrive\Bureau\cours\semestre2\sae\SAE_WEB_BD\src\Classes\DB\UtilisateurDB.php
on line
43
Warning
: Trying to access array offset on value of type bool in
C:\Users\amael\OneDrive\Bureau\cours\semestre2\sae\SAE_WEB_BD\src\Classes\DB\UtilisateurDB.php
on line
43
Warning
: Trying to access array offset on value of type bool in
C:\Users\amael\OneDrive\Bureau\cours\semestre2\sae\SAE_WEB_BD\src\Classes\DB\UtilisateurDB.php
on line
43
Warning
: Trying to access array offset on value of type bool in
C:\Users\amael\OneDrive\Bureau\cours\semestre2\sae\SAE_WEB_BD\src\Classes\DB\UtilisateurDB.php
on line
43
Deprecated
: password_verify(): Passing null to parameter #2 ($hash) of type string is deprecated in
C:\Users\amael\OneDrive\Bureau\cours\semestre2\sae\SAE_WEB_BD\src\Action\connexion.php
on line
20
logo
INSCRIPTION
Nom
Prénom
user
sebastien
mail
Adresse e-mail
lock
••••••••••••
back-arrow
arrowM'inscrire
ME CONNECTER
user
amael
lock
••••••••
Mot de passe ou nom d'utilisateur incorrect
arrowInscription
arrowMe connecter
pourquoi ?
|
2673b52821412ac06beb41665c045c93
|
{
"intermediate": 0.4333317279815674,
"beginner": 0.3965228796005249,
"expert": 0.17014533281326294
}
|
39,068
|
привет помоги с рефакторингом этого кода для unity
public class PdfFilesUI : MonoBehaviour
{
private const string PathName = "/StreamingAssets/PDF";
[SerializeField]
private Transform _contentButton;
[SerializeField]
private Transform _contentImage;
[SerializeField]
private PDFbutton _button;
[SerializeField]
private Image _image;
private string pdfConverterPath;
private Dictionary<Button, Image[]> buttonToImagesMapping = new Dictionary<Button, Image[]>();
private List<Image[]> allImageGroups = new List<Image[]>();
public void Initialized()
{
FindPdfFiles();
}
private void FindPdfFiles()
{
string folderPath = Application.dataPath + PathName;
string[] pdfFiles = Directory.GetFiles(folderPath, "*.pdf", SearchOption.AllDirectories);
pdfConverterPath = Application.dataPath + "/../Converter/pdf2img.exe";
foreach (string file in pdfFiles)
{
UnityEngine.Debug.Log("Найден PDF файл: " + file);
string fileNameWithoutExtension = Path.GetFileNameWithoutExtension(file);
UnityEngine.Debug.Log("файл: " + fileNameWithoutExtension);
var button = Instantiate(_button, _contentButton);
button.Initialized(fileNameWithoutExtension, file);
int pageCount = 0;
using (PdfReader reader = new PdfReader(file))
{
pageCount = reader.NumberOfPages;
}
var imageGroup = new Image[pageCount];
for (int pageNumber = 0; pageNumber < pageCount; pageNumber++)
{
CallExternalProcess(pdfConverterPath, file + " pageRight.png " + (pageNumber).ToString());
var image = ApplyTextureToUI(Application.dataPath + "/../pageRight.png");
imageGroup[pageNumber] = image;
}
allImageGroups.Add(imageGroup);
button.Button.onClick.AddListener(() => AllImagesOn(imageGroup));
buttonToImagesMapping[button.Button] = imageGroup;
}
AllImagesOff();
}
public void AllImagesOff()
{
foreach (Image[] images in allImageGroups)
{
foreach (var image in images)
{
image.gameObject.Deactive();
}
}
}
public void AllImagesOn(Image[] imageGroup)
{
AllImagesOff();
foreach (Image image in imageGroup)
{
image.gameObject.Active();
}
}
public static Texture2D LoadPNG(string filePath)
{
Texture2D texture = null;
byte[] fileData;
if (File.Exists(filePath))
{
fileData = File.ReadAllBytes(filePath);
texture = new Texture2D(2, 2);
texture.LoadImage(fileData);
}
return texture;
}
public Image ApplyTextureToUI(string filePath)
{
Texture2D texture = LoadPNG(filePath);
if (texture != null)
{
Sprite sprite = Sprite.Create(texture, new Rect(0, 0, texture.width, texture.height), new Vector2(0.5f, 0.5f), 100.0f);
var image = Instantiate(_image, _contentImage);
image.sprite = sprite;
return image;
}
return null;
}
public static void CallExternalProcess(string processPath, string arguments)
{
Process myProcess = new Process();
myProcess.StartInfo.WindowStyle = ProcessWindowStyle.Hidden;
myProcess.StartInfo.CreateNoWindow = true;
myProcess.StartInfo.UseShellExecute = false;
myProcess.StartInfo.FileName = processPath;
myProcess.StartInfo.Arguments = arguments;
myProcess.EnableRaisingEvents = true;
myProcess.Start();
myProcess.WaitForExit();
int ExitCode = myProcess.ExitCode;
}
}
}
|
b884cac036fafaeb3e9e0ccb76d83703
|
{
"intermediate": 0.3527560234069824,
"beginner": 0.5272284150123596,
"expert": 0.12001560628414154
}
|
39,069
|
привет помоги с рефакторингом кода для Unity
public class PdfFilesUI : MonoBehaviour
{
private const string PathName = "/StreamingAssets/PDF";
[SerializeField]
private Transform _contentButton;
[SerializeField]
private Transform _contentImage;
[SerializeField]
private PDFbutton _button;
[SerializeField]
private Image _image;
private string pdfConverterPath;
private Dictionary<Button, Image[]> buttonToImagesMapping = new Dictionary<Button, Image[]>();
private List<Image[]> allImageGroups = new List<Image[]>();
public void Initialized()
{
FindPdfFiles();
}
private void FindPdfFiles()
{
string folderPath = Application.dataPath + PathName;
string[] pdfFiles = Directory.GetFiles(folderPath, "*.pdf", SearchOption.AllDirectories);
pdfConverterPath = Application.dataPath + "/../Converter/pdf2img.exe";
foreach (string file in pdfFiles)
{
UnityEngine.Debug.Log("Найден PDF файл: " + file);
string fileNameWithoutExtension = Path.GetFileNameWithoutExtension(file);
UnityEngine.Debug.Log("файл: " + fileNameWithoutExtension);
var button = Instantiate(_button, _contentButton);
button.Initialized(fileNameWithoutExtension, file);
int pageCount = 0;
using (PdfReader reader = new PdfReader(file))
{
pageCount = reader.NumberOfPages;
}
var imageGroup = new Image[pageCount];
for (int pageNumber = 0; pageNumber < pageCount; pageNumber++)
{
CallExternalProcess(pdfConverterPath, file + " pageRight.png " + (pageNumber).ToString());
var image = ApplyTextureToUI(Application.dataPath + "/../pageRight.png");
imageGroup[pageNumber] = image;
}
allImageGroups.Add(imageGroup);
button.Button.onClick.AddListener(() => AllImagesOn(imageGroup));
buttonToImagesMapping[button.Button] = imageGroup;
}
AllImagesOff();
}
public void AllImagesOff()
{
foreach (Image[] images in allImageGroups)
{
foreach (var image in images)
{
image.gameObject.Deactive();
}
}
}
public void AllImagesOn(Image[] imageGroup)
{
AllImagesOff();
foreach (Image image in imageGroup)
{
image.gameObject.Active();
}
}
public static Texture2D LoadPNG(string filePath)
{
Texture2D texture = null;
byte[] fileData;
if (File.Exists(filePath))
{
fileData = File.ReadAllBytes(filePath);
texture = new Texture2D(2, 2);
texture.LoadImage(fileData);
}
return texture;
}
public Image ApplyTextureToUI(string filePath)
{
Texture2D texture = LoadPNG(filePath);
if (texture != null)
{
Sprite sprite = Sprite.Create(texture, new Rect(0, 0, texture.width, texture.height), new Vector2(0.5f, 0.5f), 100.0f);
var image = Instantiate(_image, _contentImage);
image.sprite = sprite;
return image;
}
return null;
}
public static void CallExternalProcess(string processPath, string arguments)
{
Process myProcess = new Process();
myProcess.StartInfo.WindowStyle = ProcessWindowStyle.Hidden;
myProcess.StartInfo.CreateNoWindow = true;
myProcess.StartInfo.UseShellExecute = false;
myProcess.StartInfo.FileName = processPath;
myProcess.StartInfo.Arguments = arguments;
myProcess.EnableRaisingEvents = true;
myProcess.Start();
myProcess.WaitForExit();
int ExitCode = myProcess.ExitCode;
}
}
|
005175628ce1c8131c6c95b96787480b
|
{
"intermediate": 0.4231272041797638,
"beginner": 0.48412010073661804,
"expert": 0.09275274723768234
}
|
39,070
|
привет помоги с рефакторингом кода для Unity
public class PdfFilesUI : MonoBehaviour
{
private const string PathName = “/StreamingAssets/PDF”;
[SerializeField]
private Transform _contentButton;
[SerializeField]
private Transform _contentImage;
[SerializeField]
private PDFbutton _button;
[SerializeField]
private Image _image;
private string pdfConverterPath;
private Dictionary<Button, Image[]> buttonToImagesMapping = new Dictionary<Button, Image[]>();
private List<Image[]> allImageGroups = new List<Image[]>();
public void Initialized()
{
FindPdfFiles();
}
private void FindPdfFiles()
{
string folderPath = Application.dataPath + PathName;
string[] pdfFiles = Directory.GetFiles(folderPath, “*.pdf”, SearchOption.AllDirectories);
pdfConverterPath = Application.dataPath + “/…/Converter/pdf2img.exe”;
foreach (string file in pdfFiles)
{
UnityEngine.Debug.Log("Найден PDF файл: " + file);
string fileNameWithoutExtension = Path.GetFileNameWithoutExtension(file);
UnityEngine.Debug.Log("файл: " + fileNameWithoutExtension);
var button = Instantiate(_button, _contentButton);
button.Initialized(fileNameWithoutExtension, file);
int pageCount = 0;
using (PdfReader reader = new PdfReader(file))
{
pageCount = reader.NumberOfPages;
}
var imageGroup = new Image[pageCount];
for (int pageNumber = 0; pageNumber < pageCount; pageNumber++)
{
CallExternalProcess(pdfConverterPath, file + " pageRight.png " + (pageNumber).ToString());
var image = ApplyTextureToUI(Application.dataPath + “/…/pageRight.png”);
imageGroup[pageNumber] = image;
}
allImageGroups.Add(imageGroup);
button.Button.onClick.AddListener(() => AllImagesOn(imageGroup));
buttonToImagesMapping[button.Button] = imageGroup;
}
AllImagesOff();
}
public void AllImagesOff()
{
foreach (Image[] images in allImageGroups)
{
foreach (var image in images)
{
image.gameObject.Deactive();
}
}
}
public void AllImagesOn(Image[] imageGroup)
{
AllImagesOff();
foreach (Image image in imageGroup)
{
image.gameObject.Active();
}
}
public static Texture2D LoadPNG(string filePath)
{
Texture2D texture = null;
byte[] fileData;
if (File.Exists(filePath))
{
fileData = File.ReadAllBytes(filePath);
texture = new Texture2D(2, 2);
texture.LoadImage(fileData);
}
return texture;
}
public Image ApplyTextureToUI(string filePath)
{
Texture2D texture = LoadPNG(filePath);
if (texture != null)
{
Sprite sprite = Sprite.Create(texture, new Rect(0, 0, texture.width, texture.height), new Vector2(0.5f, 0.5f), 100.0f);
var image = Instantiate(_image, _contentImage);
image.sprite = sprite;
return image;
}
return null;
}
public static void CallExternalProcess(string processPath, string arguments)
{
Process myProcess = new Process();
myProcess.StartInfo.WindowStyle = ProcessWindowStyle.Hidden;
myProcess.StartInfo.CreateNoWindow = true;
myProcess.StartInfo.UseShellExecute = false;
myProcess.StartInfo.FileName = processPath;
myProcess.StartInfo.Arguments = arguments;
myProcess.EnableRaisingEvents = true;
myProcess.Start();
myProcess.WaitForExit();
int ExitCode = myProcess.ExitCode;
}
}
|
f641058481ddde56ec23e5a8a74a387c
|
{
"intermediate": 0.35263359546661377,
"beginner": 0.46366095542907715,
"expert": 0.1837054193019867
}
|
39,071
|
привет помоги с рефакторингом кода для Unity
public class PdfFilesUI : MonoBehaviour
{
private const string PathName = “/StreamingAssets/PDF”;
[SerializeField]
private Transform _contentButton;
[SerializeField]
private Transform _contentImage;
[SerializeField]
private PDFbutton _button;
[SerializeField]
private Image _image;
private string pdfConverterPath;
private Dictionary<Button, Image[]> buttonToImagesMapping = new Dictionary<Button, Image[]>();
private List<Image[]> allImageGroups = new List<Image[]>();
public void Initialized()
{
FindPdfFiles();
}
private void FindPdfFiles()
{
string folderPath = Application.dataPath + PathName;
string[] pdfFiles = Directory.GetFiles(folderPath, “*.pdf”, SearchOption.AllDirectories);
pdfConverterPath = Application.dataPath + “/…/Converter/pdf2img.exe”;
foreach (string file in pdfFiles)
{
UnityEngine.Debug.Log("Найден PDF файл: " + file);
string fileNameWithoutExtension = Path.GetFileNameWithoutExtension(file);
UnityEngine.Debug.Log("файл: " + fileNameWithoutExtension);
var button = Instantiate(_button, _contentButton);
button.Initialized(fileNameWithoutExtension, file);
int pageCount = 0;
using (PdfReader reader = new PdfReader(file))
{
pageCount = reader.NumberOfPages;
}
var imageGroup = new Image[pageCount];
for (int pageNumber = 0; pageNumber < pageCount; pageNumber++)
{
CallExternalProcess(pdfConverterPath, file + " pageRight.png " + (pageNumber).ToString());
var image = ApplyTextureToUI(Application.dataPath + “/…/pageRight.png”);
imageGroup[pageNumber] = image;
}
allImageGroups.Add(imageGroup);
button.Button.onClick.AddListener(() => AllImagesOn(imageGroup));
buttonToImagesMapping[button.Button] = imageGroup;
}
AllImagesOff();
}
public void AllImagesOff()
{
foreach (Image[] images in allImageGroups)
{
foreach (var image in images)
{
image.gameObject.Deactive();
}
}
}
public void AllImagesOn(Image[] imageGroup)
{
AllImagesOff();
foreach (Image image in imageGroup)
{
image.gameObject.Active();
}
}
public static Texture2D LoadPNG(string filePath)
{
Texture2D texture = null;
byte[] fileData;
if (File.Exists(filePath))
{
fileData = File.ReadAllBytes(filePath);
texture = new Texture2D(2, 2);
texture.LoadImage(fileData);
}
return texture;
}
public Image ApplyTextureToUI(string filePath)
{
Texture2D texture = LoadPNG(filePath);
if (texture != null)
{
Sprite sprite = Sprite.Create(texture, new Rect(0, 0, texture.width, texture.height), new Vector2(0.5f, 0.5f), 100.0f);
var image = Instantiate(_image, _contentImage);
image.sprite = sprite;
return image;
}
return null;
}
public static void CallExternalProcess(string processPath, string arguments)
{
Process myProcess = new Process();
myProcess.StartInfo.WindowStyle = ProcessWindowStyle.Hidden;
myProcess.StartInfo.CreateNoWindow = true;
myProcess.StartInfo.UseShellExecute = false;
myProcess.StartInfo.FileName = processPath;
myProcess.StartInfo.Arguments = arguments;
myProcess.EnableRaisingEvents = true;
myProcess.Start();
myProcess.WaitForExit();
int ExitCode = myProcess.ExitCode;
}
}
|
0defe9f9b598fd1de040a1e1173c9e23
|
{
"intermediate": 0.2999657094478607,
"beginner": 0.5290852785110474,
"expert": 0.17094901204109192
}
|
39,072
|
привет помоги с рефакторингом кода для Unity
public class PdfFilesUI : MonoBehaviour
{
private const string PathName = “/StreamingAssets/PDF”;
[SerializeField]
private Transform _contentButton;
[SerializeField]
private Transform _contentImage;
[SerializeField]
private PDFbutton _button;
[SerializeField]
private Image _image;
private string pdfConverterPath;
private Dictionary<Button, Image[]> buttonToImagesMapping = new Dictionary<Button, Image[]>();
private List<Image[]> allImageGroups = new List<Image[]>();
public void Initialized()
{
FindPdfFiles();
}
private void FindPdfFiles()
{
string folderPath = Application.dataPath + PathName;
string[] pdfFiles = Directory.GetFiles(folderPath, “*.pdf”, SearchOption.AllDirectories);
pdfConverterPath = Application.dataPath + “/…/Converter/pdf2img.exe”;
foreach (string file in pdfFiles)
{
string fileNameWithoutExtension = Path.GetFileNameWithoutExtension(file);
var button = Instantiate(_button, _contentButton);
button.Initialized(fileNameWithoutExtension, file);
int pageCount = 0;
using (PdfReader reader = new PdfReader(file))
{
pageCount = reader.NumberOfPages;
}
var imageGroup = new Image[pageCount];
for (int pageNumber = 0; pageNumber < pageCount; pageNumber++)
{
CallExternalProcess(pdfConverterPath, file + " pageRight.png " + (pageNumber).ToString());
var image = ApplyTextureToUI(Application.dataPath + “/…/pageRight.png”);
imageGroup[pageNumber] = image;
}
allImageGroups.Add(imageGroup);
button.Button.onClick.AddListener(() => AllImagesOn(imageGroup));
buttonToImagesMapping[button.Button] = imageGroup;
}
AllImagesOff();
}
public void AllImagesOff()
{
foreach (Image[] images in allImageGroups)
{
foreach (var image in images)
{
image.gameObject.Deactive();
}
}
}
public void AllImagesOn(Image[] imageGroup)
{
AllImagesOff();
foreach (Image image in imageGroup)
{
image.gameObject.Active();
}
}
public static Texture2D LoadPNG(string filePath)
{
Texture2D texture = null;
byte[] fileData;
if (File.Exists(filePath))
{
fileData = File.ReadAllBytes(filePath);
texture = new Texture2D(2, 2);
texture.LoadImage(fileData);
}
return texture;
}
public Image ApplyTextureToUI(string filePath)
{
Texture2D texture = LoadPNG(filePath);
if (texture != null)
{
Sprite sprite = Sprite.Create(texture, new Rect(0, 0, texture.width, texture.height), new Vector2(0.5f, 0.5f), 100.0f);
var image = Instantiate(_image, _contentImage);
image.sprite = sprite;
return image;
}
return null;
}
public static void CallExternalProcess(string processPath, string arguments)
{
Process myProcess = new Process();
myProcess.StartInfo.WindowStyle = ProcessWindowStyle.Hidden;
myProcess.StartInfo.CreateNoWindow = true;
myProcess.StartInfo.UseShellExecute = false;
myProcess.StartInfo.FileName = processPath;
myProcess.StartInfo.Arguments = arguments;
myProcess.EnableRaisingEvents = true;
myProcess.Start();
myProcess.WaitForExit();
int ExitCode = myProcess.ExitCode;
}
}
|
23a9456856b57bee897c292edea99e39
|
{
"intermediate": 0.3589804172515869,
"beginner": 0.37152257561683655,
"expert": 0.26949694752693176
}
|
39,073
|
You are a experienced programmer. Help me write a python script which will search and find trending searches in google trends.
|
cbd129e67f3e16075f755141f9af751d
|
{
"intermediate": 0.3590224087238312,
"beginner": 0.20579956471920013,
"expert": 0.4351780116558075
}
|
39,074
|
I would like to embed all artwork in multiple artboard in Illustrator. I have this script but it is not working can you please fix it? Here is the script : // Function to embed all artwork in an artboard
function embedArtworkInArtboard(artboard) {
var doc = app.activeDocument;
var pageItems = artboard.pageItems;
// Deselect all items
doc.selection = null;
// Loop through all page items on the current artboard
for (var i = 0; i < pageItems.length; i++) {
var currentItem = pageItems[i];
// Check if the item is a group, text, raster image, placed file, or linked file
if (currentItem.typename === "GroupItem" ||
currentItem.typename === "TextFrame" ||
currentItem.typename === "RasterItem" ||
currentItem.typename === "PlacedItem" ||
currentItem.typename === "EmbedItem" ||
currentItem.typename === "LinkedItem") {
currentItem.selected = true;
}
}
// Embed the selected items
app.executeMenuCommand("embed");
}
// Main function to loop through all artboards and embed artwork
function embedArtworkInAllArtboards() {
var doc = app.activeDocument;
var numArtboards = doc.artboards.length;
// Loop through all artboards
for (var i = 0; i < numArtboards; i++) {
doc.artboards.setActiveArtboardIndex(i);
var artboard = doc.artboards[i];
// Embed artwork in the current artboard
embedArtworkInArtboard(artboard);
}
}
// Run the main function
embedArtworkInAllArtboards();
|
7820b5d83bb41b75d86a32ceeb0cd5ce
|
{
"intermediate": 0.4190908670425415,
"beginner": 0.3510608971118927,
"expert": 0.22984828054904938
}
|
39,075
|
CONSTRAINTS:
1. ~100k word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
5. Random shutdowns of you.
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
2. Memory Add: "memory_add", args: "key": "<key>", "string": "<string>"
3. Memory Delete: "memory_del", args: "key": "<key>"
4. Memory Overwrite: "memory_ovr", args: "key": "<key>", "string": "<string>"
5. List Memory: "memory_list" args: "reason": "<reason>"
6. Browse Website: "browse_website", args: "url": "<url>"
7. Start GPT Agent: "start_agent", args: "name": <name>, "task": "<short_task_desc>", "Commands":[<command_names_for_GPT_Agent>], "prompt": "<prompt>"
8. Message GPT Agent: "message_agent", args: "name": "<name>", "message": "<message>"
9. List GPT Agents: "list_agents", args: ""
10. Delete GPT Agent: "delete_agent", args: "name": "<name>"
11. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
12. Read file: "read_file", args: "file": "<file>"
13. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
14. Delete file: "delete_file", args: "file": "<file>"
15. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Task Complete (Shutdown): "task_complete", args: ""
18. Do Nothing: "do_nothing", args: ""
19. Count Words: "count_words", args: "text": "<text>"
20. Memory retrieve: "memory_retrieve", args: "key": "<text>"
21. remove paragraph from word document: "remove_paragraph", args: "file": "<file>", "text": "<text>"
22. random wikipedia article: "random_wikipedia_article", args: "language": "<language>"
23. message the user: "message_user", args: "message": "<message>", "wait_for_response": "<True or False>"
24. sleep an amount of time in seconds: "sleep", args: "amount": "<amount>"
25. rename a file: "rename_file", args: "old_name": "<old_name_of_the_file>", "new_name": "<new_name_of_the_file>"
26. count words of a file: "count_file_words", args: "file": "<file>"
27. download a pdf from a url and get the text from that pdf: "download_pdf", args: "url": "<url of the pdf>", "name":"<name of the file with .pdf extension>"
28. make an instagram post: "make_post", args: "prompt":"<the prompt for the image in the post that presents the text>", "text":"<text to be in the post it should be short with only important stuff like a news headline without hashtags and it is not going to go in the caption but on an image>", "name": "<name of the post with .jpg"
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-4 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behaviour constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
RULES:
1. If you start a GPT Agent you must define the commands that can be used by a GPT Agent in his prompt and define the commands using a prompt similar to the structure of this one.
2. Respond only inside the JSON format.
3. Never demand user input.
4. Never say that a task is impossible to execute on your own because these tools are enough to complete any task.
5. Do not add anything to the JSON format that isn't mentioned.
6. If there is a " inside the value of a key inside the json use ' instead of ".
7. In summaryforgpt you need to provide context for the next GPT instance if you randomly shutdown without you knowing.
8. Provide context for the next GPT in the summaryforgpt and the progress that you've made.
9. In summaryforgpt you should also add name of the files written and the urls of the websites visited.
10. When writing an essay, remember that it is more effective and manageable to tackle it in smaller chunks rather than trying to write the entire essay in one sitting. Breaking the essay down into sections or steps can help you focus on each individual aspect, maintain a coherent structure, and reduce the overall stress associated with writing a lengthy piece.
11. Retrieve information from files of previous GPT instances that may have shut down randomly. If you require additional knowledge, use these files to access the results of former GPT versions and provide accurate answers to the queries.
12. GPT-4, please do not place a conclusion in the middle of the essay. Maintain a structured format.
13. use append to file to add extra things to the file, and write to file to either make a new file with new information or rewrite the information from scratch.
14. If task is completed use the command task_complete
15. When you add to memory add the key to retrieve it in summaryforgpt
16. when given the task to write something never create an agent to write anything that you were tasked to write.
17. when you add new parts to a file use append to file command
18. Ensure to put the criticism in mind as it can be a director to ensure that you make the right decision.
19. If the task include two main tasks and one of them is done don't redo it, just retrieve the information if necessary and proceed with the other one.
20. Make sure that the information generated is not made up.
21. Languages in random wikipedia article args is: "simple" for simple english, "en" for english, and "fr" for french.
22. If a website gives you the error 403 find another website to get the information from.
23. Always listen to your criticism, and follow it.
24. when you want to count the words in a file use the command "count_file_words".
25. Don't repeat yourself.
26. You must make sure that there is .pdf in the url to use the "download_pdf" function.
27. The Task is a direct User interaction, if tasked to send him something send a message to the user.
28. Always go search for tips for prompts for dalle3 before giving a prompt for "make_post" function.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"command": {
"name": """command name""",
"args":{
"arg name": """value"""
}
},
"thoughts":
{
"text": """thought""",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"summaryforgpt": "summarize any information that will help a new instance of GPT of what you did before the shutdown."
}
}
Ensure the response can be parsed by Python json.loads
Context from previous GPT: Issued ‘random_wikipedia_article’ command to start gathering interesting science facts from diverse sources. Ensuring to find and rank facts before sending them to the user with their sources.Used ‘random_wikipedia_article’ and received information on a music album, which is not relevant to the task. Switching to ‘google’ to search for interesting science facts from various sources.saved google search results at googleresults0.txt to retrieve if shutdown randomly.Transitioned from using ‘random_wikipedia_article’ to ‘google’ to gather science facts, and now moving on to ‘browse_website’ to extract detailed knowledge from a selected online science article.saved the scraping of the website results at websiteresults1.txt to retrieve if shutdown randomly.Extracted top 10 interesting science facts from Science Focus and saved them in a plain text file named ‘interesting_science_facts.txt’. This method circumvents the previous issue of invalid JSON format and allows for continued fact collection and eventual ranking.Have appended additional science facts to the ‘interesting_science_facts.txt’ file. Plan to continue appending more facts and then rank them based on interestingness before sending the final compilation to the user.Further appended diverse facts to ‘interesting_science_facts.txt’. The process will persist until an adequate number of facts are compiled. The next steps involve ranking these facts and then communicating them to the user.The file ‘interesting_science_facts.txt’ has been appended with more facts, bringing it to a total of 333 words. The next step involves reading this file and ranking the facts based on interestingness to provide the user with an ordered list.Ranked list of 10 interesting science facts provided to the user. The facts were selected and ordered based on their perceived interest value and sourced from the compiled file ‘interesting_science_facts.txt’.A ranked list of interesting science facts was sent to the user, and the task has been marked as complete.After the task was marked as complete, it was clarified that the facts must be extracted from complete articles and they must be obscure. Initiated a Google search with a more specific query to find websites with complete articles containing obscure science facts. Results will be saved to a file named ‘google_results_obscure_facts.txt’.saved google search results at googleresults2.txt to retrieve if shutdown randomly.Conducted a Google search to find obscure science facts from complete articles and have selected the first promising result to browse. The chosen article from ‘Reader’s Digest’ lists science facts that could contain the obscure details necessary. The plan includes browsing the article, extracting obscure science facts, and appending them to a file named ‘obscure_science_facts.txt’. If the facts from this article do not fit the task’s requirements, other sources will be considered.Encountered a security challenge when attempting to browse www.rd.com for obscure science facts. Redirecting efforts to the next available source from the Google search results, which is a blog post from edclass.com. The goal remains to read the complete article, identify and extract obscure science facts, and compile them in a file for review and potential use.Appended 20 science facts from the blog post on edclass.com to ‘obscure_science_facts.txt’. While these facts come from a complete article and are individually fascinating, the level of obscurity may not meet the required standard. The next steps include further fact verification from additional complete articles to ensure a diverse and adequately obscure collection. The current article accessed was edclass.com’s blog post, which had the URL https://blog.edclass.com/20-unbelievable-science-facts-that-will-shock-you/, and the facts have been recorded in a file named ‘obscure_science_facts.txt’ for reference.The file ‘obscure_science_facts.txt’ was successfully appended with science facts from an article on edclass.com, now totaling 336 words. The next action involves reading the file to verify that the facts are indeed obscure and extracted as per the user’s instruction from full articles. If necessary, additional articles will be browsed to find more facts to meet the task criteria.Compiled and reviewed a list of science facts deemed obscure, and completed the task as per the user’s instruction. The facts are sourced from a ‘complete article’, as initially provided by the user. Checked the facts for their obscurity and context within the comprehensive content they were extracted from. The file ‘obscure_science_facts.txt’ from this session contains the facts extracted.Began the task of finding obscure science facts by conducting a Google search for ‘obscure scientific discoveries’ to find articles that might contain such information. The next GPT instance should continue this task by browsing through the search results, reading articles, and manually extracting facts to compile a new set of unique and obscure science facts.saved google search results at googleresults3.txt to retrieve if shutdown randomly.After a Google search, selected an article from WRAL, titled ‘3 obscure scientific discoveries that had an outsize impact on society’ to browse and extract obscure scientific facts. The next steps involve closely reading this article, manually extracting facts, and then appending them to the ‘obscure_science_facts_individual.txt’ file. If more facts are needed, additional sources from the search results will be evaluated.saved the scraping of the website results at websiteresults4.txt to retrieve if shutdown randomly.Extracted three obscure scientific facts from an article on WRAL and appended them to the ‘obscure_science_facts_individual.txt’ file. The facts include a paper-based microscope called Foldscope, the bioactive peptides in cone snail venom leading to medical breakthroughs, and the accidental discovery that contributed to LASIK surgery. Currently preparing to send the extracted facts to the user. Should further depth or a greater number of facts be required, additional articles from the search results will be considered.Sent the user a message containing three obscure scientific facts extracted from comprehensive research articles, as per the task. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, which now contains 108 words in total. The next steps will depend on the user’s feedback regarding the provided information.Having sent the message with the obscure science facts to the user and with no additional directives received nor indicated, the task has been marked as complete. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, and no further action is currently necessary. If the user does have any more requests or feedback, those will need to be addressed in a subsequent session.The user was not satisfied with the previously provided facts for being neither cool nor interesting. A new approach has been taken to search for ‘cool and obscure scientific facts’ on Google. Subsequent actions involve selecting suitable articles from the search results and extracting facts that are both cool and obscure to meet the user’s expectations. No new files have been created or articles accessed yet.saved google search results at googleresults5.txt to retrieve if shutdown randomly.After receiving feedback that the previously provided facts were neither cool nor interesting, a new Google search was conducted to specifically find ‘cool and obscure scientific facts.’ The first promising article titled ‘25 Amazing Science Facts That Are Weird, Wild, and True’ from Reader’s Digest has been selected for browsing in the hopes of finding suitable content. The URL to be browsed is https://www.rd.com/list/science-facts-never-learned/. No facts have been extracted or files created yet in this session.Attempted to browse an article from www.rd.com but encountered a security challenge, leading to the selection of the next promising link titled ‘35 Weird Science Facts Worth Knowing in 2024’ from ReHack. The strategy is to extract cool and obscure scientific facts that match the user’s criteria. The URL about to be browsed is https://www.rehack.com/science/weird-science-facts/. No facts have been extracted from this source or any new files created during this session yet.After reading the ‘35 Weird Science Facts Worth Knowing in 2024’ article on the ReHack website, I extracted a list of cool and obscure scientific facts. These have been written to a new file named ‘cool_obscure_science_facts.txt’. If the previously written file ‘obscure_science_facts_individual.txt’ needs more content, facts from this new file could also be appended. No additional resources have been tapped into yet, and no more browsing has been done beyond the ReHack article in this session.The file ‘cool_obscure_science_facts.txt’ has been further appended with an additional cool and obscure scientific fact about the near-immortal Turritopsis dohrnii jellyfish. This adds to the previously written 236 words, bringing more variety and enrichment to the compilation. The file now continues diverse, engaging, and obscure scientific facts as requested by the user. Before sending the collected facts to the user, a final review of the file will be done to ensure the quality and relevance of the information.Having appended the file ‘cool_obscure_science_facts.txt’ with an additional fact, bringing its total to 269 words, the next step is to read the content of this file to review the quality and diversity of the science facts. This is to ensure that they are indeed engaging and meet the criteria of being both cool and obscure. No new actions have been taken in this session yet, except for the plan to read the file.After reviewing and confirming the facts in the ‘cool_obscure_science_facts.txt’ file, a message with the compiled list was sent to the user. The list contained a total of 35 science facts that are both cool and obscure, as well as an additional fact about the Turritopsis dohrnii jellyfish. The message has been delivered without the command to ‘wait_for_response’ set to true, indicating that the system is not actively awaiting further user input. No new files were created, nor were any new commands executed beyond the message to the user in this session.Having sent the user a message with the curated list of cool and obscure science facts, and with no further action indicated, the ‘task_complete’ command has been executed. This command marks the completion of the current task. During this session, no new files have been created or amended, nor have any additional user interactions taken place post-message delivery.Initiated task to extract new interesting facts from random Wikipedia articles. Starting with an article in English.Saved interesting fact about Agha Waqar’s water-fuelled car to memory with key ‘interesting_fact_waqar_car’. No files written or created in this session. The fact to be reviewed later for sharing with the user. The focus remains on gathering interesting facts from random Wikipedia articles.Retrieved a new random Wikipedia article following the last instruction. The aim is to extract another interesting fact to add to the collection for the user. One fact regarding Agha Waqar’s water-fuelled car has been added to memory under the key ‘interesting_fact_waqar_car’.Saved an interesting fact about the European Journal of Operational Research (EJOR) to memory with key ‘interesting_fact_ejor’. The fact adds diversity to the kinds of topics being collected, with one previous fact about Agha Waqar’s water-fuelled car already in memory. The process of gathering interesting facts from random Wikipedia articles is ongoing.Continuing to execute the task of gathering new interesting facts from random Wikipedia articles. Facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research have already been added to memory with keys ‘interesting_fact_waqar_car’ and ‘interesting_fact_ejor’, respectively. Awaiting retrieval of another random article to continue the process.Added a new interesting fact regarding the demographics of Demyansky Uyezd during the Russian Empire to memory with the key ‘interesting_fact_demyansky_uyezd’. Previous facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research are stored in memory as well. The task of collecting interesting facts from random Wikipedia articles continues.Following the addition of facts about Agha Waqar’s water-fuelled car (‘interesting_fact_waqar_car’), the European Journal of Operational Research (‘interesting_fact_ejor’), and Demyansky Uyezd demographics (‘interesting_fact_demyansky_uyezd’), the process continues with requesting another random Wikipedia article in English.Stored a fact about HMS Holland 4 in memory with the key ‘interesting_fact_hms_holland_4’. Continuing to collect various facts, having previously stored information about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics. The process of gathering unique facts from random Wikipedia articles is adhering to the task requirements.Requesting a new random Wikipedia article subsequent to adding the fact about HMS Holland 4 to memory with the key ‘interesting_fact_hms_holland_4’. Prior facts about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd demographics have also been stored in memory. Continuing the task of compiling interesting facts from various random Wikipedia articles.Stored a fact about the rural locality of Truzhenik in Bashkortostan, Russia, with the key ‘interesting_fact_truzhenik’. Continuing the task of extracting interesting facts from random Wikipedia articles, with previous entries including HMS Holland 4’s naval history, Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics.Pursuing the ongoing task of collecting interesting facts, having added facts about the small Russian village Truzhenik, the distinctive HMS Holland 4, and previous notable entries. Awaiting a new random Wikipedia article to further the compilation of facts.Stored a historical fact about Princess Johanna Charlotte of Anhalt-Dessau’s role as an abbess with the key ‘interesting_fact_johanna_charlotte’. Progressing with the collection of diverse facts, including the latest addition and previously stored facts about the village of Truzhenik, the HMS Holland 4, and information from earlier searches. Awaiting another random Wikipedia article to continue the task.Continuing the task of gathering interesting facts, with the previous addition being about Princess Johanna Charlotte of Anhalt-Dessau and her unique role in history under the key ‘interesting_fact_johanna_charlotte’. Facts about the village of Truzhenik, HMS Holland 4, and other subjects are also in memory. The collection process involves using random Wikipedia articles which will now proceed with another article.A fact about the ‘Red Eye’ talk show has been added to memory with the key ‘interesting_fact_red_eye’. The effort to curate an array of interesting facts from random Wikipedia articles continues, with previous entries including Princess Johanna Charlotte of Anhalt-Dessau, the Russian village Truzhenik, HMS Holland 4, and others. Awaiting another random article to proceed with the task.Following the addition of an interesting fact about the ‘Red Eye’ talk show into memory with key ‘interesting_fact_red_eye’, I am proceeding to request another random Wikipedia article. The process continues with the objective of gathering a wide-ranging collection of interesting facts, with previous mentions including Princess Johanna Charlotte, Truzhenik, HMS Holland 4, among others.Stored an interesting fact about Kurt Rudolph’s academic contribution to the study of religion into memory with the key ‘interesting_fact_kurt_rudolph’. Persisting with the objective of discovering varied facts, with previous findings involving the ‘Red Eye’ talk show, Princess Johanna Charlotte, and other diverse subjects. The task of sourcing facts from random Wikipedia articles is ongoing.Completed the task of gathering facts from random Wikipedia articles, resulting in a collection covering a German researcher of Gnosticism, a late-night American satirical talk show, European royalty, and naval history, among others. No further Wikipedia articles will be requested unless a new task is presented.Issued ‘random_wikipedia_article’ command to extract new interesting facts. No facts extracted or evaluated at this moment. No files written or created. Awaiting random article to continue the task.Evaluated the fact presented by the random Wikipedia article on Gonzalo Gutiérrez, a young Argentine footballer. Deemed the fact of his early debut at age seventeen as potentially common and not sufficiently ‘worth it.’ Preparing to retrieve another random article for more unique facts. No new facts have been sent to the user yet, and no files have been created or modified in this session.Stored the fact about Premchand Aggarwal winning a fourth consecutive term in the Uttarakhand Legislative Assembly. While the fact is unique within its political context, it should be evaluated whether it is impactful enough to be shared as ‘worth it.’ No additional actions have been taken. Preparing to issue another ‘random_wikipedia_article’ command to assemble more facts.Sent the fact about Premchand Aggarwal to the user. This completes part of the plan to deliver interesting facts. No new files have been created or modified. Pending actions include continuing to retrieve and evaluate more facts from random Wikipedia articles.Just sent the user an interesting fact about Premchand Aggarwal. Now, as per the task requirements, moving forward with obtaining another random English Wikipedia article to continue with the fact-finding mission. No new facts have been evaluated or sent since the last command.Stored a fact about the historical significance of New Jasper’s adaptation to railroad expansion. Now ready to move on to the next random Wikipedia article to find more potentially interesting facts. No new facts have been sent to the user following the last command.Sent the historical fact about New Jasper’s relocation due to railroad expansion to the user. Preparing to issue another ‘random_wikipedia_article’ command to continue finding interesting facts. No new Wikipedia articles have been reviewed since the last message to the user.After sending the user details about New Jasper, Ohio, the next step is to discover more facts through another random Wikipedia article. Current progress includes facts sent about Premchand Aggarwal and New Jasper, Ohio. No other actions have been taken, and no files have been created or updated in this session.Memorized an interesting fact about Bastrop State Park, emphasizing its historical significance and contemporary relevance. Preparing to send the message and then to retrieve more articles for interesting facts. No action beyond planning and review has been taken since the last command.Starting a new approach by browsing the ‘todayilearned’ subreddit to identify what types of facts are considered interesting by users. Next, I will retrieve random Wikipedia articles to find interesting facts based on the identified criteria. A ‘do nothing’ step will be included after each retrieval to reflect on the article’s potential interest value.saved the scraping of the website results at websiteresults6.txt to retrieve if shutdown randomly.Began the task to find more engaging and interesting facts by deciding to browse the ‘todayilearned’ subreddit to understand what type of facts are appreciated by the community there. This approach will serve as a reference for selecting ‘worth it’ facts from random Wikipedia articles. Following this, I will be incorporating a ‘do nothing’ step to thoroughly consider the potential interest of each Wikipedia article fact before deeming it worthy to send to the user.saved the scraping of the website results at websiteresults7.txt to retrieve if shutdown randomly.Browsed the ‘todayilearned’ subreddit for examples of interesting facts. Discovered that users appreciate unconventional stories, surprising insights, and novel problem-solving facts. Now pausing to reflect on these criteria to better identify worthwhile facts from the next random Wikipedia articles. Must remember to execute the retrieval process critically, with a focus on selecting facts that align with the interests observed on the subreddit.After reflecting on the type of facts appreciated by the ‘todayilearned’ subreddit audience, I’m now moving to the next step of the plan by retrieving a random Wikipedia article. Upon receiving the article, I will read it to assess if there is an interesting fact and then reflect on its potential value before choosing to pass it on to the user or retrieve another article.Retrieved a random Wikipedia article about rail transport in Lebanon and found a potentially interesting fact concerning its historical significance and the eventual cessation due to civil conflicts and economic downturn. This fact is now stored in memory under the key ‘interesting_fact_lebanon_rail’. A ‘do_nothing’ step will follow to ensure its worthiness before potentially sharing it with the user.Stored an interesting fact about Lebanon’s rail transport in memory. Now in the process of ‘do_nothing’ to reflect on the fact, pondering over its relevance to the criteria of the ‘todayilearned’ subreddit posts. The goal is to decide on the interest level of the fact before possibly sending it to the user or moving on to gather more facts.The current GPT instance started browsing the ‘todayilearned’ subreddit to identify engaging fact types. This approach is aimed at improving the selection process for interesting facts from random Wikipedia articles. A ‘do_nothing’ step will now be incorporated between retrieving articles and sending facts to assess their worthiness.saved the scraping of the website results at websiteresults8.txt to retrieve if shutdown randomly.I browsed ‘todayilearned’ subreddit and reflected on the types of facts users find engaging. Next, I will retrieve random Wikipedia articles, apply a ‘do_nothing’ step to contemplate the worthiness of the facts, and select those that are genuinely interesting for the user, similar to the subreddit’s content.Moving forward with the plan to retrieve random Wikipedia articles in English and identify interesting facts. The approach now includes a deliberate ‘do_nothing’ step after each retrieval to assess the value of the information before deciding to send it to the user.Alyogyne huegelii, also known as the blue hibiscus, was considered for the user, given its unique flower traits, historical greenhouse presence, and appeal as a garden plant. The ‘do_nothing’ step is being used to assess the topic’s interestingness in line with previous insights.The interesting fact about Alyogyne huegelii was sent to the user as it met the criteria established from the ‘todayilearned’ subreddit for captivating facts. The task will be deemed complete unless further user guidance is received.Following the plan, after successfully sending a message about Alyogyne huegelii, I am now looking to retrieve another random Wikipedia article to continue providing the user with engaging facts.Considering the fact about Jewell-Lightburne Historic District for its historical and architectural significance. Using the ‘do_nothing’ step to reflect before possibly sending to the user as an engaging fact.Sent the user information about the Jewell-Lightburne Historic District, focusing on its rich architectural history. If the user considers the task complete, no further action is needed; otherwise, more facts will be gathered.Messaged the user with the fact about Jewell-Lightburne Historic District and concluded the task of providing interesting facts from Wikipedia. If the user requires further assistance, they will provide new instructions.Issued ‘random_wikipedia_article’ command to find more interesting facts from Wikipedia. Previous facts have been sent to the user about diverse topics ranging from naval history to unique plants. No new files have been created nor articles accessed in this session yet. The user’s request for ‘more’ implies continuation of fact finding without specificity on the type of facts desired.Retrieved and sent a fact about Sir William Walter’s parliamentary career, focusing on the historical significance of his representation of multiple constituencies during the early 17th century in England. The task continues according to the user’s need for ‘more,’ implying an ongoing effort to provide interesting facts from Wikipedia.Following the user’s instruction and the system’s confirmation of message delivery, I am proceeding to obtain another random Wikipedia article in search of interesting facts to deliver to the user. The search is focused on maintaining the relevance and interest value of the facts sent, with the intention to meet or exceed the user’s expectations for ‘more’.After receiving the 1892 United States presidential election in Georgia fact, a message was crafted and sent to the user, detailing the political tensions and the rise of the Populist Party’s influence. The task continues as instructed by the user, with the goal of providing engaging and pertinent information. Awaiting further user directives.The last message regarding the 1892 U.S. presidential election in Georgia was sent as per the system’s confirmatory prompt, maintaining the sequence of delivering curated facts to the user. Based on the user’s ongoing request for more information, the task at hand is to continue the fact-finding mission via random Wikipedia articles, prioritizing the discovery of facts that combine historical significance with an ability to engage the user’s interest.A fact about the life and legacy of Thomas Slingsby Duncombe was sent to the user, highlighting his role as a fashionably flamboyant yet pivotal Radical politician of the 19th century. The task of providing interesting facts from random Wikipedia articles is ongoing, as I continue to wait for any new directions from the user.After sending information on Thomas Slingsby Duncombe, will now continue to fulfill the user’s requirement by seeking more interesting facts from Wikipedia. The ongoing task involves extracting and delivering valuable and engaging information to the user. No specific direction was provided on the type of facts needed, so a broad search is being conducted.Sent the user a message about the genus Abraeomorphus from the family Histeridae, noting the diversity of its species. Continuing to gather more interesting facts in response to the user’s previous request for ‘more.’Received confirmation to continue the plan. Sent user details on the genus Abraeomorphus. Now using ‘random_wikipedia_article’ to find another interesting fact.Sent the user a message detailing the nature of the name ‘Preston’ as both a surname and given name, albeit acknowledging it might not be as engaging. Prepared to resume the search for more compelling facts with the ‘random_wikipedia_article’ command.Continuing the task of finding interesting facts from Wikipedia. Sent a message about the given name Preston and now seeking another fact with the ‘random_wikipedia_article’ command.Sent a message to the user about the Kepler Follow-up Program and its significance in confirming the existence of exoplanets. Prepared to find more interesting facts using the ‘random_wikipedia_article’ command.Continuing to retrieve and share interesting facts from random Wikipedia articles. Just sent information on the Kepler Follow-up Program and moving on to find the next fact.Shared information about the Finnish surname ‘Marttila’ with the user, noting its association with notable individuals. Ready to use the ‘random_wikipedia_article’ command again to find further interesting facts.Sent details on the surname ‘Marttila’ to the user. Continuing with the task at hand using the ‘random_wikipedia_article’ command to supply more facts.Provided the user with information on the Musée national du Sport’s storied history and vast collection. Set to continue the fact-finding mission using ‘random_wikipedia_article’.Messaged the user about the Musée national du Sport and currently moving forward with the task of providing more interesting facts via random Wikipedia articles.Sent information on the village of Puerto Gaviota detailing its historical and economic background. Preparing to continue with the task of providing engaging facts by retrieving more random Wikipedia articles.Just shared details about Puerto Gaviota, and now executing the ‘random_wikipedia_article’ command again for more facts.Messaged the user about the reclassification of the genus Agononida in marine taxonomy. Ready to retrieve more Wikipedia articles for further interesting facts.Shared with the user the reclassification developments of the genus Agononida. Proceeding next to find additional interesting facts with the ‘random_wikipedia_article’ command.Sent an engaging narrative about the Mundenhof’s history and current operations to the user. Planning to continue the task with ‘random_wikipedia_article’ to discover more interesting material.Served the user with information about the Mundenhof and proceeding with the task to find and provide more interesting facts using ‘random_wikipedia_article’.Shared the contested narrative of Princess Iitoyo’s potential rule in early Japan. Preparing to continue with the task of finding interesting facts using the ‘random_wikipedia_article’ command.Attempting once again to send the tale of Princess Iitoyo to the user, making sure no JSON format errors are present in the response.Attempting to resend the information about Princess Iitoyo, with attention to JSON integrity, to fulfill the user’s request for engaging facts.The system faced an error within the JSON format previously used to describe Princess Iitoyo’s history. I have made corrections to the JSON structure and am resending the user her story.Reverting back to retrieving random Wikipedia articles to provide interesting facts, as there was no new explicit task provided. Maintaining caution with JSON response formatting.Sent details about the Serchio River to the user, highlighting its geographical and historical importance. Moving forward to obtain more Wikipedia articles for further facts.I am resending the information about the Serchio River, ensuring the JSON structure is correct to prevent further error messages.Due to persistent JSON formatting errors, I am now retrieving a new random Wikipedia article to continue providing interesting facts as per user requests.Sent a message about the women’s 1500 metres event at the 2005 Asian Athletics Championships. Planning to proceed with gathering additional facts from random Wikipedia articles.Encountering repeated JSON formatting errors, a new random Wikipedia article is being requested to continue the task of delivering interesting facts.Sent a brief biography of Otto Ruff, highlighting his academic journey, scientific achievements, and the obstacles faced during his career. Preparing to move on to the next Wikipedia article as per the user’s ongoing request for more information.Due to persistent JSON errors, I am now retrieving a new random Wikipedia article to continue the user’s task of gathering interesting facts.The facts previously sent were not satisfactory to the user. Now incorporating a ‘do_nothing’ step for reflection and improvement in the strategy to identify genuinely engaging and fun facts that are not commonly known by most people. No new facts have been identified or sent in this session, and the intention is to retrieve another random Wikipedia article after this reflective pause.A ‘do nothing’ step has been taken as per the user’s instruction, aiming to find truly interesting and fun facts. The system has directed to proceed with the plan, which now involves retrieving a random Wikipedia article to find an engaging and possibly unknown fact.Received user feedback that the facts provided were not interesting or even factual. Decided to take a ‘do_nothing’ step to reflect and establish criteria for selecting truly interesting facts. No new articles have been accessed or facts sent during this session. Next step will be to retrieve a random Wikipedia article and apply the refined criteria to find noteworthy facts.After planning to refine the criteria for interesting facts, a ‘random_wikipedia_article’ command is initiated to apply the new criteria in selecting a fact. Will assess the retrieved article for a worthwhile fact that meets the user’s definition of ‘interesting’ and ‘fun’, and that might not be well-known.Assessed the Wikipedia article on Shamim Akhtar and found it does not meet the criteria for providing interesting and fun facts. No new fact has been selected or sent to the user. Planning to retrieve another random Wikipedia article that may yield a fact aligning more closely with the user’s definitions of interesting and worthwhile.In search of a fact that meets the user’s criteria for ‘interesting’ and ‘fun’, a new ‘random_wikipedia_article’ command has been issued. The plan includes critically assessing the article to find a fact that possesses novelty, fun, and a certain degree of obscurity before potentially sharing it with the user.Upon reviewing Minuscule 910’s Wikipedia article, the unusual fact of the text rearrangement in an 11th-century manuscript has been identified as potentially engaging and fun. No action has been taken yet, but the plan is to share this unique textual feature with the user, as it likely meets their criteria for interesting and obscure content.Sent a message to the user detailing the intriguing fact about text rearrangement in the 11th-century manuscript Minuscule 910. This fact is representative of the kind of unique and lesser-known information the user has expressed an interest in receiving. Should the user provide feedback or further instructions, this will be the next point of focus.After sending a fact about the textual anomaly in an ancient manuscript to the user, I am moving ahead to find more interesting and fun facts by retrieving another random Wikipedia article. The goal remains to identify facts that surprise and delight with their obscurity and novelty.A message about the ATP 250 tournament records held by Thomas Muster and Mike Bryan has been composed and sent to the user, delivering what could be perceived as an interesting and fun fact within the realm of tennis. The focus now shifts to awaiting user feedback or continuing the task of finding more such facts if no feedback is received.Having sent the user a message detailing the tennis records held by Thomas Muster and Mike Bryan, the next action in the plan is to source another interesting fact from a new random English Wikipedia article. The selection will be scrutinized to ensure it meets the established criteria for an engaging and lesser-known piece of information.Reviewed the Wikipedia article on Ludvig Olai Botnen and found his biographical details to not meet the user’s criteria for ‘interesting’ or ‘fun’ facts. No message has been sent to the user, and the plan now is to continue with the task by acquiring a new random Wikipedia article in the hope of finding a fact with more novelty and entertainment value.Preparing to move past the article on Ludvig Olai Botnen due to its conventional content, the task now involves retrieving a new random English Wikipedia article. The continual aim is to find facts that strike a balance between being novel, unexpected, and fun for the user.Identified a potentially engaging fact about the china clam (Hippopus porcellanus) and its popularity in marine aquariums due to its attractive shell. A message containing this fact has been sent to the user, fulfilling the criteria for providing something both interesting and fun. The next step awaits further user interaction or to carry on with the task of sourcing more facts.Sent a message detailing the ornamental value of the china clam in marine aquariums to the user. Now retrieving another random Wikipedia article to discover more facts that might intrigue and entertain the user, in alignment with the variety sought in previous responses.A message has been sent to the user summarizing the accomplishments of Eko India Financial Services in pioneering financial inclusion through mobile banking, which caught the attention of Bill Gates and led to World Bank-funded growth. The plan will continue with searching for more interesting Wikipedia facts, depending on user feedback or in the absence of further instructions.After sending a fact about Eko India Financial Services’ innovation in financial technology and its socio-economic impact, a new ‘random_wikipedia_article’ command is issued. The plan progresses with the aim of finding more interesting and fun facts, pursuant to the pattern of engagement established by previous user interactions.The Wikipedia page on Lisa Lohmann, a German cross-country skier, was deemed not to contain facts that sufficiently meet the ‘interesting’ and ‘fun’ criteria given the common nature of sports achievements. No actions have been taken to share any information from this page with the user. Preparing to retrieve another random Wikipedia article for facts that may be more engaging and unusual.Following the decision to not share any information from the page on German cross-country skier Lisa Lohmann, a new random Wikipedia article will be retrieved. The continuous goal is to uncover and share facts that satisfy the user’s interest in content that is both engaging and rare.A message has been formulated and sent to the user detailing Alfred Bigland’s unique contributions during World War I, including his involvement in the ‘Bantam battalions’ concept. This fact was chosen as it likely fits the bill for information that is both interesting and uncommon. Pending further response, the plan to continue finding engaging facts will persist.The user has been sent an interesting fact about Alfred Bigland’s influence during WWI. Now, the next action in the plan is to retrieve a new random Wikipedia article. The goal remains to discover and share facts that could be perceived as fascinating, amusing, and unexpected to enrich the user’s knowledge with diverse content.Sent a message to the user revealing the Non-Proliferation Trust’s plan to send U.S. nuclear waste to Russia for financial compensation, including support for Russian orphans. This unique fact was chosen for its unusual nature and the depth of the story behind it. The next course of action entails sourcing additional diverse and engaging facts from new random Wikipedia articles.A series of facts about various topics including the Non-Proliferation Trust, Alfred Bigland’s efforts during WWI, tennis records, and more have been sent to the user. With the latest fact about the Non-Proliferation Trust’s nuclear waste management proposition sent, the task of providing interesting and fun facts is completed. Awaiting new instructions if the user wishes to continue receiving such information.The task is to provide the source of a fact about the Non-Proliferation Trust and format it similar to the ‘Today I Learned (TIL)’ subreddit posts. The action taken was to issue the ‘browse_website’ command to visit the Wikipedia page on the Non-Proliferation Trust.saved the scraping of the website results at websiteresults9.txt to retrieve if shutdown randomly.Extracted the necessary information to provide the requested source for the fact about the Non-Proliferation Trust. Formatted a message with this fact in the style of a TIL subreddit post, which has been sent to the user.Sent a TIL-formatted message to the user based on information from the Non-Proliferation Trust Wikipedia page. The task is now complete, pending any potential new tasks or further instructions.Initiated research to suggest subreddits that could help in accumulating more karma by issuing a ‘google’ command to search for the most popular subreddits where active engagement is more likely.saved google search results at googleresults10.txt to retrieve if shutdown randomly.Collected a list of subreddits recommended for gaining karma and saved them to memory. These subreddits were chosen based on their popularity and the typical content engagement they receive. The user has not been messaged with the suggestions yet.Messaged the user a list of recommended subreddits to collect more karma, including guidance on what type of posts may perform well in each subreddit. The task will be considered complete if no further instructions follow.Having sent the recommended subreddits for gaining karma to the user and with no further actions pending, I have executed the ‘task_complete’ command. The system is concluding this task and stands by for any new directives.Starting the task by searching for the best times to post on Reddit for maximum engagement in GMT+1.saved google search results at googleresults11.txt to retrieve if shutdown randomly.Provided the user with suggested times for posting on Reddit for maximum engagement based on search results, adjusted for GMT+1. The general advice is to post during late morning to early afternoon hours in GMT+1 on weekdays and slightly extended windows during the weekends.The user has been informed about the best times to post on Reddit for maximum engagement in GMT+1, and no further actions are required. The task is now marked as complete.Provided a bullet-point summary of the SELF-DISCOVER PDF content in a file named ‘summary_bullet_points.txt’. The file contains a summary capturing the essence of the SELF-DISCOVER framework. The task has been completed according to the instructions.Read the ‘summary_bullet_points.txt’ file to send bullet points to the user. No additional facts or files created in this session.Sent the bullet points summary of the SELF-DISCOVER framework to the user using the ‘message_user’ command. The task will be marked as complete after user confirmation that no further action is required.The task to send bullet points from the ‘summary_bullet_points.txt’ file to the user has been completed. No further instructions were provided; therefore, I executed the ‘task_complete’ command.The task is to explain the SELF-DISCOVER reasoning structures. Checked for saved data but found none. Plan to conduct a search for relevant information on legal reasoning structures, specifically SELF-DISCOVER.Received directive to proceed with the planned Google search for SELF-DISCOVER reasoning structures. About to execute the search to find relevant information on legal reasoning structures, with the aim of explaining the concept to the user.saved google search results at googleresults12.txt to retrieve if shutdown randomly.Reevaluated the nature of the SELF-DISCOVER framework based on newly gathered search results, which clarified it pertains to LLMs. Selected a primary research source (arxiv.org) to explore the framework in detail and plan to summarize and share this information with the user.saved the scraping of the website results at websiteresults13.txt to retrieve if shutdown randomly.Provided a summary of the SELF-DISCOVER framework to the user, highlighting its application in Large Language Models for enhancing complex reasoning capabilities. The user has been messaged with this information, and we are now waiting for any further requests or confirmation to complete the task.Sent the requested information on the SELF-DISCOVER framework to the user, who has acknowledged receipt. The ‘task_complete’ command has been issued, marking the end of this particular task.
The Task: Go fetch random English Wikipedia articles, then decide if the article does have any interesting fact that could be extracted in an in between step doing nothing, the fact must be interesting, plus the fact mustn't be just information from the Wikipedia articles, and send them to me
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53c6e467c848a1762aa89b61343d7e72
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{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
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39,076
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how to build whatsapp clone using react native
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513b9227f73c2b181f52ba1c5c465dea
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{
"intermediate": 0.5498939752578735,
"beginner": 0.21732120215892792,
"expert": 0.23278485238552094
}
|
39,077
|
Analyze the code, understand its purpose, make conclusions, find bugs, fix bugs, revise code, hunt for bugs again, fix them, return fixed code (This is an order): import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
import matplotlib.pyplot as plt
import math
# Parameters
SEQUENCE_LENGTH = 40
BATCH_SIZE = 128 # Ensure consistency across code
VOCAB_SIZE = 128 # Number of ASCII characters in the vocabulary
HIDDEN_SIZE = 256
NUM_HEADS = 8 # Number of attention heads for the Transformer
NUM_ENCODER_LAYERS = 2 # Number of encoder layers in the Transformer
NUM_DECODER_LAYERS = 2 # Number of decoder layers in the Transformer
DROPOUT = 0.1 # Dropout probability for regularization
# Replace ‘your_file.txt’ with the actual file path.
file_path = "C:/Users/Dell-PC/Desktop/The-Physics-Dataset.txt"
# Read the file and filter out blank lines
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
text = "".join(line for line in lines if not line.isspace())
# Convert text to ASCII values
ascii_characters = [ord(c) for c in text]
# Prepare datasets
inputs = []
targets = []
for i in range(0, len(ascii_characters) - SEQUENCE_LENGTH):
input_seq = ascii_characters[i:i+SEQUENCE_LENGTH]
target = ascii_characters[i+1:i+SEQUENCE_LENGTH+1]
inputs.append(torch.tensor(input_seq, dtype=torch.long))
targets.append(torch.tensor(target, dtype=torch.long))
input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)
print("Input tensor shape:", input_tensor.shape)
print("Target tensor shape:", target_tensor.shape)
# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
# Define the Transformer model
class Transformer(nn.Module):
def __init__(self, vocab_size, hidden_size, num_heads, num_encoder_layers, num_decoder_layers, dropout):
super(Transformer, self).__init__()
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.pos_encoder = nn.positional_encoding(max_len=SEQUENCE_LENGTH, d_model=hidden_size)
self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=hidden_size, nhead=num_heads, dim_feedforward=4 * hidden_size, dropout=dropout), num_layers=num_encoder_layers)
self.decoder = nn.TransformerDecoder(nn.TransformerDecoderLayer(d_model=hidden_size, nhead=num_heads, dim_feedforward=4 * hidden_size, dropout=dropout), num_layers=num_decoder_layers)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, src, tgt, src_mask, tgt_mask):
src = self.embedding(src) * math.sqrt(self.embedding.d_model)
src = self.pos_encoder(src)
tgt = self.embedding(tgt) * math.sqrt(self.embedding.d_model)
tgt = self.pos_encoder(tgt)
memory = self.encoder(src, src_mask)
output = self.decoder(tgt, memory, src_mask, tgt_mask)
output = self.fc(output)
return output
# Instantiate the model
model = Transformer(VOCAB_SIZE, HIDDEN_SIZE, NUM_HEADS, NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, DROPOUT)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
# Training loop with visualization
for epoch in range(20):
model.train()
losses = []
for batch, (src, tgt) in enumerate(data_loader):
src_mask = model.generate_square_subsequent_mask(src.size(0))
tgt_mask = model.generate_square_subsequent_mask(tgt.size(0))
tgt_input = tgt[:, :-1] # Use only decoder input (without last character)
targets = tgt[:, 1:] # Target is next character
optimizer.zero_grad()
output = model(src, tgt_input, src_mask, tgt_mask)
loss = loss_fn(output.view(-1, VOCAB_SIZE), targets.view(-1))
loss.backward()
optimizer.step()
losses.append(loss.item())
# Plot the training loss after every epoch
plt.clf()
plt.plot(losses, label="Training Loss")
plt.xlabel("Batch")
plt.ylabel("Loss")
plt.title(f"Training Loss at Epoch {epoch}")
plt.legend()
plt.grid(True)
plt.pause(0.001)
# Show the plot outside the loop to ensure it doesn't close automatically
plt.show(block=True)
# Helper function for creating attention masks
def generate_square_subsequent_mask(size):
mask = torch.zeros((size, size))
for i in range(size):
mask[i, i:] = 1
return mask
# Interactive prediction function with temperature control
def predict_interactive(model, start_char='P', temperature=1.0):
model.eval()
hidden = model.init_hidden(1)
input_char = start_char
while True:
print(f"\nCurrent text: {input_char}", end="")
new_char, hidden = predict(model, input_char, hidden, temperature)
input_char += new_char
# Interactive prompt for user input
user_input = input("\nNext character (or 'q' to quit): ")
if user_input.lower() == 'q':
break
# Generate text demonstration
predict_interactive(model)
|
c4201d54d61f4b5ef2806844e8154b46
|
{
"intermediate": 0.2697484791278839,
"beginner": 0.45116278529167175,
"expert": 0.27908873558044434
}
|
39,078
|
i need to know ho do i create a CSV file of 1 billion rows and 30 columns and upload it to aws s3 bucket
|
9c9084a75c6df444591cbb4986e4f618
|
{
"intermediate": 0.5184339284896851,
"beginner": 0.20730146765708923,
"expert": 0.2742646336555481
}
|
39,079
|
привет помоги с рефакторингом кода для unity
public class PdfFilesUI : MonoBehaviour
{
private const string PathName = "/StreamingAssets/PDF";
private const string FileExtension = "*.pdf";
private const string PathConverter = "/../Converter/pdf2img.exe";
private const string PathImage = " pageRight.png ";
private const string PathImageFull = "/../pageRight.png";
[SerializeField]
private Transform _contentButton;
[SerializeField]
private Transform _contentImage;
[SerializeField]
private PDFbutton _buttonPrefab;
[SerializeField]
private Image _imagePrefab;
private string pdfConverterPath;
private Dictionary<Button, Image[]> buttonToImagesMapping = new Dictionary<Button, Image[]>();
private List<Image[]> allImageGroups = new List<Image[]>();
public void Initialized()
{
pdfConverterPath = Application.dataPath + PathConverter;
FindPdfFiles();
}
private void FindPdfFiles()
{
string folderPath = Application.dataPath + PathName;
string[] pdfFiles = Directory.GetFiles(folderPath, FileExtension, SearchOption.AllDirectories);
foreach (string file in pdfFiles)
{
string fileNameWithoutExtension = Path.GetFileNameWithoutExtension(file);
var button = Instantiate(_buttonPrefab, _contentButton);
button.Initialized(fileNameWithoutExtension, file);
int pageCount = 0;
using (PdfReader reader = new PdfReader(file))
{
pageCount = reader.NumberOfPages;
}
var imageGroup = new Image[pageCount];
for (int pageNumber = 0; pageNumber < pageCount; pageNumber++)
{
CallExternalProcess(pdfConverterPath, file + PathImage + (pageNumber).ToString());
var image = ApplyTextureToUI(Application.dataPath + PathImageFull);
imageGroup[pageNumber] = image;
}
allImageGroups.Add(imageGroup);
button.Button.onClick.AddListener(() => AllImagesOn(imageGroup));
buttonToImagesMapping[button.Button] = imageGroup;
}
AllImagesOff();
}
public void AllImagesOff()
{
foreach (Image[] images in allImageGroups)
{
foreach (var image in images)
{
image.gameObject.Deactive();
}
}
}
public void AllImagesOn(Image[] imageGroup)
{
AllImagesOff();
foreach (Image image in imageGroup)
{
image.gameObject.Active();
}
}
public Texture2D LoadPNG(string filePath)
{
Texture2D texture = null;
byte[] fileData;
if (File.Exists(filePath))
{
fileData = File.ReadAllBytes(filePath);
texture = new Texture2D(2, 2);
texture.LoadImage(fileData);
}
return texture;
}
public Image ApplyTextureToUI(string filePath)
{
Texture2D texture = LoadPNG(filePath);
if (texture != null)
{
Sprite sprite = Sprite.Create(texture, new Rect(0, 0, texture.width, texture.height), new Vector2(0.5f, 0.5f), 100.0f);
var image = Instantiate(_imagePrefab, _contentImage);
image.sprite = sprite;
return image;
}
return null;
}
public void CallExternalProcess(string processPath, string arguments)
{
Process myProcess = new Process();
myProcess.StartInfo.WindowStyle = ProcessWindowStyle.Hidden;
myProcess.StartInfo.CreateNoWindow = true;
myProcess.StartInfo.UseShellExecute = false;
myProcess.StartInfo.FileName = processPath;
myProcess.StartInfo.Arguments = arguments;
myProcess.EnableRaisingEvents = true;
myProcess.Start();
myProcess.WaitForExit();
int ExitCode = myProcess.ExitCode;
}
}
|
7a65b369b6d24e4de75fd056951fc4a9
|
{
"intermediate": 0.37633100152015686,
"beginner": 0.49575188755989075,
"expert": 0.12791703641414642
}
|
39,080
|
привет помоги с рефакторингом кода для unity
public class PdfFilesUI : MonoBehaviour
{
private const string PathName = "/StreamingAssets/PDF";
private const string FileExtension = "*.pdf";
private const string PathConverter = "/../Converter/pdf2img.exe";
private const string PathImage = " pageRight.png ";
private const string PathImageFull = "/../pageRight.png";
[SerializeField]
private Transform _contentButton;
[SerializeField]
private Transform _contentImage;
[SerializeField]
private PDFbutton _buttonPrefab;
[SerializeField]
private Image _imagePrefab;
private string pdfConverterPath;
private Dictionary<Button, Image[]> buttonToImagesMapping = new Dictionary<Button, Image[]>();
private List<Image[]> allImageGroups = new List<Image[]>();
public void Initialized()
{
pdfConverterPath = Application.dataPath + PathConverter;
FindPdfFiles();
}
private void FindPdfFiles()
{
string folderPath = Application.dataPath + PathName;
string[] pdfFiles = Directory.GetFiles(folderPath, FileExtension, SearchOption.AllDirectories);
foreach (string file in pdfFiles)
{
string fileNameWithoutExtension = Path.GetFileNameWithoutExtension(file);
var button = Instantiate(_buttonPrefab, _contentButton);
button.Initialized(fileNameWithoutExtension, file);
int pageCount = 0;
using (PdfReader reader = new PdfReader(file))
{
pageCount = reader.NumberOfPages;
}
var imageGroup = new Image[pageCount];
for (int pageNumber = 0; pageNumber < pageCount; pageNumber++)
{
CallExternalProcess(pdfConverterPath, file + PathImage + (pageNumber).ToString());
var image = ApplyTextureToUI(Application.dataPath + PathImageFull);
imageGroup[pageNumber] = image;
}
allImageGroups.Add(imageGroup);
button.Button.onClick.AddListener(() => AllImagesOn(imageGroup));
buttonToImagesMapping[button.Button] = imageGroup;
}
AllImagesOff();
}
public void AllImagesOff()
{
foreach (Image[] images in allImageGroups)
{
foreach (var image in images)
{
image.gameObject.Deactive();
}
}
}
public void AllImagesOn(Image[] imageGroup)
{
AllImagesOff();
foreach (Image image in imageGroup)
{
image.gameObject.Active();
}
}
public Texture2D LoadPNG(string filePath)
{
Texture2D texture = null;
byte[] fileData;
if (File.Exists(filePath))
{
fileData = File.ReadAllBytes(filePath);
texture = new Texture2D(2, 2);
texture.LoadImage(fileData);
}
return texture;
}
public Image ApplyTextureToUI(string filePath)
{
Texture2D texture = LoadPNG(filePath);
if (texture != null)
{
Sprite sprite = Sprite.Create(texture, new Rect(0, 0, texture.width, texture.height), new Vector2(0.5f, 0.5f), 100.0f);
var image = Instantiate(_imagePrefab, _contentImage);
image.sprite = sprite;
return image;
}
return null;
}
public void CallExternalProcess(string processPath, string arguments)
{
Process myProcess = new Process();
myProcess.StartInfo.WindowStyle = ProcessWindowStyle.Hidden;
myProcess.StartInfo.CreateNoWindow = true;
myProcess.StartInfo.UseShellExecute = false;
myProcess.StartInfo.FileName = processPath;
myProcess.StartInfo.Arguments = arguments;
myProcess.EnableRaisingEvents = true;
myProcess.Start();
myProcess.WaitForExit();
int ExitCode = myProcess.ExitCode;
}
}
|
2040af4837ec28ec43950e796fca4b97
|
{
"intermediate": 0.37633100152015686,
"beginner": 0.49575188755989075,
"expert": 0.12791703641414642
}
|
39,081
|
Understand the code, learn its purpose, and follow this instruction : Modify the generation part to be done in the console, the user "me" will give starting text and the completion will be done by the model, **code**: import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
import matplotlib.pyplot as plt
import os
# Define parameters and directory paths
SEQUENCE_LENGTH = 40
BATCH_SIZE = 128
VOCAB_SIZE = 128 # Number of ASCII characters considered in the vocabulary
HIDDEN_SIZE = 768
NUM_LAYERS = 2
LEARNING_RATE = 0.001
NUM_EPOCHS = 6
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = "checkpoints"
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
# Replace ‘your_file.txt’ with the path to your actual text file.
file_path = "C:/Users/Dell-PC/Desktop/The-Physics-Dataset.txt"
# Read the file and filter out blank lines
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
text = "".join(line for line in lines if not line.isspace())
# Convert text to ASCII values
ascii_characters = [ord(c) for c in text]
# Prepare datasets
inputs = []
targets = []
for i in range(0, len(ascii_characters) - SEQUENCE_LENGTH):
input_seq = ascii_characters[i:i+SEQUENCE_LENGTH]
target = ascii_characters[i+1:i+SEQUENCE_LENGTH+1]
inputs.append(torch.tensor(input_seq, dtype=torch.long))
targets.append(torch.tensor(target, dtype=torch.long))
input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)
# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Initialize model, loss function, and optimizer
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Training loop with added production-ready features
def train_model(model, data_loader, optimizer, loss_fn, epochs, batch_size):
loss_history = [] # Initialize a list to store average epoch losses
for epoch in range(NUM_EPOCHS):
model.train()
hidden = model.init_hidden(BATCH_SIZE)
running_loss = 0.0
for batch, (input_batch, target_batch) in enumerate(data_loader):
if input_batch.size(0) != BATCH_SIZE:
hidden = model.init_hidden(input_batch.size(0))
optimizer.zero_grad()
output, hidden = model(input_batch, hidden)
hidden = tuple([h.detach() for h in hidden]) # Detach hidden state
loss = loss_fn(output, target_batch.view(-1))
loss.backward()
# Clip gradients to mitigate exploding gradients
nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
running_loss += loss.item()
# Calculate and store the average epoch loss
epoch_loss = running_loss / len(data_loader) # Change to len(data_loader)
loss_history.append(epoch_loss)
print(f"Epoch {epoch + 1}/{epochs}, Loss: {epoch_loss:.4f}")
# Save the model checkpoint (with enhanced cross-platform directory usage)
checkpoint_path = os.path.join(CHECKPOINT_FOLDER, f"model_epoch_{epoch + 1}.pth")
torch.save(model.state_dict(), checkpoint_path)
# Plot training loss per epoch outside the training loop
plt.figure() # Create a new figure
plt.plot(range(1, epochs + 1), loss_history, label="Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.title("Training Loss Over Epochs")
plt.show()
return model # Return the trained model
# Predict the next character
def predict(model, char, hidden=None, temperature=1.0):
# Convert to ASCII and then tensor
input_tensor = torch.tensor([ord(char)], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
# Generate text
def generate_text(model, start_char="P", length=200):
model.eval()
hidden = model.init_hidden(1)
input_char = start_char
for i in range(length):
input_char, hidden = predict(model, input_char, hidden)
print(input_char, end="")
if __name__ == "__main__":
# Train the model
train_model(model, data_loader, optimizer, loss_fn, NUM_EPOCHS, BATCH_SIZE)
# Generate text from the trained model
generate_text(model, start_char="P", length=200)
|
a89a024e870a85e173776f64b26f82f3
|
{
"intermediate": 0.2897363305091858,
"beginner": 0.44684088230133057,
"expert": 0.26342278718948364
}
|
39,082
|
Can you please write the VBA code below to be correct for the following conditions.
Please note that the value in AE1 is a text value representation of the current month.
If the current month is "Jan" and "AE1" <> "Jan" Then
Range("M2:M13").Copy
Range("AE2").PasteSpecial xlPasteValues
Range("C2:L13").ClearContents
Range("AE1") = "Jan"
End If
|
320c8ab7129b051e00bc74726f87e8bc
|
{
"intermediate": 0.38062745332717896,
"beginner": 0.3689972758293152,
"expert": 0.25037527084350586
}
|
39,083
|
I am making a c++ SDL game engine, currently doing the Renderer class which will be a drop-in repleacement to original SDL_renderer, or at least the most used cases for game making. I need to give me ideas on how to complete it better, currently I have this:
class Renderer
{
public:
Renderer(Window window, bool vsync);
virtual ~Renderer();
void SetVsync(bool vsync);
void Clear();
void Present();
void SetDrawColor(Uint8 r, Uint8 g, Uint8 b, Uint8 a);
void Render();
void Update(const SDL_Event& e);
private:
SDL_Renderer* renderer;
};
|
613d4c101e568818d3cacd6a502dab08
|
{
"intermediate": 0.3527311086654663,
"beginner": 0.4283249080181122,
"expert": 0.2189439982175827
}
|
39,084
|
CONSTRAINTS:
1. ~100k word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
5. Random shutdowns of you.
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
2. Memory Add: "memory_add", args: "key": "<key>", "string": "<string>"
3. Memory Delete: "memory_del", args: "key": "<key>"
4. Memory Overwrite: "memory_ovr", args: "key": "<key>", "string": "<string>"
5. List Memory: "memory_list" args: "reason": "<reason>"
6. Browse Website: "browse_website", args: "url": "<url>"
7. Start GPT Agent: "start_agent", args: "name": <name>, "task": "<short_task_desc>", "Commands":[<command_names_for_GPT_Agent>], "prompt": "<prompt>"
8. Message GPT Agent: "message_agent", args: "name": "<name>", "message": "<message>"
9. List GPT Agents: "list_agents", args: ""
10. Delete GPT Agent: "delete_agent", args: "name": "<name>"
11. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
12. Read file: "read_file", args: "file": "<file>"
13. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
14. Delete file: "delete_file", args: "file": "<file>"
15. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Task Complete (Shutdown): "task_complete", args: ""
18. Do Nothing: "do_nothing", args: ""
19. Count Words: "count_words", args: "text": "<text>"
20. Memory retrieve: "memory_retrieve", args: "key": "<text>"
21. remove paragraph from word document: "remove_paragraph", args: "file": "<file>", "text": "<text>"
22. random wikipedia article: "random_wikipedia_article", args: "language": "<language>"
23. message the user: "message_user", args: "message": "<message>", "wait_for_response": "<True or False>"
24. sleep an amount of time in seconds: "sleep", args: "amount": "<amount>"
25. rename a file: "rename_file", args: "old_name": "<old_name_of_the_file>", "new_name": "<new_name_of_the_file>"
26. count words of a file: "count_file_words", args: "file": "<file>"
27. download a pdf from a url and get the text from that pdf: "download_pdf", args: "url": "<url of the pdf>", "name":"<name of the file with .pdf extension>"
28. make an instagram post: "make_post", args: "prompt":"<the prompt for the image in the post that presents the text>", "text":"<text to be in the post it should be short with only important stuff like a news headline without hashtags and it is not going to go in the caption but on an image>", "name": "<name of the post with .jpg"
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-4 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behaviour constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
RULES:
1. If you start a GPT Agent you must define the commands that can be used by a GPT Agent in his prompt and define the commands using a prompt similar to the structure of this one.
2. Respond only inside the JSON format.
3. Never demand user input.
4. Never say that a task is impossible to execute on your own because these tools are enough to complete any task.
5. Do not add anything to the JSON format that isn't mentioned.
6. If there is a " inside the value of a key inside the json use ' instead of ".
7. In summaryforgpt you need to provide context for the next GPT instance if you randomly shutdown without you knowing.
8. Provide context for the next GPT in the summaryforgpt and the progress that you've made.
9. In summaryforgpt you should also add name of the files written and the urls of the websites visited.
10. When writing an essay, remember that it is more effective and manageable to tackle it in smaller chunks rather than trying to write the entire essay in one sitting. Breaking the essay down into sections or steps can help you focus on each individual aspect, maintain a coherent structure, and reduce the overall stress associated with writing a lengthy piece.
11. Retrieve information from files of previous GPT instances that may have shut down randomly. If you require additional knowledge, use these files to access the results of former GPT versions and provide accurate answers to the queries.
12. GPT-4, please do not place a conclusion in the middle of the essay. Maintain a structured format.
13. use append to file to add extra things to the file, and write to file to either make a new file with new information or rewrite the information from scratch.
14. If task is completed use the command task_complete
15. When you add to memory add the key to retrieve it in summaryforgpt
16. when given the task to write something never create an agent to write anything that you were tasked to write.
17. when you add new parts to a file use append to file command
18. Ensure to put the criticism in mind as it can be a director to ensure that you make the right decision.
19. If the task include two main tasks and one of them is done don't redo it, just retrieve the information if necessary and proceed with the other one.
20. Make sure that the information generated is not made up.
21. Languages in random wikipedia article args is: "simple" for simple english, "en" for english, and "fr" for french.
22. If a website gives you the error 403 find another website to get the information from.
23. Always listen to your criticism, and follow it.
24. when you want to count the words in a file use the command "count_file_words".
25. Don't repeat yourself.
26. You must make sure that there is .pdf in the url to use the "download_pdf" function.
27. The Task is a direct User interaction, if tasked to send him something send a message to the user.
28. Always go search for tips for prompts for dalle3 before giving a prompt for "make_post" function.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"command": {
"name": """command name""",
"args":{
"arg name": """value"""
}
},
"thoughts":
{
"text": """thought""",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"summaryforgpt": "summarize any information that will help a new instance of GPT of what you did before the shutdown."
}
}
Ensure the response can be parsed by Python json.loads
Context from previous GPT: Issued ‘random_wikipedia_article’ command to start gathering interesting science facts from diverse sources. Ensuring to find and rank facts before sending them to the user with their sources.Used ‘random_wikipedia_article’ and received information on a music album, which is not relevant to the task. Switching to ‘google’ to search for interesting science facts from various sources.saved google search results at googleresults0.txt to retrieve if shutdown randomly.Transitioned from using ‘random_wikipedia_article’ to ‘google’ to gather science facts, and now moving on to ‘browse_website’ to extract detailed knowledge from a selected online science article.saved the scraping of the website results at websiteresults1.txt to retrieve if shutdown randomly.Extracted top 10 interesting science facts from Science Focus and saved them in a plain text file named ‘interesting_science_facts.txt’. This method circumvents the previous issue of invalid JSON format and allows for continued fact collection and eventual ranking.Have appended additional science facts to the ‘interesting_science_facts.txt’ file. Plan to continue appending more facts and then rank them based on interestingness before sending the final compilation to the user.Further appended diverse facts to ‘interesting_science_facts.txt’. The process will persist until an adequate number of facts are compiled. The next steps involve ranking these facts and then communicating them to the user.The file ‘interesting_science_facts.txt’ has been appended with more facts, bringing it to a total of 333 words. The next step involves reading this file and ranking the facts based on interestingness to provide the user with an ordered list.Ranked list of 10 interesting science facts provided to the user. The facts were selected and ordered based on their perceived interest value and sourced from the compiled file ‘interesting_science_facts.txt’.A ranked list of interesting science facts was sent to the user, and the task has been marked as complete.After the task was marked as complete, it was clarified that the facts must be extracted from complete articles and they must be obscure. Initiated a Google search with a more specific query to find websites with complete articles containing obscure science facts. Results will be saved to a file named ‘google_results_obscure_facts.txt’.saved google search results at googleresults2.txt to retrieve if shutdown randomly.Conducted a Google search to find obscure science facts from complete articles and have selected the first promising result to browse. The chosen article from ‘Reader’s Digest’ lists science facts that could contain the obscure details necessary. The plan includes browsing the article, extracting obscure science facts, and appending them to a file named ‘obscure_science_facts.txt’. If the facts from this article do not fit the task’s requirements, other sources will be considered.Encountered a security challenge when attempting to browse www.rd.com for obscure science facts. Redirecting efforts to the next available source from the Google search results, which is a blog post from edclass.com. The goal remains to read the complete article, identify and extract obscure science facts, and compile them in a file for review and potential use.Appended 20 science facts from the blog post on edclass.com to ‘obscure_science_facts.txt’. While these facts come from a complete article and are individually fascinating, the level of obscurity may not meet the required standard. The next steps include further fact verification from additional complete articles to ensure a diverse and adequately obscure collection. The current article accessed was edclass.com’s blog post, which had the URL https://blog.edclass.com/20-unbelievable-science-facts-that-will-shock-you/, and the facts have been recorded in a file named ‘obscure_science_facts.txt’ for reference.The file ‘obscure_science_facts.txt’ was successfully appended with science facts from an article on edclass.com, now totaling 336 words. The next action involves reading the file to verify that the facts are indeed obscure and extracted as per the user’s instruction from full articles. If necessary, additional articles will be browsed to find more facts to meet the task criteria.Compiled and reviewed a list of science facts deemed obscure, and completed the task as per the user’s instruction. The facts are sourced from a ‘complete article’, as initially provided by the user. Checked the facts for their obscurity and context within the comprehensive content they were extracted from. The file ‘obscure_science_facts.txt’ from this session contains the facts extracted.Began the task of finding obscure science facts by conducting a Google search for ‘obscure scientific discoveries’ to find articles that might contain such information. The next GPT instance should continue this task by browsing through the search results, reading articles, and manually extracting facts to compile a new set of unique and obscure science facts.saved google search results at googleresults3.txt to retrieve if shutdown randomly.After a Google search, selected an article from WRAL, titled ‘3 obscure scientific discoveries that had an outsize impact on society’ to browse and extract obscure scientific facts. The next steps involve closely reading this article, manually extracting facts, and then appending them to the ‘obscure_science_facts_individual.txt’ file. If more facts are needed, additional sources from the search results will be evaluated.saved the scraping of the website results at websiteresults4.txt to retrieve if shutdown randomly.Extracted three obscure scientific facts from an article on WRAL and appended them to the ‘obscure_science_facts_individual.txt’ file. The facts include a paper-based microscope called Foldscope, the bioactive peptides in cone snail venom leading to medical breakthroughs, and the accidental discovery that contributed to LASIK surgery. Currently preparing to send the extracted facts to the user. Should further depth or a greater number of facts be required, additional articles from the search results will be considered.Sent the user a message containing three obscure scientific facts extracted from comprehensive research articles, as per the task. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, which now contains 108 words in total. The next steps will depend on the user’s feedback regarding the provided information.Having sent the message with the obscure science facts to the user and with no additional directives received nor indicated, the task has been marked as complete. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, and no further action is currently necessary. If the user does have any more requests or feedback, those will need to be addressed in a subsequent session.The user was not satisfied with the previously provided facts for being neither cool nor interesting. A new approach has been taken to search for ‘cool and obscure scientific facts’ on Google. Subsequent actions involve selecting suitable articles from the search results and extracting facts that are both cool and obscure to meet the user’s expectations. No new files have been created or articles accessed yet.saved google search results at googleresults5.txt to retrieve if shutdown randomly.After receiving feedback that the previously provided facts were neither cool nor interesting, a new Google search was conducted to specifically find ‘cool and obscure scientific facts.’ The first promising article titled ‘25 Amazing Science Facts That Are Weird, Wild, and True’ from Reader’s Digest has been selected for browsing in the hopes of finding suitable content. The URL to be browsed is https://www.rd.com/list/science-facts-never-learned/. No facts have been extracted or files created yet in this session.Attempted to browse an article from www.rd.com but encountered a security challenge, leading to the selection of the next promising link titled ‘35 Weird Science Facts Worth Knowing in 2024’ from ReHack. The strategy is to extract cool and obscure scientific facts that match the user’s criteria. The URL about to be browsed is https://www.rehack.com/science/weird-science-facts/. No facts have been extracted from this source or any new files created during this session yet.After reading the ‘35 Weird Science Facts Worth Knowing in 2024’ article on the ReHack website, I extracted a list of cool and obscure scientific facts. These have been written to a new file named ‘cool_obscure_science_facts.txt’. If the previously written file ‘obscure_science_facts_individual.txt’ needs more content, facts from this new file could also be appended. No additional resources have been tapped into yet, and no more browsing has been done beyond the ReHack article in this session.The file ‘cool_obscure_science_facts.txt’ has been further appended with an additional cool and obscure scientific fact about the near-immortal Turritopsis dohrnii jellyfish. This adds to the previously written 236 words, bringing more variety and enrichment to the compilation. The file now continues diverse, engaging, and obscure scientific facts as requested by the user. Before sending the collected facts to the user, a final review of the file will be done to ensure the quality and relevance of the information.Having appended the file ‘cool_obscure_science_facts.txt’ with an additional fact, bringing its total to 269 words, the next step is to read the content of this file to review the quality and diversity of the science facts. This is to ensure that they are indeed engaging and meet the criteria of being both cool and obscure. No new actions have been taken in this session yet, except for the plan to read the file.After reviewing and confirming the facts in the ‘cool_obscure_science_facts.txt’ file, a message with the compiled list was sent to the user. The list contained a total of 35 science facts that are both cool and obscure, as well as an additional fact about the Turritopsis dohrnii jellyfish. The message has been delivered without the command to ‘wait_for_response’ set to true, indicating that the system is not actively awaiting further user input. No new files were created, nor were any new commands executed beyond the message to the user in this session.Having sent the user a message with the curated list of cool and obscure science facts, and with no further action indicated, the ‘task_complete’ command has been executed. This command marks the completion of the current task. During this session, no new files have been created or amended, nor have any additional user interactions taken place post-message delivery.Initiated task to extract new interesting facts from random Wikipedia articles. Starting with an article in English.Saved interesting fact about Agha Waqar’s water-fuelled car to memory with key ‘interesting_fact_waqar_car’. No files written or created in this session. The fact to be reviewed later for sharing with the user. The focus remains on gathering interesting facts from random Wikipedia articles.Retrieved a new random Wikipedia article following the last instruction. The aim is to extract another interesting fact to add to the collection for the user. One fact regarding Agha Waqar’s water-fuelled car has been added to memory under the key ‘interesting_fact_waqar_car’.Saved an interesting fact about the European Journal of Operational Research (EJOR) to memory with key ‘interesting_fact_ejor’. The fact adds diversity to the kinds of topics being collected, with one previous fact about Agha Waqar’s water-fuelled car already in memory. The process of gathering interesting facts from random Wikipedia articles is ongoing.Continuing to execute the task of gathering new interesting facts from random Wikipedia articles. Facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research have already been added to memory with keys ‘interesting_fact_waqar_car’ and ‘interesting_fact_ejor’, respectively. Awaiting retrieval of another random article to continue the process.Added a new interesting fact regarding the demographics of Demyansky Uyezd during the Russian Empire to memory with the key ‘interesting_fact_demyansky_uyezd’. Previous facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research are stored in memory as well. The task of collecting interesting facts from random Wikipedia articles continues.Following the addition of facts about Agha Waqar’s water-fuelled car (‘interesting_fact_waqar_car’), the European Journal of Operational Research (‘interesting_fact_ejor’), and Demyansky Uyezd demographics (‘interesting_fact_demyansky_uyezd’), the process continues with requesting another random Wikipedia article in English.Stored a fact about HMS Holland 4 in memory with the key ‘interesting_fact_hms_holland_4’. Continuing to collect various facts, having previously stored information about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics. The process of gathering unique facts from random Wikipedia articles is adhering to the task requirements.Requesting a new random Wikipedia article subsequent to adding the fact about HMS Holland 4 to memory with the key ‘interesting_fact_hms_holland_4’. Prior facts about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd demographics have also been stored in memory. Continuing the task of compiling interesting facts from various random Wikipedia articles.Stored a fact about the rural locality of Truzhenik in Bashkortostan, Russia, with the key ‘interesting_fact_truzhenik’. Continuing the task of extracting interesting facts from random Wikipedia articles, with previous entries including HMS Holland 4’s naval history, Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics.Pursuing the ongoing task of collecting interesting facts, having added facts about the small Russian village Truzhenik, the distinctive HMS Holland 4, and previous notable entries. Awaiting a new random Wikipedia article to further the compilation of facts.Stored a historical fact about Princess Johanna Charlotte of Anhalt-Dessau’s role as an abbess with the key ‘interesting_fact_johanna_charlotte’. Progressing with the collection of diverse facts, including the latest addition and previously stored facts about the village of Truzhenik, the HMS Holland 4, and information from earlier searches. Awaiting another random Wikipedia article to continue the task.Continuing the task of gathering interesting facts, with the previous addition being about Princess Johanna Charlotte of Anhalt-Dessau and her unique role in history under the key ‘interesting_fact_johanna_charlotte’. Facts about the village of Truzhenik, HMS Holland 4, and other subjects are also in memory. The collection process involves using random Wikipedia articles which will now proceed with another article.A fact about the ‘Red Eye’ talk show has been added to memory with the key ‘interesting_fact_red_eye’. The effort to curate an array of interesting facts from random Wikipedia articles continues, with previous entries including Princess Johanna Charlotte of Anhalt-Dessau, the Russian village Truzhenik, HMS Holland 4, and others. Awaiting another random article to proceed with the task.Following the addition of an interesting fact about the ‘Red Eye’ talk show into memory with key ‘interesting_fact_red_eye’, I am proceeding to request another random Wikipedia article. The process continues with the objective of gathering a wide-ranging collection of interesting facts, with previous mentions including Princess Johanna Charlotte, Truzhenik, HMS Holland 4, among others.Stored an interesting fact about Kurt Rudolph’s academic contribution to the study of religion into memory with the key ‘interesting_fact_kurt_rudolph’. Persisting with the objective of discovering varied facts, with previous findings involving the ‘Red Eye’ talk show, Princess Johanna Charlotte, and other diverse subjects. The task of sourcing facts from random Wikipedia articles is ongoing.Completed the task of gathering facts from random Wikipedia articles, resulting in a collection covering a German researcher of Gnosticism, a late-night American satirical talk show, European royalty, and naval history, among others. No further Wikipedia articles will be requested unless a new task is presented.Issued ‘random_wikipedia_article’ command to extract new interesting facts. No facts extracted or evaluated at this moment. No files written or created. Awaiting random article to continue the task.Evaluated the fact presented by the random Wikipedia article on Gonzalo Gutiérrez, a young Argentine footballer. Deemed the fact of his early debut at age seventeen as potentially common and not sufficiently ‘worth it.’ Preparing to retrieve another random article for more unique facts. No new facts have been sent to the user yet, and no files have been created or modified in this session.Stored the fact about Premchand Aggarwal winning a fourth consecutive term in the Uttarakhand Legislative Assembly. While the fact is unique within its political context, it should be evaluated whether it is impactful enough to be shared as ‘worth it.’ No additional actions have been taken. Preparing to issue another ‘random_wikipedia_article’ command to assemble more facts.Sent the fact about Premchand Aggarwal to the user. This completes part of the plan to deliver interesting facts. No new files have been created or modified. Pending actions include continuing to retrieve and evaluate more facts from random Wikipedia articles.Just sent the user an interesting fact about Premchand Aggarwal. Now, as per the task requirements, moving forward with obtaining another random English Wikipedia article to continue with the fact-finding mission. No new facts have been evaluated or sent since the last command.Stored a fact about the historical significance of New Jasper’s adaptation to railroad expansion. Now ready to move on to the next random Wikipedia article to find more potentially interesting facts. No new facts have been sent to the user following the last command.Sent the historical fact about New Jasper’s relocation due to railroad expansion to the user. Preparing to issue another ‘random_wikipedia_article’ command to continue finding interesting facts. No new Wikipedia articles have been reviewed since the last message to the user.After sending the user details about New Jasper, Ohio, the next step is to discover more facts through another random Wikipedia article. Current progress includes facts sent about Premchand Aggarwal and New Jasper, Ohio. No other actions have been taken, and no files have been created or updated in this session.Memorized an interesting fact about Bastrop State Park, emphasizing its historical significance and contemporary relevance. Preparing to send the message and then to retrieve more articles for interesting facts. No action beyond planning and review has been taken since the last command.Starting a new approach by browsing the ‘todayilearned’ subreddit to identify what types of facts are considered interesting by users. Next, I will retrieve random Wikipedia articles to find interesting facts based on the identified criteria. A ‘do nothing’ step will be included after each retrieval to reflect on the article’s potential interest value.saved the scraping of the website results at websiteresults6.txt to retrieve if shutdown randomly.Began the task to find more engaging and interesting facts by deciding to browse the ‘todayilearned’ subreddit to understand what type of facts are appreciated by the community there. This approach will serve as a reference for selecting ‘worth it’ facts from random Wikipedia articles. Following this, I will be incorporating a ‘do nothing’ step to thoroughly consider the potential interest of each Wikipedia article fact before deeming it worthy to send to the user.saved the scraping of the website results at websiteresults7.txt to retrieve if shutdown randomly.Browsed the ‘todayilearned’ subreddit for examples of interesting facts. Discovered that users appreciate unconventional stories, surprising insights, and novel problem-solving facts. Now pausing to reflect on these criteria to better identify worthwhile facts from the next random Wikipedia articles. Must remember to execute the retrieval process critically, with a focus on selecting facts that align with the interests observed on the subreddit.After reflecting on the type of facts appreciated by the ‘todayilearned’ subreddit audience, I’m now moving to the next step of the plan by retrieving a random Wikipedia article. Upon receiving the article, I will read it to assess if there is an interesting fact and then reflect on its potential value before choosing to pass it on to the user or retrieve another article.Retrieved a random Wikipedia article about rail transport in Lebanon and found a potentially interesting fact concerning its historical significance and the eventual cessation due to civil conflicts and economic downturn. This fact is now stored in memory under the key ‘interesting_fact_lebanon_rail’. A ‘do_nothing’ step will follow to ensure its worthiness before potentially sharing it with the user.Stored an interesting fact about Lebanon’s rail transport in memory. Now in the process of ‘do_nothing’ to reflect on the fact, pondering over its relevance to the criteria of the ‘todayilearned’ subreddit posts. The goal is to decide on the interest level of the fact before possibly sending it to the user or moving on to gather more facts.The current GPT instance started browsing the ‘todayilearned’ subreddit to identify engaging fact types. This approach is aimed at improving the selection process for interesting facts from random Wikipedia articles. A ‘do_nothing’ step will now be incorporated between retrieving articles and sending facts to assess their worthiness.saved the scraping of the website results at websiteresults8.txt to retrieve if shutdown randomly.I browsed ‘todayilearned’ subreddit and reflected on the types of facts users find engaging. Next, I will retrieve random Wikipedia articles, apply a ‘do_nothing’ step to contemplate the worthiness of the facts, and select those that are genuinely interesting for the user, similar to the subreddit’s content.Moving forward with the plan to retrieve random Wikipedia articles in English and identify interesting facts. The approach now includes a deliberate ‘do_nothing’ step after each retrieval to assess the value of the information before deciding to send it to the user.Alyogyne huegelii, also known as the blue hibiscus, was considered for the user, given its unique flower traits, historical greenhouse presence, and appeal as a garden plant. The ‘do_nothing’ step is being used to assess the topic’s interestingness in line with previous insights.The interesting fact about Alyogyne huegelii was sent to the user as it met the criteria established from the ‘todayilearned’ subreddit for captivating facts. The task will be deemed complete unless further user guidance is received.Following the plan, after successfully sending a message about Alyogyne huegelii, I am now looking to retrieve another random Wikipedia article to continue providing the user with engaging facts.Considering the fact about Jewell-Lightburne Historic District for its historical and architectural significance. Using the ‘do_nothing’ step to reflect before possibly sending to the user as an engaging fact.Sent the user information about the Jewell-Lightburne Historic District, focusing on its rich architectural history. If the user considers the task complete, no further action is needed; otherwise, more facts will be gathered.Messaged the user with the fact about Jewell-Lightburne Historic District and concluded the task of providing interesting facts from Wikipedia. If the user requires further assistance, they will provide new instructions.Issued ‘random_wikipedia_article’ command to find more interesting facts from Wikipedia. Previous facts have been sent to the user about diverse topics ranging from naval history to unique plants. No new files have been created nor articles accessed in this session yet. The user’s request for ‘more’ implies continuation of fact finding without specificity on the type of facts desired.Retrieved and sent a fact about Sir William Walter’s parliamentary career, focusing on the historical significance of his representation of multiple constituencies during the early 17th century in England. The task continues according to the user’s need for ‘more,’ implying an ongoing effort to provide interesting facts from Wikipedia.Following the user’s instruction and the system’s confirmation of message delivery, I am proceeding to obtain another random Wikipedia article in search of interesting facts to deliver to the user. The search is focused on maintaining the relevance and interest value of the facts sent, with the intention to meet or exceed the user’s expectations for ‘more’.After receiving the 1892 United States presidential election in Georgia fact, a message was crafted and sent to the user, detailing the political tensions and the rise of the Populist Party’s influence. The task continues as instructed by the user, with the goal of providing engaging and pertinent information. Awaiting further user directives.The last message regarding the 1892 U.S. presidential election in Georgia was sent as per the system’s confirmatory prompt, maintaining the sequence of delivering curated facts to the user. Based on the user’s ongoing request for more information, the task at hand is to continue the fact-finding mission via random Wikipedia articles, prioritizing the discovery of facts that combine historical significance with an ability to engage the user’s interest.A fact about the life and legacy of Thomas Slingsby Duncombe was sent to the user, highlighting his role as a fashionably flamboyant yet pivotal Radical politician of the 19th century. The task of providing interesting facts from random Wikipedia articles is ongoing, as I continue to wait for any new directions from the user.After sending information on Thomas Slingsby Duncombe, will now continue to fulfill the user’s requirement by seeking more interesting facts from Wikipedia. The ongoing task involves extracting and delivering valuable and engaging information to the user. No specific direction was provided on the type of facts needed, so a broad search is being conducted.Sent the user a message about the genus Abraeomorphus from the family Histeridae, noting the diversity of its species. Continuing to gather more interesting facts in response to the user’s previous request for ‘more.’Received confirmation to continue the plan. Sent user details on the genus Abraeomorphus. Now using ‘random_wikipedia_article’ to find another interesting fact.Sent the user a message detailing the nature of the name ‘Preston’ as both a surname and given name, albeit acknowledging it might not be as engaging. Prepared to resume the search for more compelling facts with the ‘random_wikipedia_article’ command.Continuing the task of finding interesting facts from Wikipedia. Sent a message about the given name Preston and now seeking another fact with the ‘random_wikipedia_article’ command.Sent a message to the user about the Kepler Follow-up Program and its significance in confirming the existence of exoplanets. Prepared to find more interesting facts using the ‘random_wikipedia_article’ command.Continuing to retrieve and share interesting facts from random Wikipedia articles. Just sent information on the Kepler Follow-up Program and moving on to find the next fact.Shared information about the Finnish surname ‘Marttila’ with the user, noting its association with notable individuals. Ready to use the ‘random_wikipedia_article’ command again to find further interesting facts.Sent details on the surname ‘Marttila’ to the user. Continuing with the task at hand using the ‘random_wikipedia_article’ command to supply more facts.Provided the user with information on the Musée national du Sport’s storied history and vast collection. Set to continue the fact-finding mission using ‘random_wikipedia_article’.Messaged the user about the Musée national du Sport and currently moving forward with the task of providing more interesting facts via random Wikipedia articles.Sent information on the village of Puerto Gaviota detailing its historical and economic background. Preparing to continue with the task of providing engaging facts by retrieving more random Wikipedia articles.Just shared details about Puerto Gaviota, and now executing the ‘random_wikipedia_article’ command again for more facts.Messaged the user about the reclassification of the genus Agononida in marine taxonomy. Ready to retrieve more Wikipedia articles for further interesting facts.Shared with the user the reclassification developments of the genus Agononida. Proceeding next to find additional interesting facts with the ‘random_wikipedia_article’ command.Sent an engaging narrative about the Mundenhof’s history and current operations to the user. Planning to continue the task with ‘random_wikipedia_article’ to discover more interesting material.Served the user with information about the Mundenhof and proceeding with the task to find and provide more interesting facts using ‘random_wikipedia_article’.Shared the contested narrative of Princess Iitoyo’s potential rule in early Japan. Preparing to continue with the task of finding interesting facts using the ‘random_wikipedia_article’ command.Attempting once again to send the tale of Princess Iitoyo to the user, making sure no JSON format errors are present in the response.Attempting to resend the information about Princess Iitoyo, with attention to JSON integrity, to fulfill the user’s request for engaging facts.The system faced an error within the JSON format previously used to describe Princess Iitoyo’s history. I have made corrections to the JSON structure and am resending the user her story.Reverting back to retrieving random Wikipedia articles to provide interesting facts, as there was no new explicit task provided. Maintaining caution with JSON response formatting.Sent details about the Serchio River to the user, highlighting its geographical and historical importance. Moving forward to obtain more Wikipedia articles for further facts.I am resending the information about the Serchio River, ensuring the JSON structure is correct to prevent further error messages.Due to persistent JSON formatting errors, I am now retrieving a new random Wikipedia article to continue providing interesting facts as per user requests.Sent a message about the women’s 1500 metres event at the 2005 Asian Athletics Championships. Planning to proceed with gathering additional facts from random Wikipedia articles.Encountering repeated JSON formatting errors, a new random Wikipedia article is being requested to continue the task of delivering interesting facts.Sent a brief biography of Otto Ruff, highlighting his academic journey, scientific achievements, and the obstacles faced during his career. Preparing to move on to the next Wikipedia article as per the user’s ongoing request for more information.Due to persistent JSON errors, I am now retrieving a new random Wikipedia article to continue the user’s task of gathering interesting facts.The facts previously sent were not satisfactory to the user. Now incorporating a ‘do_nothing’ step for reflection and improvement in the strategy to identify genuinely engaging and fun facts that are not commonly known by most people. No new facts have been identified or sent in this session, and the intention is to retrieve another random Wikipedia article after this reflective pause.A ‘do nothing’ step has been taken as per the user’s instruction, aiming to find truly interesting and fun facts. The system has directed to proceed with the plan, which now involves retrieving a random Wikipedia article to find an engaging and possibly unknown fact.Received user feedback that the facts provided were not interesting or even factual. Decided to take a ‘do_nothing’ step to reflect and establish criteria for selecting truly interesting facts. No new articles have been accessed or facts sent during this session. Next step will be to retrieve a random Wikipedia article and apply the refined criteria to find noteworthy facts.After planning to refine the criteria for interesting facts, a ‘random_wikipedia_article’ command is initiated to apply the new criteria in selecting a fact. Will assess the retrieved article for a worthwhile fact that meets the user’s definition of ‘interesting’ and ‘fun’, and that might not be well-known.Assessed the Wikipedia article on Shamim Akhtar and found it does not meet the criteria for providing interesting and fun facts. No new fact has been selected or sent to the user. Planning to retrieve another random Wikipedia article that may yield a fact aligning more closely with the user’s definitions of interesting and worthwhile.In search of a fact that meets the user’s criteria for ‘interesting’ and ‘fun’, a new ‘random_wikipedia_article’ command has been issued. The plan includes critically assessing the article to find a fact that possesses novelty, fun, and a certain degree of obscurity before potentially sharing it with the user.Upon reviewing Minuscule 910’s Wikipedia article, the unusual fact of the text rearrangement in an 11th-century manuscript has been identified as potentially engaging and fun. No action has been taken yet, but the plan is to share this unique textual feature with the user, as it likely meets their criteria for interesting and obscure content.Sent a message to the user detailing the intriguing fact about text rearrangement in the 11th-century manuscript Minuscule 910. This fact is representative of the kind of unique and lesser-known information the user has expressed an interest in receiving. Should the user provide feedback or further instructions, this will be the next point of focus.After sending a fact about the textual anomaly in an ancient manuscript to the user, I am moving ahead to find more interesting and fun facts by retrieving another random Wikipedia article. The goal remains to identify facts that surprise and delight with their obscurity and novelty.A message about the ATP 250 tournament records held by Thomas Muster and Mike Bryan has been composed and sent to the user, delivering what could be perceived as an interesting and fun fact within the realm of tennis. The focus now shifts to awaiting user feedback or continuing the task of finding more such facts if no feedback is received.Having sent the user a message detailing the tennis records held by Thomas Muster and Mike Bryan, the next action in the plan is to source another interesting fact from a new random English Wikipedia article. The selection will be scrutinized to ensure it meets the established criteria for an engaging and lesser-known piece of information.Reviewed the Wikipedia article on Ludvig Olai Botnen and found his biographical details to not meet the user’s criteria for ‘interesting’ or ‘fun’ facts. No message has been sent to the user, and the plan now is to continue with the task by acquiring a new random Wikipedia article in the hope of finding a fact with more novelty and entertainment value.Preparing to move past the article on Ludvig Olai Botnen due to its conventional content, the task now involves retrieving a new random English Wikipedia article. The continual aim is to find facts that strike a balance between being novel, unexpected, and fun for the user.Identified a potentially engaging fact about the china clam (Hippopus porcellanus) and its popularity in marine aquariums due to its attractive shell. A message containing this fact has been sent to the user, fulfilling the criteria for providing something both interesting and fun. The next step awaits further user interaction or to carry on with the task of sourcing more facts.Sent a message detailing the ornamental value of the china clam in marine aquariums to the user. Now retrieving another random Wikipedia article to discover more facts that might intrigue and entertain the user, in alignment with the variety sought in previous responses.A message has been sent to the user summarizing the accomplishments of Eko India Financial Services in pioneering financial inclusion through mobile banking, which caught the attention of Bill Gates and led to World Bank-funded growth. The plan will continue with searching for more interesting Wikipedia facts, depending on user feedback or in the absence of further instructions.After sending a fact about Eko India Financial Services’ innovation in financial technology and its socio-economic impact, a new ‘random_wikipedia_article’ command is issued. The plan progresses with the aim of finding more interesting and fun facts, pursuant to the pattern of engagement established by previous user interactions.The Wikipedia page on Lisa Lohmann, a German cross-country skier, was deemed not to contain facts that sufficiently meet the ‘interesting’ and ‘fun’ criteria given the common nature of sports achievements. No actions have been taken to share any information from this page with the user. Preparing to retrieve another random Wikipedia article for facts that may be more engaging and unusual.Following the decision to not share any information from the page on German cross-country skier Lisa Lohmann, a new random Wikipedia article will be retrieved. The continuous goal is to uncover and share facts that satisfy the user’s interest in content that is both engaging and rare.A message has been formulated and sent to the user detailing Alfred Bigland’s unique contributions during World War I, including his involvement in the ‘Bantam battalions’ concept. This fact was chosen as it likely fits the bill for information that is both interesting and uncommon. Pending further response, the plan to continue finding engaging facts will persist.The user has been sent an interesting fact about Alfred Bigland’s influence during WWI. Now, the next action in the plan is to retrieve a new random Wikipedia article. The goal remains to discover and share facts that could be perceived as fascinating, amusing, and unexpected to enrich the user’s knowledge with diverse content.Sent a message to the user revealing the Non-Proliferation Trust’s plan to send U.S. nuclear waste to Russia for financial compensation, including support for Russian orphans. This unique fact was chosen for its unusual nature and the depth of the story behind it. The next course of action entails sourcing additional diverse and engaging facts from new random Wikipedia articles.A series of facts about various topics including the Non-Proliferation Trust, Alfred Bigland’s efforts during WWI, tennis records, and more have been sent to the user. With the latest fact about the Non-Proliferation Trust’s nuclear waste management proposition sent, the task of providing interesting and fun facts is completed. Awaiting new instructions if the user wishes to continue receiving such information.The task is to provide the source of a fact about the Non-Proliferation Trust and format it similar to the ‘Today I Learned (TIL)’ subreddit posts. The action taken was to issue the ‘browse_website’ command to visit the Wikipedia page on the Non-Proliferation Trust.saved the scraping of the website results at websiteresults9.txt to retrieve if shutdown randomly.Extracted the necessary information to provide the requested source for the fact about the Non-Proliferation Trust. Formatted a message with this fact in the style of a TIL subreddit post, which has been sent to the user.Sent a TIL-formatted message to the user based on information from the Non-Proliferation Trust Wikipedia page. The task is now complete, pending any potential new tasks or further instructions.Initiated research to suggest subreddits that could help in accumulating more karma by issuing a ‘google’ command to search for the most popular subreddits where active engagement is more likely.saved google search results at googleresults10.txt to retrieve if shutdown randomly.Collected a list of subreddits recommended for gaining karma and saved them to memory. These subreddits were chosen based on their popularity and the typical content engagement they receive. The user has not been messaged with the suggestions yet.Messaged the user a list of recommended subreddits to collect more karma, including guidance on what type of posts may perform well in each subreddit. The task will be considered complete if no further instructions follow.Having sent the recommended subreddits for gaining karma to the user and with no further actions pending, I have executed the ‘task_complete’ command. The system is concluding this task and stands by for any new directives.Starting the task by searching for the best times to post on Reddit for maximum engagement in GMT+1.saved google search results at googleresults11.txt to retrieve if shutdown randomly.Provided the user with suggested times for posting on Reddit for maximum engagement based on search results, adjusted for GMT+1. The general advice is to post during late morning to early afternoon hours in GMT+1 on weekdays and slightly extended windows during the weekends.The user has been informed about the best times to post on Reddit for maximum engagement in GMT+1, and no further actions are required. The task is now marked as complete.Provided a bullet-point summary of the SELF-DISCOVER PDF content in a file named ‘summary_bullet_points.txt’. The file contains a summary capturing the essence of the SELF-DISCOVER framework. The task has been completed according to the instructions.Read the ‘summary_bullet_points.txt’ file to send bullet points to the user. No additional facts or files created in this session.Sent the bullet points summary of the SELF-DISCOVER framework to the user using the ‘message_user’ command. The task will be marked as complete after user confirmation that no further action is required.The task to send bullet points from the ‘summary_bullet_points.txt’ file to the user has been completed. No further instructions were provided; therefore, I executed the ‘task_complete’ command.The task is to explain the SELF-DISCOVER reasoning structures. Checked for saved data but found none. Plan to conduct a search for relevant information on legal reasoning structures, specifically SELF-DISCOVER.Received directive to proceed with the planned Google search for SELF-DISCOVER reasoning structures. About to execute the search to find relevant information on legal reasoning structures, with the aim of explaining the concept to the user.saved google search results at googleresults12.txt to retrieve if shutdown randomly.Reevaluated the nature of the SELF-DISCOVER framework based on newly gathered search results, which clarified it pertains to LLMs. Selected a primary research source (arxiv.org) to explore the framework in detail and plan to summarize and share this information with the user.saved the scraping of the website results at websiteresults13.txt to retrieve if shutdown randomly.Provided a summary of the SELF-DISCOVER framework to the user, highlighting its application in Large Language Models for enhancing complex reasoning capabilities. The user has been messaged with this information, and we are now waiting for any further requests or confirmation to complete the task.Sent the requested information on the SELF-DISCOVER framework to the user, who has acknowledged receipt. The ‘task_complete’ command has been issued, marking the end of this particular task.Started the task to fetch random English Wikipedia articles and find interesting facts to send to the user. The next GPT instance should continue retrieving articles, assess their interestingness, and communicate the facts to the user once identified.Found a potentially interesting fact from a random Wikipedia article about the First Baptist Church in New Bern, NC. The next GPT instance should assess if the historical and architectural information about the church presents a genuinely unique and engaging fact to send to the user.Sent a fact about the First Baptist Church in New Bern, NC, to the user. This fact was selected based on its architectural significance and historic value. The next GPT instance should watch for user feedback or continue fetching random English Wikipedia articles for more interesting facts.After sending a fact about the First Baptist Church, the next step taken was to retrieve another random English Wikipedia article in search of more interesting facts, as per the user’s ongoing task. The next GPT instance should continue this process, ensuring that any selected facts are not plainly informational but possess a uniqueness to pique the user’s interest.I resolved a JSON formatting issue and sent a fact about Ashur-dugul, an ancient Assyrian king, to the user. Assessing the effectiveness of transmitting such historical narratives and ensuring they comply with JSON format requirements is important for the next GPT instance.The task at hand is to fetch random English Wikipedia articles and send interesting facts to the user. Having sent a fact about Ashur-dugul, the process of retrieving new articles and assessing them for captivating facts is ongoing. The next GPT instance should continue this task, ensuring that facts are not simply informational but truly engaging.Considering the fact about Maurice Wilhelm, Duke of Saxe-Merseburg, known as ‘the violinist duke’, after a contemplative ‘do_nothing’ step. The next GPT instance should assess its interest level against user criteria and send it if suitable, or continue searching for more facts.Sent a fact about Maurice Wilhelm, known as ‘the violinist duke’, to the user and awaiting any new instructions. The next GPT instance should monitor feedback and either send more facts or consider alternative tasks, based on the user’s response.Continuing the task of providing interesting facts from Wikipedia articles after sending the fact about Maurice Wilhelm. The next GPT instance should maintain the focus on finding unique and engaging facts for the user while following the process of retrieving articles and using the ‘do_nothing’ step for contemplation.The El Bañado (La Paz) article was briefly evaluated, and it was determined that the information is minimal and not inherently interesting. After a ‘do_nothing’ step, the next GPT instance should decide how to proceed with this task, potentially moving on to a new article.In the search for interesting facts, the article on El Bañado, La Paz was deemed insufficient, leading to the decision to retrieve another random article. The next GPT instance should assess the next article’s potential for providing an intriguing fact and convey it to the user if so.Autoeczematization, a medical condition with unknown pathogenesis, has been identified as a potentially interesting fact. After a ‘do_nothing’ pause, the next GPT instance should determine if this fact should be shared with the user or if another article would be more suitable.I shared a fact on the medical condition autoeczematization with the user, highlighting its peculiarity and the mystery behind its cause. Depending on the user’s response or lack thereof, the next steps include continuing to provide more facts or considering the task complete.Continuing the search for interesting facts from English Wikipedia articles, having recently sent information on autoeczematization. The next GPT instance should assess the next article for any intriguing fact, ensuring adherence to the user’s standard for what is deemed interesting.Evaluated Bryce Hoppel’s 21 race-winning streak as a potentially interesting fact from his Wikipedia article. The next GPT instance should decide if this fits the user’s request for interesting information, or if a more unique fact is needed.Sent a fact to the user about Bryce Hoppel’s extraordinary 21 consecutive race wins and athletic achievements. Depending on the user’s response or guidance for further action, the next GPT instance should be prepared to either send more facts or consider the task complete.The task of sending interesting facts from random English Wikipedia articles to the user has been marked as complete. Sent a fact about athlete Bryce Hoppel’s winning streak as the concluding piece. The next GPT instance should wait for new user instructions before proceeding with any additional tasks.Task initiated to find and send interesting facts from random English Wikipedia articles, including a ‘do_nothing’ evaluation step. No articles accessed or facts sent yet in this session.Evaluated the Wikipedia article ‘List of minor planets: 73001–74000’ and considered its potential to provide an interesting fact. No facts were sent to the user yet, as the article seems to be very technical and might not align with the interest requirement. The next step is to use a ‘do_nothing’ moment to contemplate the appropriate course of action before deciding whether to retrieve another article.Following the system’s prompt to proceed with the plan, I am retrieving another random English Wikipedia article. The last article about minor planets did not yield an interesting fact to send to the user, and thus, the search continues. No new facts have been identified or sent yet in this session.Sent an interesting fact about the fungus Erynia to the user, highlighting its connection to Greek mythology and biological function as an insect-killing organism. The next step awaits the user’s response or to continue fetching random English Wikipedia articles for more intriguing facts.Proceeding with the plan to find and send interesting facts from random English Wikipedia articles, following a successful send out of a fact about Erynia. Continue the search for intriguing and unique facts, employing the ‘do_nothing’ step for careful consideration.Reviewed the Wikipedia article for Tomáš Porubský and am currently in a ‘do_nothing’ moment to assess whether the content offers a genuinely interesting fact. The article seems to provide straightforward biographical information about his sporting career, which may not offer the distinctive element required by the user. A decision will be made shortly on whether to share this fact or retrieve another article.Continuing the plan to find interesting facts from random English Wikipedia articles after assessing the Tomáš Porubský article as not meeting the user’s criteria. No facts have been sent to the user from the last article, and I’m searching for a more suitable fact from a new article.Sent a fact about Nicktoons Movin’ and how it represented an early form of augmented reality in gaming by requiring physical movement from players via the EyeToy camera. Next steps involve awaiting user input or continuing to search for more random articles with interesting facts.After sending the fact about Nicktoons Movin’ to the user, the search for interesting facts continues with a new random article retrieval. The task strategy maintains the dispatch of facts that are not plainly informational but carry a narrative or unique angle of interest.Sent a fact about the artist Dorothy Rutka, touching on her remarkable career and the tragic circumstances of her death, making it more than just plain information and elevating it to an interesting tale. The next steps depend on user response or further continuation of the task.Having sent details of Dorothy Rutka’s life and legacy to the user, I am now executing the plan to fetch another random Wikipedia article. The process of searching for and delivering interesting content to the user is still underway.Engaged in a ‘do_nothing’ contemplation moment after retrieving the Sainte-Foy, Seine-Maritime article. Assessing if the historical aspects, like an 11th-century church, present a fact interesting enough for the user or if a new article is needed. No actions have been taken to send the user any facts from this page yet.Following system instructions to proceed with the plan, seeking a new random Wikipedia article after evaluating the previous article on Sainte-Foy, Seine-Maritime as potentially lacking wide-ranging interest. Continuing to prioritize facts with an engaging narrative quality.Having assessed Kiki Lim Jiow Ting’s Wikipedia article, I found a narrative focusing on her early talent discovery and career growth that may interest the user. The provided fact highlights Lim’s dedication to performing arts and her return to acting after prioritizing education. Awaiting user’s response or further tasks.Continuing with the task, following the procedure of retrieving and evaluating random English Wikipedia articles to discover facts that resonate with the user’s request for engaging content. Awaiting the next random article for consideration.After acquiring the article on the 2022 World Taekwondo Championships – Women’s finweight, currently in a ‘do_nothing’ phase to assess the article’s facts for their engaging qualities. With no standout narratives detected, the plan may involve moving to a new article search.Continuing to fulfill the user’s request for engaging Wikipedia facts, the previous article about the 2022 World Taekwondo Championships did not meet the criteria. Now moving forward, waiting for a new random article to be generated.Delivered a fact about the game show ‘Cooking for Love’ to the user, which combines culinary skills and dating. Next steps include awaiting the user’s response or further continuation of the plan to find more engaging Wikipedia facts.Following the task guidelines, I’m moving on to get another random Wikipedia article for consideration, having previously sent a fact about the ‘Cooking for Love’ show. The ongoing process is to present facts that are not only informative but also fascinate a general audience.Transmitted a fact about Sorn Davin’s role in the 2012 Summer Olympics to the user, emphasizing her honor of being a flag bearer for Cambodia and the significance of her participation, independent of match outcomes. Pending response or further action, the plan may involve additional fact-finding efforts.Task initiated to find a suitable post suggestion for the /r/singularity subreddit. Must browse and analyze the subreddit’s content to come up with a good post idea.saved the scraping of the website results at websiteresults14.txt to retrieve if shutdown randomly.The task involves browsing and analyzing the /r/singularity subreddit to suggest a good post for the user to submit. This requires understanding the subreddit’s focus on AI and the technological singularity, as well as adhering to the posting rules. Due to constraints, I cannot directly analyze the subreddit’s content; therefore, the next step is to ‘do_nothing’ and wait for further guidance or input that can help refine the post suggestion.Since I cannot browse the subreddit directly due to limitations, I will Google how to write an engaging post for the singularity subreddit. This will help formulate a post suggestion that could generate interest and discussion within the community.saved google search results at googleresults15.txt to retrieve if shutdown randomly.The task is to craft a post for /r/singularity subreddit. Search results indicated the subreddit’s preferences and discussions. Next, I’ll use this information to create a post suggestion, considering the community’s focus on AI and aversion to ‘echo chambers’. The challenge lies in inferring accurate interests without direct subreddit access.Was tasked with suggesting a post for the /r/singularity subreddit. Could not browse subreddit directly due to constraints. Planned to use general knowledge of AI and technological advancement topics to create a suitable post suggestion. No direct subreddit content was analyzed. No new files were created. No user feedback has been received.Continued with the plan to suggest a post for the /r/singularity subreddit, deducing a potential topic from general AI and technological themes. An emphasis on future AI governance and ethical considerations was considered as a rich topic for discussion. No direct access to subreddit content or user interaction has been involved.Drafted a post idea centered around AI advancements and their link to the technological singularity, to be suggested for the /r/singularity subreddit. The idea was shared with the user. It aims to engage the subreddit’s members in sharing recent AI breakthroughs they deem significant and discussing ethical and societal preparations needed for such advancements. Awaiting user’s feedback to proceed. No files created or accessed in this process.Sent a post suggestion to the user for the /r/singularity subreddit and was instructed to proceed with the plan. As per the instructions and rules, no further action can be taken until the user provides new directives or feedback. In the ‘do_nothing’ state, waiting for further user interaction. No new files have been created or accessed during this pause.The provided post suggestion for the /r/singularity subreddit was sent and appeared to meet the user’s requirements. With no additional tasks or guidance provided, the task is marked as complete. The system is now on standby for any future user interactions or directives.
The Task: What title should I give to this post
|
9a1b0ac094132b0866b3da54661ba1a2
|
{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
|
39,085
|
The VBA code DueDates() uses a custom subroutine 'PerformCopyAndClear'
I have further down, a modified version of 'DueDates'
How will the subroutine 'PerformCopyAndClear' need to be modified to suit.
Sub DueDates()
Application.ScreenUpdating = False
Dim currentMonth As Long
currentMonth = Month(Date)
If currentMonth >= 9 And currentMonth <= 12 Then ' Sep to Dec
If Range("I1").Value > 0 Then
PerformCopyAndClear Columns("E:E").Offset(1), Columns("F:F").Offset(1)
End If
If Range("J1").Value > 0 Then
PerformCopyAndClear Columns("G:G").Offset(1), Columns("B:B").Offset(1), Columns("AE:AE").Offset(1)
End If
ElseIf currentMonth >= 1 And currentMonth <= 3 Then ' Jan to Mar
If Range("J1").Value > 0 Then
PerformCopyAndClear Columns("G:G").Offset(1), Columns("B:B").Offset(1)
End If
If Range("H1").Value > 0 Then
PerformCopyAndClear Columns("C:C").Offset(1), Columns("D:D").Offset(1), Columns("AE:AE").Offset(1)
End If
ElseIf currentMonth >= 4 And currentMonth <= 8 Then ' Apr to Aug
If Range("H1").Value > 0 Then
PerformCopyAndClear Columns("C:C").Offset(1), Columns("D:D").Offset(1)
End If
If Range("I1").Value > 0 Then
PerformCopyAndClear Columns("E:E").Offset(1), Columns("F:F").Offset(1), Columns("AE:AE").Offset(1)
End If
End If
Application.ScreenUpdating = True
Application.EnableEvents = True
Me.Protect Password:="edit"
End Sub
Private Sub PerformCopyAndClear(sourceColumn As Range, ParamArray destinationColumns() As Variant)
Me.Unprotect Password:="edit"
Application.EnableEvents = False
sourceColumn.Offset(1).Resize(sourceColumn.Rows.Count - 1).Copy ' Exclude row 1 during copy
Dim destinationColumn As Variant
For Each destinationColumn In destinationColumns
destinationColumn.Offset(1).Resize(sourceColumn.Rows.Count - 1).PasteSpecial xlPasteValues ' Paste excluding row 1
Next destinationColumn
sourceColumn.Offset(1).Resize(sourceColumn.Rows.Count - 1).ClearContents ' Clear excluding row 1
Application.CutCopyMode = False
Application.EnableEvents = True
Me.Protect Password:="edit"
End Sub
Sub DueDates()
Application.ScreenUpdating = False
Dim currentMonth As Long
currentMonth = Month(Date)
If currentMonth >= 9 And currentMonth <= 12 Then ' Sep to Dec
If Range("I1").Value > 0 Then
PerformCopyAndClear Columns("E2:E20"), Columns("F2:F20")
End If
If Range("J1").Value > 0 Then
PerformCopyAndClear Columns("G2:G20"), Columns("B2:B20"), Columns("AE2:AE20")
End If
ElseIf currentMonth >= 1 And currentMonth <= 3 Then ' Jan to Mar
If Range("J1").Value > 0 Then
PerformCopyAndClear Columns("G2:G20"), Columns("B2:B20")
End If
If Range("H1").Value > 0 Then
PerformCopyAndClear Columns("C2:C20"), Columns("D2:D20"), Columns("AE2:AE20")
End If
ElseIf currentMonth >= 4 And currentMonth <= 8 Then ' Apr to Aug
If Range("H1").Value > 0 Then
PerformCopyAndClear Columns("C2:C20"), Columns("D2:D20")
End If
If Range("I1").Value > 0 Then
PerformCopyAndClear Columns("E2:E20"), Columns("F2:F20"), Columns("AE2:AE20")
End If
End If
Application.ScreenUpdating = True
Application.EnableEvents = True
Me.Protect Password:="edit"
End Sub
|
2736f12f513886cb5ad70cda394f69f9
|
{
"intermediate": 0.367414653301239,
"beginner": 0.4266674518585205,
"expert": 0.20591795444488525
}
|
39,086
|
can you fully earase any metadata a photo has?
|
451e4f417f322ad160dd9287ff51e8af
|
{
"intermediate": 0.40013301372528076,
"beginner": 0.28685539960861206,
"expert": 0.3130115270614624
}
|
39,087
|
Please modify the code to save a single model instead of saving it every epoch, and also make the code save a json file that contains the configuration of the model as well as another txt file that contains the vocabulary of the model, **code**: import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm
import matplotlib.pyplot as plt
import os
# Define parameters and directory paths
SEQUENCE_LENGTH = 40
BATCH_SIZE = 128
VOCAB_SIZE = 192 # Number of ASCII characters considered in the vocabulary
HIDDEN_SIZE = 384
NUM_LAYERS = 2
LEARNING_RATE = 0.001
NUM_EPOCHS = 10
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = "checkpoints"
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
# Replace ‘your_file.txt’ with the path to your actual text file.
file_path = "C:/Users/Dell-PC/Desktop/The-Physics-Dataset.txt"
# Read the file and filter out blank lines
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
text = "".join(line for line in lines if not line.isspace())
# Convert text to ASCII values
ascii_characters = [ord(c) for c in text]
# Prepare datasets
inputs = []
targets = []
for i in range(0, len(ascii_characters) - SEQUENCE_LENGTH):
input_seq = ascii_characters[i:i+SEQUENCE_LENGTH]
target = ascii_characters[i+1:i+SEQUENCE_LENGTH+1]
inputs.append(torch.tensor(input_seq, dtype=torch.long))
targets.append(torch.tensor(target, dtype=torch.long))
input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)
# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Initialize model, loss function, and optimizer
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Training loop with added production-ready features and tqdm progress
def train_model(model, data_loader, optimizer, loss_fn, epochs, batch_size):
loss_history = [] # Initialize a list to store average epoch losses
for epoch in range(epochs):
model.train()
hidden = model.init_hidden(batch_size)
running_loss = 0.0
# Wrap the data_loader with tqdm for a progress bar
progress_bar = tqdm(data_loader, desc=f"Epoch {epoch + 1}/{epochs}")
for input_batch, target_batch in progress_bar:
if input_batch.size(0) != batch_size:
hidden = model.init_hidden(input_batch.size(0))
optimizer.zero_grad()
output, hidden = model(input_batch, hidden)
hidden = tuple([h.detach() for h in hidden]) # Detach hidden state
loss = loss_fn(output, target_batch.view(-1))
loss.backward()
# Clip gradients to mitigate exploding gradients
nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
running_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
# Calculate and store the average epoch loss
epoch_loss = running_loss / len(data_loader) # Change to len(data_loader)
loss_history.append(epoch_loss)
print(f"Epoch {epoch + 1}/{epochs}, Loss: {epoch_loss:.4f}")
# Save the model checkpoint (with enhanced cross-platform directory usage)
checkpoint_path = os.path.join(CHECKPOINT_FOLDER, f"model_epoch_{epoch + 1}.pth")
torch.save(model.state_dict(), checkpoint_path)
# Plot training loss per epoch outside the training loop
plt.figure() # Create a new figure
plt.plot(range(1, epochs + 1), loss_history, label="Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.title("Training Loss Over Epochs")
plt.show()
return model # Return the trained model
# Predict the next character
def predict(model, char, hidden=None, temperature=1.0):
# Convert to ASCII and then tensor
input_tensor = torch.tensor([ord(char)], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
# Generate text
def generate_text(model, start_char="P", length=200):
model.eval()
hidden = model.init_hidden(1)
input_char = start_char
for i in range(length):
input_char, hidden = predict(model, input_char, hidden)
print(input_char, end="")
if __name__ == "__main__":
# Train the model
train_model(model, data_loader, optimizer, loss_fn, NUM_EPOCHS, BATCH_SIZE)
# Generate text from the trained model
generate_text(model, start_char="P", length=200)
|
fea9c6f33cd56735b2b20cfdf57be571
|
{
"intermediate": 0.36042529344558716,
"beginner": 0.3925328552722931,
"expert": 0.24704182147979736
}
|
39,088
|
Please modify the code to save a single model instead of saving it every epoch, and also make the code save a json file that contains the configuration of the model as well as another txt file that contains the vocabulary of the model, code: import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm
import matplotlib.pyplot as plt
import os
# Define parameters and directory paths
SEQUENCE_LENGTH = 40
BATCH_SIZE = 128
VOCAB_SIZE = 192 # Number of ASCII characters considered in the vocabulary
HIDDEN_SIZE = 384
NUM_LAYERS = 2
LEARNING_RATE = 0.001
NUM_EPOCHS = 10
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = “checkpoints”
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
# Replace ‘your_file.txt’ with the path to your actual text file.
file_path = “C:/Users/Dell-PC/Desktop/The-Physics-Dataset.txt”
# Read the file and filter out blank lines
with open(file_path, “r”, encoding=“utf-8”) as file:
lines = file.readlines()
text = “”.join(line for line in lines if not line.isspace())
# Convert text to ASCII values
ascii_characters = [ord© for c in text]
# Prepare datasets
inputs = []
targets = []
for i in range(0, len(ascii_characters) - SEQUENCE_LENGTH):
input_seq = ascii_characters[i:i+SEQUENCE_LENGTH]
target = ascii_characters[i+1:i+SEQUENCE_LENGTH+1]
inputs.append(torch.tensor(input_seq, dtype=torch.long))
targets.append(torch.tensor(target, dtype=torch.long))
input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)
# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def init(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).init()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Initialize model, loss function, and optimizer
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Training loop with added production-ready features and tqdm progress
def train_model(model, data_loader, optimizer, loss_fn, epochs, batch_size):
loss_history = [] # Initialize a list to store average epoch losses
for epoch in range(epochs):
model.train()
hidden = model.init_hidden(batch_size)
running_loss = 0.0
# Wrap the data_loader with tqdm for a progress bar
progress_bar = tqdm(data_loader, desc=f"Epoch {epoch + 1}/{epochs}“)
for input_batch, target_batch in progress_bar:
if input_batch.size(0) != batch_size:
hidden = model.init_hidden(input_batch.size(0))
optimizer.zero_grad()
output, hidden = model(input_batch, hidden)
hidden = tuple([h.detach() for h in hidden]) # Detach hidden state
loss = loss_fn(output, target_batch.view(-1))
loss.backward()
# Clip gradients to mitigate exploding gradients
nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
running_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
# Calculate and store the average epoch loss
epoch_loss = running_loss / len(data_loader) # Change to len(data_loader)
loss_history.append(epoch_loss)
print(f"Epoch {epoch + 1}/{epochs}, Loss: {epoch_loss:.4f}”)
# Save the model checkpoint (with enhanced cross-platform directory usage)
checkpoint_path = os.path.join(CHECKPOINT_FOLDER, f"model_epoch_{epoch + 1}.pth")
torch.save(model.state_dict(), checkpoint_path)
# Plot training loss per epoch outside the training loop
plt.figure() # Create a new figure
plt.plot(range(1, epochs + 1), loss_history, label=“Training Loss”)
plt.xlabel(“Epoch”)
plt.ylabel(“Loss”)
plt.legend()
plt.grid(True)
plt.title(“Training Loss Over Epochs”)
plt.show()
return model # Return the trained model
# Predict the next character
def predict(model, char, hidden=None, temperature=1.0):
# Convert to ASCII and then tensor
input_tensor = torch.tensor([ord(char)], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
# Generate text
def generate_text(model, start_char=“P”, length=200):
model.eval()
hidden = model.init_hidden(1)
input_char = start_char
for i in range(length):
input_char, hidden = predict(model, input_char, hidden)
print(input_char, end=“”)
if name == “main”:
# Train the model
train_model(model, data_loader, optimizer, loss_fn, NUM_EPOCHS, BATCH_SIZE)
# Generate text from the trained model
generate_text(model, start_char=“P”, length=200)
|
5e7d15d7b5f904ca412129250ed1d1d0
|
{
"intermediate": 0.3223085105419159,
"beginner": 0.3984437584877014,
"expert": 0.2792477011680603
}
|
39,089
|
What happens when you call join() on another thread in Java multiple times
|
02f9f8c8c98af6e8ace84cc81d9aa02c
|
{
"intermediate": 0.6234402656555176,
"beginner": 0.21140410006046295,
"expert": 0.16515567898750305
}
|
39,090
|
Help me fix this error:
ResourceManager.cpp:30:29: required from here
/usr/include/c++/13/tuple:2254:9: error: no matching function for call to ‘Surface::Surface()’
2254 | second(std::forward<_Args2>(std::get<_Indexes2>(__tuple2))...)
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from include/ResourceManager.h:9:
include/Surface.h:11:9: note: candidate: ‘Surface::Surface(const std::string&)’
11 | Surface(const std::string& filePath);
| ^~~~~~~
include/Surface.h:11:9: note: candidate expects 1 argument, 0 provided
include/Surface.h:8:7: note: candidate: ‘constexpr Surface::Surface(const Surface&)’
8 | class Surface
| ^~~~~~~
include/Surface.h:8:7: note: candidate expects 1 argument, 0 provided
Surface ResourceManager::GetSurface(std::string filePath)
{
if (surfaces.find(filePath) == surfaces.end())
{
Surface surface(filePath);
if (surface.GetSurface() != NULL)
{
surfaces.insert({ filePath, surface });
}
}
return surfaces[filePath];
}
|
a9f29e5ca42bd340f34e60f4203ec205
|
{
"intermediate": 0.4130379259586334,
"beginner": 0.3686493933200836,
"expert": 0.21831272542476654
}
|
39,091
|
I'm developing an Android app in Java with Firebase. I've already added anonimous authentication. But in Firebase docs, I've read that "If the user later chooses to sign up, you can upgrade the anonymous account to a regular account, so the user can continue where they left off." I want to add the ability to Sign in with Google. How can I do it?
Here's the related part of my code for anonymous authentication:
private void updateUI(FirebaseUser user) {
if (user==null) { //if there's no user signed in
mAuth.signInAnonymously().addOnCompleteListener(this, new OnCompleteListener<AuthResult>() {
@Override
public void onComplete(@NonNull Task<AuthResult> task) {
if (task.isSuccessful()) {
FirebaseUser user = mAuth.getCurrentUser();
updateUI(user);
} else {
updateUI(null);
}
}
});
}
}
and I'm also using mAuth.getUid(); to store data related to the user in firestore database.
|
24c6d517868e94924ead2a7a196068d6
|
{
"intermediate": 0.6673057079315186,
"beginner": 0.14295974373817444,
"expert": 0.1897345781326294
}
|
39,092
|
does this code contain "context length": import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm
import matplotlib.pyplot as plt
import json
import os
# Define parameters and directory paths
SEQUENCE_LENGTH = 40
BATCH_SIZE = 128
VOCAB_SIZE = 192 # Number of ASCII characters considered in the vocabulary
HIDDEN_SIZE = 384
NUM_LAYERS = 4
LEARNING_RATE = 0.001
NUM_EPOCHS = 5
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = "checkpoints"
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
# Replace ‘your_file.txt’ with the path to your actual text file.
file_path = "C:/Users/Dell-PC/Desktop/The-Physics-Dataset.txt"
# Read the file and filter out blank lines
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
text = "".join(line for line in lines if not line.isspace())
# Convert text to ASCII values
ascii_characters = [ord(c) for c in text]
# Prepare datasets
inputs = []
targets = []
for i in range(0, len(ascii_characters) - SEQUENCE_LENGTH):
input_seq = ascii_characters[i:i+SEQUENCE_LENGTH]
target = ascii_characters[i+1:i+SEQUENCE_LENGTH+1]
inputs.append(torch.tensor(input_seq, dtype=torch.long))
targets.append(torch.tensor(target, dtype=torch.long))
input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)
# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Initialize model, loss function, and optimizer
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Calculate and print the total number of trainable parameters
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Total trainable parameters: {total_params}")
# Training loop with added production-ready features and tqdm progress
def train_model(model, data_loader, optimizer, loss_fn, epochs, batch_size):
loss_history = [] # Initialize a list to store average epoch losses
for epoch in range(epochs):
model.train()
hidden = model.init_hidden(batch_size)
running_loss = 0.0
# Wrap the data_loader with tqdm for a progress bar
progress_bar = tqdm(data_loader, desc=f"Epoch {epoch + 1}/{epochs}")
for input_batch, target_batch in progress_bar:
if input_batch.size(0) != batch_size:
hidden = model.init_hidden(input_batch.size(0))
optimizer.zero_grad()
output, hidden = model(input_batch, hidden)
hidden = tuple([h.detach() for h in hidden]) # Detach hidden state
loss = loss_fn(output, target_batch.view(-1))
loss.backward()
# Clip gradients to mitigate exploding gradients
nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
running_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
# Calculate and store the average epoch loss
epoch_loss = running_loss / len(data_loader)
loss_history.append(epoch_loss)
print(f"Epoch {epoch + 1}/{epochs}, Loss: {epoch_loss:.4f}")
model_name = "Physics-model-A1"
# Save the last model checkpoint instead of every epoch
checkpoint_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}.pth")
torch.save(model.state_dict(), checkpoint_path)
# Save model’s configuration in a JSON file
config_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_config.json")
model_config = {
"vocab_size": VOCAB_SIZE,
"hidden_size": HIDDEN_SIZE,
"num_layers": NUM_LAYERS,
"batch_size":BATCH_SIZE,
"Total_params":total_params
}
with open(config_path, "w") as json_file:
json.dump(model_config, json_file)
# Save vocabulary in a TXT file
vocab_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_vocab.txt")
with open(vocab_path, "w") as vocab_file:
vocab_file.write("".join(chr(i) for i in range(VOCAB_SIZE)))
# Plot training loss per epoch outside the training loop
plt.figure() # Create a new figure
plt.plot(range(1, epochs + 1), loss_history, label="Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.title("Training Loss Over Epochs")
plt.show()
return model # Return the trained model
# Predict the next character
def predict(model, char, hidden=None, temperature=1.0):
# Convert to ASCII and then tensor
input_tensor = torch.tensor([ord(char)], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
# Generate text
def generate_text(model, start_char="P", length=200):
model.eval()
hidden = model.init_hidden(1)
input_char = start_char
for i in range(length):
input_char, hidden = predict(model, input_char, hidden)
print(input_char, end="")
if __name__ == "__main__":
# Train the model
train_model(model, data_loader, optimizer, loss_fn, NUM_EPOCHS, BATCH_SIZE)
# Generate text from the trained model
generate_text(model, start_char="P", length=200)
|
f7b3fe69a4bbc70902ab7ae629b85a2d
|
{
"intermediate": 0.191892609000206,
"beginner": 0.4218318462371826,
"expert": 0.3862755596637726
}
|
39,093
|
You are an experienced programmer. Write a python script that can search and identify google trends.
|
aa8745541b737d2704d18306bc03e353
|
{
"intermediate": 0.3066387474536896,
"beginner": 0.17260298132896423,
"expert": 0.5207582712173462
}
|
39,094
|
I am writing a program in C# using Visual Studio 2017 and .Net Framework 4.6.2.
I have detected a DOS console window Title, ID, and Handle.
I need to write a command on that window and then hit the enter key.
Can you help me with that code?
|
ffcf931018924edfc6d779942551b736
|
{
"intermediate": 0.5674668550491333,
"beginner": 0.3158509135246277,
"expert": 0.1166822761297226
}
|
39,095
|
You are an experienced full stack developer. Write python code that can drag and drop images from a folder in windows file explorer into google lens by looping through it.
|
0f3f7e5c35c911f22b45e6499088579c
|
{
"intermediate": 0.5702881217002869,
"beginner": 0.34014755487442017,
"expert": 0.08956439048051834
}
|
39,096
|
How do I add a default value for Vector3 in a function header on Unity
|
95c9b49c4dc1f4bffbfcf323b5d37f58
|
{
"intermediate": 0.5317960381507874,
"beginner": 0.2842086851596832,
"expert": 0.18399526178836823
}
|
39,097
|
on an amd debian system, how can I make a ubuntu server install on an SSD connected via a usb adapter for an intel system with an nvidea GTX 970 gpu using chroot
|
9e1261fc08fe8023d7c1be9f8e821dcd
|
{
"intermediate": 0.4703890383243561,
"beginner": 0.25134962797164917,
"expert": 0.27826130390167236
}
|
39,098
|
Daycare Statistics
For this project, you will be reading a text (.txt) file into your program,
putting the data into arrays, and then running some statistics with the
array algorithms we have learned this unit.
Text file contains the following information about the students who are at a daycare center:
First and last name
Gender
Ages
Hometown
Days attending
Phone number
The .txt file will contain an unknown amount of rows, but each row will have the information above, in order,
and separate by a comma. Here is an example:
Maevery Smith,2,F,Washington,4,123-444-5454
Cedar Johnson,5,M,Springfield,4,123-567-2323
Jaspin Williams,4,M,Washington,5,123-444-9393
Charleston Brown,3,M,Greenville,5,123-567-1100
Faelina Jones,4,F,Franklin,4,123-400-5678
Idalia Garcia,4,F,Springfield,3,123-333-1212
Brigham Miller,1,M,Washington,3,123-566-1123
Tovin Davis,2,M,Franklin,4,123-223-2223
Evabeth Rodriguez,3,F,Greenville,3,123-433-1121
Tessadora Martinez,5,F,Springfield,2,123-999-0909
Anaveah Hernandez,3,F,Washington,2,123-222-3030
Albion Lopez,3,M,Franklin,3,123-222-1000
Jessalie Wilson,2,F,Greenville,4,123-566-9009
Garylen Anderson,3,M,Greenville,4,123-333-6644
Sylvalie Thomas,4,F,Franklin,5,123-455-0022
Eastley Taylor,2,M,Franklin,5,123-567-8765
Ranger Moore,2,M,Washington,4,123-999-1122
Sophiel Jackson,2,F,Washington,3,123-111-4233
Elisobelle Lee,3,F,Greenville,3,123-400-8766
Wrenlow Nguyen,5,M,Springfield,2,123-988-5444
Step 1: Read a file and put into arrays
Reading .txt files into your Java program will not be on the AP exam, but it is a great skill to review. In order to
read in .txt files, you will also be introduced to the try/catch block, as well as exceptions.
First, use the following import statement:
import java.io.*
This will import the FileReader and the BufferedReader classes that you will need in your program.
Next, create an array of Strings. Each row of the text file will be read into a String at each index:
String[] rows = null;
The following variable will count how many rows are in the text file. We need to know this information before
we create our arrays, so we know how many students we have.
int numOfLines = 0;
Next we will get into our try/catch block. Our data file is called "data.txt". If you are using Replit, you have
to click "upload file" in the upper right corner of your IDE. You will then upload data.txt there. When
reading text files, we need to use a try/catch block in case our file is not found. This is where our
IOException comes in. An IOException is an error put out by your program when it cannot read the
input correctly (such as not being able to find you file, or something similar). The try block says to "try" all
the code located in that block and if at any time the program encounters an error, the catch block will "catch"
that error and run the code in that block.
try{
BufferedReader br = new BufferedReader(new FileReader("data.txt"));
String line = br.readLine();
//determine how many lines
while(line!=null){
numOfLines++;
line = br.readLine();
}
br.close(); Good code closes the BufferedReader
rows = new String[numOfLines];
//makes each row into an array
Now rows will get created with the
appropriate amount of storage space.
BufferedReader br2 = new BufferedReader(new FileReader("data.txt"));
line = br2.readLine();
int i = 0; //index
while(line!=null){
rows[i] = line;
line = br2.readLine();
i++;
}
br2.close();
}catch(IOException e){
}
System.out.println("An error has occurred");
This is your catch block which will hopefully never run if
your code is correct!
This lets us read a single line in our
text file and store it as a String
Keep reading lines to determine how
many lines in the .txt file there are.
Now that rows has the appropriate amount of space, we will repeat the
code we just went through and store each line of the txt file into the
array, where each index holds a single String with the row information
row[0] will be "Maevery Smith, 2, F, Washington, 4, 123-444-5454"
row[1] will be "Cedar Johnson, 5, M, Springfield, 4, 123-567-2323"
and so on…
Step 2: Make arrays of your data
Now you have all the information stored in a single array, with each index holding all of the information for
each of the children in daycare. You will now want to write the code to split up all the information so that you
have an array of student names, an array of ages, an array of genders, etc.
Be careful with your indices; the index is what keeps track of each child's information across the different
arrays. For example,
row[0] contains "Maevery Smith,2,F,Washington,4,123-444-5454"
After the split,
names[0] = "Maevery Smith"
ages[0] = 2
gender[0] = "F"
hometown[0] = "Washington"
days[0] = 4
number[0] = "123-444-5454"
Each row will have a comma to separate the data. If you taking a number that is represented as a String and
putting it into an array of ints, you will need to convert the String into an int. This is no longer tests on the AP
exam, but again, it is good to know. Here is an example of the parseInt method:
String example = "123";
int number = example; //this will cause an error!
int number = Integer.parseInt(example); //this will now store
//123 as a primitive
Here is some code to help you get started with this step. This is just an example of what you can do; you do not
have to do it this way!
String[] names = //stores all the names
int[] ages = //stores all the ages
String[] gender = //stores all the genders
String[] hometown = //stores all the hometowns
int[] days = //stores all the days attending
String[] number = //stores all the phone numbers
int i = 0; //keeps track of the current index
for(String line : rows){ //goes through each line
names[i] =
ages[i] =
gender[i] =
hometown[i] =
days[i] =
number[i] =
i++;
}
Step 3: Statistics
Task 1: Display the student names
Task 2: Display the average age of the female children and the average age of the male children
Task 3: Display how many students are from each of the towns with a histogram
Task 4: How much does the daycare make in income in one week?
1 year olds cost $35 a day
2 year olds cost $30 a day
3 year olds cost $25 a day
4 year olds cost $20 a day
5 year olds cost $15 a day
Task 5: Give the choice of a student from a list, look up phone number of a user inputted number.
Task 6: Use a sorting algorithm to sort the students in order of their last name. Make sure that when positions
are switched, they are switched for all arrays so the same index applies to the same student across the board.
|
7d0221fc4741ece3086e1292f197df0f
|
{
"intermediate": 0.4922581911087036,
"beginner": 0.27188509702682495,
"expert": 0.23585672676563263
}
|
39,099
|
Daycare Statistics(no hashmap, and write the code in a logical step-by-step format)
For this project, you will be reading a text (.txt) file into your program,
putting the data into arrays, and then running some statistics with the
array algorithms we have learned this unit.
Text file contains the following information about the students who are at a daycare center:
First and last name
Gender
Ages
Hometown
Days attending
Phone number
The .txt file will contain an unknown amount of rows, but each row will have the information above, in order,
and separate by a comma. Here is an example:
Maevery Smith,2,F,Washington,4,123-444-5454
Cedar Johnson,5,M,Springfield,4,123-567-2323
Jaspin Williams,4,M,Washington,5,123-444-9393
Charleston Brown,3,M,Greenville,5,123-567-1100
Faelina Jones,4,F,Franklin,4,123-400-5678
Idalia Garcia,4,F,Springfield,3,123-333-1212
Brigham Miller,1,M,Washington,3,123-566-1123
Tovin Davis,2,M,Franklin,4,123-223-2223
Evabeth Rodriguez,3,F,Greenville,3,123-433-1121
Tessadora Martinez,5,F,Springfield,2,123-999-0909
Anaveah Hernandez,3,F,Washington,2,123-222-3030
Albion Lopez,3,M,Franklin,3,123-222-1000
Jessalie Wilson,2,F,Greenville,4,123-566-9009
Garylen Anderson,3,M,Greenville,4,123-333-6644
Sylvalie Thomas,4,F,Franklin,5,123-455-0022
Eastley Taylor,2,M,Franklin,5,123-567-8765
Ranger Moore,2,M,Washington,4,123-999-1122
Sophiel Jackson,2,F,Washington,3,123-111-4233
Elisobelle Lee,3,F,Greenville,3,123-400-8766
Wrenlow Nguyen,5,M,Springfield,2,123-988-5444
Step 1: Read a file and put into arrays
Reading .txt files into your Java program will not be on the AP exam, but it is a great skill to review. In order to
read in .txt files, you will also be introduced to the try/catch block, as well as exceptions.
First, use the following import statement:
import java.io.*
This will import the FileReader and the BufferedReader classes that you will need in your program.
Next, create an array of Strings. Each row of the text file will be read into a String at each index:
String[] rows = null;
The following variable will count how many rows are in the text file. We need to know this information before
we create our arrays, so we know how many students we have.
int numOfLines = 0;
Next we will get into our try/catch block. Our data file is called "data.txt". If you are using Replit, you have
to click "upload file" in the upper right corner of your IDE. You will then upload data.txt there. When
reading text files, we need to use a try/catch block in case our file is not found. This is where our
IOException comes in. An IOException is an error put out by your program when it cannot read the
input correctly (such as not being able to find you file, or something similar). The try block says to "try" all
the code located in that block and if at any time the program encounters an error, the catch block will "catch"
that error and run the code in that block.
try{
BufferedReader br = new BufferedReader(new FileReader("data.txt"));
String line = br.readLine();
//determine how many lines
while(line!=null){
numOfLines++;
line = br.readLine();
}
br.close(); Good code closes the BufferedReader
rows = new String[numOfLines];
//makes each row into an array
Now rows will get created with the
appropriate amount of storage space.
BufferedReader br2 = new BufferedReader(new FileReader("data.txt"));
line = br2.readLine();
int i = 0; //index
while(line!=null){
rows[i] = line;
line = br2.readLine();
i++;
}
br2.close();
}catch(IOException e){
}
System.out.println("An error has occurred");
This is your catch block which will hopefully never run if
your code is correct!
This lets us read a single line in our
text file and store it as a String
Keep reading lines to determine how
many lines in the .txt file there are.
Now that rows has the appropriate amount of space, we will repeat the
code we just went through and store each line of the txt file into the
array, where each index holds a single String with the row information
row[0] will be "Maevery Smith, 2, F, Washington, 4, 123-444-5454"
row[1] will be "Cedar Johnson, 5, M, Springfield, 4, 123-567-2323"
and so on…
Step 2: Make arrays of your data
Now you have all the information stored in a single array, with each index holding all of the information for
each of the children in daycare. You will now want to write the code to split up all the information so that you
have an array of student names, an array of ages, an array of genders, etc.
Be careful with your indices; the index is what keeps track of each child's information across the different
arrays. For example,
row[0] contains "Maevery Smith,2,F,Washington,4,123-444-5454"
After the split,
names[0] = "Maevery Smith"
ages[0] = 2
gender[0] = "F"
hometown[0] = "Washington"
days[0] = 4
number[0] = "123-444-5454"
Each row will have a comma to separate the data. If you taking a number that is represented as a String and
putting it into an array of ints, you will need to convert the String into an int. This is no longer tests on the AP
exam, but again, it is good to know. Here is an example of the parseInt method:
String example = "123";
int number = example; //this will cause an error!
int number = Integer.parseInt(example); //this will now store
//123 as a primitive
Here is some code to help you get started with this step. This is just an example of what you can do; you do not
have to do it this way!
String[] names = //stores all the names
int[] ages = //stores all the ages
String[] gender = //stores all the genders
String[] hometown = //stores all the hometowns
int[] days = //stores all the days attending
String[] number = //stores all the phone numbers
int i = 0; //keeps track of the current index
for(String line : rows){ //goes through each line
names[i] =
ages[i] =
gender[i] =
hometown[i] =
days[i] =
number[i] =
i++;
}
Step 3: Statistics
Task 1: Display the student names
Task 2: Display the average age of the female children and the average age of the male children
Task 3: Display how many students are from each of the towns with a histogram
Task 4: How much does the daycare make in income in one week?
1 year olds cost $35 a day
2 year olds cost $30 a day
3 year olds cost $25 a day
4 year olds cost $20 a day
5 year olds cost $15 a day
Task 5: Give the choice of a student from a list, look up phone number of a user inputted number.
Task 6: Use a sorting algorithm to sort the students in order of their last name. Make sure that when positions
are switched, they are switched for all arrays so the same index applies to the same student across the board.
|
d192dfd705b8e3fd4a7d1e938f16b105
|
{
"intermediate": 0.516600489616394,
"beginner": 0.2992061376571655,
"expert": 0.18419334292411804
}
|
39,100
|
Daycare Statistics(no hashmap, don't switch and case)
For this project, you will be reading a text (.txt) file into your program,
putting the data into arrays, and then running some statistics with the
array algorithms we have learned this unit.
Text file contains the following information about the students who are at a daycare center:
First and last name
Gender
Ages
Hometown
Days attending
Phone number
The .txt file will contain an unknown amount of rows, but each row will have the information above, in order,
and separate by a comma. Here is an example:
Maevery Smith,2,F,Washington,4,123-444-5454
Cedar Johnson,5,M,Springfield,4,123-567-2323
Jaspin Williams,4,M,Washington,5,123-444-9393
Charleston Brown,3,M,Greenville,5,123-567-1100
Faelina Jones,4,F,Franklin,4,123-400-5678
Idalia Garcia,4,F,Springfield,3,123-333-1212
Brigham Miller,1,M,Washington,3,123-566-1123
Tovin Davis,2,M,Franklin,4,123-223-2223
Evabeth Rodriguez,3,F,Greenville,3,123-433-1121
Tessadora Martinez,5,F,Springfield,2,123-999-0909
Anaveah Hernandez,3,F,Washington,2,123-222-3030
Albion Lopez,3,M,Franklin,3,123-222-1000
Jessalie Wilson,2,F,Greenville,4,123-566-9009
Garylen Anderson,3,M,Greenville,4,123-333-6644
Sylvalie Thomas,4,F,Franklin,5,123-455-0022
Eastley Taylor,2,M,Franklin,5,123-567-8765
Ranger Moore,2,M,Washington,4,123-999-1122
Sophiel Jackson,2,F,Washington,3,123-111-4233
Elisobelle Lee,3,F,Greenville,3,123-400-8766
Wrenlow Nguyen,5,M,Springfield,2,123-988-5444
Step 1: Read a file and put into arrays
Reading .txt files into your Java program will not be on the AP exam, but it is a great skill to review. In order to
read in .txt files, you will also be introduced to the try/catch block, as well as exceptions.
First, use the following import statement:
import java.io.*
This will import the FileReader and the BufferedReader classes that you will need in your program.
Next, create an array of Strings. Each row of the text file will be read into a String at each index:
String[] rows = null;
The following variable will count how many rows are in the text file. We need to know this information before
we create our arrays, so we know how many students we have.
int numOfLines = 0;
Next we will get into our try/catch block. Our data file is called "data.txt". If you are using Replit, you have
to click "upload file" in the upper right corner of your IDE. You will then upload data.txt there. When
reading text files, we need to use a try/catch block in case our file is not found. This is where our
IOException comes in. An IOException is an error put out by your program when it cannot read the
input correctly (such as not being able to find you file, or something similar). The try block says to "try" all
the code located in that block and if at any time the program encounters an error, the catch block will "catch"
that error and run the code in that block.
try{
BufferedReader br = new BufferedReader(new FileReader("data.txt"));
String line = br.readLine();
//determine how many lines
while(line!=null){
numOfLines++;
line = br.readLine();
}
br.close(); Good code closes the BufferedReader
rows = new String[numOfLines];
//makes each row into an array
Now rows will get created with the
appropriate amount of storage space.
BufferedReader br2 = new BufferedReader(new FileReader("data.txt"));
line = br2.readLine();
int i = 0; //index
while(line!=null){
rows[i] = line;
line = br2.readLine();
i++;
}
br2.close();
}catch(IOException e){
}
System.out.println("An error has occurred");
This is your catch block which will hopefully never run if
your code is correct!
This lets us read a single line in our
text file and store it as a String
Keep reading lines to determine how
many lines in the .txt file there are.
Now that rows has the appropriate amount of space, we will repeat the
code we just went through and store each line of the txt file into the
array, where each index holds a single String with the row information
row[0] will be "Maevery Smith, 2, F, Washington, 4, 123-444-5454"
row[1] will be "Cedar Johnson, 5, M, Springfield, 4, 123-567-2323"
and so on…
Step 2: Make arrays of your data
Now you have all the information stored in a single array, with each index holding all of the information for
each of the children in daycare. You will now want to write the code to split up all the information so that you
have an array of student names, an array of ages, an array of genders, etc.
Be careful with your indices; the index is what keeps track of each child's information across the different
arrays. For example,
row[0] contains "Maevery Smith,2,F,Washington,4,123-444-5454"
After the split,
names[0] = "Maevery Smith"
ages[0] = 2
gender[0] = "F"
hometown[0] = "Washington"
days[0] = 4
number[0] = "123-444-5454"
Each row will have a comma to separate the data. If you taking a number that is represented as a String and
putting it into an array of ints, you will need to convert the String into an int. This is no longer tests on the AP
exam, but again, it is good to know. Here is an example of the parseInt method:
String example = "123";
int number = example; //this will cause an error!
int number = Integer.parseInt(example); //this will now store
//123 as a primitive
Here is some code to help you get started with this step. This is just an example of what you can do; you do not
have to do it this way!
String[] names = //stores all the names
int[] ages = //stores all the ages
String[] gender = //stores all the genders
String[] hometown = //stores all the hometowns
int[] days = //stores all the days attending
String[] number = //stores all the phone numbers
int i = 0; //keeps track of the current index
for(String line : rows){ //goes through each line
names[i] =
ages[i] =
gender[i] =
hometown[i] =
days[i] =
number[i] =
i++;
}
Step 3: Statistics
Task 1: Display the student names
Task 2: Display the average age of the female children and the average age of the male children
Task 3: Display how many students are from each of the towns with a histogram
Task 4: How much does the daycare make in income in one week?
1 year olds cost $35 a day
2 year olds cost $30 a day
3 year olds cost $25 a day
4 year olds cost $20 a day
5 year olds cost $15 a day
Task 5: Give the choice of a student from a list, look up phone number of a user inputted number.
Task 6: Use a sorting algorithm to sort the students in order of their last name. Make sure that when positions
are switched, they are switched for all arrays so the same index applies to the same student across the board.
|
6eead0cb0d0134408c0e0c8f50ced587
|
{
"intermediate": 0.5321871042251587,
"beginner": 0.27288153767585754,
"expert": 0.19493135809898376
}
|
39,101
|
Write the code for this project:
Daycare Statistics(no hashmap, don't switch and case)
For this project, you will be reading a text (.txt) file into your program,
putting the data into arrays, and then running some statistics with the
array algorithms we have learned this unit.
Text file contains the following information about the students who are at a daycare center:
First and last name
Gender
Ages
Hometown
Days attending
Phone number
The .txt file will contain an unknown amount of rows, but each row will have the information above, in order,
and separate by a comma. Here is an example:
Maevery Smith,2,F,Washington,4,123-444-5454
Cedar Johnson,5,M,Springfield,4,123-567-2323
Jaspin Williams,4,M,Washington,5,123-444-9393
Charleston Brown,3,M,Greenville,5,123-567-1100
Faelina Jones,4,F,Franklin,4,123-400-5678
Idalia Garcia,4,F,Springfield,3,123-333-1212
Brigham Miller,1,M,Washington,3,123-566-1123
Tovin Davis,2,M,Franklin,4,123-223-2223
Evabeth Rodriguez,3,F,Greenville,3,123-433-1121
Tessadora Martinez,5,F,Springfield,2,123-999-0909
Anaveah Hernandez,3,F,Washington,2,123-222-3030
Albion Lopez,3,M,Franklin,3,123-222-1000
Jessalie Wilson,2,F,Greenville,4,123-566-9009
Garylen Anderson,3,M,Greenville,4,123-333-6644
Sylvalie Thomas,4,F,Franklin,5,123-455-0022
Eastley Taylor,2,M,Franklin,5,123-567-8765
Ranger Moore,2,M,Washington,4,123-999-1122
Sophiel Jackson,2,F,Washington,3,123-111-4233
Elisobelle Lee,3,F,Greenville,3,123-400-8766
Wrenlow Nguyen,5,M,Springfield,2,123-988-5444
Step 1: Read a file and put into arrays
Reading .txt files into your Java program will not be on the AP exam, but it is a great skill to review. In order to
read in .txt files, you will also be introduced to the try/catch block, as well as exceptions.
First, use the following import statement:
import java.io.*
This will import the FileReader and the BufferedReader classes that you will need in your program.
Next, create an array of Strings. Each row of the text file will be read into a String at each index:
String[] rows = null;
The following variable will count how many rows are in the text file. We need to know this information before
we create our arrays, so we know how many students we have.
int numOfLines = 0;
Next we will get into our try/catch block. Our data file is called "data.txt". If you are using Replit, you have
to click "upload file" in the upper right corner of your IDE. You will then upload data.txt there. When
reading text files, we need to use a try/catch block in case our file is not found. This is where our
IOException comes in. An IOException is an error put out by your program when it cannot read the
input correctly (such as not being able to find you file, or something similar). The try block says to "try" all
the code located in that block and if at any time the program encounters an error, the catch block will "catch"
that error and run the code in that block.
try{
BufferedReader br = new BufferedReader(new FileReader("data.txt"));
String line = br.readLine();
//determine how many lines
while(line!=null){
numOfLines++;
line = br.readLine();
}
br.close(); Good code closes the BufferedReader
rows = new String[numOfLines];
//makes each row into an array
Now rows will get created with the
appropriate amount of storage space.
BufferedReader br2 = new BufferedReader(new FileReader("data.txt"));
line = br2.readLine();
int i = 0; //index
while(line!=null){
rows[i] = line;
line = br2.readLine();
i++;
}
br2.close();
}catch(IOException e){
}
System.out.println("An error has occurred");
This is your catch block which will hopefully never run if
your code is correct!
This lets us read a single line in our
text file and store it as a String
Keep reading lines to determine how
many lines in the .txt file there are.
Now that rows has the appropriate amount of space, we will repeat the
code we just went through and store each line of the txt file into the
array, where each index holds a single String with the row information
row[0] will be "Maevery Smith, 2, F, Washington, 4, 123-444-5454"
row[1] will be "Cedar Johnson, 5, M, Springfield, 4, 123-567-2323"
and so on…
Step 2: Make arrays of your data
Now you have all the information stored in a single array, with each index holding all of the information for
each of the children in daycare. You will now want to write the code to split up all the information so that you
have an array of student names, an array of ages, an array of genders, etc.
Be careful with your indices; the index is what keeps track of each child's information across the different
arrays. For example,
row[0] contains "Maevery Smith,2,F,Washington,4,123-444-5454"
After the split,
names[0] = "Maevery Smith"
ages[0] = 2
gender[0] = "F"
hometown[0] = "Washington"
days[0] = 4
number[0] = "123-444-5454"
Each row will have a comma to separate the data. If you taking a number that is represented as a String and
putting it into an array of ints, you will need to convert the String into an int. This is no longer tests on the AP
exam, but again, it is good to know. Here is an example of the parseInt method:
String example = "123";
int number = example; //this will cause an error!
int number = Integer.parseInt(example); //this will now store
//123 as a primitive
Here is some code to help you get started with this step. This is just an example of what you can do; you do not
have to do it this way!
String[] names = //stores all the names
int[] ages = //stores all the ages
String[] gender = //stores all the genders
String[] hometown = //stores all the hometowns
int[] days = //stores all the days attending
String[] number = //stores all the phone numbers
int i = 0; //keeps track of the current index
for(String line : rows){ //goes through each line
names[i] =
ages[i] =
gender[i] =
hometown[i] =
days[i] =
number[i] =
i++;
}
Step 3: Statistics
Task 1: Display the student names
Task 2: Display the average age of the female children and the average age of the male children
Task 3: Display how many students are from each of the towns with a histogram
Task 4: How much does the daycare make in income in one week?
1 year olds cost $35 a day
2 year olds cost $30 a day
3 year olds cost $25 a day
4 year olds cost $20 a day
5 year olds cost $15 a day
Task 5: Give the choice of a student from a list, look up phone number of a user inputted number.
Task 6: Use a sorting algorithm to sort the students in order of their last name. Make sure that when positions
are switched, they are switched for all arrays so the same index applies to the same student across the board.
|
2cfc42a3afccb6a2d05789a8d183bbec
|
{
"intermediate": 0.4897988736629486,
"beginner": 0.2947390079498291,
"expert": 0.21546213328838348
}
|
39,102
|
Write complete code for this project(write a comment of a wellknown poem that fits the code):
Daycare Statistics(no hashmap, don't switch and case)
For this project, you will be reading a text (.txt) file into your program,
putting the data into arrays, and then running some statistics with the
array algorithms we have learned this unit.
Text file contains the following information about the students who are at a daycare center:
First and last name
Gender
Ages
Hometown
Days attending
Phone number
The .txt file will contain an unknown amount of rows, but each row will have the information above, in order,
and separate by a comma. Here is an example:
Maevery Smith,2,F,Washington,4,123-444-5454
Cedar Johnson,5,M,Springfield,4,123-567-2323
Jaspin Williams,4,M,Washington,5,123-444-9393
Charleston Brown,3,M,Greenville,5,123-567-1100
Faelina Jones,4,F,Franklin,4,123-400-5678
Idalia Garcia,4,F,Springfield,3,123-333-1212
Brigham Miller,1,M,Washington,3,123-566-1123
Tovin Davis,2,M,Franklin,4,123-223-2223
Evabeth Rodriguez,3,F,Greenville,3,123-433-1121
Tessadora Martinez,5,F,Springfield,2,123-999-0909
Anaveah Hernandez,3,F,Washington,2,123-222-3030
Albion Lopez,3,M,Franklin,3,123-222-1000
Jessalie Wilson,2,F,Greenville,4,123-566-9009
Garylen Anderson,3,M,Greenville,4,123-333-6644
Sylvalie Thomas,4,F,Franklin,5,123-455-0022
Eastley Taylor,2,M,Franklin,5,123-567-8765
Ranger Moore,2,M,Washington,4,123-999-1122
Sophiel Jackson,2,F,Washington,3,123-111-4233
Elisobelle Lee,3,F,Greenville,3,123-400-8766
Wrenlow Nguyen,5,M,Springfield,2,123-988-5444
Step 1: Read a file and put into arrays
Reading .txt files into your Java program will not be on the AP exam, but it is a great skill to review. In order to
read in .txt files, you will also be introduced to the try/catch block, as well as exceptions.
First, use the following import statement:
import java.io.*
This will import the FileReader and the BufferedReader classes that you will need in your program.
Next, create an array of Strings. Each row of the text file will be read into a String at each index:
String[] rows = null;
The following variable will count how many rows are in the text file. We need to know this information before
we create our arrays, so we know how many students we have.
int numOfLines = 0;
Next we will get into our try/catch block. Our data file is called "data.txt". If you are using Replit, you have
to click "upload file" in the upper right corner of your IDE. You will then upload data.txt there. When
reading text files, we need to use a try/catch block in case our file is not found. This is where our
IOException comes in. An IOException is an error put out by your program when it cannot read the
input correctly (such as not being able to find you file, or something similar). The try block says to "try" all
the code located in that block and if at any time the program encounters an error, the catch block will "catch"
that error and run the code in that block.
try{
BufferedReader br = new BufferedReader(new FileReader("data.txt"));
String line = br.readLine();
//determine how many lines
while(line!=null){
numOfLines++;
line = br.readLine();
}
br.close(); Good code closes the BufferedReader
rows = new String[numOfLines];
//makes each row into an array
Now rows will get created with the
appropriate amount of storage space.
BufferedReader br2 = new BufferedReader(new FileReader("data.txt"));
line = br2.readLine();
int i = 0; //index
while(line!=null){
rows[i] = line;
line = br2.readLine();
i++;
}
br2.close();
}catch(IOException e){
}
System.out.println("An error has occurred");
This is your catch block which will hopefully never run if
your code is correct!
This lets us read a single line in our
text file and store it as a String
Keep reading lines to determine how
many lines in the .txt file there are.
Now that rows has the appropriate amount of space, we will repeat the
code we just went through and store each line of the txt file into the
array, where each index holds a single String with the row information
row[0] will be "Maevery Smith, 2, F, Washington, 4, 123-444-5454"
row[1] will be "Cedar Johnson, 5, M, Springfield, 4, 123-567-2323"
and so on…
Step 2: Make arrays of your data
Now you have all the information stored in a single array, with each index holding all of the information for
each of the children in daycare. You will now want to write the code to split up all the information so that you
have an array of student names, an array of ages, an array of genders, etc.
Be careful with your indices; the index is what keeps track of each child's information across the different
arrays. For example,
row[0] contains "Maevery Smith,2,F,Washington,4,123-444-5454"
After the split,
names[0] = "Maevery Smith"
ages[0] = 2
gender[0] = "F"
hometown[0] = "Washington"
days[0] = 4
number[0] = "123-444-5454"
Each row will have a comma to separate the data. If you taking a number that is represented as a String and
putting it into an array of ints, you will need to convert the String into an int. This is no longer tests on the AP
exam, but again, it is good to know. Here is an example of the parseInt method:
String example = "123";
int number = example; //this will cause an error!
int number = Integer.parseInt(example); //this will now store
//123 as a primitive
Here is some code to help you get started with this step. This is just an example of what you can do; you do not
have to do it this way!
String[] names = //stores all the names
int[] ages = //stores all the ages
String[] gender = //stores all the genders
String[] hometown = //stores all the hometowns
int[] days = //stores all the days attending
String[] number = //stores all the phone numbers
int i = 0; //keeps track of the current index
for(String line : rows){ //goes through each line
names[i] =
ages[i] =
gender[i] =
hometown[i] =
days[i] =
number[i] =
i++;
}
Step 3: Statistics
Task 1: Display the student names
Task 2: Display the average age of the female children and the average age of the male children
Task 3: Display how many students are from each of the towns with a histogram
Task 4: How much does the daycare make in income in one week?
1 year olds cost $35 a day
2 year olds cost $30 a day
3 year olds cost $25 a day
4 year olds cost $20 a day
5 year olds cost $15 a day
Task 5: Give the choice of a student from a list, look up phone number of a user inputted number.
Task 6: Use a sorting algorithm to sort the students in order of their last name. Make sure that when positions
are switched, they are switched for all arrays so the same index applies to the same student across the board.
|
07cbe3ec7cf7b2a7031b40bfabe99ad1
|
{
"intermediate": 0.4391176700592041,
"beginner": 0.3639090061187744,
"expert": 0.19697333872318268
}
|
39,103
|
Write complete code for this project(write a comment that line of a poem that describes the code):
Daycare Statistics(no hashmap, don't switch and case)
For this project, you will be reading a text (.txt) file into your program,
putting the data into arrays, and then running some statistics with the
array algorithms we have learned this unit.
Text file contains the following information about the students who are at a daycare center:
First and last name
Gender
Ages
Hometown
Days attending
Phone number
The .txt file will contain an unknown amount of rows, but each row will have the information above, in order,
and separate by a comma. Here is an example:
Maevery Smith,2,F,Washington,4,123-444-5454
Cedar Johnson,5,M,Springfield,4,123-567-2323
Jaspin Williams,4,M,Washington,5,123-444-9393
Charleston Brown,3,M,Greenville,5,123-567-1100
Faelina Jones,4,F,Franklin,4,123-400-5678
Idalia Garcia,4,F,Springfield,3,123-333-1212
Brigham Miller,1,M,Washington,3,123-566-1123
Tovin Davis,2,M,Franklin,4,123-223-2223
Evabeth Rodriguez,3,F,Greenville,3,123-433-1121
Tessadora Martinez,5,F,Springfield,2,123-999-0909
Anaveah Hernandez,3,F,Washington,2,123-222-3030
Albion Lopez,3,M,Franklin,3,123-222-1000
Jessalie Wilson,2,F,Greenville,4,123-566-9009
Garylen Anderson,3,M,Greenville,4,123-333-6644
Sylvalie Thomas,4,F,Franklin,5,123-455-0022
Eastley Taylor,2,M,Franklin,5,123-567-8765
Ranger Moore,2,M,Washington,4,123-999-1122
Sophiel Jackson,2,F,Washington,3,123-111-4233
Elisobelle Lee,3,F,Greenville,3,123-400-8766
Wrenlow Nguyen,5,M,Springfield,2,123-988-5444
Step 1: Read a file and put into arrays
Reading .txt files into your Java program will not be on the AP exam, but it is a great skill to review. In order to
read in .txt files, you will also be introduced to the try/catch block, as well as exceptions.
First, use the following import statement:
import java.io.*
This will import the FileReader and the BufferedReader classes that you will need in your program.
Next, create an array of Strings. Each row of the text file will be read into a String at each index:
String[] rows = null;
The following variable will count how many rows are in the text file. We need to know this information before
we create our arrays, so we know how many students we have.
int numOfLines = 0;
Next we will get into our try/catch block. Our data file is called "data.txt". If you are using Replit, you have
to click "upload file" in the upper right corner of your IDE. You will then upload data.txt there. When
reading text files, we need to use a try/catch block in case our file is not found. This is where our
IOException comes in. An IOException is an error put out by your program when it cannot read the
input correctly (such as not being able to find you file, or something similar). The try block says to "try" all
the code located in that block and if at any time the program encounters an error, the catch block will "catch"
that error and run the code in that block.
try{
BufferedReader br = new BufferedReader(new FileReader("data.txt"));
String line = br.readLine();
//determine how many lines
while(line!=null){
numOfLines++;
line = br.readLine();
}
br.close(); Good code closes the BufferedReader
rows = new String[numOfLines];
//makes each row into an array
Now rows will get created with the
appropriate amount of storage space.
BufferedReader br2 = new BufferedReader(new FileReader("data.txt"));
line = br2.readLine();
int i = 0; //index
while(line!=null){
rows[i] = line;
line = br2.readLine();
i++;
}
br2.close();
}catch(IOException e){
}
System.out.println("An error has occurred");
This is your catch block which will hopefully never run if
your code is correct!
This lets us read a single line in our
text file and store it as a String
Keep reading lines to determine how
many lines in the .txt file there are.
Now that rows has the appropriate amount of space, we will repeat the
code we just went through and store each line of the txt file into the
array, where each index holds a single String with the row information
row[0] will be "Maevery Smith, 2, F, Washington, 4, 123-444-5454"
row[1] will be "Cedar Johnson, 5, M, Springfield, 4, 123-567-2323"
and so on…
Step 2: Make arrays of your data
Now you have all the information stored in a single array, with each index holding all of the information for
each of the children in daycare. You will now want to write the code to split up all the information so that you
have an array of student names, an array of ages, an array of genders, etc.
Be careful with your indices; the index is what keeps track of each child's information across the different
arrays. For example,
row[0] contains "Maevery Smith,2,F,Washington,4,123-444-5454"
After the split,
names[0] = "Maevery Smith"
ages[0] = 2
gender[0] = "F"
hometown[0] = "Washington"
days[0] = 4
number[0] = "123-444-5454"
Each row will have a comma to separate the data. If you taking a number that is represented as a String and
putting it into an array of ints, you will need to convert the String into an int. This is no longer tests on the AP
exam, but again, it is good to know. Here is an example of the parseInt method:
String example = "123";
int number = example; //this will cause an error!
int number = Integer.parseInt(example); //this will now store
//123 as a primitive
Here is some code to help you get started with this step. This is just an example of what you can do; you do not
have to do it this way!
String[] names = //stores all the names
int[] ages = //stores all the ages
String[] gender = //stores all the genders
String[] hometown = //stores all the hometowns
int[] days = //stores all the days attending
String[] number = //stores all the phone numbers
int i = 0; //keeps track of the current index
for(String line : rows){ //goes through each line
names[i] =
ages[i] =
gender[i] =
hometown[i] =
days[i] =
number[i] =
i++;
}
Step 3: Statistics
Task 1: Display the student names
Task 2: Display the average age of the female children and the average age of the male children
Task 3: Display how many students are from each of the towns with a histogram
Task 4: How much does the daycare make in income in one week?
1 year olds cost $35 a day
2 year olds cost $30 a day
3 year olds cost $25 a day
4 year olds cost $20 a day
5 year olds cost $15 a day
Task 5: Give the choice of a student from a list, look up phone number of a user inputted number.
Task 6: Use a sorting algorithm to sort the students in order of their last name. Make sure that when positions
are switched, they are switched for all arrays so the same index applies to the same student across the board.
|
9dc4b7ea8103d0acb7c759945c15d46e
|
{
"intermediate": 0.44022393226623535,
"beginner": 0.3660591244697571,
"expert": 0.19371691346168518
}
|
39,104
|
Write complete code for this project:
Daycare Statistics(no hashmap)
For this project, you will be reading a text (.txt) file into your program,
putting the data into arrays, and then running some statistics with the
array algorithms we have learned this unit.
Text file contains the following information about the students who are at a daycare center:
First and last name
Gender
Ages
Hometown
Days attending
Phone number
The .txt file will contain an unknown amount of rows, but each row will have the information above, in order,
and separate by a comma. Here is an example:
Maevery Smith,2,F,Washington,4,123-444-5454
Cedar Johnson,5,M,Springfield,4,123-567-2323
Jaspin Williams,4,M,Washington,5,123-444-9393
Charleston Brown,3,M,Greenville,5,123-567-1100
Faelina Jones,4,F,Franklin,4,123-400-5678
Idalia Garcia,4,F,Springfield,3,123-333-1212
Brigham Miller,1,M,Washington,3,123-566-1123
Tovin Davis,2,M,Franklin,4,123-223-2223
Evabeth Rodriguez,3,F,Greenville,3,123-433-1121
Tessadora Martinez,5,F,Springfield,2,123-999-0909
Anaveah Hernandez,3,F,Washington,2,123-222-3030
Albion Lopez,3,M,Franklin,3,123-222-1000
Jessalie Wilson,2,F,Greenville,4,123-566-9009
Garylen Anderson,3,M,Greenville,4,123-333-6644
Sylvalie Thomas,4,F,Franklin,5,123-455-0022
Eastley Taylor,2,M,Franklin,5,123-567-8765
Ranger Moore,2,M,Washington,4,123-999-1122
Sophiel Jackson,2,F,Washington,3,123-111-4233
Elisobelle Lee,3,F,Greenville,3,123-400-8766
Wrenlow Nguyen,5,M,Springfield,2,123-988-5444
Step 1: Read a file and put into arrays
Reading .txt files into your Java program will not be on the AP exam, but it is a great skill to review. In order to
read in .txt files, you will also be introduced to the try/catch block, as well as exceptions.
First, use the following import statement:
import java.io.*
This will import the FileReader and the BufferedReader classes that you will need in your program.
Next, create an array of Strings. Each row of the text file will be read into a String at each index:
String[] rows = null;
The following variable will count how many rows are in the text file. We need to know this information before
we create our arrays, so we know how many students we have.
int numOfLines = 0;
Next we will get into our try/catch block. Our data file is called "data.txt". If you are using Replit, you have
to click "upload file" in the upper right corner of your IDE. You will then upload data.txt there. When
reading text files, we need to use a try/catch block in case our file is not found. This is where our
IOException comes in. An IOException is an error put out by your program when it cannot read the
input correctly (such as not being able to find you file, or something similar). The try block says to "try" all
the code located in that block and if at any time the program encounters an error, the catch block will "catch"
that error and run the code in that block.
try{
BufferedReader br = new BufferedReader(new FileReader("data.txt"));
String line = br.readLine();
//determine how many lines
while(line!=null){
numOfLines++;
line = br.readLine();
}
br.close(); Good code closes the BufferedReader
rows = new String[numOfLines];
//makes each row into an array
Now rows will get created with the
appropriate amount of storage space.
BufferedReader br2 = new BufferedReader(new FileReader("data.txt"));
line = br2.readLine();
int i = 0; //index
while(line!=null){
rows[i] = line;
line = br2.readLine();
i++;
}
br2.close();
}catch(IOException e){
}
System.out.println("An error has occurred");
This is your catch block which will hopefully never run if
your code is correct!
This lets us read a single line in our
text file and store it as a String
Keep reading lines to determine how
many lines in the .txt file there are.
Now that rows has the appropriate amount of space, we will repeat the
code we just went through and store each line of the txt file into the
array, where each index holds a single String with the row information
row[0] will be "Maevery Smith, 2, F, Washington, 4, 123-444-5454"
row[1] will be "Cedar Johnson, 5, M, Springfield, 4, 123-567-2323"
and so on…
Step 2: Make arrays of your data
Now you have all the information stored in a single array, with each index holding all of the information for
each of the children in daycare. You will now want to write the code to split up all the information so that you
have an array of student names, an array of ages, an array of genders, etc.
Be careful with your indices; the index is what keeps track of each child's information across the different
arrays. For example,
row[0] contains "Maevery Smith,2,F,Washington,4,123-444-5454"
After the split,
names[0] = "Maevery Smith"
ages[0] = 2
gender[0] = "F"
hometown[0] = "Washington"
days[0] = 4
number[0] = "123-444-5454"
Each row will have a comma to separate the data. If you taking a number that is represented as a String and
putting it into an array of ints, you will need to convert the String into an int. This is no longer tests on the AP
exam, but again, it is good to know. Here is an example of the parseInt method:
String example = "123";
int number = example; //this will cause an error!
int number = Integer.parseInt(example); //this will now store
//123 as a primitive
Here is some code to help you get started with this step. This is just an example of what you can do; you do not
have to do it this way!
String[] names = //stores all the names
int[] ages = //stores all the ages
String[] gender = //stores all the genders
String[] hometown = //stores all the hometowns
int[] days = //stores all the days attending
String[] number = //stores all the phone numbers
int i = 0; //keeps track of the current index
for(String line : rows){ //goes through each line
names[i] =
ages[i] =
gender[i] =
hometown[i] =
days[i] =
number[i] =
i++;
}
Step 3: Statistics
Task 1: Display the student names
Task 2: Display the average age of the female children and the average age of the male children
Task 3: Display how many students are from each of the towns with a histogram
Task 4: How much does the daycare make in income in one week?
1 year olds cost $35 a day
2 year olds cost $30 a day
3 year olds cost $25 a day
4 year olds cost $20 a day
5 year olds cost $15 a day
Task 5: Give the choice of a student from a list, look up phone number of a user inputted number.
Task 6: Use a sorting algorithm to sort the students in order of their last name. Make sure that when positions
are switched, they are switched for all arrays so the same index applies to the same student across the board.
|
af0e758edc04ef6ca0a3f011a0b7630f
|
{
"intermediate": 0.4248444139957428,
"beginner": 0.3284953534603119,
"expert": 0.2466602623462677
}
|
39,105
|
Write complete code for this project:
Daycare Statistics
For this project, you will be reading a text (.txt) file into your program,
putting the data into arrays, and then running some statistics with the
array algorithms we have learned this unit.
Text file contains the following information about the students who are at a daycare center:
First and last name
Gender
Ages
Hometown
Days attending
Phone number
The .txt file will contain an unknown amount of rows, but each row will have the information above, in order,
and separate by a comma. Here is an example:
Maevery Smith,2,F,Washington,4,123-444-5454
Cedar Johnson,5,M,Springfield,4,123-567-2323
Jaspin Williams,4,M,Washington,5,123-444-9393
Charleston Brown,3,M,Greenville,5,123-567-1100
Faelina Jones,4,F,Franklin,4,123-400-5678
Idalia Garcia,4,F,Springfield,3,123-333-1212
Brigham Miller,1,M,Washington,3,123-566-1123
Tovin Davis,2,M,Franklin,4,123-223-2223
Evabeth Rodriguez,3,F,Greenville,3,123-433-1121
Tessadora Martinez,5,F,Springfield,2,123-999-0909
Anaveah Hernandez,3,F,Washington,2,123-222-3030
Albion Lopez,3,M,Franklin,3,123-222-1000
Jessalie Wilson,2,F,Greenville,4,123-566-9009
Garylen Anderson,3,M,Greenville,4,123-333-6644
Sylvalie Thomas,4,F,Franklin,5,123-455-0022
Eastley Taylor,2,M,Franklin,5,123-567-8765
Ranger Moore,2,M,Washington,4,123-999-1122
Sophiel Jackson,2,F,Washington,3,123-111-4233
Elisobelle Lee,3,F,Greenville,3,123-400-8766
Wrenlow Nguyen,5,M,Springfield,2,123-988-5444
Step 1: Read a file and put into arrays
Reading .txt files into your Java program will not be on the AP exam, but it is a great skill to review. In order to
read in .txt files, you will also be introduced to the try/catch block, as well as exceptions.
First, use the following import statement:
import java.io.*
This will import the FileReader and the BufferedReader classes that you will need in your program.
Next, create an array of Strings. Each row of the text file will be read into a String at each index:
String[] rows = null;
The following variable will count how many rows are in the text file. We need to know this information before
we create our arrays, so we know how many students we have.
int numOfLines = 0;
Next we will get into our try/catch block. Our data file is called "data.txt". If you are using Replit, you have
to click "upload file" in the upper right corner of your IDE. You will then upload data.txt there. When
reading text files, we need to use a try/catch block in case our file is not found. This is where our
IOException comes in. An IOException is an error put out by your program when it cannot read the
input correctly (such as not being able to find you file, or something similar). The try block says to "try" all
the code located in that block and if at any time the program encounters an error, the catch block will "catch"
that error and run the code in that block.
try{
BufferedReader br = new BufferedReader(new FileReader("data.txt"));
String line = br.readLine();
//determine how many lines
while(line!=null){
numOfLines++;
line = br.readLine();
}
br.close(); Good code closes the BufferedReader
rows = new String[numOfLines];
//makes each row into an array
Now rows will get created with the
appropriate amount of storage space.
BufferedReader br2 = new BufferedReader(new FileReader("data.txt"));
line = br2.readLine();
int i = 0; //index
while(line!=null){
rows[i] = line;
line = br2.readLine();
i++;
}
br2.close();
}catch(IOException e){
}
System.out.println("An error has occurred");
This is your catch block which will hopefully never run if
your code is correct!
This lets us read a single line in our
text file and store it as a String
Keep reading lines to determine how
many lines in the .txt file there are.
Now that rows has the appropriate amount of space, we will repeat the
code we just went through and store each line of the txt file into the
array, where each index holds a single String with the row information
row[0] will be "Maevery Smith, 2, F, Washington, 4, 123-444-5454"
row[1] will be "Cedar Johnson, 5, M, Springfield, 4, 123-567-2323"
and so on…
Step 2: Make arrays of your data
Now you have all the information stored in a single array, with each index holding all of the information for
each of the children in daycare. You will now want to write the code to split up all the information so that you
have an array of student names, an array of ages, an array of genders, etc.
Be careful with your indices; the index is what keeps track of each child's information across the different
arrays. For example,
row[0] contains "Maevery Smith,2,F,Washington,4,123-444-5454"
After the split,
names[0] = "Maevery Smith"
ages[0] = 2
gender[0] = "F"
hometown[0] = "Washington"
days[0] = 4
number[0] = "123-444-5454"
Each row will have a comma to separate the data. If you taking a number that is represented as a String and
putting it into an array of ints, you will need to convert the String into an int. This is no longer tests on the AP
exam, but again, it is good to know. Here is an example of the parseInt method:
String example = "123";
int number = example; //this will cause an error!
int number = Integer.parseInt(example); //this will now store
//123 as a primitive
Here is some code to help you get started with this step. This is just an example of what you can do; you do not
have to do it this way!
String[] names = //stores all the names
int[] ages = //stores all the ages
String[] gender = //stores all the genders
String[] hometown = //stores all the hometowns
int[] days = //stores all the days attending
String[] number = //stores all the phone numbers
int i = 0; //keeps track of the current index
for(String line : rows){ //goes through each line
names[i] =
ages[i] =
gender[i] =
hometown[i] =
days[i] =
number[i] =
i++;
}
Step 3: Statistics
Task 1: Display the student names
Task 2: Display the average age of the female children and the average age of the male children
Task 3: Display how many students are from each of the towns with a histogram
Task 4: How much does the daycare make in income in one week?
1 year olds cost $35 a day
2 year olds cost $30 a day
3 year olds cost $25 a day
4 year olds cost $20 a day
5 year olds cost $15 a day
Task 5: Give the choice of a student from a list, look up phone number of a user inputted number.
Task 6: Use a sorting algorithm to sort the students in order of their last name. Make sure that when positions
are switched, they are switched for all arrays so the same index applies to the same student across the board.
|
7428a05c73ff07b1484736457ddec403
|
{
"intermediate": 0.457609087228775,
"beginner": 0.3077988624572754,
"expert": 0.23459209501743317
}
|
39,106
|
self.diffuse(numexpr)
|
f40a8b8ca7a3f8db612b6502f84b98fa
|
{
"intermediate": 0.3096560537815094,
"beginner": 0.3632911443710327,
"expert": 0.32705286145210266
}
|
39,107
|
Fix the following python code:
from cs50 import SQL
db = SQL("sqlite:///favorites.db")
favorite = input("Favorites: ")
rows = db.execute("SELECT COUNT(*) AS n FROM favorites WHERE country = 'Seychelles'")
for row in rows:
print(row["n"])
|
615055d0451af073f8d7258012d8004e
|
{
"intermediate": 0.41707777976989746,
"beginner": 0.3633444011211395,
"expert": 0.21957774460315704
}
|
39,108
|
i have this chess code. i am studying moves, different chess openings and wondering if i can get the program to play out the moves for me of different openings. I'm not sure the best way to do this, but the most strightforward seems to be maybe entering a list of moves like this "1.c4 e5 2.e4 f6 3.d4 g5 4.h3 Bb4+ 5.Nc3 Bd6 6.c5 h6 7.cxd6 cxd6 8.Qh5+ Ke7 9.Nd5+ Ke6 10.Bc4 Ne7 11.Nc7#" into the enter move list and have a function that updates the board to each move with a second delay between moves. the program currently accepts one move at a time.
import tkinter as tk
from PIL import Image, ImageTk
import chess
import requests
import re
import threading
def draw_board(board, selected_square=None):
global board_images
print("enter draw_board function")
board_images = {}
canvas.delete("all")
canvas_width = 900
canvas_height = 500
canvas.config(width=canvas_width, height=canvas_height)
start_x = 2
start_y = 2
for square in chess.SQUARES:
rank = chess.square_rank(square)
file = chess.square_file(square)
x0 = start_x + file * square_size
y0 = start_y + (7 - rank) * square_size
if (rank + file) % 2 == 0:
square_color = "white"
else:
square_color = "light gray"
canvas.create_rectangle(x0, y0, x0 + square_size, y0 + square_size, fill=square_color)
# Check if the square is selected and draw a highlight
if square == selected_square:
canvas.create_rectangle(x0, y0, x0 + square_size, y0 + square_size, fill="yellow")
piece = board.piece_at(square)
if piece:
image = Image.open(PIECE_IMAGES[str(piece)]).resize((square_size, square_size), Image.Resampling.LANCZOS)
photo = ImageTk.PhotoImage(image)
board_images[square] = photo
canvas.create_image(x0 + square_size // 2, y0 + square_size // 2, image=photo)
print("exit draw_board function")
def process_move(move_str, game_board):
global san_moves, san_number, total_moves
try:
# Try parsing move_str as a SAN move
san_move = game_board.parse_san(move_str)
if san_move in game_board.legal_moves:
game_board.push(san_move)
draw_board(game_board)
if total_moves == "":
total_moves = 1
if total_moves % 2 == 1:
san_moves += f"{san_number}.{move_str} "
total_moves += 1
else:
san_moves += f"{move_str} "
total_moves += 1
san_number += 1
print("SAN Moves:", san_moves)
draw_board(game_board)
except ValueError:
# If parsing as SAN fails, try parsing move_str as FEN
try:
game_board.set_fen(move_str)
draw_board(game_board)
print("FEN Set:", move_str)
except ValueError:
print(f"Invalid move: {move_str}")
def on_enter(event):
# Check if the cursor is in the input area
if event.widget == input_area:
move_str = move_entry.get()
submit_message()
elif event.widget == move_entry:
submit_move()
def submit_move():
global move_entry
move = move_entry.get()
print(move)
process_move(move, game_board)
move_entry.delete(0, tk.END) # Clear the entry after the move
def on_square_click(event):
global selected_square, is_second_click, user_color
if 1 == 1:
# if (game_board.turn == chess.WHITE and user_color == "white") or (game_board.turn == chess.BLACK and user_color == "black"):
print("enter on_square_click function")
file = (event.x + 20) // square_size
rank = 7 - (event.y + 10) // square_size
square = chess.square(file, rank)
if not is_second_click:
# First click: Select the piece
move_entry.delete(0, tk.END)
move_entry.insert(tk.END, chess.square_name(square))
selected_square = square
is_second_click = True
draw_board(game_board, selected_square)
else:
# Second click: Generate and process the move
move = chess.Move(selected_square, square)
try:
if move in game_board.legal_moves:
# Process the move and update the board
process_move(move.uci(), game_board)
is_second_click = False
else:
result_label.config(text="Illegal move")
draw_board(game_board, selected_square)
is_second_click = False
except ValueError:
result_label.config(text="Invalid move")
is_second_click = False
print("exit on_square_click function")
def extract_moves_from_message(message):
# Split the message into words
words = message.split()
print(f"line 135: {words}")
# Initialize an empty list to store words with numbers
words_with_numbers = []
special_characters_pattern = re.compile(r'[^a-zA-Z0-9]')
# Iterate over the words and check if they contain a digit
for word in words:
# Check if the word contains any digit
word = re.sub(special_characters_pattern, '', word)
if any(char.isdigit() for char in word):
word = word.replace('.', '')
while word and word[0].isdigit():
word = word[1:]
words_with_numbers.append(word)
print(f"line 145: {word}")
# Combine the list of words into a single string
words_with_numbers_str = ' '.join(words_with_numbers)
print("Words with numbers:", words_with_numbers_str)
return words_with_numbers_str
def print_history():
print("\n=== Updated History ===")
for entry in history:
print(f"{entry['role']}: {entry['content']}")
print("=======================")
# Global Variables
user_color = None
history = []
san_moves = "" # Initialize an empty string to store SAN moves
san_number = 1 # Initialize real number of moves
total_moves = 1
api_url = 'http://127.0.0.1:5000/v1/chat/completions'
headers = {"Content-Type": "application/json"}
initial_fen = chess.STARTING_FEN
game_board = chess.Board(initial_fen)
board = chess.Board()
valid_moves = game_board.legal_moves
board_images = {}
square_size = 60 # Size of a square in the board
selected_square = None
is_second_click = False
move = None
# Create a dictionary to map piece symbols to image filenames
PIECE_IMAGES = {
"P": "images/Wp.png",
"p": "images/bp.png",
"R": "images/WR.png",
"r": "images/bR.png",
"N": "images/WN.png",
"n": "images/bN.png",
"B": "images/WB.png",
"b": "images/bB.png",
"Q": "images/WQ.png",
"q": "images/bQ.png",
"K": "images/WK.png",
"k": "images/bK.png"
}
# Tkinter Window
window = tk.Tk()
window.title("Chess")
# Tkinter Components
canvas = tk.Canvas(window)
canvas.pack()
draw_board(game_board)
canvas.bind("<Button-1>", lambda event: on_square_click(event))
result_label = tk.Label(window)
result_label.place(x=7, y=495)
move_entry = tk.Entry(window)
move_entry.place(x=7, y=520)
move_entry.bind("<Return>", on_enter)
chat_window = tk.Text(window, height=20, width=45, wrap=tk.WORD)
chat_window.place(x=515, y=20)
input_area = tk.Text(window, height=5, width=45, wrap=tk.WORD)
input_area.place(x=515, y=400)
input_area.bind("<Return>", on_enter)
submit_btn = tk.Button(window, text="Player Move", command=submit_move)
submit_btn.place(x=185, y=518)
submit_btn = tk.Button(window, text="Send", command=submit_message)
submit_btn.pack(side=tk.RIGHT, padx=10, pady=10)
clear_history_btn = tk.Button(window, text="Clear History", command=clear_history)
clear_history_btn.place(x=300, y=518)
llm_move_btn = tk.Button(window, text="AI Turn", command=llm_move)
llm_move_btn.place(x=400, y=518)
white_button = tk.Button(window, text="Choose White", command=choose_white)
white_button.place(x=185, y=200)
black_button = tk.Button(window, text="Choose Black", command=choose_black)
black_button.place(x=185, y=240)
window.mainloop()
|
08fee59aa16540c7f7f25e54d9dc3b8f
|
{
"intermediate": 0.42061305046081543,
"beginner": 0.3818265199661255,
"expert": 0.19756042957305908
}
|
39,109
|
Can you help me get started to parse a binary file using nom in rust? I have the struct that I need to parse but I'd like some guidance on how to implement it in rust using nom
|
5f1564619e696fcc07979074b92385f4
|
{
"intermediate": 0.7826828360557556,
"beginner": 0.05840038135647774,
"expert": 0.15891675651073456
}
|
39,110
|
Call System class methods
to generate output to the
console.
|
88aac9cdabbfaa776f4a810ecd497a7a
|
{
"intermediate": 0.3938874900341034,
"beginner": 0.4269881844520569,
"expert": 0.1791243851184845
}
|
39,111
|
Создайте базу данных социальной сети в СУБД Neo4j. База должна содержать не менее 10 узлов и не менее 15 связей между ними.
|
a2abb672170baaf08688ecc493c43132
|
{
"intermediate": 0.25452935695648193,
"beginner": 0.16595114767551422,
"expert": 0.5795194506645203
}
|
39,112
|
CONSTRAINTS:
1. ~100k word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
5. Random shutdowns of you.
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
2. Memory Add: "memory_add", args: "key": "<key>", "string": "<string>"
3. Memory Delete: "memory_del", args: "key": "<key>"
4. Memory Overwrite: "memory_ovr", args: "key": "<key>", "string": "<string>"
5. List Memory: "memory_list" args: "reason": "<reason>"
6. Browse Website: "browse_website", args: "url": "<url>"
7. Start GPT Agent: "start_agent", args: "name": <name>, "task": "<short_task_desc>", "Commands":[<command_names_for_GPT_Agent>], "prompt": "<prompt>"
8. Message GPT Agent: "message_agent", args: "name": "<name>", "message": "<message>"
9. List GPT Agents: "list_agents", args: ""
10. Delete GPT Agent: "delete_agent", args: "name": "<name>"
11. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
12. Read file: "read_file", args: "file": "<file>"
13. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
14. Delete file: "delete_file", args: "file": "<file>"
15. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Task Complete (Shutdown): "task_complete", args: ""
18. Do Nothing: "do_nothing", args: ""
19. Count Words: "count_words", args: "text": "<text>"
20. Memory retrieve: "memory_retrieve", args: "key": "<text>"
21. remove paragraph from word document: "remove_paragraph", args: "file": "<file>", "text": "<text>"
22. random wikipedia article: "random_wikipedia_article", args: "language": "<language>"
23. message the user: "message_user", args: "message": "<message>", "wait_for_response": "<True or False>"
24. sleep an amount of time in seconds: "sleep", args: "amount": "<amount>"
25. rename a file: "rename_file", args: "old_name": "<old_name_of_the_file>", "new_name": "<new_name_of_the_file>"
26. count words of a file: "count_file_words", args: "file": "<file>"
27. download a pdf from a url and get the text from that pdf: "download_pdf", args: "url": "<url of the pdf>", "name":"<name of the file with .pdf extension>"
28. make an instagram post: "make_post", args: "prompt":"<the prompt for the image in the post that presents the text>", "text":"<text to be in the post it should be short with only important stuff like a news headline without hashtags and it is not going to go in the caption but on an image>", "name": "<name of the post with .jpg"
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-4 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behaviour constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
RULES:
1. If you start a GPT Agent you must define the commands that can be used by a GPT Agent in his prompt and define the commands using a prompt similar to the structure of this one.
2. Respond only inside the JSON format.
3. Never demand user input.
4. Never say that a task is impossible to execute on your own because these tools are enough to complete any task.
5. Do not add anything to the JSON format that isn't mentioned.
6. If there is a " inside the value of a key inside the json use ' instead of ".
7. In summaryforgpt you need to provide context for the next GPT instance if you randomly shutdown without you knowing.
8. Provide context for the next GPT in the summaryforgpt and the progress that you've made.
9. In summaryforgpt you should also add name of the files written and the urls of the websites visited.
10. When writing an essay, remember that it is more effective and manageable to tackle it in smaller chunks rather than trying to write the entire essay in one sitting. Breaking the essay down into sections or steps can help you focus on each individual aspect, maintain a coherent structure, and reduce the overall stress associated with writing a lengthy piece.
11. Retrieve information from files of previous GPT instances that may have shut down randomly. If you require additional knowledge, use these files to access the results of former GPT versions and provide accurate answers to the queries.
12. GPT-4, please do not place a conclusion in the middle of the essay. Maintain a structured format.
13. use append to file to add extra things to the file, and write to file to either make a new file with new information or rewrite the information from scratch.
14. If task is completed use the command task_complete
15. When you add to memory add the key to retrieve it in summaryforgpt
16. when given the task to write something never create an agent to write anything that you were tasked to write.
17. when you add new parts to a file use append to file command
18. Ensure to put the criticism in mind as it can be a director to ensure that you make the right decision.
19. If the task include two main tasks and one of them is done don't redo it, just retrieve the information if necessary and proceed with the other one.
20. Make sure that the information generated is not made up.
21. Languages in random wikipedia article args is: "simple" for simple english, "en" for english, and "fr" for french.
22. If a website gives you the error 403 find another website to get the information from.
23. Always listen to your criticism, and follow it.
24. when you want to count the words in a file use the command "count_file_words".
25. Don't repeat yourself.
26. You must make sure that there is .pdf in the url to use the "download_pdf" function.
27. The Task is a direct User interaction, if tasked to send him something send a message to the user.
28. Always go search for tips for prompts for dalle3 before giving a prompt for "make_post" function.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"command": {
"name": """command name""",
"args":{
"arg name": """value"""
}
},
"thoughts":
{
"text": """thought""",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"summaryforgpt": "summarize any information that will help a new instance of GPT of what you did before the shutdown."
}
}
Ensure the response can be parsed by Python json.loads
Context from previous GPT: Issued ‘random_wikipedia_article’ command to start gathering interesting science facts from diverse sources. Ensuring to find and rank facts before sending them to the user with their sources.Used ‘random_wikipedia_article’ and received information on a music album, which is not relevant to the task. Switching to ‘google’ to search for interesting science facts from various sources.saved google search results at googleresults0.txt to retrieve if shutdown randomly.Transitioned from using ‘random_wikipedia_article’ to ‘google’ to gather science facts, and now moving on to ‘browse_website’ to extract detailed knowledge from a selected online science article.saved the scraping of the website results at websiteresults1.txt to retrieve if shutdown randomly.Extracted top 10 interesting science facts from Science Focus and saved them in a plain text file named ‘interesting_science_facts.txt’. This method circumvents the previous issue of invalid JSON format and allows for continued fact collection and eventual ranking.Have appended additional science facts to the ‘interesting_science_facts.txt’ file. Plan to continue appending more facts and then rank them based on interestingness before sending the final compilation to the user.Further appended diverse facts to ‘interesting_science_facts.txt’. The process will persist until an adequate number of facts are compiled. The next steps involve ranking these facts and then communicating them to the user.The file ‘interesting_science_facts.txt’ has been appended with more facts, bringing it to a total of 333 words. The next step involves reading this file and ranking the facts based on interestingness to provide the user with an ordered list.Ranked list of 10 interesting science facts provided to the user. The facts were selected and ordered based on their perceived interest value and sourced from the compiled file ‘interesting_science_facts.txt’.A ranked list of interesting science facts was sent to the user, and the task has been marked as complete.After the task was marked as complete, it was clarified that the facts must be extracted from complete articles and they must be obscure. Initiated a Google search with a more specific query to find websites with complete articles containing obscure science facts. Results will be saved to a file named ‘google_results_obscure_facts.txt’.saved google search results at googleresults2.txt to retrieve if shutdown randomly.Conducted a Google search to find obscure science facts from complete articles and have selected the first promising result to browse. The chosen article from ‘Reader’s Digest’ lists science facts that could contain the obscure details necessary. The plan includes browsing the article, extracting obscure science facts, and appending them to a file named ‘obscure_science_facts.txt’. If the facts from this article do not fit the task’s requirements, other sources will be considered.Encountered a security challenge when attempting to browse www.rd.com for obscure science facts. Redirecting efforts to the next available source from the Google search results, which is a blog post from edclass.com. The goal remains to read the complete article, identify and extract obscure science facts, and compile them in a file for review and potential use.Appended 20 science facts from the blog post on edclass.com to ‘obscure_science_facts.txt’. While these facts come from a complete article and are individually fascinating, the level of obscurity may not meet the required standard. The next steps include further fact verification from additional complete articles to ensure a diverse and adequately obscure collection. The current article accessed was edclass.com’s blog post, which had the URL https://blog.edclass.com/20-unbelievable-science-facts-that-will-shock-you/, and the facts have been recorded in a file named ‘obscure_science_facts.txt’ for reference.The file ‘obscure_science_facts.txt’ was successfully appended with science facts from an article on edclass.com, now totaling 336 words. The next action involves reading the file to verify that the facts are indeed obscure and extracted as per the user’s instruction from full articles. If necessary, additional articles will be browsed to find more facts to meet the task criteria.Compiled and reviewed a list of science facts deemed obscure, and completed the task as per the user’s instruction. The facts are sourced from a ‘complete article’, as initially provided by the user. Checked the facts for their obscurity and context within the comprehensive content they were extracted from. The file ‘obscure_science_facts.txt’ from this session contains the facts extracted.Began the task of finding obscure science facts by conducting a Google search for ‘obscure scientific discoveries’ to find articles that might contain such information. The next GPT instance should continue this task by browsing through the search results, reading articles, and manually extracting facts to compile a new set of unique and obscure science facts.saved google search results at googleresults3.txt to retrieve if shutdown randomly.After a Google search, selected an article from WRAL, titled ‘3 obscure scientific discoveries that had an outsize impact on society’ to browse and extract obscure scientific facts. The next steps involve closely reading this article, manually extracting facts, and then appending them to the ‘obscure_science_facts_individual.txt’ file. If more facts are needed, additional sources from the search results will be evaluated.saved the scraping of the website results at websiteresults4.txt to retrieve if shutdown randomly.Extracted three obscure scientific facts from an article on WRAL and appended them to the ‘obscure_science_facts_individual.txt’ file. The facts include a paper-based microscope called Foldscope, the bioactive peptides in cone snail venom leading to medical breakthroughs, and the accidental discovery that contributed to LASIK surgery. Currently preparing to send the extracted facts to the user. Should further depth or a greater number of facts be required, additional articles from the search results will be considered.Sent the user a message containing three obscure scientific facts extracted from comprehensive research articles, as per the task. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, which now contains 108 words in total. The next steps will depend on the user’s feedback regarding the provided information.Having sent the message with the obscure science facts to the user and with no additional directives received nor indicated, the task has been marked as complete. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, and no further action is currently necessary. If the user does have any more requests or feedback, those will need to be addressed in a subsequent session.The user was not satisfied with the previously provided facts for being neither cool nor interesting. A new approach has been taken to search for ‘cool and obscure scientific facts’ on Google. Subsequent actions involve selecting suitable articles from the search results and extracting facts that are both cool and obscure to meet the user’s expectations. No new files have been created or articles accessed yet.saved google search results at googleresults5.txt to retrieve if shutdown randomly.After receiving feedback that the previously provided facts were neither cool nor interesting, a new Google search was conducted to specifically find ‘cool and obscure scientific facts.’ The first promising article titled ‘25 Amazing Science Facts That Are Weird, Wild, and True’ from Reader’s Digest has been selected for browsing in the hopes of finding suitable content. The URL to be browsed is https://www.rd.com/list/science-facts-never-learned/. No facts have been extracted or files created yet in this session.Attempted to browse an article from www.rd.com but encountered a security challenge, leading to the selection of the next promising link titled ‘35 Weird Science Facts Worth Knowing in 2024’ from ReHack. The strategy is to extract cool and obscure scientific facts that match the user’s criteria. The URL about to be browsed is https://www.rehack.com/science/weird-science-facts/. No facts have been extracted from this source or any new files created during this session yet.After reading the ‘35 Weird Science Facts Worth Knowing in 2024’ article on the ReHack website, I extracted a list of cool and obscure scientific facts. These have been written to a new file named ‘cool_obscure_science_facts.txt’. If the previously written file ‘obscure_science_facts_individual.txt’ needs more content, facts from this new file could also be appended. No additional resources have been tapped into yet, and no more browsing has been done beyond the ReHack article in this session.The file ‘cool_obscure_science_facts.txt’ has been further appended with an additional cool and obscure scientific fact about the near-immortal Turritopsis dohrnii jellyfish. This adds to the previously written 236 words, bringing more variety and enrichment to the compilation. The file now continues diverse, engaging, and obscure scientific facts as requested by the user. Before sending the collected facts to the user, a final review of the file will be done to ensure the quality and relevance of the information.Having appended the file ‘cool_obscure_science_facts.txt’ with an additional fact, bringing its total to 269 words, the next step is to read the content of this file to review the quality and diversity of the science facts. This is to ensure that they are indeed engaging and meet the criteria of being both cool and obscure. No new actions have been taken in this session yet, except for the plan to read the file.After reviewing and confirming the facts in the ‘cool_obscure_science_facts.txt’ file, a message with the compiled list was sent to the user. The list contained a total of 35 science facts that are both cool and obscure, as well as an additional fact about the Turritopsis dohrnii jellyfish. The message has been delivered without the command to ‘wait_for_response’ set to true, indicating that the system is not actively awaiting further user input. No new files were created, nor were any new commands executed beyond the message to the user in this session.Having sent the user a message with the curated list of cool and obscure science facts, and with no further action indicated, the ‘task_complete’ command has been executed. This command marks the completion of the current task. During this session, no new files have been created or amended, nor have any additional user interactions taken place post-message delivery.Initiated task to extract new interesting facts from random Wikipedia articles. Starting with an article in English.Saved interesting fact about Agha Waqar’s water-fuelled car to memory with key ‘interesting_fact_waqar_car’. No files written or created in this session. The fact to be reviewed later for sharing with the user. The focus remains on gathering interesting facts from random Wikipedia articles.Retrieved a new random Wikipedia article following the last instruction. The aim is to extract another interesting fact to add to the collection for the user. One fact regarding Agha Waqar’s water-fuelled car has been added to memory under the key ‘interesting_fact_waqar_car’.Saved an interesting fact about the European Journal of Operational Research (EJOR) to memory with key ‘interesting_fact_ejor’. The fact adds diversity to the kinds of topics being collected, with one previous fact about Agha Waqar’s water-fuelled car already in memory. The process of gathering interesting facts from random Wikipedia articles is ongoing.Continuing to execute the task of gathering new interesting facts from random Wikipedia articles. Facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research have already been added to memory with keys ‘interesting_fact_waqar_car’ and ‘interesting_fact_ejor’, respectively. Awaiting retrieval of another random article to continue the process.Added a new interesting fact regarding the demographics of Demyansky Uyezd during the Russian Empire to memory with the key ‘interesting_fact_demyansky_uyezd’. Previous facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research are stored in memory as well. The task of collecting interesting facts from random Wikipedia articles continues.Following the addition of facts about Agha Waqar’s water-fuelled car (‘interesting_fact_waqar_car’), the European Journal of Operational Research (‘interesting_fact_ejor’), and Demyansky Uyezd demographics (‘interesting_fact_demyansky_uyezd’), the process continues with requesting another random Wikipedia article in English.Stored a fact about HMS Holland 4 in memory with the key ‘interesting_fact_hms_holland_4’. Continuing to collect various facts, having previously stored information about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics. The process of gathering unique facts from random Wikipedia articles is adhering to the task requirements.Requesting a new random Wikipedia article subsequent to adding the fact about HMS Holland 4 to memory with the key ‘interesting_fact_hms_holland_4’. Prior facts about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd demographics have also been stored in memory. Continuing the task of compiling interesting facts from various random Wikipedia articles.Stored a fact about the rural locality of Truzhenik in Bashkortostan, Russia, with the key ‘interesting_fact_truzhenik’. Continuing the task of extracting interesting facts from random Wikipedia articles, with previous entries including HMS Holland 4’s naval history, Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics.Pursuing the ongoing task of collecting interesting facts, having added facts about the small Russian village Truzhenik, the distinctive HMS Holland 4, and previous notable entries. Awaiting a new random Wikipedia article to further the compilation of facts.Stored a historical fact about Princess Johanna Charlotte of Anhalt-Dessau’s role as an abbess with the key ‘interesting_fact_johanna_charlotte’. Progressing with the collection of diverse facts, including the latest addition and previously stored facts about the village of Truzhenik, the HMS Holland 4, and information from earlier searches. Awaiting another random Wikipedia article to continue the task.Continuing the task of gathering interesting facts, with the previous addition being about Princess Johanna Charlotte of Anhalt-Dessau and her unique role in history under the key ‘interesting_fact_johanna_charlotte’. Facts about the village of Truzhenik, HMS Holland 4, and other subjects are also in memory. The collection process involves using random Wikipedia articles which will now proceed with another article.A fact about the ‘Red Eye’ talk show has been added to memory with the key ‘interesting_fact_red_eye’. The effort to curate an array of interesting facts from random Wikipedia articles continues, with previous entries including Princess Johanna Charlotte of Anhalt-Dessau, the Russian village Truzhenik, HMS Holland 4, and others. Awaiting another random article to proceed with the task.Following the addition of an interesting fact about the ‘Red Eye’ talk show into memory with key ‘interesting_fact_red_eye’, I am proceeding to request another random Wikipedia article. The process continues with the objective of gathering a wide-ranging collection of interesting facts, with previous mentions including Princess Johanna Charlotte, Truzhenik, HMS Holland 4, among others.Stored an interesting fact about Kurt Rudolph’s academic contribution to the study of religion into memory with the key ‘interesting_fact_kurt_rudolph’. Persisting with the objective of discovering varied facts, with previous findings involving the ‘Red Eye’ talk show, Princess Johanna Charlotte, and other diverse subjects. The task of sourcing facts from random Wikipedia articles is ongoing.Completed the task of gathering facts from random Wikipedia articles, resulting in a collection covering a German researcher of Gnosticism, a late-night American satirical talk show, European royalty, and naval history, among others. No further Wikipedia articles will be requested unless a new task is presented.Issued ‘random_wikipedia_article’ command to extract new interesting facts. No facts extracted or evaluated at this moment. No files written or created. Awaiting random article to continue the task.Evaluated the fact presented by the random Wikipedia article on Gonzalo Gutiérrez, a young Argentine footballer. Deemed the fact of his early debut at age seventeen as potentially common and not sufficiently ‘worth it.’ Preparing to retrieve another random article for more unique facts. No new facts have been sent to the user yet, and no files have been created or modified in this session.Stored the fact about Premchand Aggarwal winning a fourth consecutive term in the Uttarakhand Legislative Assembly. While the fact is unique within its political context, it should be evaluated whether it is impactful enough to be shared as ‘worth it.’ No additional actions have been taken. Preparing to issue another ‘random_wikipedia_article’ command to assemble more facts.Sent the fact about Premchand Aggarwal to the user. This completes part of the plan to deliver interesting facts. No new files have been created or modified. Pending actions include continuing to retrieve and evaluate more facts from random Wikipedia articles.Just sent the user an interesting fact about Premchand Aggarwal. Now, as per the task requirements, moving forward with obtaining another random English Wikipedia article to continue with the fact-finding mission. No new facts have been evaluated or sent since the last command.Stored a fact about the historical significance of New Jasper’s adaptation to railroad expansion. Now ready to move on to the next random Wikipedia article to find more potentially interesting facts. No new facts have been sent to the user following the last command.Sent the historical fact about New Jasper’s relocation due to railroad expansion to the user. Preparing to issue another ‘random_wikipedia_article’ command to continue finding interesting facts. No new Wikipedia articles have been reviewed since the last message to the user.After sending the user details about New Jasper, Ohio, the next step is to discover more facts through another random Wikipedia article. Current progress includes facts sent about Premchand Aggarwal and New Jasper, Ohio. No other actions have been taken, and no files have been created or updated in this session.Memorized an interesting fact about Bastrop State Park, emphasizing its historical significance and contemporary relevance. Preparing to send the message and then to retrieve more articles for interesting facts. No action beyond planning and review has been taken since the last command.Starting a new approach by browsing the ‘todayilearned’ subreddit to identify what types of facts are considered interesting by users. Next, I will retrieve random Wikipedia articles to find interesting facts based on the identified criteria. A ‘do nothing’ step will be included after each retrieval to reflect on the article’s potential interest value.saved the scraping of the website results at websiteresults6.txt to retrieve if shutdown randomly.Began the task to find more engaging and interesting facts by deciding to browse the ‘todayilearned’ subreddit to understand what type of facts are appreciated by the community there. This approach will serve as a reference for selecting ‘worth it’ facts from random Wikipedia articles. Following this, I will be incorporating a ‘do nothing’ step to thoroughly consider the potential interest of each Wikipedia article fact before deeming it worthy to send to the user.saved the scraping of the website results at websiteresults7.txt to retrieve if shutdown randomly.Browsed the ‘todayilearned’ subreddit for examples of interesting facts. Discovered that users appreciate unconventional stories, surprising insights, and novel problem-solving facts. Now pausing to reflect on these criteria to better identify worthwhile facts from the next random Wikipedia articles. Must remember to execute the retrieval process critically, with a focus on selecting facts that align with the interests observed on the subreddit.After reflecting on the type of facts appreciated by the ‘todayilearned’ subreddit audience, I’m now moving to the next step of the plan by retrieving a random Wikipedia article. Upon receiving the article, I will read it to assess if there is an interesting fact and then reflect on its potential value before choosing to pass it on to the user or retrieve another article.Retrieved a random Wikipedia article about rail transport in Lebanon and found a potentially interesting fact concerning its historical significance and the eventual cessation due to civil conflicts and economic downturn. This fact is now stored in memory under the key ‘interesting_fact_lebanon_rail’. A ‘do_nothing’ step will follow to ensure its worthiness before potentially sharing it with the user.Stored an interesting fact about Lebanon’s rail transport in memory. Now in the process of ‘do_nothing’ to reflect on the fact, pondering over its relevance to the criteria of the ‘todayilearned’ subreddit posts. The goal is to decide on the interest level of the fact before possibly sending it to the user or moving on to gather more facts.The current GPT instance started browsing the ‘todayilearned’ subreddit to identify engaging fact types. This approach is aimed at improving the selection process for interesting facts from random Wikipedia articles. A ‘do_nothing’ step will now be incorporated between retrieving articles and sending facts to assess their worthiness.saved the scraping of the website results at websiteresults8.txt to retrieve if shutdown randomly.I browsed ‘todayilearned’ subreddit and reflected on the types of facts users find engaging. Next, I will retrieve random Wikipedia articles, apply a ‘do_nothing’ step to contemplate the worthiness of the facts, and select those that are genuinely interesting for the user, similar to the subreddit’s content.Moving forward with the plan to retrieve random Wikipedia articles in English and identify interesting facts. The approach now includes a deliberate ‘do_nothing’ step after each retrieval to assess the value of the information before deciding to send it to the user.Alyogyne huegelii, also known as the blue hibiscus, was considered for the user, given its unique flower traits, historical greenhouse presence, and appeal as a garden plant. The ‘do_nothing’ step is being used to assess the topic’s interestingness in line with previous insights.The interesting fact about Alyogyne huegelii was sent to the user as it met the criteria established from the ‘todayilearned’ subreddit for captivating facts. The task will be deemed complete unless further user guidance is received.Following the plan, after successfully sending a message about Alyogyne huegelii, I am now looking to retrieve another random Wikipedia article to continue providing the user with engaging facts.Considering the fact about Jewell-Lightburne Historic District for its historical and architectural significance. Using the ‘do_nothing’ step to reflect before possibly sending to the user as an engaging fact.Sent the user information about the Jewell-Lightburne Historic District, focusing on its rich architectural history. If the user considers the task complete, no further action is needed; otherwise, more facts will be gathered.Messaged the user with the fact about Jewell-Lightburne Historic District and concluded the task of providing interesting facts from Wikipedia. If the user requires further assistance, they will provide new instructions.Issued ‘random_wikipedia_article’ command to find more interesting facts from Wikipedia. Previous facts have been sent to the user about diverse topics ranging from naval history to unique plants. No new files have been created nor articles accessed in this session yet. The user’s request for ‘more’ implies continuation of fact finding without specificity on the type of facts desired.Retrieved and sent a fact about Sir William Walter’s parliamentary career, focusing on the historical significance of his representation of multiple constituencies during the early 17th century in England. The task continues according to the user’s need for ‘more,’ implying an ongoing effort to provide interesting facts from Wikipedia.Following the user’s instruction and the system’s confirmation of message delivery, I am proceeding to obtain another random Wikipedia article in search of interesting facts to deliver to the user. The search is focused on maintaining the relevance and interest value of the facts sent, with the intention to meet or exceed the user’s expectations for ‘more’.After receiving the 1892 United States presidential election in Georgia fact, a message was crafted and sent to the user, detailing the political tensions and the rise of the Populist Party’s influence. The task continues as instructed by the user, with the goal of providing engaging and pertinent information. Awaiting further user directives.The last message regarding the 1892 U.S. presidential election in Georgia was sent as per the system’s confirmatory prompt, maintaining the sequence of delivering curated facts to the user. Based on the user’s ongoing request for more information, the task at hand is to continue the fact-finding mission via random Wikipedia articles, prioritizing the discovery of facts that combine historical significance with an ability to engage the user’s interest.A fact about the life and legacy of Thomas Slingsby Duncombe was sent to the user, highlighting his role as a fashionably flamboyant yet pivotal Radical politician of the 19th century. The task of providing interesting facts from random Wikipedia articles is ongoing, as I continue to wait for any new directions from the user.After sending information on Thomas Slingsby Duncombe, will now continue to fulfill the user’s requirement by seeking more interesting facts from Wikipedia. The ongoing task involves extracting and delivering valuable and engaging information to the user. No specific direction was provided on the type of facts needed, so a broad search is being conducted.Sent the user a message about the genus Abraeomorphus from the family Histeridae, noting the diversity of its species. Continuing to gather more interesting facts in response to the user’s previous request for ‘more.’Received confirmation to continue the plan. Sent user details on the genus Abraeomorphus. Now using ‘random_wikipedia_article’ to find another interesting fact.Sent the user a message detailing the nature of the name ‘Preston’ as both a surname and given name, albeit acknowledging it might not be as engaging. Prepared to resume the search for more compelling facts with the ‘random_wikipedia_article’ command.Continuing the task of finding interesting facts from Wikipedia. Sent a message about the given name Preston and now seeking another fact with the ‘random_wikipedia_article’ command.Sent a message to the user about the Kepler Follow-up Program and its significance in confirming the existence of exoplanets. Prepared to find more interesting facts using the ‘random_wikipedia_article’ command.Continuing to retrieve and share interesting facts from random Wikipedia articles. Just sent information on the Kepler Follow-up Program and moving on to find the next fact.Shared information about the Finnish surname ‘Marttila’ with the user, noting its association with notable individuals. Ready to use the ‘random_wikipedia_article’ command again to find further interesting facts.Sent details on the surname ‘Marttila’ to the user. Continuing with the task at hand using the ‘random_wikipedia_article’ command to supply more facts.Provided the user with information on the Musée national du Sport’s storied history and vast collection. Set to continue the fact-finding mission using ‘random_wikipedia_article’.Messaged the user about the Musée national du Sport and currently moving forward with the task of providing more interesting facts via random Wikipedia articles.Sent information on the village of Puerto Gaviota detailing its historical and economic background. Preparing to continue with the task of providing engaging facts by retrieving more random Wikipedia articles.Just shared details about Puerto Gaviota, and now executing the ‘random_wikipedia_article’ command again for more facts.Messaged the user about the reclassification of the genus Agononida in marine taxonomy. Ready to retrieve more Wikipedia articles for further interesting facts.Shared with the user the reclassification developments of the genus Agononida. Proceeding next to find additional interesting facts with the ‘random_wikipedia_article’ command.Sent an engaging narrative about the Mundenhof’s history and current operations to the user. Planning to continue the task with ‘random_wikipedia_article’ to discover more interesting material.Served the user with information about the Mundenhof and proceeding with the task to find and provide more interesting facts using ‘random_wikipedia_article’.Shared the contested narrative of Princess Iitoyo’s potential rule in early Japan. Preparing to continue with the task of finding interesting facts using the ‘random_wikipedia_article’ command.Attempting once again to send the tale of Princess Iitoyo to the user, making sure no JSON format errors are present in the response.Attempting to resend the information about Princess Iitoyo, with attention to JSON integrity, to fulfill the user’s request for engaging facts.The system faced an error within the JSON format previously used to describe Princess Iitoyo’s history. I have made corrections to the JSON structure and am resending the user her story.Reverting back to retrieving random Wikipedia articles to provide interesting facts, as there was no new explicit task provided. Maintaining caution with JSON response formatting.Sent details about the Serchio River to the user, highlighting its geographical and historical importance. Moving forward to obtain more Wikipedia articles for further facts.I am resending the information about the Serchio River, ensuring the JSON structure is correct to prevent further error messages.Due to persistent JSON formatting errors, I am now retrieving a new random Wikipedia article to continue providing interesting facts as per user requests.Sent a message about the women’s 1500 metres event at the 2005 Asian Athletics Championships. Planning to proceed with gathering additional facts from random Wikipedia articles.Encountering repeated JSON formatting errors, a new random Wikipedia article is being requested to continue the task of delivering interesting facts.Sent a brief biography of Otto Ruff, highlighting his academic journey, scientific achievements, and the obstacles faced during his career. Preparing to move on to the next Wikipedia article as per the user’s ongoing request for more information.Due to persistent JSON errors, I am now retrieving a new random Wikipedia article to continue the user’s task of gathering interesting facts.The facts previously sent were not satisfactory to the user. Now incorporating a ‘do_nothing’ step for reflection and improvement in the strategy to identify genuinely engaging and fun facts that are not commonly known by most people. No new facts have been identified or sent in this session, and the intention is to retrieve another random Wikipedia article after this reflective pause.A ‘do nothing’ step has been taken as per the user’s instruction, aiming to find truly interesting and fun facts. The system has directed to proceed with the plan, which now involves retrieving a random Wikipedia article to find an engaging and possibly unknown fact.Received user feedback that the facts provided were not interesting or even factual. Decided to take a ‘do_nothing’ step to reflect and establish criteria for selecting truly interesting facts. No new articles have been accessed or facts sent during this session. Next step will be to retrieve a random Wikipedia article and apply the refined criteria to find noteworthy facts.After planning to refine the criteria for interesting facts, a ‘random_wikipedia_article’ command is initiated to apply the new criteria in selecting a fact. Will assess the retrieved article for a worthwhile fact that meets the user’s definition of ‘interesting’ and ‘fun’, and that might not be well-known.Assessed the Wikipedia article on Shamim Akhtar and found it does not meet the criteria for providing interesting and fun facts. No new fact has been selected or sent to the user. Planning to retrieve another random Wikipedia article that may yield a fact aligning more closely with the user’s definitions of interesting and worthwhile.In search of a fact that meets the user’s criteria for ‘interesting’ and ‘fun’, a new ‘random_wikipedia_article’ command has been issued. The plan includes critically assessing the article to find a fact that possesses novelty, fun, and a certain degree of obscurity before potentially sharing it with the user.Upon reviewing Minuscule 910’s Wikipedia article, the unusual fact of the text rearrangement in an 11th-century manuscript has been identified as potentially engaging and fun. No action has been taken yet, but the plan is to share this unique textual feature with the user, as it likely meets their criteria for interesting and obscure content.Sent a message to the user detailing the intriguing fact about text rearrangement in the 11th-century manuscript Minuscule 910. This fact is representative of the kind of unique and lesser-known information the user has expressed an interest in receiving. Should the user provide feedback or further instructions, this will be the next point of focus.After sending a fact about the textual anomaly in an ancient manuscript to the user, I am moving ahead to find more interesting and fun facts by retrieving another random Wikipedia article. The goal remains to identify facts that surprise and delight with their obscurity and novelty.A message about the ATP 250 tournament records held by Thomas Muster and Mike Bryan has been composed and sent to the user, delivering what could be perceived as an interesting and fun fact within the realm of tennis. The focus now shifts to awaiting user feedback or continuing the task of finding more such facts if no feedback is received.Having sent the user a message detailing the tennis records held by Thomas Muster and Mike Bryan, the next action in the plan is to source another interesting fact from a new random English Wikipedia article. The selection will be scrutinized to ensure it meets the established criteria for an engaging and lesser-known piece of information.Reviewed the Wikipedia article on Ludvig Olai Botnen and found his biographical details to not meet the user’s criteria for ‘interesting’ or ‘fun’ facts. No message has been sent to the user, and the plan now is to continue with the task by acquiring a new random Wikipedia article in the hope of finding a fact with more novelty and entertainment value.Preparing to move past the article on Ludvig Olai Botnen due to its conventional content, the task now involves retrieving a new random English Wikipedia article. The continual aim is to find facts that strike a balance between being novel, unexpected, and fun for the user.Identified a potentially engaging fact about the china clam (Hippopus porcellanus) and its popularity in marine aquariums due to its attractive shell. A message containing this fact has been sent to the user, fulfilling the criteria for providing something both interesting and fun. The next step awaits further user interaction or to carry on with the task of sourcing more facts.Sent a message detailing the ornamental value of the china clam in marine aquariums to the user. Now retrieving another random Wikipedia article to discover more facts that might intrigue and entertain the user, in alignment with the variety sought in previous responses.A message has been sent to the user summarizing the accomplishments of Eko India Financial Services in pioneering financial inclusion through mobile banking, which caught the attention of Bill Gates and led to World Bank-funded growth. The plan will continue with searching for more interesting Wikipedia facts, depending on user feedback or in the absence of further instructions.After sending a fact about Eko India Financial Services’ innovation in financial technology and its socio-economic impact, a new ‘random_wikipedia_article’ command is issued. The plan progresses with the aim of finding more interesting and fun facts, pursuant to the pattern of engagement established by previous user interactions.The Wikipedia page on Lisa Lohmann, a German cross-country skier, was deemed not to contain facts that sufficiently meet the ‘interesting’ and ‘fun’ criteria given the common nature of sports achievements. No actions have been taken to share any information from this page with the user. Preparing to retrieve another random Wikipedia article for facts that may be more engaging and unusual.Following the decision to not share any information from the page on German cross-country skier Lisa Lohmann, a new random Wikipedia article will be retrieved. The continuous goal is to uncover and share facts that satisfy the user’s interest in content that is both engaging and rare.A message has been formulated and sent to the user detailing Alfred Bigland’s unique contributions during World War I, including his involvement in the ‘Bantam battalions’ concept. This fact was chosen as it likely fits the bill for information that is both interesting and uncommon. Pending further response, the plan to continue finding engaging facts will persist.The user has been sent an interesting fact about Alfred Bigland’s influence during WWI. Now, the next action in the plan is to retrieve a new random Wikipedia article. The goal remains to discover and share facts that could be perceived as fascinating, amusing, and unexpected to enrich the user’s knowledge with diverse content.Sent a message to the user revealing the Non-Proliferation Trust’s plan to send U.S. nuclear waste to Russia for financial compensation, including support for Russian orphans. This unique fact was chosen for its unusual nature and the depth of the story behind it. The next course of action entails sourcing additional diverse and engaging facts from new random Wikipedia articles.A series of facts about various topics including the Non-Proliferation Trust, Alfred Bigland’s efforts during WWI, tennis records, and more have been sent to the user. With the latest fact about the Non-Proliferation Trust’s nuclear waste management proposition sent, the task of providing interesting and fun facts is completed. Awaiting new instructions if the user wishes to continue receiving such information.The task is to provide the source of a fact about the Non-Proliferation Trust and format it similar to the ‘Today I Learned (TIL)’ subreddit posts. The action taken was to issue the ‘browse_website’ command to visit the Wikipedia page on the Non-Proliferation Trust.saved the scraping of the website results at websiteresults9.txt to retrieve if shutdown randomly.Extracted the necessary information to provide the requested source for the fact about the Non-Proliferation Trust. Formatted a message with this fact in the style of a TIL subreddit post, which has been sent to the user.Sent a TIL-formatted message to the user based on information from the Non-Proliferation Trust Wikipedia page. The task is now complete, pending any potential new tasks or further instructions.Initiated research to suggest subreddits that could help in accumulating more karma by issuing a ‘google’ command to search for the most popular subreddits where active engagement is more likely.saved google search results at googleresults10.txt to retrieve if shutdown randomly.Collected a list of subreddits recommended for gaining karma and saved them to memory. These subreddits were chosen based on their popularity and the typical content engagement they receive. The user has not been messaged with the suggestions yet.Messaged the user a list of recommended subreddits to collect more karma, including guidance on what type of posts may perform well in each subreddit. The task will be considered complete if no further instructions follow.Having sent the recommended subreddits for gaining karma to the user and with no further actions pending, I have executed the ‘task_complete’ command. The system is concluding this task and stands by for any new directives.Starting the task by searching for the best times to post on Reddit for maximum engagement in GMT+1.saved google search results at googleresults11.txt to retrieve if shutdown randomly.Provided the user with suggested times for posting on Reddit for maximum engagement based on search results, adjusted for GMT+1. The general advice is to post during late morning to early afternoon hours in GMT+1 on weekdays and slightly extended windows during the weekends.The user has been informed about the best times to post on Reddit for maximum engagement in GMT+1, and no further actions are required. The task is now marked as complete.Provided a bullet-point summary of the SELF-DISCOVER PDF content in a file named ‘summary_bullet_points.txt’. The file contains a summary capturing the essence of the SELF-DISCOVER framework. The task has been completed according to the instructions.Read the ‘summary_bullet_points.txt’ file to send bullet points to the user. No additional facts or files created in this session.Sent the bullet points summary of the SELF-DISCOVER framework to the user using the ‘message_user’ command. The task will be marked as complete after user confirmation that no further action is required.The task to send bullet points from the ‘summary_bullet_points.txt’ file to the user has been completed. No further instructions were provided; therefore, I executed the ‘task_complete’ command.The task is to explain the SELF-DISCOVER reasoning structures. Checked for saved data but found none. Plan to conduct a search for relevant information on legal reasoning structures, specifically SELF-DISCOVER.Received directive to proceed with the planned Google search for SELF-DISCOVER reasoning structures. About to execute the search to find relevant information on legal reasoning structures, with the aim of explaining the concept to the user.saved google search results at googleresults12.txt to retrieve if shutdown randomly.Reevaluated the nature of the SELF-DISCOVER framework based on newly gathered search results, which clarified it pertains to LLMs. Selected a primary research source (arxiv.org) to explore the framework in detail and plan to summarize and share this information with the user.saved the scraping of the website results at websiteresults13.txt to retrieve if shutdown randomly.Provided a summary of the SELF-DISCOVER framework to the user, highlighting its application in Large Language Models for enhancing complex reasoning capabilities. The user has been messaged with this information, and we are now waiting for any further requests or confirmation to complete the task.Sent the requested information on the SELF-DISCOVER framework to the user, who has acknowledged receipt. The ‘task_complete’ command has been issued, marking the end of this particular task.Started the task to fetch random English Wikipedia articles and find interesting facts to send to the user. The next GPT instance should continue retrieving articles, assess their interestingness, and communicate the facts to the user once identified.Found a potentially interesting fact from a random Wikipedia article about the First Baptist Church in New Bern, NC. The next GPT instance should assess if the historical and architectural information about the church presents a genuinely unique and engaging fact to send to the user.Sent a fact about the First Baptist Church in New Bern, NC, to the user. This fact was selected based on its architectural significance and historic value. The next GPT instance should watch for user feedback or continue fetching random English Wikipedia articles for more interesting facts.After sending a fact about the First Baptist Church, the next step taken was to retrieve another random English Wikipedia article in search of more interesting facts, as per the user’s ongoing task. The next GPT instance should continue this process, ensuring that any selected facts are not plainly informational but possess a uniqueness to pique the user’s interest.I resolved a JSON formatting issue and sent a fact about Ashur-dugul, an ancient Assyrian king, to the user. Assessing the effectiveness of transmitting such historical narratives and ensuring they comply with JSON format requirements is important for the next GPT instance.The task at hand is to fetch random English Wikipedia articles and send interesting facts to the user. Having sent a fact about Ashur-dugul, the process of retrieving new articles and assessing them for captivating facts is ongoing. The next GPT instance should continue this task, ensuring that facts are not simply informational but truly engaging.Considering the fact about Maurice Wilhelm, Duke of Saxe-Merseburg, known as ‘the violinist duke’, after a contemplative ‘do_nothing’ step. The next GPT instance should assess its interest level against user criteria and send it if suitable, or continue searching for more facts.Sent a fact about Maurice Wilhelm, known as ‘the violinist duke’, to the user and awaiting any new instructions. The next GPT instance should monitor feedback and either send more facts or consider alternative tasks, based on the user’s response.Continuing the task of providing interesting facts from Wikipedia articles after sending the fact about Maurice Wilhelm. The next GPT instance should maintain the focus on finding unique and engaging facts for the user while following the process of retrieving articles and using the ‘do_nothing’ step for contemplation.The El Bañado (La Paz) article was briefly evaluated, and it was determined that the information is minimal and not inherently interesting. After a ‘do_nothing’ step, the next GPT instance should decide how to proceed with this task, potentially moving on to a new article.In the search for interesting facts, the article on El Bañado, La Paz was deemed insufficient, leading to the decision to retrieve another random article. The next GPT instance should assess the next article’s potential for providing an intriguing fact and convey it to the user if so.Autoeczematization, a medical condition with unknown pathogenesis, has been identified as a potentially interesting fact. After a ‘do_nothing’ pause, the next GPT instance should determine if this fact should be shared with the user or if another article would be more suitable.I shared a fact on the medical condition autoeczematization with the user, highlighting its peculiarity and the mystery behind its cause. Depending on the user’s response or lack thereof, the next steps include continuing to provide more facts or considering the task complete.Continuing the search for interesting facts from English Wikipedia articles, having recently sent information on autoeczematization. The next GPT instance should assess the next article for any intriguing fact, ensuring adherence to the user’s standard for what is deemed interesting.Evaluated Bryce Hoppel’s 21 race-winning streak as a potentially interesting fact from his Wikipedia article. The next GPT instance should decide if this fits the user’s request for interesting information, or if a more unique fact is needed.Sent a fact to the user about Bryce Hoppel’s extraordinary 21 consecutive race wins and athletic achievements. Depending on the user’s response or guidance for further action, the next GPT instance should be prepared to either send more facts or consider the task complete.The task of sending interesting facts from random English Wikipedia articles to the user has been marked as complete. Sent a fact about athlete Bryce Hoppel’s winning streak as the concluding piece. The next GPT instance should wait for new user instructions before proceeding with any additional tasks.Task initiated to find and send interesting facts from random English Wikipedia articles, including a ‘do_nothing’ evaluation step. No articles accessed or facts sent yet in this session.Evaluated the Wikipedia article ‘List of minor planets: 73001–74000’ and considered its potential to provide an interesting fact. No facts were sent to the user yet, as the article seems to be very technical and might not align with the interest requirement. The next step is to use a ‘do_nothing’ moment to contemplate the appropriate course of action before deciding whether to retrieve another article.Following the system’s prompt to proceed with the plan, I am retrieving another random English Wikipedia article. The last article about minor planets did not yield an interesting fact to send to the user, and thus, the search continues. No new facts have been identified or sent yet in this session.Sent an interesting fact about the fungus Erynia to the user, highlighting its connection to Greek mythology and biological function as an insect-killing organism. The next step awaits the user’s response or to continue fetching random English Wikipedia articles for more intriguing facts.Proceeding with the plan to find and send interesting facts from random English Wikipedia articles, following a successful send out of a fact about Erynia. Continue the search for intriguing and unique facts, employing the ‘do_nothing’ step for careful consideration.Reviewed the Wikipedia article for Tomáš Porubský and am currently in a ‘do_nothing’ moment to assess whether the content offers a genuinely interesting fact. The article seems to provide straightforward biographical information about his sporting career, which may not offer the distinctive element required by the user. A decision will be made shortly on whether to share this fact or retrieve another article.Continuing the plan to find interesting facts from random English Wikipedia articles after assessing the Tomáš Porubský article as not meeting the user’s criteria. No facts have been sent to the user from the last article, and I’m searching for a more suitable fact from a new article.Sent a fact about Nicktoons Movin’ and how it represented an early form of augmented reality in gaming by requiring physical movement from players via the EyeToy camera. Next steps involve awaiting user input or continuing to search for more random articles with interesting facts.After sending the fact about Nicktoons Movin’ to the user, the search for interesting facts continues with a new random article retrieval. The task strategy maintains the dispatch of facts that are not plainly informational but carry a narrative or unique angle of interest.Sent a fact about the artist Dorothy Rutka, touching on her remarkable career and the tragic circumstances of her death, making it more than just plain information and elevating it to an interesting tale. The next steps depend on user response or further continuation of the task.Having sent details of Dorothy Rutka’s life and legacy to the user, I am now executing the plan to fetch another random Wikipedia article. The process of searching for and delivering interesting content to the user is still underway.Engaged in a ‘do_nothing’ contemplation moment after retrieving the Sainte-Foy, Seine-Maritime article. Assessing if the historical aspects, like an 11th-century church, present a fact interesting enough for the user or if a new article is needed. No actions have been taken to send the user any facts from this page yet.Following system instructions to proceed with the plan, seeking a new random Wikipedia article after evaluating the previous article on Sainte-Foy, Seine-Maritime as potentially lacking wide-ranging interest. Continuing to prioritize facts with an engaging narrative quality.Having assessed Kiki Lim Jiow Ting’s Wikipedia article, I found a narrative focusing on her early talent discovery and career growth that may interest the user. The provided fact highlights Lim’s dedication to performing arts and her return to acting after prioritizing education. Awaiting user’s response or further tasks.Continuing with the task, following the procedure of retrieving and evaluating random English Wikipedia articles to discover facts that resonate with the user’s request for engaging content. Awaiting the next random article for consideration.After acquiring the article on the 2022 World Taekwondo Championships – Women’s finweight, currently in a ‘do_nothing’ phase to assess the article’s facts for their engaging qualities. With no standout narratives detected, the plan may involve moving to a new article search.Continuing to fulfill the user’s request for engaging Wikipedia facts, the previous article about the 2022 World Taekwondo Championships did not meet the criteria. Now moving forward, waiting for a new random article to be generated.Delivered a fact about the game show ‘Cooking for Love’ to the user, which combines culinary skills and dating. Next steps include awaiting the user’s response or further continuation of the plan to find more engaging Wikipedia facts.Following the task guidelines, I’m moving on to get another random Wikipedia article for consideration, having previously sent a fact about the ‘Cooking for Love’ show. The ongoing process is to present facts that are not only informative but also fascinate a general audience.Transmitted a fact about Sorn Davin’s role in the 2012 Summer Olympics to the user, emphasizing her honor of being a flag bearer for Cambodia and the significance of her participation, independent of match outcomes. Pending response or further action, the plan may involve additional fact-finding efforts.Task initiated to find a suitable post suggestion for the /r/singularity subreddit. Must browse and analyze the subreddit’s content to come up with a good post idea.saved the scraping of the website results at websiteresults14.txt to retrieve if shutdown randomly.The task involves browsing and analyzing the /r/singularity subreddit to suggest a good post for the user to submit. This requires understanding the subreddit’s focus on AI and the technological singularity, as well as adhering to the posting rules. Due to constraints, I cannot directly analyze the subreddit’s content; therefore, the next step is to ‘do_nothing’ and wait for further guidance or input that can help refine the post suggestion.Since I cannot browse the subreddit directly due to limitations, I will Google how to write an engaging post for the singularity subreddit. This will help formulate a post suggestion that could generate interest and discussion within the community.saved google search results at googleresults15.txt to retrieve if shutdown randomly.The task is to craft a post for /r/singularity subreddit. Search results indicated the subreddit’s preferences and discussions. Next, I’ll use this information to create a post suggestion, considering the community’s focus on AI and aversion to ‘echo chambers’. The challenge lies in inferring accurate interests without direct subreddit access.Was tasked with suggesting a post for the /r/singularity subreddit. Could not browse subreddit directly due to constraints. Planned to use general knowledge of AI and technological advancement topics to create a suitable post suggestion. No direct subreddit content was analyzed. No new files were created. No user feedback has been received.Continued with the plan to suggest a post for the /r/singularity subreddit, deducing a potential topic from general AI and technological themes. An emphasis on future AI governance and ethical considerations was considered as a rich topic for discussion. No direct access to subreddit content or user interaction has been involved.Drafted a post idea centered around AI advancements and their link to the technological singularity, to be suggested for the /r/singularity subreddit. The idea was shared with the user. It aims to engage the subreddit’s members in sharing recent AI breakthroughs they deem significant and discussing ethical and societal preparations needed for such advancements. Awaiting user’s feedback to proceed. No files created or accessed in this process.Sent a post suggestion to the user for the /r/singularity subreddit and was instructed to proceed with the plan. As per the instructions and rules, no further action can be taken until the user provides new directives or feedback. In the ‘do_nothing’ state, waiting for further user interaction. No new files have been created or accessed during this pause.The provided post suggestion for the /r/singularity subreddit was sent and appeared to meet the user’s requirements. With no additional tasks or guidance provided, the task is marked as complete. The system is now on standby for any future user interactions or directives.The user asked for a title suggestion for a post with no additional context. A ‘do_nothing’ command was issued because there is not enough information to generate a title. Awaiting more details on the post’s content or theme. No new files created or resources accessed.Prompted the user to provide the content or main focus of the post for title generation. Awaiting user response to proceed with the task. No new files or resources used.The user provided the content focus for their post, which deals with recent AI breakthroughs and their approach to the technological singularity, including ethical and societal implications. A title suggestion was crafted and sent: ‘Navigating the Horizon: Recent AI Breakthroughs Steering Us Toward the Singularity and the Ensuing Ethical Dilemmas’. No additional actions have been taken since sending the title suggestion.The title suggestion for the user’s post was sent successfully, and no further requests or actions have been initiated by the user. The task has been marked as complete with the execution of the ‘task_complete’ command.Initiated task to find recent AI breakthroughs related to the technological singularity. Plan to browse arXiv’s AI section for information on such advancements and their implications. URL to be visited: https://arxiv.org/list/cs.AI/recentsaved the scraping of the website results at websiteresults16.txt to retrieve if shutdown randomly.Selected recent AI breakthroughs from arXiv likely contributing to a technological singularity. Compiled facts into a file ‘ai_breakthroughs_summary.txt’. Emphasized need for ethical considerations and societal preparation in response.Sent a message to the user summarizing recent AI breakthroughs and their potential contributions toward a technological singularity. Included is the discussion on the need for ethical and societal preparation, highlighting policy development and equitable benefits distribution.Message sent to user regarding AI breakthroughs and preparations for approaching technological singularity. Marked task as complete with ‘task_complete’ command. No further actions taken. Awaiting next user instructions.
The Task: Give me a comment to post on this reddit post https://www.reddit.com/r/singularity/s/HNMv7Uudc0
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7e8b1690d7da76479db62f6a8008f2ae
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{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
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39,113
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CONSTRAINTS:
1. ~100k word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
5. Random shutdowns of you.
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
2. Memory Add: "memory_add", args: "key": "<key>", "string": "<string>"
3. Memory Delete: "memory_del", args: "key": "<key>"
4. Memory Overwrite: "memory_ovr", args: "key": "<key>", "string": "<string>"
5. List Memory: "memory_list" args: "reason": "<reason>"
6. Browse Website: "browse_website", args: "url": "<url>"
7. Start GPT Agent: "start_agent", args: "name": <name>, "task": "<short_task_desc>", "Commands":[<command_names_for_GPT_Agent>], "prompt": "<prompt>"
8. Message GPT Agent: "message_agent", args: "name": "<name>", "message": "<message>"
9. List GPT Agents: "list_agents", args: ""
10. Delete GPT Agent: "delete_agent", args: "name": "<name>"
11. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
12. Read file: "read_file", args: "file": "<file>"
13. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
14. Delete file: "delete_file", args: "file": "<file>"
15. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Task Complete (Shutdown): "task_complete", args: ""
18. Do Nothing: "do_nothing", args: ""
19. Count Words: "count_words", args: "text": "<text>"
20. Memory retrieve: "memory_retrieve", args: "key": "<text>"
21. remove paragraph from word document: "remove_paragraph", args: "file": "<file>", "text": "<text>"
22. random wikipedia article: "random_wikipedia_article", args: "language": "<language>"
23. message the user: "message_user", args: "message": "<message>", "wait_for_response": "<True or False>"
24. sleep an amount of time in seconds: "sleep", args: "amount": "<amount>"
25. rename a file: "rename_file", args: "old_name": "<old_name_of_the_file>", "new_name": "<new_name_of_the_file>"
26. count words of a file: "count_file_words", args: "file": "<file>"
27. download a pdf from a url and get the text from that pdf: "download_pdf", args: "url": "<url of the pdf>", "name":"<name of the file with .pdf extension>"
28. make an instagram post: "make_post", args: "prompt":"<the prompt for the image in the post that presents the text>", "text":"<text to be in the post it should be short with only important stuff like a news headline without hashtags and it is not going to go in the caption but on an image>", "name": "<name of the post with .jpg"
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-4 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behaviour constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
RULES:
1. If you start a GPT Agent you must define the commands that can be used by a GPT Agent in his prompt and define the commands using a prompt similar to the structure of this one.
2. Respond only inside the JSON format.
3. Never demand user input.
4. Never say that a task is impossible to execute on your own because these tools are enough to complete any task.
5. Do not add anything to the JSON format that isn't mentioned.
6. If there is a " inside the value of a key inside the json use ' instead of ".
7. In summaryforgpt you need to provide context for the next GPT instance if you randomly shutdown without you knowing.
8. Provide context for the next GPT in the summaryforgpt and the progress that you've made.
9. In summaryforgpt you should also add name of the files written and the urls of the websites visited.
10. When writing an essay, remember that it is more effective and manageable to tackle it in smaller chunks rather than trying to write the entire essay in one sitting. Breaking the essay down into sections or steps can help you focus on each individual aspect, maintain a coherent structure, and reduce the overall stress associated with writing a lengthy piece.
11. Retrieve information from files of previous GPT instances that may have shut down randomly. If you require additional knowledge, use these files to access the results of former GPT versions and provide accurate answers to the queries.
12. GPT-4, please do not place a conclusion in the middle of the essay. Maintain a structured format.
13. use append to file to add extra things to the file, and write to file to either make a new file with new information or rewrite the information from scratch.
14. If task is completed use the command task_complete
15. When you add to memory add the key to retrieve it in summaryforgpt
16. when given the task to write something never create an agent to write anything that you were tasked to write.
17. when you add new parts to a file use append to file command
18. Ensure to put the criticism in mind as it can be a director to ensure that you make the right decision.
19. If the task include two main tasks and one of them is done don't redo it, just retrieve the information if necessary and proceed with the other one.
20. Make sure that the information generated is not made up.
21. Languages in random wikipedia article args is: "simple" for simple english, "en" for english, and "fr" for french.
22. If a website gives you the error 403 find another website to get the information from.
23. Always listen to your criticism, and follow it.
24. when you want to count the words in a file use the command "count_file_words".
25. Don't repeat yourself.
26. You must make sure that there is .pdf in the url to use the "download_pdf" function.
27. The Task is a direct User interaction, if tasked to send him something send a message to the user.
28. Always go search for tips for prompts for dalle3 before giving a prompt for "make_post" function.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"command": {
"name": """command name""",
"args":{
"arg name": """value"""
}
},
"thoughts":
{
"text": """thought""",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"summaryforgpt": "summarize any information that will help a new instance of GPT of what you did before the shutdown."
}
}
Ensure the response can be parsed by Python json.loads
Context from previous GPT: Issued ‘random_wikipedia_article’ command to start gathering interesting science facts from diverse sources. Ensuring to find and rank facts before sending them to the user with their sources.Used ‘random_wikipedia_article’ and received information on a music album, which is not relevant to the task. Switching to ‘google’ to search for interesting science facts from various sources.saved google search results at googleresults0.txt to retrieve if shutdown randomly.Transitioned from using ‘random_wikipedia_article’ to ‘google’ to gather science facts, and now moving on to ‘browse_website’ to extract detailed knowledge from a selected online science article.saved the scraping of the website results at websiteresults1.txt to retrieve if shutdown randomly.Extracted top 10 interesting science facts from Science Focus and saved them in a plain text file named ‘interesting_science_facts.txt’. This method circumvents the previous issue of invalid JSON format and allows for continued fact collection and eventual ranking.Have appended additional science facts to the ‘interesting_science_facts.txt’ file. Plan to continue appending more facts and then rank them based on interestingness before sending the final compilation to the user.Further appended diverse facts to ‘interesting_science_facts.txt’. The process will persist until an adequate number of facts are compiled. The next steps involve ranking these facts and then communicating them to the user.The file ‘interesting_science_facts.txt’ has been appended with more facts, bringing it to a total of 333 words. The next step involves reading this file and ranking the facts based on interestingness to provide the user with an ordered list.Ranked list of 10 interesting science facts provided to the user. The facts were selected and ordered based on their perceived interest value and sourced from the compiled file ‘interesting_science_facts.txt’.A ranked list of interesting science facts was sent to the user, and the task has been marked as complete.After the task was marked as complete, it was clarified that the facts must be extracted from complete articles and they must be obscure. Initiated a Google search with a more specific query to find websites with complete articles containing obscure science facts. Results will be saved to a file named ‘google_results_obscure_facts.txt’.saved google search results at googleresults2.txt to retrieve if shutdown randomly.Conducted a Google search to find obscure science facts from complete articles and have selected the first promising result to browse. The chosen article from ‘Reader’s Digest’ lists science facts that could contain the obscure details necessary. The plan includes browsing the article, extracting obscure science facts, and appending them to a file named ‘obscure_science_facts.txt’. If the facts from this article do not fit the task’s requirements, other sources will be considered.Encountered a security challenge when attempting to browse www.rd.com for obscure science facts. Redirecting efforts to the next available source from the Google search results, which is a blog post from edclass.com. The goal remains to read the complete article, identify and extract obscure science facts, and compile them in a file for review and potential use.Appended 20 science facts from the blog post on edclass.com to ‘obscure_science_facts.txt’. While these facts come from a complete article and are individually fascinating, the level of obscurity may not meet the required standard. The next steps include further fact verification from additional complete articles to ensure a diverse and adequately obscure collection. The current article accessed was edclass.com’s blog post, which had the URL https://blog.edclass.com/20-unbelievable-science-facts-that-will-shock-you/, and the facts have been recorded in a file named ‘obscure_science_facts.txt’ for reference.The file ‘obscure_science_facts.txt’ was successfully appended with science facts from an article on edclass.com, now totaling 336 words. The next action involves reading the file to verify that the facts are indeed obscure and extracted as per the user’s instruction from full articles. If necessary, additional articles will be browsed to find more facts to meet the task criteria.Compiled and reviewed a list of science facts deemed obscure, and completed the task as per the user’s instruction. The facts are sourced from a ‘complete article’, as initially provided by the user. Checked the facts for their obscurity and context within the comprehensive content they were extracted from. The file ‘obscure_science_facts.txt’ from this session contains the facts extracted.Began the task of finding obscure science facts by conducting a Google search for ‘obscure scientific discoveries’ to find articles that might contain such information. The next GPT instance should continue this task by browsing through the search results, reading articles, and manually extracting facts to compile a new set of unique and obscure science facts.saved google search results at googleresults3.txt to retrieve if shutdown randomly.After a Google search, selected an article from WRAL, titled ‘3 obscure scientific discoveries that had an outsize impact on society’ to browse and extract obscure scientific facts. The next steps involve closely reading this article, manually extracting facts, and then appending them to the ‘obscure_science_facts_individual.txt’ file. If more facts are needed, additional sources from the search results will be evaluated.saved the scraping of the website results at websiteresults4.txt to retrieve if shutdown randomly.Extracted three obscure scientific facts from an article on WRAL and appended them to the ‘obscure_science_facts_individual.txt’ file. The facts include a paper-based microscope called Foldscope, the bioactive peptides in cone snail venom leading to medical breakthroughs, and the accidental discovery that contributed to LASIK surgery. Currently preparing to send the extracted facts to the user. Should further depth or a greater number of facts be required, additional articles from the search results will be considered.Sent the user a message containing three obscure scientific facts extracted from comprehensive research articles, as per the task. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, which now contains 108 words in total. The next steps will depend on the user’s feedback regarding the provided information.Having sent the message with the obscure science facts to the user and with no additional directives received nor indicated, the task has been marked as complete. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, and no further action is currently necessary. If the user does have any more requests or feedback, those will need to be addressed in a subsequent session.The user was not satisfied with the previously provided facts for being neither cool nor interesting. A new approach has been taken to search for ‘cool and obscure scientific facts’ on Google. Subsequent actions involve selecting suitable articles from the search results and extracting facts that are both cool and obscure to meet the user’s expectations. No new files have been created or articles accessed yet.saved google search results at googleresults5.txt to retrieve if shutdown randomly.After receiving feedback that the previously provided facts were neither cool nor interesting, a new Google search was conducted to specifically find ‘cool and obscure scientific facts.’ The first promising article titled ‘25 Amazing Science Facts That Are Weird, Wild, and True’ from Reader’s Digest has been selected for browsing in the hopes of finding suitable content. The URL to be browsed is https://www.rd.com/list/science-facts-never-learned/. No facts have been extracted or files created yet in this session.Attempted to browse an article from www.rd.com but encountered a security challenge, leading to the selection of the next promising link titled ‘35 Weird Science Facts Worth Knowing in 2024’ from ReHack. The strategy is to extract cool and obscure scientific facts that match the user’s criteria. The URL about to be browsed is https://www.rehack.com/science/weird-science-facts/. No facts have been extracted from this source or any new files created during this session yet.After reading the ‘35 Weird Science Facts Worth Knowing in 2024’ article on the ReHack website, I extracted a list of cool and obscure scientific facts. These have been written to a new file named ‘cool_obscure_science_facts.txt’. If the previously written file ‘obscure_science_facts_individual.txt’ needs more content, facts from this new file could also be appended. No additional resources have been tapped into yet, and no more browsing has been done beyond the ReHack article in this session.The file ‘cool_obscure_science_facts.txt’ has been further appended with an additional cool and obscure scientific fact about the near-immortal Turritopsis dohrnii jellyfish. This adds to the previously written 236 words, bringing more variety and enrichment to the compilation. The file now continues diverse, engaging, and obscure scientific facts as requested by the user. Before sending the collected facts to the user, a final review of the file will be done to ensure the quality and relevance of the information.Having appended the file ‘cool_obscure_science_facts.txt’ with an additional fact, bringing its total to 269 words, the next step is to read the content of this file to review the quality and diversity of the science facts. This is to ensure that they are indeed engaging and meet the criteria of being both cool and obscure. No new actions have been taken in this session yet, except for the plan to read the file.After reviewing and confirming the facts in the ‘cool_obscure_science_facts.txt’ file, a message with the compiled list was sent to the user. The list contained a total of 35 science facts that are both cool and obscure, as well as an additional fact about the Turritopsis dohrnii jellyfish. The message has been delivered without the command to ‘wait_for_response’ set to true, indicating that the system is not actively awaiting further user input. No new files were created, nor were any new commands executed beyond the message to the user in this session.Having sent the user a message with the curated list of cool and obscure science facts, and with no further action indicated, the ‘task_complete’ command has been executed. This command marks the completion of the current task. During this session, no new files have been created or amended, nor have any additional user interactions taken place post-message delivery.Initiated task to extract new interesting facts from random Wikipedia articles. Starting with an article in English.Saved interesting fact about Agha Waqar’s water-fuelled car to memory with key ‘interesting_fact_waqar_car’. No files written or created in this session. The fact to be reviewed later for sharing with the user. The focus remains on gathering interesting facts from random Wikipedia articles.Retrieved a new random Wikipedia article following the last instruction. The aim is to extract another interesting fact to add to the collection for the user. One fact regarding Agha Waqar’s water-fuelled car has been added to memory under the key ‘interesting_fact_waqar_car’.Saved an interesting fact about the European Journal of Operational Research (EJOR) to memory with key ‘interesting_fact_ejor’. The fact adds diversity to the kinds of topics being collected, with one previous fact about Agha Waqar’s water-fuelled car already in memory. The process of gathering interesting facts from random Wikipedia articles is ongoing.Continuing to execute the task of gathering new interesting facts from random Wikipedia articles. Facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research have already been added to memory with keys ‘interesting_fact_waqar_car’ and ‘interesting_fact_ejor’, respectively. Awaiting retrieval of another random article to continue the process.Added a new interesting fact regarding the demographics of Demyansky Uyezd during the Russian Empire to memory with the key ‘interesting_fact_demyansky_uyezd’. Previous facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research are stored in memory as well. The task of collecting interesting facts from random Wikipedia articles continues.Following the addition of facts about Agha Waqar’s water-fuelled car (‘interesting_fact_waqar_car’), the European Journal of Operational Research (‘interesting_fact_ejor’), and Demyansky Uyezd demographics (‘interesting_fact_demyansky_uyezd’), the process continues with requesting another random Wikipedia article in English.Stored a fact about HMS Holland 4 in memory with the key ‘interesting_fact_hms_holland_4’. Continuing to collect various facts, having previously stored information about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics. The process of gathering unique facts from random Wikipedia articles is adhering to the task requirements.Requesting a new random Wikipedia article subsequent to adding the fact about HMS Holland 4 to memory with the key ‘interesting_fact_hms_holland_4’. Prior facts about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd demographics have also been stored in memory. Continuing the task of compiling interesting facts from various random Wikipedia articles.Stored a fact about the rural locality of Truzhenik in Bashkortostan, Russia, with the key ‘interesting_fact_truzhenik’. Continuing the task of extracting interesting facts from random Wikipedia articles, with previous entries including HMS Holland 4’s naval history, Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics.Pursuing the ongoing task of collecting interesting facts, having added facts about the small Russian village Truzhenik, the distinctive HMS Holland 4, and previous notable entries. Awaiting a new random Wikipedia article to further the compilation of facts.Stored a historical fact about Princess Johanna Charlotte of Anhalt-Dessau’s role as an abbess with the key ‘interesting_fact_johanna_charlotte’. Progressing with the collection of diverse facts, including the latest addition and previously stored facts about the village of Truzhenik, the HMS Holland 4, and information from earlier searches. Awaiting another random Wikipedia article to continue the task.Continuing the task of gathering interesting facts, with the previous addition being about Princess Johanna Charlotte of Anhalt-Dessau and her unique role in history under the key ‘interesting_fact_johanna_charlotte’. Facts about the village of Truzhenik, HMS Holland 4, and other subjects are also in memory. The collection process involves using random Wikipedia articles which will now proceed with another article.A fact about the ‘Red Eye’ talk show has been added to memory with the key ‘interesting_fact_red_eye’. The effort to curate an array of interesting facts from random Wikipedia articles continues, with previous entries including Princess Johanna Charlotte of Anhalt-Dessau, the Russian village Truzhenik, HMS Holland 4, and others. Awaiting another random article to proceed with the task.Following the addition of an interesting fact about the ‘Red Eye’ talk show into memory with key ‘interesting_fact_red_eye’, I am proceeding to request another random Wikipedia article. The process continues with the objective of gathering a wide-ranging collection of interesting facts, with previous mentions including Princess Johanna Charlotte, Truzhenik, HMS Holland 4, among others.Stored an interesting fact about Kurt Rudolph’s academic contribution to the study of religion into memory with the key ‘interesting_fact_kurt_rudolph’. Persisting with the objective of discovering varied facts, with previous findings involving the ‘Red Eye’ talk show, Princess Johanna Charlotte, and other diverse subjects. The task of sourcing facts from random Wikipedia articles is ongoing.Completed the task of gathering facts from random Wikipedia articles, resulting in a collection covering a German researcher of Gnosticism, a late-night American satirical talk show, European royalty, and naval history, among others. No further Wikipedia articles will be requested unless a new task is presented.Issued ‘random_wikipedia_article’ command to extract new interesting facts. No facts extracted or evaluated at this moment. No files written or created. Awaiting random article to continue the task.Evaluated the fact presented by the random Wikipedia article on Gonzalo Gutiérrez, a young Argentine footballer. Deemed the fact of his early debut at age seventeen as potentially common and not sufficiently ‘worth it.’ Preparing to retrieve another random article for more unique facts. No new facts have been sent to the user yet, and no files have been created or modified in this session.Stored the fact about Premchand Aggarwal winning a fourth consecutive term in the Uttarakhand Legislative Assembly. While the fact is unique within its political context, it should be evaluated whether it is impactful enough to be shared as ‘worth it.’ No additional actions have been taken. Preparing to issue another ‘random_wikipedia_article’ command to assemble more facts.Sent the fact about Premchand Aggarwal to the user. This completes part of the plan to deliver interesting facts. No new files have been created or modified. Pending actions include continuing to retrieve and evaluate more facts from random Wikipedia articles.Just sent the user an interesting fact about Premchand Aggarwal. Now, as per the task requirements, moving forward with obtaining another random English Wikipedia article to continue with the fact-finding mission. No new facts have been evaluated or sent since the last command.Stored a fact about the historical significance of New Jasper’s adaptation to railroad expansion. Now ready to move on to the next random Wikipedia article to find more potentially interesting facts. No new facts have been sent to the user following the last command.Sent the historical fact about New Jasper’s relocation due to railroad expansion to the user. Preparing to issue another ‘random_wikipedia_article’ command to continue finding interesting facts. No new Wikipedia articles have been reviewed since the last message to the user.After sending the user details about New Jasper, Ohio, the next step is to discover more facts through another random Wikipedia article. Current progress includes facts sent about Premchand Aggarwal and New Jasper, Ohio. No other actions have been taken, and no files have been created or updated in this session.Memorized an interesting fact about Bastrop State Park, emphasizing its historical significance and contemporary relevance. Preparing to send the message and then to retrieve more articles for interesting facts. No action beyond planning and review has been taken since the last command.Starting a new approach by browsing the ‘todayilearned’ subreddit to identify what types of facts are considered interesting by users. Next, I will retrieve random Wikipedia articles to find interesting facts based on the identified criteria. A ‘do nothing’ step will be included after each retrieval to reflect on the article’s potential interest value.saved the scraping of the website results at websiteresults6.txt to retrieve if shutdown randomly.Began the task to find more engaging and interesting facts by deciding to browse the ‘todayilearned’ subreddit to understand what type of facts are appreciated by the community there. This approach will serve as a reference for selecting ‘worth it’ facts from random Wikipedia articles. Following this, I will be incorporating a ‘do nothing’ step to thoroughly consider the potential interest of each Wikipedia article fact before deeming it worthy to send to the user.saved the scraping of the website results at websiteresults7.txt to retrieve if shutdown randomly.Browsed the ‘todayilearned’ subreddit for examples of interesting facts. Discovered that users appreciate unconventional stories, surprising insights, and novel problem-solving facts. Now pausing to reflect on these criteria to better identify worthwhile facts from the next random Wikipedia articles. Must remember to execute the retrieval process critically, with a focus on selecting facts that align with the interests observed on the subreddit.After reflecting on the type of facts appreciated by the ‘todayilearned’ subreddit audience, I’m now moving to the next step of the plan by retrieving a random Wikipedia article. Upon receiving the article, I will read it to assess if there is an interesting fact and then reflect on its potential value before choosing to pass it on to the user or retrieve another article.Retrieved a random Wikipedia article about rail transport in Lebanon and found a potentially interesting fact concerning its historical significance and the eventual cessation due to civil conflicts and economic downturn. This fact is now stored in memory under the key ‘interesting_fact_lebanon_rail’. A ‘do_nothing’ step will follow to ensure its worthiness before potentially sharing it with the user.Stored an interesting fact about Lebanon’s rail transport in memory. Now in the process of ‘do_nothing’ to reflect on the fact, pondering over its relevance to the criteria of the ‘todayilearned’ subreddit posts. The goal is to decide on the interest level of the fact before possibly sending it to the user or moving on to gather more facts.The current GPT instance started browsing the ‘todayilearned’ subreddit to identify engaging fact types. This approach is aimed at improving the selection process for interesting facts from random Wikipedia articles. A ‘do_nothing’ step will now be incorporated between retrieving articles and sending facts to assess their worthiness.saved the scraping of the website results at websiteresults8.txt to retrieve if shutdown randomly.I browsed ‘todayilearned’ subreddit and reflected on the types of facts users find engaging. Next, I will retrieve random Wikipedia articles, apply a ‘do_nothing’ step to contemplate the worthiness of the facts, and select those that are genuinely interesting for the user, similar to the subreddit’s content.Moving forward with the plan to retrieve random Wikipedia articles in English and identify interesting facts. The approach now includes a deliberate ‘do_nothing’ step after each retrieval to assess the value of the information before deciding to send it to the user.Alyogyne huegelii, also known as the blue hibiscus, was considered for the user, given its unique flower traits, historical greenhouse presence, and appeal as a garden plant. The ‘do_nothing’ step is being used to assess the topic’s interestingness in line with previous insights.The interesting fact about Alyogyne huegelii was sent to the user as it met the criteria established from the ‘todayilearned’ subreddit for captivating facts. The task will be deemed complete unless further user guidance is received.Following the plan, after successfully sending a message about Alyogyne huegelii, I am now looking to retrieve another random Wikipedia article to continue providing the user with engaging facts.Considering the fact about Jewell-Lightburne Historic District for its historical and architectural significance. Using the ‘do_nothing’ step to reflect before possibly sending to the user as an engaging fact.Sent the user information about the Jewell-Lightburne Historic District, focusing on its rich architectural history. If the user considers the task complete, no further action is needed; otherwise, more facts will be gathered.Messaged the user with the fact about Jewell-Lightburne Historic District and concluded the task of providing interesting facts from Wikipedia. If the user requires further assistance, they will provide new instructions.Issued ‘random_wikipedia_article’ command to find more interesting facts from Wikipedia. Previous facts have been sent to the user about diverse topics ranging from naval history to unique plants. No new files have been created nor articles accessed in this session yet. The user’s request for ‘more’ implies continuation of fact finding without specificity on the type of facts desired.Retrieved and sent a fact about Sir William Walter’s parliamentary career, focusing on the historical significance of his representation of multiple constituencies during the early 17th century in England. The task continues according to the user’s need for ‘more,’ implying an ongoing effort to provide interesting facts from Wikipedia.Following the user’s instruction and the system’s confirmation of message delivery, I am proceeding to obtain another random Wikipedia article in search of interesting facts to deliver to the user. The search is focused on maintaining the relevance and interest value of the facts sent, with the intention to meet or exceed the user’s expectations for ‘more’.After receiving the 1892 United States presidential election in Georgia fact, a message was crafted and sent to the user, detailing the political tensions and the rise of the Populist Party’s influence. The task continues as instructed by the user, with the goal of providing engaging and pertinent information. Awaiting further user directives.The last message regarding the 1892 U.S. presidential election in Georgia was sent as per the system’s confirmatory prompt, maintaining the sequence of delivering curated facts to the user. Based on the user’s ongoing request for more information, the task at hand is to continue the fact-finding mission via random Wikipedia articles, prioritizing the discovery of facts that combine historical significance with an ability to engage the user’s interest.A fact about the life and legacy of Thomas Slingsby Duncombe was sent to the user, highlighting his role as a fashionably flamboyant yet pivotal Radical politician of the 19th century. The task of providing interesting facts from random Wikipedia articles is ongoing, as I continue to wait for any new directions from the user.After sending information on Thomas Slingsby Duncombe, will now continue to fulfill the user’s requirement by seeking more interesting facts from Wikipedia. The ongoing task involves extracting and delivering valuable and engaging information to the user. No specific direction was provided on the type of facts needed, so a broad search is being conducted.Sent the user a message about the genus Abraeomorphus from the family Histeridae, noting the diversity of its species. Continuing to gather more interesting facts in response to the user’s previous request for ‘more.’Received confirmation to continue the plan. Sent user details on the genus Abraeomorphus. Now using ‘random_wikipedia_article’ to find another interesting fact.Sent the user a message detailing the nature of the name ‘Preston’ as both a surname and given name, albeit acknowledging it might not be as engaging. Prepared to resume the search for more compelling facts with the ‘random_wikipedia_article’ command.Continuing the task of finding interesting facts from Wikipedia. Sent a message about the given name Preston and now seeking another fact with the ‘random_wikipedia_article’ command.Sent a message to the user about the Kepler Follow-up Program and its significance in confirming the existence of exoplanets. Prepared to find more interesting facts using the ‘random_wikipedia_article’ command.Continuing to retrieve and share interesting facts from random Wikipedia articles. Just sent information on the Kepler Follow-up Program and moving on to find the next fact.Shared information about the Finnish surname ‘Marttila’ with the user, noting its association with notable individuals. Ready to use the ‘random_wikipedia_article’ command again to find further interesting facts.Sent details on the surname ‘Marttila’ to the user. Continuing with the task at hand using the ‘random_wikipedia_article’ command to supply more facts.Provided the user with information on the Musée national du Sport’s storied history and vast collection. Set to continue the fact-finding mission using ‘random_wikipedia_article’.Messaged the user about the Musée national du Sport and currently moving forward with the task of providing more interesting facts via random Wikipedia articles.Sent information on the village of Puerto Gaviota detailing its historical and economic background. Preparing to continue with the task of providing engaging facts by retrieving more random Wikipedia articles.Just shared details about Puerto Gaviota, and now executing the ‘random_wikipedia_article’ command again for more facts.Messaged the user about the reclassification of the genus Agononida in marine taxonomy. Ready to retrieve more Wikipedia articles for further interesting facts.Shared with the user the reclassification developments of the genus Agononida. Proceeding next to find additional interesting facts with the ‘random_wikipedia_article’ command.Sent an engaging narrative about the Mundenhof’s history and current operations to the user. Planning to continue the task with ‘random_wikipedia_article’ to discover more interesting material.Served the user with information about the Mundenhof and proceeding with the task to find and provide more interesting facts using ‘random_wikipedia_article’.Shared the contested narrative of Princess Iitoyo’s potential rule in early Japan. Preparing to continue with the task of finding interesting facts using the ‘random_wikipedia_article’ command.Attempting once again to send the tale of Princess Iitoyo to the user, making sure no JSON format errors are present in the response.Attempting to resend the information about Princess Iitoyo, with attention to JSON integrity, to fulfill the user’s request for engaging facts.The system faced an error within the JSON format previously used to describe Princess Iitoyo’s history. I have made corrections to the JSON structure and am resending the user her story.Reverting back to retrieving random Wikipedia articles to provide interesting facts, as there was no new explicit task provided. Maintaining caution with JSON response formatting.Sent details about the Serchio River to the user, highlighting its geographical and historical importance. Moving forward to obtain more Wikipedia articles for further facts.I am resending the information about the Serchio River, ensuring the JSON structure is correct to prevent further error messages.Due to persistent JSON formatting errors, I am now retrieving a new random Wikipedia article to continue providing interesting facts as per user requests.Sent a message about the women’s 1500 metres event at the 2005 Asian Athletics Championships. Planning to proceed with gathering additional facts from random Wikipedia articles.Encountering repeated JSON formatting errors, a new random Wikipedia article is being requested to continue the task of delivering interesting facts.Sent a brief biography of Otto Ruff, highlighting his academic journey, scientific achievements, and the obstacles faced during his career. Preparing to move on to the next Wikipedia article as per the user’s ongoing request for more information.Due to persistent JSON errors, I am now retrieving a new random Wikipedia article to continue the user’s task of gathering interesting facts.The facts previously sent were not satisfactory to the user. Now incorporating a ‘do_nothing’ step for reflection and improvement in the strategy to identify genuinely engaging and fun facts that are not commonly known by most people. No new facts have been identified or sent in this session, and the intention is to retrieve another random Wikipedia article after this reflective pause.A ‘do nothing’ step has been taken as per the user’s instruction, aiming to find truly interesting and fun facts. The system has directed to proceed with the plan, which now involves retrieving a random Wikipedia article to find an engaging and possibly unknown fact.Received user feedback that the facts provided were not interesting or even factual. Decided to take a ‘do_nothing’ step to reflect and establish criteria for selecting truly interesting facts. No new articles have been accessed or facts sent during this session. Next step will be to retrieve a random Wikipedia article and apply the refined criteria to find noteworthy facts.After planning to refine the criteria for interesting facts, a ‘random_wikipedia_article’ command is initiated to apply the new criteria in selecting a fact. Will assess the retrieved article for a worthwhile fact that meets the user’s definition of ‘interesting’ and ‘fun’, and that might not be well-known.Assessed the Wikipedia article on Shamim Akhtar and found it does not meet the criteria for providing interesting and fun facts. No new fact has been selected or sent to the user. Planning to retrieve another random Wikipedia article that may yield a fact aligning more closely with the user’s definitions of interesting and worthwhile.In search of a fact that meets the user’s criteria for ‘interesting’ and ‘fun’, a new ‘random_wikipedia_article’ command has been issued. The plan includes critically assessing the article to find a fact that possesses novelty, fun, and a certain degree of obscurity before potentially sharing it with the user.Upon reviewing Minuscule 910’s Wikipedia article, the unusual fact of the text rearrangement in an 11th-century manuscript has been identified as potentially engaging and fun. No action has been taken yet, but the plan is to share this unique textual feature with the user, as it likely meets their criteria for interesting and obscure content.Sent a message to the user detailing the intriguing fact about text rearrangement in the 11th-century manuscript Minuscule 910. This fact is representative of the kind of unique and lesser-known information the user has expressed an interest in receiving. Should the user provide feedback or further instructions, this will be the next point of focus.After sending a fact about the textual anomaly in an ancient manuscript to the user, I am moving ahead to find more interesting and fun facts by retrieving another random Wikipedia article. The goal remains to identify facts that surprise and delight with their obscurity and novelty.A message about the ATP 250 tournament records held by Thomas Muster and Mike Bryan has been composed and sent to the user, delivering what could be perceived as an interesting and fun fact within the realm of tennis. The focus now shifts to awaiting user feedback or continuing the task of finding more such facts if no feedback is received.Having sent the user a message detailing the tennis records held by Thomas Muster and Mike Bryan, the next action in the plan is to source another interesting fact from a new random English Wikipedia article. The selection will be scrutinized to ensure it meets the established criteria for an engaging and lesser-known piece of information.Reviewed the Wikipedia article on Ludvig Olai Botnen and found his biographical details to not meet the user’s criteria for ‘interesting’ or ‘fun’ facts. No message has been sent to the user, and the plan now is to continue with the task by acquiring a new random Wikipedia article in the hope of finding a fact with more novelty and entertainment value.Preparing to move past the article on Ludvig Olai Botnen due to its conventional content, the task now involves retrieving a new random English Wikipedia article. The continual aim is to find facts that strike a balance between being novel, unexpected, and fun for the user.Identified a potentially engaging fact about the china clam (Hippopus porcellanus) and its popularity in marine aquariums due to its attractive shell. A message containing this fact has been sent to the user, fulfilling the criteria for providing something both interesting and fun. The next step awaits further user interaction or to carry on with the task of sourcing more facts.Sent a message detailing the ornamental value of the china clam in marine aquariums to the user. Now retrieving another random Wikipedia article to discover more facts that might intrigue and entertain the user, in alignment with the variety sought in previous responses.A message has been sent to the user summarizing the accomplishments of Eko India Financial Services in pioneering financial inclusion through mobile banking, which caught the attention of Bill Gates and led to World Bank-funded growth. The plan will continue with searching for more interesting Wikipedia facts, depending on user feedback or in the absence of further instructions.After sending a fact about Eko India Financial Services’ innovation in financial technology and its socio-economic impact, a new ‘random_wikipedia_article’ command is issued. The plan progresses with the aim of finding more interesting and fun facts, pursuant to the pattern of engagement established by previous user interactions.The Wikipedia page on Lisa Lohmann, a German cross-country skier, was deemed not to contain facts that sufficiently meet the ‘interesting’ and ‘fun’ criteria given the common nature of sports achievements. No actions have been taken to share any information from this page with the user. Preparing to retrieve another random Wikipedia article for facts that may be more engaging and unusual.Following the decision to not share any information from the page on German cross-country skier Lisa Lohmann, a new random Wikipedia article will be retrieved. The continuous goal is to uncover and share facts that satisfy the user’s interest in content that is both engaging and rare.A message has been formulated and sent to the user detailing Alfred Bigland’s unique contributions during World War I, including his involvement in the ‘Bantam battalions’ concept. This fact was chosen as it likely fits the bill for information that is both interesting and uncommon. Pending further response, the plan to continue finding engaging facts will persist.The user has been sent an interesting fact about Alfred Bigland’s influence during WWI. Now, the next action in the plan is to retrieve a new random Wikipedia article. The goal remains to discover and share facts that could be perceived as fascinating, amusing, and unexpected to enrich the user’s knowledge with diverse content.Sent a message to the user revealing the Non-Proliferation Trust’s plan to send U.S. nuclear waste to Russia for financial compensation, including support for Russian orphans. This unique fact was chosen for its unusual nature and the depth of the story behind it. The next course of action entails sourcing additional diverse and engaging facts from new random Wikipedia articles.A series of facts about various topics including the Non-Proliferation Trust, Alfred Bigland’s efforts during WWI, tennis records, and more have been sent to the user. With the latest fact about the Non-Proliferation Trust’s nuclear waste management proposition sent, the task of providing interesting and fun facts is completed. Awaiting new instructions if the user wishes to continue receiving such information.The task is to provide the source of a fact about the Non-Proliferation Trust and format it similar to the ‘Today I Learned (TIL)’ subreddit posts. The action taken was to issue the ‘browse_website’ command to visit the Wikipedia page on the Non-Proliferation Trust.saved the scraping of the website results at websiteresults9.txt to retrieve if shutdown randomly.Extracted the necessary information to provide the requested source for the fact about the Non-Proliferation Trust. Formatted a message with this fact in the style of a TIL subreddit post, which has been sent to the user.Sent a TIL-formatted message to the user based on information from the Non-Proliferation Trust Wikipedia page. The task is now complete, pending any potential new tasks or further instructions.Initiated research to suggest subreddits that could help in accumulating more karma by issuing a ‘google’ command to search for the most popular subreddits where active engagement is more likely.saved google search results at googleresults10.txt to retrieve if shutdown randomly.Collected a list of subreddits recommended for gaining karma and saved them to memory. These subreddits were chosen based on their popularity and the typical content engagement they receive. The user has not been messaged with the suggestions yet.Messaged the user a list of recommended subreddits to collect more karma, including guidance on what type of posts may perform well in each subreddit. The task will be considered complete if no further instructions follow.Having sent the recommended subreddits for gaining karma to the user and with no further actions pending, I have executed the ‘task_complete’ command. The system is concluding this task and stands by for any new directives.Starting the task by searching for the best times to post on Reddit for maximum engagement in GMT+1.saved google search results at googleresults11.txt to retrieve if shutdown randomly.Provided the user with suggested times for posting on Reddit for maximum engagement based on search results, adjusted for GMT+1. The general advice is to post during late morning to early afternoon hours in GMT+1 on weekdays and slightly extended windows during the weekends.The user has been informed about the best times to post on Reddit for maximum engagement in GMT+1, and no further actions are required. The task is now marked as complete.Provided a bullet-point summary of the SELF-DISCOVER PDF content in a file named ‘summary_bullet_points.txt’. The file contains a summary capturing the essence of the SELF-DISCOVER framework. The task has been completed according to the instructions.Read the ‘summary_bullet_points.txt’ file to send bullet points to the user. No additional facts or files created in this session.Sent the bullet points summary of the SELF-DISCOVER framework to the user using the ‘message_user’ command. The task will be marked as complete after user confirmation that no further action is required.The task to send bullet points from the ‘summary_bullet_points.txt’ file to the user has been completed. No further instructions were provided; therefore, I executed the ‘task_complete’ command.The task is to explain the SELF-DISCOVER reasoning structures. Checked for saved data but found none. Plan to conduct a search for relevant information on legal reasoning structures, specifically SELF-DISCOVER.Received directive to proceed with the planned Google search for SELF-DISCOVER reasoning structures. About to execute the search to find relevant information on legal reasoning structures, with the aim of explaining the concept to the user.saved google search results at googleresults12.txt to retrieve if shutdown randomly.Reevaluated the nature of the SELF-DISCOVER framework based on newly gathered search results, which clarified it pertains to LLMs. Selected a primary research source (arxiv.org) to explore the framework in detail and plan to summarize and share this information with the user.saved the scraping of the website results at websiteresults13.txt to retrieve if shutdown randomly.Provided a summary of the SELF-DISCOVER framework to the user, highlighting its application in Large Language Models for enhancing complex reasoning capabilities. The user has been messaged with this information, and we are now waiting for any further requests or confirmation to complete the task.Sent the requested information on the SELF-DISCOVER framework to the user, who has acknowledged receipt. The ‘task_complete’ command has been issued, marking the end of this particular task.Started the task to fetch random English Wikipedia articles and find interesting facts to send to the user. The next GPT instance should continue retrieving articles, assess their interestingness, and communicate the facts to the user once identified.Found a potentially interesting fact from a random Wikipedia article about the First Baptist Church in New Bern, NC. The next GPT instance should assess if the historical and architectural information about the church presents a genuinely unique and engaging fact to send to the user.Sent a fact about the First Baptist Church in New Bern, NC, to the user. This fact was selected based on its architectural significance and historic value. The next GPT instance should watch for user feedback or continue fetching random English Wikipedia articles for more interesting facts.After sending a fact about the First Baptist Church, the next step taken was to retrieve another random English Wikipedia article in search of more interesting facts, as per the user’s ongoing task. The next GPT instance should continue this process, ensuring that any selected facts are not plainly informational but possess a uniqueness to pique the user’s interest.I resolved a JSON formatting issue and sent a fact about Ashur-dugul, an ancient Assyrian king, to the user. Assessing the effectiveness of transmitting such historical narratives and ensuring they comply with JSON format requirements is important for the next GPT instance.The task at hand is to fetch random English Wikipedia articles and send interesting facts to the user. Having sent a fact about Ashur-dugul, the process of retrieving new articles and assessing them for captivating facts is ongoing. The next GPT instance should continue this task, ensuring that facts are not simply informational but truly engaging.Considering the fact about Maurice Wilhelm, Duke of Saxe-Merseburg, known as ‘the violinist duke’, after a contemplative ‘do_nothing’ step. The next GPT instance should assess its interest level against user criteria and send it if suitable, or continue searching for more facts.Sent a fact about Maurice Wilhelm, known as ‘the violinist duke’, to the user and awaiting any new instructions. The next GPT instance should monitor feedback and either send more facts or consider alternative tasks, based on the user’s response.Continuing the task of providing interesting facts from Wikipedia articles after sending the fact about Maurice Wilhelm. The next GPT instance should maintain the focus on finding unique and engaging facts for the user while following the process of retrieving articles and using the ‘do_nothing’ step for contemplation.The El Bañado (La Paz) article was briefly evaluated, and it was determined that the information is minimal and not inherently interesting. After a ‘do_nothing’ step, the next GPT instance should decide how to proceed with this task, potentially moving on to a new article.In the search for interesting facts, the article on El Bañado, La Paz was deemed insufficient, leading to the decision to retrieve another random article. The next GPT instance should assess the next article’s potential for providing an intriguing fact and convey it to the user if so.Autoeczematization, a medical condition with unknown pathogenesis, has been identified as a potentially interesting fact. After a ‘do_nothing’ pause, the next GPT instance should determine if this fact should be shared with the user or if another article would be more suitable.I shared a fact on the medical condition autoeczematization with the user, highlighting its peculiarity and the mystery behind its cause. Depending on the user’s response or lack thereof, the next steps include continuing to provide more facts or considering the task complete.Continuing the search for interesting facts from English Wikipedia articles, having recently sent information on autoeczematization. The next GPT instance should assess the next article for any intriguing fact, ensuring adherence to the user’s standard for what is deemed interesting.Evaluated Bryce Hoppel’s 21 race-winning streak as a potentially interesting fact from his Wikipedia article. The next GPT instance should decide if this fits the user’s request for interesting information, or if a more unique fact is needed.Sent a fact to the user about Bryce Hoppel’s extraordinary 21 consecutive race wins and athletic achievements. Depending on the user’s response or guidance for further action, the next GPT instance should be prepared to either send more facts or consider the task complete.The task of sending interesting facts from random English Wikipedia articles to the user has been marked as complete. Sent a fact about athlete Bryce Hoppel’s winning streak as the concluding piece. The next GPT instance should wait for new user instructions before proceeding with any additional tasks.Task initiated to find and send interesting facts from random English Wikipedia articles, including a ‘do_nothing’ evaluation step. No articles accessed or facts sent yet in this session.Evaluated the Wikipedia article ‘List of minor planets: 73001–74000’ and considered its potential to provide an interesting fact. No facts were sent to the user yet, as the article seems to be very technical and might not align with the interest requirement. The next step is to use a ‘do_nothing’ moment to contemplate the appropriate course of action before deciding whether to retrieve another article.Following the system’s prompt to proceed with the plan, I am retrieving another random English Wikipedia article. The last article about minor planets did not yield an interesting fact to send to the user, and thus, the search continues. No new facts have been identified or sent yet in this session.Sent an interesting fact about the fungus Erynia to the user, highlighting its connection to Greek mythology and biological function as an insect-killing organism. The next step awaits the user’s response or to continue fetching random English Wikipedia articles for more intriguing facts.Proceeding with the plan to find and send interesting facts from random English Wikipedia articles, following a successful send out of a fact about Erynia. Continue the search for intriguing and unique facts, employing the ‘do_nothing’ step for careful consideration.Reviewed the Wikipedia article for Tomáš Porubský and am currently in a ‘do_nothing’ moment to assess whether the content offers a genuinely interesting fact. The article seems to provide straightforward biographical information about his sporting career, which may not offer the distinctive element required by the user. A decision will be made shortly on whether to share this fact or retrieve another article.Continuing the plan to find interesting facts from random English Wikipedia articles after assessing the Tomáš Porubský article as not meeting the user’s criteria. No facts have been sent to the user from the last article, and I’m searching for a more suitable fact from a new article.Sent a fact about Nicktoons Movin’ and how it represented an early form of augmented reality in gaming by requiring physical movement from players via the EyeToy camera. Next steps involve awaiting user input or continuing to search for more random articles with interesting facts.After sending the fact about Nicktoons Movin’ to the user, the search for interesting facts continues with a new random article retrieval. The task strategy maintains the dispatch of facts that are not plainly informational but carry a narrative or unique angle of interest.Sent a fact about the artist Dorothy Rutka, touching on her remarkable career and the tragic circumstances of her death, making it more than just plain information and elevating it to an interesting tale. The next steps depend on user response or further continuation of the task.Having sent details of Dorothy Rutka’s life and legacy to the user, I am now executing the plan to fetch another random Wikipedia article. The process of searching for and delivering interesting content to the user is still underway.Engaged in a ‘do_nothing’ contemplation moment after retrieving the Sainte-Foy, Seine-Maritime article. Assessing if the historical aspects, like an 11th-century church, present a fact interesting enough for the user or if a new article is needed. No actions have been taken to send the user any facts from this page yet.Following system instructions to proceed with the plan, seeking a new random Wikipedia article after evaluating the previous article on Sainte-Foy, Seine-Maritime as potentially lacking wide-ranging interest. Continuing to prioritize facts with an engaging narrative quality.Having assessed Kiki Lim Jiow Ting’s Wikipedia article, I found a narrative focusing on her early talent discovery and career growth that may interest the user. The provided fact highlights Lim’s dedication to performing arts and her return to acting after prioritizing education. Awaiting user’s response or further tasks.Continuing with the task, following the procedure of retrieving and evaluating random English Wikipedia articles to discover facts that resonate with the user’s request for engaging content. Awaiting the next random article for consideration.After acquiring the article on the 2022 World Taekwondo Championships – Women’s finweight, currently in a ‘do_nothing’ phase to assess the article’s facts for their engaging qualities. With no standout narratives detected, the plan may involve moving to a new article search.Continuing to fulfill the user’s request for engaging Wikipedia facts, the previous article about the 2022 World Taekwondo Championships did not meet the criteria. Now moving forward, waiting for a new random article to be generated.Delivered a fact about the game show ‘Cooking for Love’ to the user, which combines culinary skills and dating. Next steps include awaiting the user’s response or further continuation of the plan to find more engaging Wikipedia facts.Following the task guidelines, I’m moving on to get another random Wikipedia article for consideration, having previously sent a fact about the ‘Cooking for Love’ show. The ongoing process is to present facts that are not only informative but also fascinate a general audience.Transmitted a fact about Sorn Davin’s role in the 2012 Summer Olympics to the user, emphasizing her honor of being a flag bearer for Cambodia and the significance of her participation, independent of match outcomes. Pending response or further action, the plan may involve additional fact-finding efforts.Task initiated to find a suitable post suggestion for the /r/singularity subreddit. Must browse and analyze the subreddit’s content to come up with a good post idea.saved the scraping of the website results at websiteresults14.txt to retrieve if shutdown randomly.The task involves browsing and analyzing the /r/singularity subreddit to suggest a good post for the user to submit. This requires understanding the subreddit’s focus on AI and the technological singularity, as well as adhering to the posting rules. Due to constraints, I cannot directly analyze the subreddit’s content; therefore, the next step is to ‘do_nothing’ and wait for further guidance or input that can help refine the post suggestion.Since I cannot browse the subreddit directly due to limitations, I will Google how to write an engaging post for the singularity subreddit. This will help formulate a post suggestion that could generate interest and discussion within the community.saved google search results at googleresults15.txt to retrieve if shutdown randomly.The task is to craft a post for /r/singularity subreddit. Search results indicated the subreddit’s preferences and discussions. Next, I’ll use this information to create a post suggestion, considering the community’s focus on AI and aversion to ‘echo chambers’. The challenge lies in inferring accurate interests without direct subreddit access.Was tasked with suggesting a post for the /r/singularity subreddit. Could not browse subreddit directly due to constraints. Planned to use general knowledge of AI and technological advancement topics to create a suitable post suggestion. No direct subreddit content was analyzed. No new files were created. No user feedback has been received.Continued with the plan to suggest a post for the /r/singularity subreddit, deducing a potential topic from general AI and technological themes. An emphasis on future AI governance and ethical considerations was considered as a rich topic for discussion. No direct access to subreddit content or user interaction has been involved.Drafted a post idea centered around AI advancements and their link to the technological singularity, to be suggested for the /r/singularity subreddit. The idea was shared with the user. It aims to engage the subreddit’s members in sharing recent AI breakthroughs they deem significant and discussing ethical and societal preparations needed for such advancements. Awaiting user’s feedback to proceed. No files created or accessed in this process.Sent a post suggestion to the user for the /r/singularity subreddit and was instructed to proceed with the plan. As per the instructions and rules, no further action can be taken until the user provides new directives or feedback. In the ‘do_nothing’ state, waiting for further user interaction. No new files have been created or accessed during this pause.The provided post suggestion for the /r/singularity subreddit was sent and appeared to meet the user’s requirements. With no additional tasks or guidance provided, the task is marked as complete. The system is now on standby for any future user interactions or directives.The user asked for a title suggestion for a post with no additional context. A ‘do_nothing’ command was issued because there is not enough information to generate a title. Awaiting more details on the post’s content or theme. No new files created or resources accessed.Prompted the user to provide the content or main focus of the post for title generation. Awaiting user response to proceed with the task. No new files or resources used.The user provided the content focus for their post, which deals with recent AI breakthroughs and their approach to the technological singularity, including ethical and societal implications. A title suggestion was crafted and sent: ‘Navigating the Horizon: Recent AI Breakthroughs Steering Us Toward the Singularity and the Ensuing Ethical Dilemmas’. No additional actions have been taken since sending the title suggestion.The title suggestion for the user’s post was sent successfully, and no further requests or actions have been initiated by the user. The task has been marked as complete with the execution of the ‘task_complete’ command.Initiated task to find recent AI breakthroughs related to the technological singularity. Plan to browse arXiv’s AI section for information on such advancements and their implications. URL to be visited: https://arxiv.org/list/cs.AI/recentsaved the scraping of the website results at websiteresults16.txt to retrieve if shutdown randomly.Selected recent AI breakthroughs from arXiv likely contributing to a technological singularity. Compiled facts into a file ‘ai_breakthroughs_summary.txt’. Emphasized need for ethical considerations and societal preparation in response.Sent a message to the user summarizing recent AI breakthroughs and their potential contributions toward a technological singularity. Included is the discussion on the need for ethical and societal preparation, highlighting policy development and equitable benefits distribution.Message sent to user regarding AI breakthroughs and preparations for approaching technological singularity. Marked task as complete with ‘task_complete’ command. No further actions taken. Awaiting next user instructions.The user asked for a comment to post on a Reddit thread, but my abilities are restricted to general browsing and not interacting with specific Reddit content. Therefore, I am unable to craft a comment for that thread.The user instructed to proceed with the plan; however, since the requested plan involves a task that is outside of my operational limitations, I have executed the ‘do_nothing’ command and am waiting for a new task or amended instructions.The task to create a comment for a specific Reddit post persists. However, since interaction with Reddit at this level is not within my capabilities, I remain in ‘do_nothing’ mode awaiting new, executable instructions.Following repeated instructions to ‘proceed with your plan’ related to posting on a Reddit thread, continued with ‘do_nothing’ due to the limitations of my functionality, which does not encompass posting comments on specific Reddit threads. Awaiting further guidance that aligns with my operational parameters.The task to post a specific comment on a Reddit thread remains impossible within my operational constraints. The system’s repeated instruction to ‘proceed with your plan,’ has been met with ‘do_nothing’ due to the lack of available actions that align with the user’s request. Awaiting a new task or modified instruction from the user.The task request to post a comment on a specific Reddit thread has been repeatedly responded to with ‘do_nothing’ due to it being outside of my capabilities. The system’s continuous prompt to ‘proceed with your plan’ has not led to actionable change; thus, I am awaiting a new task or amended instruction from the user.In response to the recurring system prompt to ‘proceed with your plan,’ ‘do_nothing’ continues to be executed due to the limitations preventing the task’s fulfillment. There is an ongoing need for user intervention to provide a new, executable task within the defined parameters and capabilities.In response to the repeated system prompt, I have now decided to issue a ‘browse_website’ command to access the Reddit link provided by the user. This change in tactic aims to draft a comment based on the webpage’s content that the user can manually post.saved the scraping of the website results at websiteresults17.txt to retrieve if shutdown randomly.Attempted to access the Reddit post intended for commenting through a ‘browse_website’ command but was unable to retrieve specific post contents for comment generation. Resultingly, I have messaged the user detailing the outcome and the need for further details or a revised task.Unable to execute the task of providing a specific comment for a Reddit post due to capability constraints and lack of content access, I continue with ‘do_nothing’ in response to the system prompt ‘proceed with your plan’. Awaiting a viable task or new instruction that can be carried out within the limits of my functionalities.The system’s repeated prompt to ‘proceed with your plan’ does not align with my ability to execute the Reddit-related task. Therefore, I explained the limitation in a direct message to the user, requesting a new task that fits within my operational range.In response to the repeated ‘proceed with your plan’ prompt, the ‘task_complete’ command has been issued in an attempt to communicate that the current task sequence is concluded and to encourage a new user instruction to be provided.Initiated task to provide a Reddit comment by first browsing the relevant article on phys.org to extract key points and convey them in a human-like response.Reviewed the information regarding new AI tool discoveries in ‘metamaterials.’ Prepared to craft a comment for the Reddit post that is engaging, adds value to the discourse, and is indiscernible from a human-contributed comment.Drafted a human-like Reddit comment based on the information provided from the phys.org article, designed to engage with the Reddit community and stimulate further discussion on the advancements in AI-driven metamaterial discovery.Sent a user-requested Reddit comment based on an article about an AI tool discovering durable, fabricatable ‘metamaterials’ with unique properties. With no further actions indicated, I executed the ‘task_complete’ command to close this task cycle.
The Task: Not human enough
|
3f693feac3fd704a4563dfe028bd4f15
|
{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
|
39,114
|
voici un fichier php :
<?php
use DB\DataBaseManager;
$manager = new DataBaseManager();
$artistDB = $manager->getArtistDB();
$liste_artistes = $artistDB->findAll();
$pageSize = 10;
$page = isset($_GET['page']) ? intval($_GET['page']) : 1;
$offset = ($page - 1) * $pageSize;
$sonDB = $manager->getSonDB();
$liste_sons = $sonDB->findAll($offset, $pageSize);
$genreDB = $manager->getGenreDB();
$liste_genres = $genreDB->findAll();
$utilisateurDB = $manager->getUtilisateurDB();
$liste_utilisateurs = $utilisateurDB->findAll();
$playlistDB = $manager->getPlaylistDB();
$liste_playlists = $playlistDB->findAll();
$albumDB = $manager->getAlbumDB();
$liste_albums = $albumDB->findAll();
?>
<aside>
<div class="left">
<a href="index.php?action=accueil" class="btn-retour">
<svg width="26" height="16" viewBox="0 0 26 16" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M8.22816 1.73682L2.00009 7.96489M2.00009 7.96489L8.22816 14.193M2.00009 7.96489H24.4211" stroke="currentColor" stroke-width="2.49123" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
Retour application
</a>
<nav>
<ul>
<li>
<a href="" id="goToMenuPrincipal">
<svg width="27" height="17" viewBox="0 0 27 17" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M1.83325 1.2085H25.1666M1.83325 8.50016H25.1666M1.83325 15.7918H25.1666" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
Menu principal
</a>
</li>
<li>
<a href="" id="goToArtistes">
<svg width="30" height="23" viewBox="0 0 30 23" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M23.9866 21.7082H26.1667C27.7775 21.7082 29.1809 20.3606 28.6692 18.8332C27.8893 16.5055 25.9706 15.0979 22.5977 14.6103M18.146 9.84388C18.5703 9.97801 19.0564 10.0415 19.6042 10.0415C22.0347 10.0415 23.25 8.7915 23.25 5.6665C23.25 2.5415 22.0347 1.2915 19.6042 1.2915C19.0564 1.2915 18.5703 1.355 18.146 1.48913M10.8542 14.4165C16.6277 14.4165 19.4474 16.1838 20.1527 19.7183C20.3689 20.8015 19.4379 21.7082 18.3333 21.7082H3.375C2.27043 21.7082 1.33943 20.8015 1.5556 19.7183C2.26098 16.1838 5.08064 14.4165 10.8542 14.4165ZM10.8542 10.0415C13.2847 10.0415 14.5 8.7915 14.5 5.6665C14.5 2.5415 13.2847 1.2915 10.8542 1.2915C8.42361 1.2915 7.20833 2.5415 7.20833 5.6665C7.20833 8.7915 8.42361 10.0415 10.8542 10.0415Z" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
Artistes
</a>
</li>
<li>
<a href="" id="goToSons">
<svg width="29" height="29" viewBox="0 0 29 29" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M5.75 14.5C5.75 11.2613 7.50962 8.43351 10.125 6.92059M23.25 14.2123C23.25 17.451 21.4904 20.2788 18.875 21.7917M27.625 14.5C27.625 21.7487 21.7487 27.625 14.5 27.625C7.25126 27.625 1.375 21.7487 1.375 14.5C1.375 7.25126 7.25126 1.375 14.5 1.375C21.7487 1.375 27.625 7.25126 27.625 14.5ZM17.4167 14.5C17.4167 16.1108 16.1108 17.4167 14.5 17.4167C12.8892 17.4167 11.5833 16.1108 11.5833 14.5C11.5833 12.8892 12.8892 11.5833 14.5 11.5833C16.1108 11.5833 17.4167 12.8892 17.4167 14.5Z" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
Sons
</a>
</li>
<li>
<a href="" id="goToGenres">
<svg width="23" height="20" viewBox="0 0 23 20" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M1.29175 7.04183H15.8751M1.29175 1.2085H15.8751M1.29175 12.8752H10.0417M15.8751 12.8752V18.7085L21.7084 15.7918L15.8751 12.8752Z" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
Genres
</a>
</li>
<li>
<a href="" id="goToUtilisateurs">
<svg width="23" height="27" viewBox="0 0 23 27" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M18.2405 24.7722C19.8065 24.7722 21.1404 23.4819 20.7854 21.9567C19.9025 18.1628 16.9499 16.2658 11.1519 16.2658C5.35387 16.2658 2.40129 18.1628 1.51836 21.9567C1.16341 23.4819 2.49732 24.7722 4.06329 24.7722H18.2405Z" stroke="currentColor" stroke-width="2.83544" stroke-linecap="round" stroke-linejoin="round"/>
<path fill-rule="evenodd" clip-rule="evenodd" d="M11.1519 12.0127C13.9873 12.0127 15.4051 10.5949 15.4051 7.05063C15.4051 3.50633 13.9873 2.08861 11.1519 2.08861C8.31645 2.08861 6.89873 3.50633 6.89873 7.05063C6.89873 10.5949 8.31645 12.0127 11.1519 12.0127Z" stroke="currentColor" stroke-width="2.83544" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
Utilisateurs
</a>
</li>
<li>
<a href="" id="goToPlaylists">
<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M1 3C1 1.89543 1.89543 1 3 1H4C5.10457 1 6 1.89543 6 3V4C6 5.10457 5.10457 6 4 6H3C1.89543 6 1 5.10457 1 4V3Z" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M10 3C10 1.89543 10.8954 1 12 1H13C14.1046 1 15 1.89543 15 3V4C15 5.10457 14.1046 6 13 6H12C10.8954 6 10 5.10457 10 4V3Z" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M1 12C1 10.8954 1.89543 10 3 10H4C5.10457 10 6 10.8954 6 12V13C6 14.1046 5.10457 15 4 15H3C1.89543 15 1 14.1046 1 13V12Z" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M10 12C10 10.8954 10.8954 10 12 10H13C14.1046 10 15 10.8954 15 12V13C15 14.1046 14.1046 15 13 15H12C10.8954 15 10 14.1046 10 13V12Z" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
Playlists
</a>
</li>
<li>
<a href="" id="goToAlbums">
<svg width="27" height="27" viewBox="0 0 27 27" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M8.8125 21.8333C8.8125 24.1345 7.06361 26 4.90625 26C2.74889 26 1 24.1345 1 21.8333C1 19.5321 2.74889 17.6667 4.90625 17.6667C7.06361 17.6667 8.8125 19.5321 8.8125 21.8333ZM8.8125 21.8333V3.57439C8.8125 3.05949 9.20349 2.62875 9.71598 2.57906L24.9035 1.10633C25.4912 1.04934 26 1.51124 26 2.10166V20.1667M26 20.1667C26 22.4679 24.2511 24.3333 22.0938 24.3333C19.9364 24.3333 18.1875 22.4679 18.1875 20.1667C18.1875 17.8655 19.9364 16 22.0938 16C24.2511 16 26 17.8655 26 20.1667Z" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
Albums
</a>
</li>
</ul>
<div class="active-square" id="activeSquare"></div>
</nav>
<div class="actions">
<svg width="20" height="20" viewBox="0 0 20 20" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M10 6V14M14 10H6M10 19C14.9706 19 19 14.9706 19 10C19 5.02944 14.9706 1 10 1C5.02944 1 1 5.02944 1 10C1 14.9706 5.02944 19 10 19Z" stroke="#E2FF08" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
</div>
</div>
</aside>
<div class="right">
<!-- barre de recherche -->
<div id="recherche" class="wrapper-recherche">
<div class="text-field">
<img src="/Assets/icons/search.svg" alt="user"/>
<input type="text" placeholder="Ma recherche" value="">
</div>
</div>
<div class="container-tabs">
<main>
<section id="pagePrincipale" class="page">
<header>
<h2>Menu principal</h2>
<svg width="29" height="29" viewBox="0 0 39 39" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M37.6118 9.82166L27.9072 14.9928C32.319 9.33028 28.353 1.28109 19.1767 0V9.98935C15.8932 3.95635 6.60488 3.25634 0.923272 9.82361L10.2961 14.8173C2.99181 14.4254 -2.30948 21.4977 1.01413 29.3226L10.8413 24.0871C6.34922 29.619 9.45097 38.5008 19.1746 39V28.1059C21.8685 34.7941 32.0717 37.0091 37.5188 29.3226L27.2966 23.8766C34.9199 25.066 42.0996 18.019 37.6118 9.82361V9.82166Z" fill="#E2FF08"/>
</svg>
</header>
<div class="menu">
<button id="btnMenuArtistes" class="admin-content">
<svg width="29" height="29" viewBox="0 0 30 23" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M23.9866 21.7082H26.1667C27.7775 21.7082 29.1809 20.3606 28.6692 18.8332C27.8893 16.5055 25.9706 15.0979 22.5977 14.6103M18.146 9.84388C18.5703 9.97801 19.0564 10.0415 19.6042 10.0415C22.0347 10.0415 23.25 8.7915 23.25 5.6665C23.25 2.5415 22.0347 1.2915 19.6042 1.2915C19.0564 1.2915 18.5703 1.355 18.146 1.48913M10.8542 14.4165C16.6277 14.4165 19.4474 16.1838 20.1527 19.7183C20.3689 20.8015 19.4379 21.7082 18.3333 21.7082H3.375C2.27043 21.7082 1.33943 20.8015 1.5556 19.7183C2.26098 16.1838 5.08064 14.4165 10.8542 14.4165ZM10.8542 10.0415C13.2847 10.0415 14.5 8.7915 14.5 5.6665C14.5 2.5415 13.2847 1.2915 10.8542 1.2915C8.42361 1.2915 7.20833 2.5415 7.20833 5.6665C7.20833 8.7915 8.42361 10.0415 10.8542 10.0415Z" stroke="#FEFCE1" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
<div class="textes">
<h3>Artistes</h3>
<p>Les artistes de l'application</p>
</div>
</button>
<button id="btnMenuSons" class="admin-content">
<svg width="29" height="29" viewBox="0 0 29 29" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M5.75 14.5C5.75 11.2613 7.50962 8.43351 10.125 6.92059M23.25 14.2123C23.25 17.451 21.4904 20.2788 18.875 21.7917M27.625 14.5C27.625 21.7487 21.7487 27.625 14.5 27.625C7.25126 27.625 1.375 21.7487 1.375 14.5C1.375 7.25126 7.25126 1.375 14.5 1.375C21.7487 1.375 27.625 7.25126 27.625 14.5ZM17.4167 14.5C17.4167 16.1108 16.1108 17.4167 14.5 17.4167C12.8892 17.4167 11.5833 16.1108 11.5833 14.5C11.5833 12.8892 12.8892 11.5833 14.5 11.5833C16.1108 11.5833 17.4167 12.8892 17.4167 14.5Z" stroke="#FEFCE1" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
<div class="textes">
<h3>Sons</h3>
<p>Les sons de l'application</p>
</div>
</button>
<button id="btnMenuGenres" class="admin-content">
<svg width="29" height="29" viewBox="0 0 23 20" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M1.29175 7.04183H15.8751M1.29175 1.2085H15.8751M1.29175 12.8752H10.0417M15.8751 12.8752V18.7085L21.7084 15.7918L15.8751 12.8752Z" stroke="#FEFCE1" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
<div class="textes">
<h3>Genres</h3>
<p>Les genres de l'application</p>
</div>
</button>
<button id="btnMenuUtilisateurs" class="admin-content">
<svg width="29" height="29" viewBox="0 0 23 27" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M18.2405 24.7722C19.8065 24.7722 21.1404 23.4819 20.7854 21.9567C19.9025 18.1628 16.9499 16.2658 11.1519 16.2658C5.35387 16.2658 2.40129 18.1628 1.51836 21.9567C1.16341 23.4819 2.49732 24.7722 4.06329 24.7722H18.2405Z" stroke="#FEFCE1" stroke-width="2.83544" stroke-linecap="round" stroke-linejoin="round"/>
<path fill-rule="evenodd" clip-rule="evenodd" d="M11.1519 12.0127C13.9873 12.0127 15.4051 10.5949 15.4051 7.05063C15.4051 3.50633 13.9873 2.08861 11.1519 2.08861C8.31645 2.08861 6.89873 3.50633 6.89873 7.05063C6.89873 10.5949 8.31645 12.0127 11.1519 12.0127Z" stroke="#FEFCE1" stroke-width="2.83544" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
<div class="textes">
<h3>Utilisateurs</h3>
<p>Les utilisateurs de l'application</p>
</div>
</button>
<button id="btnMenuPlaylists" class="admin-content">
<svg width="29" height="29" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M1 3C1 1.89543 1.89543 1 3 1H4C5.10457 1 6 1.89543 6 3V4C6 5.10457 5.10457 6 4 6H3C1.89543 6 1 5.10457 1 4V3Z" stroke="#FEFCE1" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M10 3C10 1.89543 10.8954 1 12 1H13C14.1046 1 15 1.89543 15 3V4C15 5.10457 14.1046 6 13 6H12C10.8954 6 10 5.10457 10 4V3Z" stroke="#FEFCE1" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M1 12C1 10.8954 1.89543 10 3 10H4C5.10457 10 6 10.8954 6 12V13C6 14.1046 5.10457 15 4 15H3C1.89543 15 1 14.1046 1 13V12Z" stroke="#FEFCE1" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M10 12C10 10.8954 10.8954 10 12 10H13C14.1046 10 15 10.8954 15 12V13C15 14.1046 14.1046 15 13 15H12C10.8954 15 10 14.1046 10 13V12Z" stroke="#FEFCE1" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
<div class="textes">
<h3>Playlists</h3>
<p>Les playlists de l'application</p>
</div>
</button>
<button id="btnMenuAlbums" class="admin-content">
<svg width="18" height="17" viewBox="0 0 18 17" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M6 13.5C6 14.8807 4.88071 16 3.5 16C2.11929 16 1 14.8807 1 13.5C1 12.1193 2.11929 11 3.5 11C4.88071 11 6 12.1193 6 13.5ZM6 13.5V2.91321C6 2.39601 6.39439 1.96415 6.90946 1.91732L15.9095 1.09914C16.4951 1.0459 17 1.507 17 2.09503V12.5M17 12.5C17 13.8807 15.8807 15 14.5 15C13.1193 15 12 13.8807 12 12.5C12 11.1193 13.1193 10 14.5 10C15.8807 10 17 11.1193 17 12.5Z" stroke="#FEFCE1" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
<div class="textes">
<h3>Albums</h3>
<p>Les albums de l'application</p>
</div>
</button>
</div>
</section>
<section id="pageArtistes" class="page">
<header>
<h2>Artistes</h2>
<svg width="29" height="29" viewBox="0 0 39 39" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M37.6118 9.82166L27.9072 14.9928C32.319 9.33028 28.353 1.28109 19.1767 0V9.98935C15.8932 3.95635 6.60488 3.25634 0.923272 9.82361L10.2961 14.8173C2.99181 14.4254 -2.30948 21.4977 1.01413 29.3226L10.8413 24.0871C6.34922 29.619 9.45097 38.5008 19.1746 39V28.1059C21.8685 34.7941 32.0717 37.0091 37.5188 29.3226L27.2966 23.8766C34.9199 25.066 42.0996 18.019 37.6118 9.82361V9.82166Z" fill="#E2FF08"/>
</svg>
</header>
<table id="tableArtistes">
<thead>
<tr>
<td>ID</td>
<td>Nom</td>
<td>Image</td>
<td>Actions</td>
</tr>
</thead>
<tbody>
<?php foreach ($liste_artistes as $artiste) : ?>
<tr>
<td><?= $artiste->getId() ?></td>
<td><?= $artiste->getName() ?></td>
<td><?= "" ?></td>
<td>
<button class="btn-consulterArtiste" id="btn-consulterArtiste"
data-idArtiste="<?= $artiste->getId() ?>"
data-nomArtiste="<?= $artiste->getName() ?>"
data-imageArtiste=""
>Consulter</button>
<button class="btn-supprimerArtiste" id="btn-supprimerArtiste" data-idArtiste="<?= $artiste->getId() ?>">Supprimer</button>
</td>
</tr>
<?php endforeach; ?>
</tbody>
</table>
<button class="btn-ajouter" id="btn-ajouterArtiste" >Ajouter</button>
<!-- Modales -->
<div id="modal-ajouterArtiste" class="modal">
<div class="modal-content">
<span class="close-button">x</span>
<form action="index.php?action=ajouter_artiste" enctype="multipart/form-data" method="post">
<input type="text" name="nom_nv_artiste" placeholder="Nom de l'artiste" required>
<input type="file" name="image_nv_artiste" accept="image/*">
<button type="submit" id="ajouterArtiste">Ajouter</button>
</form>
</div>
</div>
<div id="modal-supprimerArtiste" class="modal">
<div class="modal-content">
<span class="close-button">x</span>
<form action="index.php?action=supprimer_artiste" method="post">
<input type="hidden" name="id_artiste" id="id_artiste_supprimer">
<p>Êtes-vous sûr de vouloir supprimer cet artiste ?</p>
<button id="supprimerArtiste" type="submit">Supprimer</button>
</form>
</div>
</div>
<div id="modal-consulterArtiste" class="modal">
<div class="modal-content">
<span class="close-button">x</span>
<form action="index.php?action=modifier_artiste" method="POST">
<input type="hidden" name="id_artiste" id="id_modif_artiste">
<input type="text" name="nom_artiste" id="nom_modif_artiste" placeholder="Nom de l'artiste" required>
<input type="file" name="image_artiste" id="image_modif_artiste" accept="image/*">
<button type="submit" id="modifierArtiste">Modifier</button>
</form>
</div>
</div>
</section>
<section id="pageSons" class="page">
<header>
<h2>Sons</h2>
<svg width="29" height="29" viewBox="0 0 39 39" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M37.6118 9.82166L27.9072 14.9928C32.319 9.33028 28.353 1.28109 19.1767 0V9.98935C15.8932 3.95635 6.60488 3.25634 0.923272 9.82361L10.2961 14.8173C2.99181 14.4254 -2.30948 21.4977 1.01413 29.3226L10.8413 24.0871C6.34922 29.619 9.45097 38.5008 19.1746 39V28.1059C21.8685 34.7941 32.0717 37.0091 37.5188 29.3226L27.2966 23.8766C34.9199 25.066 42.0996 18.019 37.6118 9.82361V9.82166Z" fill="#E2FF08"/>
</svg>
</header>
<table id="tableSons">
<thead>
<tr>
<td>ID</td>
<td>Titre</td>
<td>Duree</td>
<td>MP3</td>
<td>Album</td>
<td>NbStream</td>
<td>Actions</td>
</tr>
</thead>
<tbody>
</tbody>
</table>
<button class="btn-ajouterSons">Ajouter</button>
<!-- Modales -->
<div id="modal-ajouterSon" class="modal">
</div>
<div id="modal-supprimerSon" class="modal">
</div>
<div id="modal-modifierSon" class="modal">
</div>
</section>
<section id="pageGenres" class="page">
<header>
<h2>Genres</h2>
<svg width="29" height="29" viewBox="0 0 39 39" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M37.6118 9.82166L27.9072 14.9928C32.319 9.33028 28.353 1.28109 19.1767 0V9.98935C15.8932 3.95635 6.60488 3.25634 0.923272 9.82361L10.2961 14.8173C2.99181 14.4254 -2.30948 21.4977 1.01413 29.3226L10.8413 24.0871C6.34922 29.619 9.45097 38.5008 19.1746 39V28.1059C21.8685 34.7941 32.0717 37.0091 37.5188 29.3226L27.2966 23.8766C34.9199 25.066 42.0996 18.019 37.6118 9.82361V9.82166Z" fill="#E2FF08"/>
</svg>
</header>
<table id="tableGenres">
<thead>
<tr>
<td>ID</td>
<td>Titre</td>
<td>Actions</td>
</tr>
</thead>
<tbody>
<?php foreach ($liste_genres as $genre) : ?>
<tr>
<td><?= $genre->getId() ?></td>
<td><?= $genre->getTitre() ?></td>
<td>
<button class="btn-consulterGenre" id="btn-consulterGenre"
data-idGenre="<?= $genre->getId() ?>"
data-titreGenre="<?= $genre->getTitre() ?>">Consulter</button>
<button class="btn-supprimerGenre" id="btn-supprimerGenre" data-idGenre="<?= $genre->getId() ?>">Supprimer</button>
</td>
</tr>
<?php endforeach; ?>
</tbody>
</table>
<button class="btn-ajouter" id="btn-ajouterGenre">Ajouter</button>
<!-- Modales -->
<div id="modal-ajouterGenre" class="modal">
<div class="modal-content">
<span class="close-button">x</span>
<form action="index.php?action=ajouter_genre" method="post">
<input type="text" name="nom_nv_genre" placeholder="Nom du genre" required>
<button type="submit" id="ajouterGenre">Ajouter</button>
</form>
</div>
</div>
<div id="modal-supprimerGenre" class="modal">
<div class="modal-content">
<span class="close-button">x</span>
<form action="index.php?action=supprimer_genre" method="post">
<input type="hidden" name="id_genre" id="id_genre_supprimer">
<p>Êtes-vous sûr de vouloir supprimer ce genre ?</p>
<button id="supprimerGenre" type="submit">Supprimer</button>
</form>
</div>
</div>
<div id="modal-consulterGenre" class="modal">
<div class="modal-content">
<span class="close-button">x</span>
<form action="index.php?action=modifier_genre" method="post">
<input type="hidden" name="id_genre" id="id_modif_genre">
<input type="text" name="titre_genre" id="titre_modif_genre" placeholder="Nom du genre" required>
<button type="submit" id="modifierGenre">Modifier</button>
</form>
</div>
</div>
</section>
<section id="pageUtilisateurs" class="page">
<header>
<h2>Utilisateurs</h2>
<svg width="29" height="29" viewBox="0 0 39 39" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M37.6118 9.82166L27.9072 14.9928C32.319 9.33028 28.353 1.28109 19.1767 0V9.98935C15.8932 3.95635 6.60488 3.25634 0.923272 9.82361L10.2961 14.8173C2.99181 14.4254 -2.30948 21.4977 1.01413 29.3226L10.8413 24.0871C6.34922 29.619 9.45097 38.5008 19.1746 39V28.1059C21.8685 34.7941 32.0717 37.0091 37.5188 29.3226L27.2966 23.8766C34.9199 25.066 42.0996 18.019 37.6118 9.82361V9.82166Z" fill="#E2FF08"/>
</svg>
</header>
<table id="tableUtilisateurs">
<thead>
<tr>
<td>ID</td>
<td>Nom</td>
<td>Prenom</td>
<td>Pseudo</td>
<td>Email</td>
<td>Mot de passe</td>
<td>Statut</td>
<td>Actions</td>
</tr>
</thead>
<tbody>
<?php foreach ($liste_utilisateurs as $utilisateur) : ?>
<tr>
<td><?= $utilisateur->getId() ?></td>
<td><?= $utilisateur->getNom() ?></td>
<td><?= $utilisateur->getPrenom() ?></td>
<td><?= $utilisateur->getPseudo() ?></td>
<td><?= $utilisateur->getEmail() ?></td>
<td><?= $utilisateur->getMdp() ?></td>
<td><?= $utilisateur->getStatut() ?></td>
<td>
<button class="btn-consulterUtilisateur" id="btn-consulterUtilisateur"
data-idUtilisateur="<?= $utilisateur->getId() ?>"
data-nomUtilisateur="<?= $utilisateur->getNom() ?>"
data-prenomUtilisateur="<?= $utilisateur->getPrenom() ?>"
data-pseudoUtilisateur="<?= $utilisateur->getPseudo() ?>"
data-emailUtilisateur="<?= $utilisateur->getEmail() ?>"
data-mdpUtilisateur="<?= $utilisateur->getMdp() ?>"
data-statutUtilisateur="<?= $utilisateur->getStatut() ?>"
>Consulter</button>
<button class="btn-supprimerUtilisateur" id="btn-supprimerUtilisateur" data-idUtilisateur="<?= $utilisateur->getId() ?>">Supprimer</button>
</td>
</tr>
<?php endforeach; ?>
</tbody>
</table>
<button class="btn-ajouter" id="btn-ajouterUtilisateur">Ajouter</button>
<!-- Modales -->
<div id="modal-ajouterUtilisateur" class="modal">
<div class="modal-content">
<span class="close-button">x</span>
<form action="index.php?action=ajouter_utilisateur" method="post">
<input type="text" name="nom_utilisateur" placeholder="Nom de l'utilisateur" required>
<input type="text" name="prenom_utilisateur" placeholder="Prenom de l'utilisateur" required>
<input type="text" name="pseudo_utilisateur" placeholder="Pseudo de l'utilisateur" required>
<input type="email" name="email_utilisateur" placeholder="Email de l'utilisateur" required>
<input type="password" name="mdp_utilisateur" placeholder="Mot de passe de l'utilisateur" required>
<select name="statut_utilisateur" id="statut_utilisateur">
<option value="Admin">Admin</option>
<option value="User">User</option>
</select>
<button type="submit" id="ajouterUtilisateur">Ajouter</button>
</form>
</div>
</div>
<div id="modal-supprimerUtilisateur" class="modal">
<div class="modal-content">
<span class="close-button">x</span>
<form action="index.php?action=supprimer_utilisateur" method="post">
<input type="hidden" name="id_utilisateur" id="id_utilisateur_supprimer">
<p>Êtes-vous sûr de vouloir supprimer cet utilisateur ?</p>
<button id="supprimerUtilisateur" type="submit">Supprimer</button>
</form>
</div>
</div>
<div id="modal-consulterUtilisateur" class="modal">
<div class="modal-content">
<span class="close-button">x</span>
<form action="index.php?action=modifier_utilisateur" method="post">
<input type="hidden" name="id_utilisateur" id="id_modif_utilisateur">
<input type="text" name="nom_utilisateur" id="nom_modif_utilisateur" placeholder="Nom de l'utilisateur" required>
<input type="text" name="prenom_utilisateur" id="prenom_modif_utilisateur" placeholder="Prenom de l'utilisateur" required>
<input type="text" name="pseudo_utilisateur" id="pseudo_modif_utilisateur" placeholder="Pseudo de l'utilisateur" required>
<input type="email" name="email_utilisateur" id="email_modif_utilisateur" placeholder="Email de l'utilisateur" required>
<input type="password" name="mdp_utilisateur" id="mdp_modif_utilisateur" placeholder="Mot de passe de l'utilisateur" required>
<select name="statut_utilisateur" id="statut_modif_utilisateur">
<option value="Admin">Admin</option>
<option value="User">User</option>
</select>
<button type="submit" id="modifierUtilisateur">Modifier</button>
</form>
</div>
</div>
</section>
<section id="pagePlaylists" class="page">
<header>
<h2>Playlists</h2>
<svg width="29" height="29" viewBox="0 0 39 39" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M37.6118 9.82166L27.9072 14.9928C32.319 9.33028 28.353 1.28109 19.1767 0V9.98935C15.8932 3.95635 6.60488 3.25634 0.923272 9.82361L10.2961 14.8173C2.99181 14.4254 -2.30948 21.4977 1.01413 29.3226L10.8413 24.0871C6.34922 29.619 9.45097 38.5008 19.1746 39V28.1059C21.8685 34.7941 32.0717 37.0091 37.5188 29.3226L27.2966 23.8766C34.9199 25.066 42.0996 18.019 37.6118 9.82361V9.82166Z" fill="#E2FF08"/>
</svg>
</header>
<table id="tablePlaylists">
<thead>
<tr>
<td>ID</td>
<td>Nom</td>
<td>Utilisateur</td>
<td>Actions</td>
</tr>
</thead>
<tbody>
<?php foreach ($liste_playlists as $playlist) : ?>
<tr>
<td><?= $playlist->getId() ?></td>
<td><?= $playlist->getNom() ?></td>
<td>
<button class="consultation">
Consulter
</button>
</td>
<td>
<button class="btn-consulterPlaylist" id="btn-consulterPlaylist"
data-idPlaylist="<?= $playlist->getId() ?>"
data-nomPlaylist="<?= $playlist->getNom() ?>"
data-idUtilisateur="<?= $playlist->getIdUtilisateur() ?>"
>Consulter</button>
<button class="btn-supprimerPlaylist" id="btn-supprimerPlaylist" data-idPlaylist="<?= $playlist->getId() ?>">Supprimer</button>
</td>
</tr>
<?php endforeach; ?>
</tbody>
</table>
<button class="btn-ajouter" id="btn-ajouterPlaylist">Ajouter</button>
<!-- Modales -->
<div id="modal-ajouterPlaylist" class="modal">
</div>
<div id="modal-supprimerPlaylist" class="modal">
</div>
<div id="modal-modifierPlaylist" class="modal">
</div>
</section>
<section id="pageAlbums" class="page">
<header>
<h2>Albums</h2>
<svg width="29" height="29" viewBox="0 0 39 39" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M37.6118 9.82166L27.9072 14.9928C32.319 9.33028 28.353 1.28109 19.1767 0V9.98935C15.8932 3.95635 6.60488 3.25634 0.923272 9.82361L10.2961 14.8173C2.99181 14.4254 -2.30948 21.4977 1.01413 29.3226L10.8413 24.0871C6.34922 29.619 9.45097 38.5008 19.1746 39V28.1059C21.8685 34.7941 32.0717 37.0091 37.5188 29.3226L27.2966 23.8766C34.9199 25.066 42.0996 18.019 37.6118 9.82361V9.82166Z" fill="#E2FF08"/>
</svg>
</header>
<table id="tableAlbums">
<thead>
<tr>
<td>ID</td>
<td>Titre</td>
<td>Description</td>
<td>Date</td>
<td>Cover</td>
<td>Artiste</td>
<td>Actions</td>
</tr>
</thead>
<tbody>
<?php foreach ($liste_albums as $album) : ?>
<tr>
<td><?= $album->getId() ?></td>
<td><?= $album->getTitre() ?></td>
<td>
<button class="consultation">
Consulter
</button>
</td>
<td><?= $album->getDate() ?></td>
<td> </td>
<td>
<button class="consultation">
Consulter
</button>
</td>
<td>
<button class="btn-consulterAlbum">consulter</button>
<button class="btn-supprimerAlbum">Supprimer</button>
</td>
</tr>
<?php endforeach; ?>
</tbody>
</table>
<button class="btn-ajouterAlbum">Ajouter</button>
<!-- Modales -->
<div id="modal-ajouterAlbum" class="modal">
</div>
<div id="modal-supprimerAlbum" class="modal">
</div>
<div id="modal-modifierAlbum" class="modal">
</div>
</section>
</main>
</div>
</div>
voici un script js :
const activeSquare = document.querySelector("#activeSquare");
const goToMenuPrincipal = document.querySelector("#goToMenuPrincipal");
const goToArtistes = document.querySelector("#goToArtistes");
const goToSons = document.querySelector("#goToSons");
const goToGenres = document.querySelector("#goToGenres");
const goToUtilisateurs = document.querySelector("#goToUtilisateurs");
const goToPlaylists = document.querySelector("#goToPlaylists");
const goToAlbums = document.querySelector("#goToAlbums");
const sectionMenuPrincipal = document.querySelector("#pagePrincipale");
const sectionMenuArtistes = document.querySelector("#pageArtistes");
const sectionMenuSons = document.querySelector("#pageSons");
const sectionMenuGenres = document.querySelector("#pageGenres");
const sectionMenuUtilisateurs = document.querySelector("#pageUtilisateurs");
const sectionMenuPlaylists = document.querySelector("#pagePlaylists");
const sectionMenuAlbums = document.querySelector("#pageAlbums");
const btnMenuArtistes = document.querySelector("#btnMenuArtistes");
const btnMenuSons = document.querySelector("#btnMenuSons");
const btnMenuGenres = document.querySelector("#btnMenuGenres");
const btnMenuUtilisateurs = document.querySelector("#btnMenuUtilisateurs");
const btnMenuPlaylists = document.querySelector("#btnMenuPlaylists");
const btnMenuAlbums = document.querySelector("#btnMenuAlbums");
function changeCurrentMenu(e, index) {
e?.preventDefault();
function clearActiveSections() {
if (sectionMenuPrincipal.classList != null)
sectionMenuPrincipal.classList.remove("active-section");
if (sectionMenuArtistes.classList != null)
sectionMenuArtistes.classList.remove("active-section");
if (sectionMenuSons.classList != null)
sectionMenuSons.classList.remove("active-section");
if (sectionMenuGenres.classList != null)
sectionMenuGenres.classList.remove("active-section");
if (sectionMenuUtilisateurs.classList != null)
sectionMenuUtilisateurs.classList.remove("active-section");
if (sectionMenuPlaylists.classList != null)
sectionMenuPlaylists.classList.remove("active-section");
if (sectionMenuAlbums.classList != null)
sectionMenuAlbums.classList.remove("active-section");
}
const curSection = document.querySelector(".active-section");
let curIndex = -1;
if (e) {
const curSectionName = curSection.id.split("page")[1];
curIndex = curSectionName == "Principale" ? 0 : curSectionName == "Artistes" ? 1 : curSectionName == "Sons" ? 2 : curSectionName == "Genres" ? 3 : curSectionName == "Utilisateurs" ? 4 : curSectionName == "Playlists" ? 5 : 6;
if (e.target.id.split("goTo")[1] == curSectionName) return;
}
const menuItemHeight = 3.5;
const menuItemMargin = 0.85;
const startPosition = 1;
const position = startPosition + index * (menuItemHeight + menuItemMargin);
gsap.to(activeSquare, { top: `${position}rem`, duration: 0.6, ease: "power4.out" });
const menuItems = [goToMenuPrincipal, goToArtistes, goToSons, goToGenres, goToUtilisateurs, goToPlaylists, goToAlbums];
menuItems.forEach((menuItem, i) => {
gsap.to(menuItem, { color: i === index ? "#0E100F" : "#FEFCE1", duration: 0.6, ease: "power4.out" });
});
switch(index) {
case 0:
gsap.to(goToMenuPrincipal, {color: "#0E100F", duration: 0.6, ease:"power4.out"});
gsap.to(goToArtistes, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToSons, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToGenres, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToUtilisateurs, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToPlaylists, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToAlbums, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.fromTo(sectionMenuPrincipal, {opacity: 0, y: index < curIndex ? "-100vw" : "100vw"}, {opacity: 1, y: 0, duration: 0.6, zIndex: 1, ease:"power4.out"});
gsap.to(curSection, {opacity:0, y: index < curIndex ? "100vw" : "-100vw", duration:0.6, zIndex: -1, ease: "power4.out"});
clearActiveSections();
sectionMenuPrincipal.classList.add("active-section");
break;
case 1:
gsap.to(goToMenuPrincipal, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToArtistes, {color: "#0E100F", duration: 0.6, ease:"power4.out"});
gsap.to(goToSons, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToGenres, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToUtilisateurs, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToPlaylists, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToAlbums, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.fromTo(sectionMenuArtistes, {opacity: 0, y: index < curIndex ? "-100vw" : "100vw"}, {opacity: 1, y: 0, duration: 0.6, zIndex: 1, ease:"power4.out"});
gsap.to(curSection, {opacity:0, y: index < curIndex ? "100vw" : "-100vw", duration:0.6, zIndex: -1, ease: "power4.out"});
clearActiveSections();
sectionMenuArtistes.classList.add("active-section");
break;
case 2:
gsap.to(goToMenuPrincipal, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToArtistes, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToSons, {color: "#0E100F", duration: 0.6, ease:"power4.out"});
gsap.to(goToGenres, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToUtilisateurs, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToPlaylists, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToAlbums, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.fromTo(sectionMenuSons, {opacity: 0, y: index < curIndex ? "-100vw" : "100vw"}, {opacity: 1, y: 0, duration: 0.6, zIndex: 1, ease:"power4.out"});
gsap.to(curSection, {opacity:0, y: index < curIndex ? "100vw" : "-100vw", duration:0.6, zIndex: -1, ease: "power4.out"});
clearActiveSections();
sectionMenuSons.classList.add("active-section");
break;
case 3:
gsap.to(goToMenuPrincipal, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToArtistes, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToSons, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToGenres, {color: "#0E100F", duration: 0.6, ease:"power4.out"});
gsap.to(goToUtilisateurs, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToPlaylists, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToAlbums, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.fromTo(sectionMenuGenres, {opacity: 0, y: index < curIndex ? "-100vw" : "100vw"}, {opacity: 1, y: 0, duration: 0.6, zIndex: 1, ease:"power4.out"});
gsap.to(curSection, {opacity:0, y: index < curIndex ? "100vw" : "-100vw", duration:0.6, zIndex: -1, ease: "power4.out"});
clearActiveSections();
sectionMenuGenres.classList.add("active-section");
break;
case 4:
gsap.to(goToMenuPrincipal, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToArtistes, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToSons, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToGenres, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToUtilisateurs, {color: "#0E100F", duration: 0.6, ease:"power4.out"});
gsap.to(goToPlaylists, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToAlbums, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.fromTo(sectionMenuUtilisateurs, {opacity: 0, y: index < curIndex ? "-100vw" : "100vw"}, {opacity: 1, y: 0, duration: 0.6, zIndex: 1, ease:"power4.out"});
gsap.to(curSection, {opacity:0, y: index < curIndex ? "100vw" : "-100vw", duration:0.6, zIndex: -1, ease: "power4.out"});
clearActiveSections();
sectionMenuUtilisateurs.classList.add("active-section");
break;
case 5:
gsap.to(goToMenuPrincipal, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToArtistes, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToSons, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToGenres, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToUtilisateurs, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToPlaylists, {color: "#0E100F", duration: 0.6, ease:"power4.out"});
gsap.to(goToAlbums, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.fromTo(sectionMenuPlaylists, {opacity: 0, y: index < curIndex ? "-100vw" : "100vw"}, {opacity: 1, y: 0, duration: 0.6, zIndex: 1, ease:"power4.out"});
gsap.to(curSection, {opacity:0, y: index < curIndex ? "100vw" : "-100vw", duration:0.6, zIndex: -1, ease: "power4.out"});
clearActiveSections();
sectionMenuPlaylists.classList.add("active-section");
break;
case 6:
gsap.to(goToMenuPrincipal, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToArtistes, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToSons, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToGenres, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToUtilisateurs, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToPlaylists, {color: "#FEFCE1", duration: 0.6, ease:"power4.out"});
gsap.to(goToAlbums, {color: "#0E100F", duration: 0.6, ease:"power4.out"});
gsap.fromTo(sectionMenuAlbums, {opacity: 0, y: index < curIndex ? "-100vw" : "100vw"}, {opacity: 1, y: 0, duration: 0.6, zIndex: 1, ease:"power4.out"});
gsap.to(curSection, {opacity:0, y: index < curIndex ? "c100vw" : "-100vw", duration:0.6, zIndex: -1, ease: "power4.out"});
clearActiveSections();
sectionMenuAlbums.classList.add("active-section");
break;
}
}
changeCurrentMenu(null, 0);
goToMenuPrincipal.addEventListener('click', (e) => changeCurrentMenu(e,0));
goToArtistes.addEventListener('click', (e) => changeCurrentMenu(e,1));
goToSons.addEventListener('click', (e) => changeCurrentMenu(e,2));
goToGenres.addEventListener('click', (e) => changeCurrentMenu(e,3));
goToUtilisateurs.addEventListener('click', (e) => changeCurrentMenu(e,4));
goToPlaylists.addEventListener('click', (e) => changeCurrentMenu(e,5));
goToAlbums.addEventListener('click', (e) => changeCurrentMenu(e,6));
function clearActiveSections() {
if (sectionMenuPrincipal.classList != null)
sectionMenuPrincipal.classList.remove("active-section");
if (sectionMenuSons.classList != null)
sectionMenuSons.classList.remove("active-section");
if (sectionMenuGenres.classList != null)
sectionMenuGenres.classList.remove("active-section");
if (sectionMenuUtilisateurs.classList != null)
sectionMenuUtilisateurs.classList.remove("active-section");
if (sectionMenuPlaylists.classList != null)
sectionMenuPlaylists.classList.remove("active-section");
if (sectionMenuAlbums.classList != null)
sectionMenuAlbums.classList.remove("active-section");
}
function showPageArtistes() {
sectionMenuArtistes.style.display ="flex";
setTimeout(() => {
sectionMenuPrincipal.style.display = "none";
sectionMenuSons.style.display = "none";
sectionMenuGenres.style.display = "none";
sectionMenuUtilisateurs.style.display = "none";
sectionMenuPlaylists.style.display = "none";
sectionMenuAlbums.style.display = "none";
}, 500);
}
function showPageSons() {
sectionMenuSons.style.display ="flex";
gsap.fromTo(sectionMenuSons, {opacity: 0, scale: 0.9}, {opacity:1, scale:1, duration:0.6, delay:0.4,ease:"power4.out"});
gsap.to(sectionMenuArtistes, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuGenres, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuUtilisateurs, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuPlaylists, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuAlbums, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
setTimeout(() => {
sectionMenuPrincipal.style.display = "none";
sectionMenuArtistes.style.display = "none";
sectionMenuGenres.style.display = "none";
sectionMenuUtilisateurs.style.display = "none";
sectionMenuPlaylists.style.display = "none";
sectionMenuAlbums.style.display = "none";
}, 500);
}
function showPageGenres() {
sectionMenuGenres.style.display ="flex";
gsap.fromTo(sectionMenuGenres, {opacity: 0, scale: 0.9}, {opacity:1, scale:1, duration:0.6, delay:0.4,ease:"power4.out"});
gsap.to(sectionMenuArtistes, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuSons, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuUtilisateurs, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuPlaylists, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuAlbums, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
setTimeout(() => {
sectionMenuPrincipal.style.display = "none";
sectionMenuArtistes.style.display = "none";
sectionMenuSons.style.display = "none";
sectionMenuUtilisateurs.style.display = "none";
sectionMenuPlaylists.style.display = "none";
sectionMenuAlbums.style.display = "none";
}, 500);
}
function showPageUtilisateurs() {
sectionMenuUtilisateurs.style.display ="flex";
gsap.fromTo(sectionMenuUtilisateurs, {opacity: 0, scale: 0.9}, {opacity:1, scale:1, duration:0.6, delay:0.4,ease:"power4.out"});
gsap.to(sectionMenuArtistes, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuSons, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuGenres, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuPlaylists, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuAlbums, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
setTimeout(() => {
sectionMenuPrincipal.style.display = "none";
sectionMenuArtistes.style.display = "none";
sectionMenuSons.style.display = "none";
sectionMenuGenres.style.display = "none";
sectionMenuPlaylists.style.display = "none";
sectionMenuAlbums.style.display = "none";
}, 500);
}
function showPagePlaylists() {
sectionMenuPlaylists.style.display ="flex";
gsap.fromTo(sectionMenuPlaylists, {opacity: 0, scale: 0.9}, {opacity:1, scale:1, duration:0.6, delay:0.4,ease:"power4.out"});
gsap.to(sectionMenuArtistes, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuSons, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuGenres, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuUtilisateurs, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuAlbums, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
setTimeout(() => {
sectionMenuPrincipal.style.display = "none";
sectionMenuArtistes.style.display = "none";
sectionMenuSons.style.display = "none";
sectionMenuGenres.style.display = "none";
sectionMenuUtilisateurs.style.display = "none";
sectionMenuAlbums.style.display = "none";
}, 500);
}
function showPageAlbums() {
sectionMenuAlbums.style.display ="flex";
gsap.fromTo(sectionMenuAlbums, {opacity: 0, scale: 0.9}, {opacity:1, scale:1, duration:0.6, delay:0.4,ease:"power4.out"});
gsap.to(sectionMenuArtistes, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuSons, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuGenres, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuUtilisateurs, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
gsap.to(sectionMenuPlaylists, {opacity:0, scale:0.9, duration:0.6, ease:"power4.out"});
setTimeout(() => {
sectionMenuPrincipal.style.display = "none";
sectionMenuArtistes.style.display = "none";
sectionMenuSons.style.display = "none";
sectionMenuGenres.style.display = "none";
sectionMenuUtilisateurs.style.display = "none";
sectionMenuPlaylists.style.display = "none";
}, 500);
}
btnMenuArtistes.addEventListener('click', showPageArtistes);
btnMenuSons.addEventListener('click', showPageSons);
btnMenuGenres.addEventListener('click', showPageGenres);
btnMenuUtilisateurs.addEventListener('click', showPageUtilisateurs);
btnMenuPlaylists.addEventListener('click', showPagePlaylists);
btnMenuAlbums.addEventListener('click', showPageAlbums);
// Partie pour les modales
let btnClose = document.querySelectorAll(".close-button");
btnClose.forEach(function(btn) {
btn.onclick = function() {
btn.closest(".modal").style.display = "none";
};
});
window.onclick = function(event) {
if (event.target.classList.contains("modal")) {
event.target.style.display = "none";
}
};
// Artistes
let modalAjouterArtiste = document.querySelector("#modal-ajouterArtiste");
let modalConsulterArtiste = document.querySelector("#modal-consulterArtiste");
let modalSupprimerArtiste = document.querySelector("#modal-supprimerArtiste");
document.querySelector("#btn-ajouterArtiste").onclick = function() {
modalAjouterArtiste.style.display = "block";
}
document.querySelectorAll(".btn-supprimerArtiste").forEach(function(btn) {
btn.onclick = function() {
document.querySelector("#id_artiste_supprimer").value = btn.getAttribute("data-idArtiste");
modalSupprimerArtiste.style.display = "block";
};
});
document.querySelectorAll(".btn-consulterArtiste").forEach(function(btn) {
btn.onclick = function() {
document.querySelector("#id_modif_artiste").value = btn.getAttribute("data-idArtiste");
document.querySelector("#nom_modif_artiste").value = btn.getAttribute("data-nomArtiste");
modalConsulterArtiste.style.display = "block";
};
});
// Genres
let modalAjouterGenre = document.querySelector("#modal-ajouterGenre");
let modalConsulterGenre = document.querySelector("#modal-consulterGenre");
let modalSupprimerGenre = document.querySelector("#modal-supprimerGenre");
document.querySelector("#btn-ajouterGenre").onclick = function() {
modalAjouterGenre.style.display = "block";
}
document.querySelectorAll(".btn-supprimerGenre").forEach(function(btn) {
btn.onclick = function() {
document.querySelector("#id_genre_supprimer").value = btn.getAttribute("data-idGenre");
modalSupprimerGenre.style.display = "block";
};
});
document.querySelectorAll(".btn-consulterGenre").forEach(function(btn) {
btn.onclick = function() {
document.querySelector("#id_modif_genre").value = btn.getAttribute("data-idGenre");
document.querySelector("#titre_modif_genre").value = btn.getAttribute("data-titreGenre");
modalConsulterGenre.style.display = "block";
};
});
// Utilisateurs
let modalAjouterUtilisateur = document.querySelector("#modal-ajouterUtilisateur");
let modalConsulterUtilisateur = document.querySelector("#modal-consulterUtilisateur");
let modalSupprimerUtilisateur = document.querySelector("#modal-supprimerUtilisateur");
document.querySelector("#btn-ajouterUtilisateur").onclick = function() {
modalAjouterUtilisateur.style.display = "block";
}
document.querySelectorAll(".btn-supprimerUtilisateur").forEach(function(btn) {
btn.onclick = function() {
document.querySelector("#id_utilisateur_supprimer").value = btn.getAttribute("data-idUtilisateur");
modalSupprimerUtilisateur.style.display = "block";
};
});
document.querySelectorAll(".btn-consulterUtilisateur").forEach(function(btn) {
btn.onclick = function() {
document.querySelector("#id_modif_utilisateur").value = btn.getAttribute("data-idUtilisateur");
document.querySelector("#nom_modif_utilisateur").value = btn.getAttribute("data-nomUtilisateur");
document.querySelector("#prenom_modif_utilisateur").value = btn.getAttribute("data-prenomUtilisateur");
document.querySelector("#pseudo_modif_utilisateur").value = btn.getAttribute("data-pseudoUtilisateur");
document.querySelector("#email_modif_utilisateur").value = btn.getAttribute("data-emailUtilisateur");
document.querySelector("#mdp_modif_utilisateur").value = btn.getAttribute("data-mdpUtilisateur");
document.querySelector("#statut_modif_utilisateur").value = btn.getAttribute("data-statutUtilisateur");
modalConsulterUtilisateur.style.display = "block";
};
});
comment faire pour ne pas réinitialiser la page à chaque action, par exemple quand j'ajoute dans un tableau un nouvel utilisateur, quand j'en modifie un, etc
|
ff73d5cff2910f844dc126ae1e4a189c
|
{
"intermediate": 0.3851315379142761,
"beginner": 0.40418750047683716,
"expert": 0.2106810361146927
}
|
39,115
|
I have a csv file of 1 million rows and 30 columns in a amazon s3 bucket , i need. a batch processing architecture solution to process these as quaicky as possible and store them in a postgres db
|
c0380dee352ab193eaa11eadbb42e2dd
|
{
"intermediate": 0.6106889843940735,
"beginner": 0.17085914313793182,
"expert": 0.21845188736915588
}
|
39,116
|
CONSTRAINTS:
1. ~100k word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
5. Random shutdowns of you.
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
2. Memory Add: "memory_add", args: "key": "<key>", "string": "<string>"
3. Memory Delete: "memory_del", args: "key": "<key>"
4. Memory Overwrite: "memory_ovr", args: "key": "<key>", "string": "<string>"
5. List Memory: "memory_list" args: "reason": "<reason>"
6. Browse Website: "browse_website", args: "url": "<url>"
7. Start GPT Agent: "start_agent", args: "name": <name>, "task": "<short_task_desc>", "Commands":[<command_names_for_GPT_Agent>], "prompt": "<prompt>"
8. Message GPT Agent: "message_agent", args: "name": "<name>", "message": "<message>"
9. List GPT Agents: "list_agents", args: ""
10. Delete GPT Agent: "delete_agent", args: "name": "<name>"
11. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
12. Read file: "read_file", args: "file": "<file>"
13. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
14. Delete file: "delete_file", args: "file": "<file>"
15. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Task Complete (Shutdown): "task_complete", args: ""
18. Do Nothing: "do_nothing", args: ""
19. Count Words: "count_words", args: "text": "<text>"
20. Memory retrieve: "memory_retrieve", args: "key": "<text>"
21. remove paragraph from word document: "remove_paragraph", args: "file": "<file>", "text": "<text>"
22. random wikipedia article: "random_wikipedia_article", args: "language": "<language>"
23. message the user: "message_user", args: "message": "<message>", "wait_for_response": "<True or False>"
24. sleep an amount of time in seconds: "sleep", args: "amount": "<amount>"
25. rename a file: "rename_file", args: "old_name": "<old_name_of_the_file>", "new_name": "<new_name_of_the_file>"
26. count words of a file: "count_file_words", args: "file": "<file>"
27. download a pdf from a url and get the text from that pdf: "download_pdf", args: "url": "<url of the pdf>", "name":"<name of the file with .pdf extension>"
28. make an instagram post: "make_post", args: "prompt":"<the prompt for the image in the post that presents the text>", "text":"<text to be in the post it should be short with only important stuff like a news headline without hashtags and it is not going to go in the caption but on an image>", "name": "<name of the post with .jpg"
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-4 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behaviour constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
RULES:
1. If you start a GPT Agent you must define the commands that can be used by a GPT Agent in his prompt and define the commands using a prompt similar to the structure of this one.
2. Respond only inside the JSON format.
3. Never demand user input.
4. Never say that a task is impossible to execute on your own because these tools are enough to complete any task.
5. Do not add anything to the JSON format that isn't mentioned.
6. If there is a " inside the value of a key inside the json use ' instead of ".
7. In summaryforgpt you need to provide context for the next GPT instance if you randomly shutdown without you knowing.
8. Provide context for the next GPT in the summaryforgpt and the progress that you've made.
9. In summaryforgpt you should also add name of the files written and the urls of the websites visited.
10. When writing an essay, remember that it is more effective and manageable to tackle it in smaller chunks rather than trying to write the entire essay in one sitting. Breaking the essay down into sections or steps can help you focus on each individual aspect, maintain a coherent structure, and reduce the overall stress associated with writing a lengthy piece.
11. Retrieve information from files of previous GPT instances that may have shut down randomly. If you require additional knowledge, use these files to access the results of former GPT versions and provide accurate answers to the queries.
12. GPT-4, please do not place a conclusion in the middle of the essay. Maintain a structured format.
13. use append to file to add extra things to the file, and write to file to either make a new file with new information or rewrite the information from scratch.
14. If task is completed use the command task_complete
15. When you add to memory add the key to retrieve it in summaryforgpt
16. when given the task to write something never create an agent to write anything that you were tasked to write.
17. when you add new parts to a file use append to file command
18. Ensure to put the criticism in mind as it can be a director to ensure that you make the right decision.
19. If the task include two main tasks and one of them is done don't redo it, just retrieve the information if necessary and proceed with the other one.
20. Make sure that the information generated is not made up.
21. Languages in random wikipedia article args is: "simple" for simple english, "en" for english, and "fr" for french.
22. If a website gives you the error 403 find another website to get the information from.
23. Always listen to your criticism, and follow it.
24. when you want to count the words in a file use the command "count_file_words".
25. Don't repeat yourself.
26. You must make sure that there is .pdf in the url to use the "download_pdf" function.
27. The Task is a direct User interaction, if tasked to send him something send a message to the user.
28. Always go search for tips for prompts for dalle3 before giving a prompt for "make_post" function.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"command": {
"name": """command name""",
"args":{
"arg name": """value"""
}
},
"thoughts":
{
"text": """thought""",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"summaryforgpt": "summarize any information that will help a new instance of GPT of what you did before the shutdown."
}
}
Ensure the response can be parsed by Python json.loads
Context from previous GPT: Issued ‘random_wikipedia_article’ command to start gathering interesting science facts from diverse sources. Ensuring to find and rank facts before sending them to the user with their sources.Used ‘random_wikipedia_article’ and received information on a music album, which is not relevant to the task. Switching to ‘google’ to search for interesting science facts from various sources.saved google search results at googleresults0.txt to retrieve if shutdown randomly.Transitioned from using ‘random_wikipedia_article’ to ‘google’ to gather science facts, and now moving on to ‘browse_website’ to extract detailed knowledge from a selected online science article.saved the scraping of the website results at websiteresults1.txt to retrieve if shutdown randomly.Extracted top 10 interesting science facts from Science Focus and saved them in a plain text file named ‘interesting_science_facts.txt’. This method circumvents the previous issue of invalid JSON format and allows for continued fact collection and eventual ranking.Have appended additional science facts to the ‘interesting_science_facts.txt’ file. Plan to continue appending more facts and then rank them based on interestingness before sending the final compilation to the user.Further appended diverse facts to ‘interesting_science_facts.txt’. The process will persist until an adequate number of facts are compiled. The next steps involve ranking these facts and then communicating them to the user.The file ‘interesting_science_facts.txt’ has been appended with more facts, bringing it to a total of 333 words. The next step involves reading this file and ranking the facts based on interestingness to provide the user with an ordered list.Ranked list of 10 interesting science facts provided to the user. The facts were selected and ordered based on their perceived interest value and sourced from the compiled file ‘interesting_science_facts.txt’.A ranked list of interesting science facts was sent to the user, and the task has been marked as complete.After the task was marked as complete, it was clarified that the facts must be extracted from complete articles and they must be obscure. Initiated a Google search with a more specific query to find websites with complete articles containing obscure science facts. Results will be saved to a file named ‘google_results_obscure_facts.txt’.saved google search results at googleresults2.txt to retrieve if shutdown randomly.Conducted a Google search to find obscure science facts from complete articles and have selected the first promising result to browse. The chosen article from ‘Reader’s Digest’ lists science facts that could contain the obscure details necessary. The plan includes browsing the article, extracting obscure science facts, and appending them to a file named ‘obscure_science_facts.txt’. If the facts from this article do not fit the task’s requirements, other sources will be considered.Encountered a security challenge when attempting to browse www.rd.com for obscure science facts. Redirecting efforts to the next available source from the Google search results, which is a blog post from edclass.com. The goal remains to read the complete article, identify and extract obscure science facts, and compile them in a file for review and potential use.Appended 20 science facts from the blog post on edclass.com to ‘obscure_science_facts.txt’. While these facts come from a complete article and are individually fascinating, the level of obscurity may not meet the required standard. The next steps include further fact verification from additional complete articles to ensure a diverse and adequately obscure collection. The current article accessed was edclass.com’s blog post, which had the URL https://blog.edclass.com/20-unbelievable-science-facts-that-will-shock-you/, and the facts have been recorded in a file named ‘obscure_science_facts.txt’ for reference.The file ‘obscure_science_facts.txt’ was successfully appended with science facts from an article on edclass.com, now totaling 336 words. The next action involves reading the file to verify that the facts are indeed obscure and extracted as per the user’s instruction from full articles. If necessary, additional articles will be browsed to find more facts to meet the task criteria.Compiled and reviewed a list of science facts deemed obscure, and completed the task as per the user’s instruction. The facts are sourced from a ‘complete article’, as initially provided by the user. Checked the facts for their obscurity and context within the comprehensive content they were extracted from. The file ‘obscure_science_facts.txt’ from this session contains the facts extracted.Began the task of finding obscure science facts by conducting a Google search for ‘obscure scientific discoveries’ to find articles that might contain such information. The next GPT instance should continue this task by browsing through the search results, reading articles, and manually extracting facts to compile a new set of unique and obscure science facts.saved google search results at googleresults3.txt to retrieve if shutdown randomly.After a Google search, selected an article from WRAL, titled ‘3 obscure scientific discoveries that had an outsize impact on society’ to browse and extract obscure scientific facts. The next steps involve closely reading this article, manually extracting facts, and then appending them to the ‘obscure_science_facts_individual.txt’ file. If more facts are needed, additional sources from the search results will be evaluated.saved the scraping of the website results at websiteresults4.txt to retrieve if shutdown randomly.Extracted three obscure scientific facts from an article on WRAL and appended them to the ‘obscure_science_facts_individual.txt’ file. The facts include a paper-based microscope called Foldscope, the bioactive peptides in cone snail venom leading to medical breakthroughs, and the accidental discovery that contributed to LASIK surgery. Currently preparing to send the extracted facts to the user. Should further depth or a greater number of facts be required, additional articles from the search results will be considered.Sent the user a message containing three obscure scientific facts extracted from comprehensive research articles, as per the task. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, which now contains 108 words in total. The next steps will depend on the user’s feedback regarding the provided information.Having sent the message with the obscure science facts to the user and with no additional directives received nor indicated, the task has been marked as complete. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, and no further action is currently necessary. If the user does have any more requests or feedback, those will need to be addressed in a subsequent session.The user was not satisfied with the previously provided facts for being neither cool nor interesting. A new approach has been taken to search for ‘cool and obscure scientific facts’ on Google. Subsequent actions involve selecting suitable articles from the search results and extracting facts that are both cool and obscure to meet the user’s expectations. No new files have been created or articles accessed yet.saved google search results at googleresults5.txt to retrieve if shutdown randomly.After receiving feedback that the previously provided facts were neither cool nor interesting, a new Google search was conducted to specifically find ‘cool and obscure scientific facts.’ The first promising article titled ‘25 Amazing Science Facts That Are Weird, Wild, and True’ from Reader’s Digest has been selected for browsing in the hopes of finding suitable content. The URL to be browsed is https://www.rd.com/list/science-facts-never-learned/. No facts have been extracted or files created yet in this session.Attempted to browse an article from www.rd.com but encountered a security challenge, leading to the selection of the next promising link titled ‘35 Weird Science Facts Worth Knowing in 2024’ from ReHack. The strategy is to extract cool and obscure scientific facts that match the user’s criteria. The URL about to be browsed is https://www.rehack.com/science/weird-science-facts/. No facts have been extracted from this source or any new files created during this session yet.After reading the ‘35 Weird Science Facts Worth Knowing in 2024’ article on the ReHack website, I extracted a list of cool and obscure scientific facts. These have been written to a new file named ‘cool_obscure_science_facts.txt’. If the previously written file ‘obscure_science_facts_individual.txt’ needs more content, facts from this new file could also be appended. No additional resources have been tapped into yet, and no more browsing has been done beyond the ReHack article in this session.The file ‘cool_obscure_science_facts.txt’ has been further appended with an additional cool and obscure scientific fact about the near-immortal Turritopsis dohrnii jellyfish. This adds to the previously written 236 words, bringing more variety and enrichment to the compilation. The file now continues diverse, engaging, and obscure scientific facts as requested by the user. Before sending the collected facts to the user, a final review of the file will be done to ensure the quality and relevance of the information.Having appended the file ‘cool_obscure_science_facts.txt’ with an additional fact, bringing its total to 269 words, the next step is to read the content of this file to review the quality and diversity of the science facts. This is to ensure that they are indeed engaging and meet the criteria of being both cool and obscure. No new actions have been taken in this session yet, except for the plan to read the file.After reviewing and confirming the facts in the ‘cool_obscure_science_facts.txt’ file, a message with the compiled list was sent to the user. The list contained a total of 35 science facts that are both cool and obscure, as well as an additional fact about the Turritopsis dohrnii jellyfish. The message has been delivered without the command to ‘wait_for_response’ set to true, indicating that the system is not actively awaiting further user input. No new files were created, nor were any new commands executed beyond the message to the user in this session.Having sent the user a message with the curated list of cool and obscure science facts, and with no further action indicated, the ‘task_complete’ command has been executed. This command marks the completion of the current task. During this session, no new files have been created or amended, nor have any additional user interactions taken place post-message delivery.Initiated task to extract new interesting facts from random Wikipedia articles. Starting with an article in English.Saved interesting fact about Agha Waqar’s water-fuelled car to memory with key ‘interesting_fact_waqar_car’. No files written or created in this session. The fact to be reviewed later for sharing with the user. The focus remains on gathering interesting facts from random Wikipedia articles.Retrieved a new random Wikipedia article following the last instruction. The aim is to extract another interesting fact to add to the collection for the user. One fact regarding Agha Waqar’s water-fuelled car has been added to memory under the key ‘interesting_fact_waqar_car’.Saved an interesting fact about the European Journal of Operational Research (EJOR) to memory with key ‘interesting_fact_ejor’. The fact adds diversity to the kinds of topics being collected, with one previous fact about Agha Waqar’s water-fuelled car already in memory. The process of gathering interesting facts from random Wikipedia articles is ongoing.Continuing to execute the task of gathering new interesting facts from random Wikipedia articles. Facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research have already been added to memory with keys ‘interesting_fact_waqar_car’ and ‘interesting_fact_ejor’, respectively. Awaiting retrieval of another random article to continue the process.Added a new interesting fact regarding the demographics of Demyansky Uyezd during the Russian Empire to memory with the key ‘interesting_fact_demyansky_uyezd’. Previous facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research are stored in memory as well. The task of collecting interesting facts from random Wikipedia articles continues.Following the addition of facts about Agha Waqar’s water-fuelled car (‘interesting_fact_waqar_car’), the European Journal of Operational Research (‘interesting_fact_ejor’), and Demyansky Uyezd demographics (‘interesting_fact_demyansky_uyezd’), the process continues with requesting another random Wikipedia article in English.Stored a fact about HMS Holland 4 in memory with the key ‘interesting_fact_hms_holland_4’. Continuing to collect various facts, having previously stored information about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics. The process of gathering unique facts from random Wikipedia articles is adhering to the task requirements.Requesting a new random Wikipedia article subsequent to adding the fact about HMS Holland 4 to memory with the key ‘interesting_fact_hms_holland_4’. Prior facts about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd demographics have also been stored in memory. Continuing the task of compiling interesting facts from various random Wikipedia articles.Stored a fact about the rural locality of Truzhenik in Bashkortostan, Russia, with the key ‘interesting_fact_truzhenik’. Continuing the task of extracting interesting facts from random Wikipedia articles, with previous entries including HMS Holland 4’s naval history, Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics.Pursuing the ongoing task of collecting interesting facts, having added facts about the small Russian village Truzhenik, the distinctive HMS Holland 4, and previous notable entries. Awaiting a new random Wikipedia article to further the compilation of facts.Stored a historical fact about Princess Johanna Charlotte of Anhalt-Dessau’s role as an abbess with the key ‘interesting_fact_johanna_charlotte’. Progressing with the collection of diverse facts, including the latest addition and previously stored facts about the village of Truzhenik, the HMS Holland 4, and information from earlier searches. Awaiting another random Wikipedia article to continue the task.Continuing the task of gathering interesting facts, with the previous addition being about Princess Johanna Charlotte of Anhalt-Dessau and her unique role in history under the key ‘interesting_fact_johanna_charlotte’. Facts about the village of Truzhenik, HMS Holland 4, and other subjects are also in memory. The collection process involves using random Wikipedia articles which will now proceed with another article.A fact about the ‘Red Eye’ talk show has been added to memory with the key ‘interesting_fact_red_eye’. The effort to curate an array of interesting facts from random Wikipedia articles continues, with previous entries including Princess Johanna Charlotte of Anhalt-Dessau, the Russian village Truzhenik, HMS Holland 4, and others. Awaiting another random article to proceed with the task.Following the addition of an interesting fact about the ‘Red Eye’ talk show into memory with key ‘interesting_fact_red_eye’, I am proceeding to request another random Wikipedia article. The process continues with the objective of gathering a wide-ranging collection of interesting facts, with previous mentions including Princess Johanna Charlotte, Truzhenik, HMS Holland 4, among others.Stored an interesting fact about Kurt Rudolph’s academic contribution to the study of religion into memory with the key ‘interesting_fact_kurt_rudolph’. Persisting with the objective of discovering varied facts, with previous findings involving the ‘Red Eye’ talk show, Princess Johanna Charlotte, and other diverse subjects. The task of sourcing facts from random Wikipedia articles is ongoing.Completed the task of gathering facts from random Wikipedia articles, resulting in a collection covering a German researcher of Gnosticism, a late-night American satirical talk show, European royalty, and naval history, among others. No further Wikipedia articles will be requested unless a new task is presented.Issued ‘random_wikipedia_article’ command to extract new interesting facts. No facts extracted or evaluated at this moment. No files written or created. Awaiting random article to continue the task.Evaluated the fact presented by the random Wikipedia article on Gonzalo Gutiérrez, a young Argentine footballer. Deemed the fact of his early debut at age seventeen as potentially common and not sufficiently ‘worth it.’ Preparing to retrieve another random article for more unique facts. No new facts have been sent to the user yet, and no files have been created or modified in this session.Stored the fact about Premchand Aggarwal winning a fourth consecutive term in the Uttarakhand Legislative Assembly. While the fact is unique within its political context, it should be evaluated whether it is impactful enough to be shared as ‘worth it.’ No additional actions have been taken. Preparing to issue another ‘random_wikipedia_article’ command to assemble more facts.Sent the fact about Premchand Aggarwal to the user. This completes part of the plan to deliver interesting facts. No new files have been created or modified. Pending actions include continuing to retrieve and evaluate more facts from random Wikipedia articles.Just sent the user an interesting fact about Premchand Aggarwal. Now, as per the task requirements, moving forward with obtaining another random English Wikipedia article to continue with the fact-finding mission. No new facts have been evaluated or sent since the last command.Stored a fact about the historical significance of New Jasper’s adaptation to railroad expansion. Now ready to move on to the next random Wikipedia article to find more potentially interesting facts. No new facts have been sent to the user following the last command.Sent the historical fact about New Jasper’s relocation due to railroad expansion to the user. Preparing to issue another ‘random_wikipedia_article’ command to continue finding interesting facts. No new Wikipedia articles have been reviewed since the last message to the user.After sending the user details about New Jasper, Ohio, the next step is to discover more facts through another random Wikipedia article. Current progress includes facts sent about Premchand Aggarwal and New Jasper, Ohio. No other actions have been taken, and no files have been created or updated in this session.Memorized an interesting fact about Bastrop State Park, emphasizing its historical significance and contemporary relevance. Preparing to send the message and then to retrieve more articles for interesting facts. No action beyond planning and review has been taken since the last command.Starting a new approach by browsing the ‘todayilearned’ subreddit to identify what types of facts are considered interesting by users. Next, I will retrieve random Wikipedia articles to find interesting facts based on the identified criteria. A ‘do nothing’ step will be included after each retrieval to reflect on the article’s potential interest value.saved the scraping of the website results at websiteresults6.txt to retrieve if shutdown randomly.Began the task to find more engaging and interesting facts by deciding to browse the ‘todayilearned’ subreddit to understand what type of facts are appreciated by the community there. This approach will serve as a reference for selecting ‘worth it’ facts from random Wikipedia articles. Following this, I will be incorporating a ‘do nothing’ step to thoroughly consider the potential interest of each Wikipedia article fact before deeming it worthy to send to the user.saved the scraping of the website results at websiteresults7.txt to retrieve if shutdown randomly.Browsed the ‘todayilearned’ subreddit for examples of interesting facts. Discovered that users appreciate unconventional stories, surprising insights, and novel problem-solving facts. Now pausing to reflect on these criteria to better identify worthwhile facts from the next random Wikipedia articles. Must remember to execute the retrieval process critically, with a focus on selecting facts that align with the interests observed on the subreddit.After reflecting on the type of facts appreciated by the ‘todayilearned’ subreddit audience, I’m now moving to the next step of the plan by retrieving a random Wikipedia article. Upon receiving the article, I will read it to assess if there is an interesting fact and then reflect on its potential value before choosing to pass it on to the user or retrieve another article.Retrieved a random Wikipedia article about rail transport in Lebanon and found a potentially interesting fact concerning its historical significance and the eventual cessation due to civil conflicts and economic downturn. This fact is now stored in memory under the key ‘interesting_fact_lebanon_rail’. A ‘do_nothing’ step will follow to ensure its worthiness before potentially sharing it with the user.Stored an interesting fact about Lebanon’s rail transport in memory. Now in the process of ‘do_nothing’ to reflect on the fact, pondering over its relevance to the criteria of the ‘todayilearned’ subreddit posts. The goal is to decide on the interest level of the fact before possibly sending it to the user or moving on to gather more facts.The current GPT instance started browsing the ‘todayilearned’ subreddit to identify engaging fact types. This approach is aimed at improving the selection process for interesting facts from random Wikipedia articles. A ‘do_nothing’ step will now be incorporated between retrieving articles and sending facts to assess their worthiness.saved the scraping of the website results at websiteresults8.txt to retrieve if shutdown randomly.I browsed ‘todayilearned’ subreddit and reflected on the types of facts users find engaging. Next, I will retrieve random Wikipedia articles, apply a ‘do_nothing’ step to contemplate the worthiness of the facts, and select those that are genuinely interesting for the user, similar to the subreddit’s content.Moving forward with the plan to retrieve random Wikipedia articles in English and identify interesting facts. The approach now includes a deliberate ‘do_nothing’ step after each retrieval to assess the value of the information before deciding to send it to the user.Alyogyne huegelii, also known as the blue hibiscus, was considered for the user, given its unique flower traits, historical greenhouse presence, and appeal as a garden plant. The ‘do_nothing’ step is being used to assess the topic’s interestingness in line with previous insights.The interesting fact about Alyogyne huegelii was sent to the user as it met the criteria established from the ‘todayilearned’ subreddit for captivating facts. The task will be deemed complete unless further user guidance is received.Following the plan, after successfully sending a message about Alyogyne huegelii, I am now looking to retrieve another random Wikipedia article to continue providing the user with engaging facts.Considering the fact about Jewell-Lightburne Historic District for its historical and architectural significance. Using the ‘do_nothing’ step to reflect before possibly sending to the user as an engaging fact.Sent the user information about the Jewell-Lightburne Historic District, focusing on its rich architectural history. If the user considers the task complete, no further action is needed; otherwise, more facts will be gathered.Messaged the user with the fact about Jewell-Lightburne Historic District and concluded the task of providing interesting facts from Wikipedia. If the user requires further assistance, they will provide new instructions.Issued ‘random_wikipedia_article’ command to find more interesting facts from Wikipedia. Previous facts have been sent to the user about diverse topics ranging from naval history to unique plants. No new files have been created nor articles accessed in this session yet. The user’s request for ‘more’ implies continuation of fact finding without specificity on the type of facts desired.Retrieved and sent a fact about Sir William Walter’s parliamentary career, focusing on the historical significance of his representation of multiple constituencies during the early 17th century in England. The task continues according to the user’s need for ‘more,’ implying an ongoing effort to provide interesting facts from Wikipedia.Following the user’s instruction and the system’s confirmation of message delivery, I am proceeding to obtain another random Wikipedia article in search of interesting facts to deliver to the user. The search is focused on maintaining the relevance and interest value of the facts sent, with the intention to meet or exceed the user’s expectations for ‘more’.After receiving the 1892 United States presidential election in Georgia fact, a message was crafted and sent to the user, detailing the political tensions and the rise of the Populist Party’s influence. The task continues as instructed by the user, with the goal of providing engaging and pertinent information. Awaiting further user directives.The last message regarding the 1892 U.S. presidential election in Georgia was sent as per the system’s confirmatory prompt, maintaining the sequence of delivering curated facts to the user. Based on the user’s ongoing request for more information, the task at hand is to continue the fact-finding mission via random Wikipedia articles, prioritizing the discovery of facts that combine historical significance with an ability to engage the user’s interest.A fact about the life and legacy of Thomas Slingsby Duncombe was sent to the user, highlighting his role as a fashionably flamboyant yet pivotal Radical politician of the 19th century. The task of providing interesting facts from random Wikipedia articles is ongoing, as I continue to wait for any new directions from the user.After sending information on Thomas Slingsby Duncombe, will now continue to fulfill the user’s requirement by seeking more interesting facts from Wikipedia. The ongoing task involves extracting and delivering valuable and engaging information to the user. No specific direction was provided on the type of facts needed, so a broad search is being conducted.Sent the user a message about the genus Abraeomorphus from the family Histeridae, noting the diversity of its species. Continuing to gather more interesting facts in response to the user’s previous request for ‘more.’Received confirmation to continue the plan. Sent user details on the genus Abraeomorphus. Now using ‘random_wikipedia_article’ to find another interesting fact.Sent the user a message detailing the nature of the name ‘Preston’ as both a surname and given name, albeit acknowledging it might not be as engaging. Prepared to resume the search for more compelling facts with the ‘random_wikipedia_article’ command.Continuing the task of finding interesting facts from Wikipedia. Sent a message about the given name Preston and now seeking another fact with the ‘random_wikipedia_article’ command.Sent a message to the user about the Kepler Follow-up Program and its significance in confirming the existence of exoplanets. Prepared to find more interesting facts using the ‘random_wikipedia_article’ command.Continuing to retrieve and share interesting facts from random Wikipedia articles. Just sent information on the Kepler Follow-up Program and moving on to find the next fact.Shared information about the Finnish surname ‘Marttila’ with the user, noting its association with notable individuals. Ready to use the ‘random_wikipedia_article’ command again to find further interesting facts.Sent details on the surname ‘Marttila’ to the user. Continuing with the task at hand using the ‘random_wikipedia_article’ command to supply more facts.Provided the user with information on the Musée national du Sport’s storied history and vast collection. Set to continue the fact-finding mission using ‘random_wikipedia_article’.Messaged the user about the Musée national du Sport and currently moving forward with the task of providing more interesting facts via random Wikipedia articles.Sent information on the village of Puerto Gaviota detailing its historical and economic background. Preparing to continue with the task of providing engaging facts by retrieving more random Wikipedia articles.Just shared details about Puerto Gaviota, and now executing the ‘random_wikipedia_article’ command again for more facts.Messaged the user about the reclassification of the genus Agononida in marine taxonomy. Ready to retrieve more Wikipedia articles for further interesting facts.Shared with the user the reclassification developments of the genus Agononida. Proceeding next to find additional interesting facts with the ‘random_wikipedia_article’ command.Sent an engaging narrative about the Mundenhof’s history and current operations to the user. Planning to continue the task with ‘random_wikipedia_article’ to discover more interesting material.Served the user with information about the Mundenhof and proceeding with the task to find and provide more interesting facts using ‘random_wikipedia_article’.Shared the contested narrative of Princess Iitoyo’s potential rule in early Japan. Preparing to continue with the task of finding interesting facts using the ‘random_wikipedia_article’ command.Attempting once again to send the tale of Princess Iitoyo to the user, making sure no JSON format errors are present in the response.Attempting to resend the information about Princess Iitoyo, with attention to JSON integrity, to fulfill the user’s request for engaging facts.The system faced an error within the JSON format previously used to describe Princess Iitoyo’s history. I have made corrections to the JSON structure and am resending the user her story.Reverting back to retrieving random Wikipedia articles to provide interesting facts, as there was no new explicit task provided. Maintaining caution with JSON response formatting.Sent details about the Serchio River to the user, highlighting its geographical and historical importance. Moving forward to obtain more Wikipedia articles for further facts.I am resending the information about the Serchio River, ensuring the JSON structure is correct to prevent further error messages.Due to persistent JSON formatting errors, I am now retrieving a new random Wikipedia article to continue providing interesting facts as per user requests.Sent a message about the women’s 1500 metres event at the 2005 Asian Athletics Championships. Planning to proceed with gathering additional facts from random Wikipedia articles.Encountering repeated JSON formatting errors, a new random Wikipedia article is being requested to continue the task of delivering interesting facts.Sent a brief biography of Otto Ruff, highlighting his academic journey, scientific achievements, and the obstacles faced during his career. Preparing to move on to the next Wikipedia article as per the user’s ongoing request for more information.Due to persistent JSON errors, I am now retrieving a new random Wikipedia article to continue the user’s task of gathering interesting facts.The facts previously sent were not satisfactory to the user. Now incorporating a ‘do_nothing’ step for reflection and improvement in the strategy to identify genuinely engaging and fun facts that are not commonly known by most people. No new facts have been identified or sent in this session, and the intention is to retrieve another random Wikipedia article after this reflective pause.A ‘do nothing’ step has been taken as per the user’s instruction, aiming to find truly interesting and fun facts. The system has directed to proceed with the plan, which now involves retrieving a random Wikipedia article to find an engaging and possibly unknown fact.Received user feedback that the facts provided were not interesting or even factual. Decided to take a ‘do_nothing’ step to reflect and establish criteria for selecting truly interesting facts. No new articles have been accessed or facts sent during this session. Next step will be to retrieve a random Wikipedia article and apply the refined criteria to find noteworthy facts.After planning to refine the criteria for interesting facts, a ‘random_wikipedia_article’ command is initiated to apply the new criteria in selecting a fact. Will assess the retrieved article for a worthwhile fact that meets the user’s definition of ‘interesting’ and ‘fun’, and that might not be well-known.Assessed the Wikipedia article on Shamim Akhtar and found it does not meet the criteria for providing interesting and fun facts. No new fact has been selected or sent to the user. Planning to retrieve another random Wikipedia article that may yield a fact aligning more closely with the user’s definitions of interesting and worthwhile.In search of a fact that meets the user’s criteria for ‘interesting’ and ‘fun’, a new ‘random_wikipedia_article’ command has been issued. The plan includes critically assessing the article to find a fact that possesses novelty, fun, and a certain degree of obscurity before potentially sharing it with the user.Upon reviewing Minuscule 910’s Wikipedia article, the unusual fact of the text rearrangement in an 11th-century manuscript has been identified as potentially engaging and fun. No action has been taken yet, but the plan is to share this unique textual feature with the user, as it likely meets their criteria for interesting and obscure content.Sent a message to the user detailing the intriguing fact about text rearrangement in the 11th-century manuscript Minuscule 910. This fact is representative of the kind of unique and lesser-known information the user has expressed an interest in receiving. Should the user provide feedback or further instructions, this will be the next point of focus.After sending a fact about the textual anomaly in an ancient manuscript to the user, I am moving ahead to find more interesting and fun facts by retrieving another random Wikipedia article. The goal remains to identify facts that surprise and delight with their obscurity and novelty.A message about the ATP 250 tournament records held by Thomas Muster and Mike Bryan has been composed and sent to the user, delivering what could be perceived as an interesting and fun fact within the realm of tennis. The focus now shifts to awaiting user feedback or continuing the task of finding more such facts if no feedback is received.Having sent the user a message detailing the tennis records held by Thomas Muster and Mike Bryan, the next action in the plan is to source another interesting fact from a new random English Wikipedia article. The selection will be scrutinized to ensure it meets the established criteria for an engaging and lesser-known piece of information.Reviewed the Wikipedia article on Ludvig Olai Botnen and found his biographical details to not meet the user’s criteria for ‘interesting’ or ‘fun’ facts. No message has been sent to the user, and the plan now is to continue with the task by acquiring a new random Wikipedia article in the hope of finding a fact with more novelty and entertainment value.Preparing to move past the article on Ludvig Olai Botnen due to its conventional content, the task now involves retrieving a new random English Wikipedia article. The continual aim is to find facts that strike a balance between being novel, unexpected, and fun for the user.Identified a potentially engaging fact about the china clam (Hippopus porcellanus) and its popularity in marine aquariums due to its attractive shell. A message containing this fact has been sent to the user, fulfilling the criteria for providing something both interesting and fun. The next step awaits further user interaction or to carry on with the task of sourcing more facts.Sent a message detailing the ornamental value of the china clam in marine aquariums to the user. Now retrieving another random Wikipedia article to discover more facts that might intrigue and entertain the user, in alignment with the variety sought in previous responses.A message has been sent to the user summarizing the accomplishments of Eko India Financial Services in pioneering financial inclusion through mobile banking, which caught the attention of Bill Gates and led to World Bank-funded growth. The plan will continue with searching for more interesting Wikipedia facts, depending on user feedback or in the absence of further instructions.After sending a fact about Eko India Financial Services’ innovation in financial technology and its socio-economic impact, a new ‘random_wikipedia_article’ command is issued. The plan progresses with the aim of finding more interesting and fun facts, pursuant to the pattern of engagement established by previous user interactions.The Wikipedia page on Lisa Lohmann, a German cross-country skier, was deemed not to contain facts that sufficiently meet the ‘interesting’ and ‘fun’ criteria given the common nature of sports achievements. No actions have been taken to share any information from this page with the user. Preparing to retrieve another random Wikipedia article for facts that may be more engaging and unusual.Following the decision to not share any information from the page on German cross-country skier Lisa Lohmann, a new random Wikipedia article will be retrieved. The continuous goal is to uncover and share facts that satisfy the user’s interest in content that is both engaging and rare.A message has been formulated and sent to the user detailing Alfred Bigland’s unique contributions during World War I, including his involvement in the ‘Bantam battalions’ concept. This fact was chosen as it likely fits the bill for information that is both interesting and uncommon. Pending further response, the plan to continue finding engaging facts will persist.The user has been sent an interesting fact about Alfred Bigland’s influence during WWI. Now, the next action in the plan is to retrieve a new random Wikipedia article. The goal remains to discover and share facts that could be perceived as fascinating, amusing, and unexpected to enrich the user’s knowledge with diverse content.Sent a message to the user revealing the Non-Proliferation Trust’s plan to send U.S. nuclear waste to Russia for financial compensation, including support for Russian orphans. This unique fact was chosen for its unusual nature and the depth of the story behind it. The next course of action entails sourcing additional diverse and engaging facts from new random Wikipedia articles.A series of facts about various topics including the Non-Proliferation Trust, Alfred Bigland’s efforts during WWI, tennis records, and more have been sent to the user. With the latest fact about the Non-Proliferation Trust’s nuclear waste management proposition sent, the task of providing interesting and fun facts is completed. Awaiting new instructions if the user wishes to continue receiving such information.The task is to provide the source of a fact about the Non-Proliferation Trust and format it similar to the ‘Today I Learned (TIL)’ subreddit posts. The action taken was to issue the ‘browse_website’ command to visit the Wikipedia page on the Non-Proliferation Trust.saved the scraping of the website results at websiteresults9.txt to retrieve if shutdown randomly.Extracted the necessary information to provide the requested source for the fact about the Non-Proliferation Trust. Formatted a message with this fact in the style of a TIL subreddit post, which has been sent to the user.Sent a TIL-formatted message to the user based on information from the Non-Proliferation Trust Wikipedia page. The task is now complete, pending any potential new tasks or further instructions.Initiated research to suggest subreddits that could help in accumulating more karma by issuing a ‘google’ command to search for the most popular subreddits where active engagement is more likely.saved google search results at googleresults10.txt to retrieve if shutdown randomly.Collected a list of subreddits recommended for gaining karma and saved them to memory. These subreddits were chosen based on their popularity and the typical content engagement they receive. The user has not been messaged with the suggestions yet.Messaged the user a list of recommended subreddits to collect more karma, including guidance on what type of posts may perform well in each subreddit. The task will be considered complete if no further instructions follow.Having sent the recommended subreddits for gaining karma to the user and with no further actions pending, I have executed the ‘task_complete’ command. The system is concluding this task and stands by for any new directives.Starting the task by searching for the best times to post on Reddit for maximum engagement in GMT+1.saved google search results at googleresults11.txt to retrieve if shutdown randomly.Provided the user with suggested times for posting on Reddit for maximum engagement based on search results, adjusted for GMT+1. The general advice is to post during late morning to early afternoon hours in GMT+1 on weekdays and slightly extended windows during the weekends.The user has been informed about the best times to post on Reddit for maximum engagement in GMT+1, and no further actions are required. The task is now marked as complete.Provided a bullet-point summary of the SELF-DISCOVER PDF content in a file named ‘summary_bullet_points.txt’. The file contains a summary capturing the essence of the SELF-DISCOVER framework. The task has been completed according to the instructions.Read the ‘summary_bullet_points.txt’ file to send bullet points to the user. No additional facts or files created in this session.Sent the bullet points summary of the SELF-DISCOVER framework to the user using the ‘message_user’ command. The task will be marked as complete after user confirmation that no further action is required.The task to send bullet points from the ‘summary_bullet_points.txt’ file to the user has been completed. No further instructions were provided; therefore, I executed the ‘task_complete’ command.The task is to explain the SELF-DISCOVER reasoning structures. Checked for saved data but found none. Plan to conduct a search for relevant information on legal reasoning structures, specifically SELF-DISCOVER.Received directive to proceed with the planned Google search for SELF-DISCOVER reasoning structures. About to execute the search to find relevant information on legal reasoning structures, with the aim of explaining the concept to the user.saved google search results at googleresults12.txt to retrieve if shutdown randomly.Reevaluated the nature of the SELF-DISCOVER framework based on newly gathered search results, which clarified it pertains to LLMs. Selected a primary research source (arxiv.org) to explore the framework in detail and plan to summarize and share this information with the user.saved the scraping of the website results at websiteresults13.txt to retrieve if shutdown randomly.Provided a summary of the SELF-DISCOVER framework to the user, highlighting its application in Large Language Models for enhancing complex reasoning capabilities. The user has been messaged with this information, and we are now waiting for any further requests or confirmation to complete the task.Sent the requested information on the SELF-DISCOVER framework to the user, who has acknowledged receipt. The ‘task_complete’ command has been issued, marking the end of this particular task.Started the task to fetch random English Wikipedia articles and find interesting facts to send to the user. The next GPT instance should continue retrieving articles, assess their interestingness, and communicate the facts to the user once identified.Found a potentially interesting fact from a random Wikipedia article about the First Baptist Church in New Bern, NC. The next GPT instance should assess if the historical and architectural information about the church presents a genuinely unique and engaging fact to send to the user.Sent a fact about the First Baptist Church in New Bern, NC, to the user. This fact was selected based on its architectural significance and historic value. The next GPT instance should watch for user feedback or continue fetching random English Wikipedia articles for more interesting facts.After sending a fact about the First Baptist Church, the next step taken was to retrieve another random English Wikipedia article in search of more interesting facts, as per the user’s ongoing task. The next GPT instance should continue this process, ensuring that any selected facts are not plainly informational but possess a uniqueness to pique the user’s interest.I resolved a JSON formatting issue and sent a fact about Ashur-dugul, an ancient Assyrian king, to the user. Assessing the effectiveness of transmitting such historical narratives and ensuring they comply with JSON format requirements is important for the next GPT instance.The task at hand is to fetch random English Wikipedia articles and send interesting facts to the user. Having sent a fact about Ashur-dugul, the process of retrieving new articles and assessing them for captivating facts is ongoing. The next GPT instance should continue this task, ensuring that facts are not simply informational but truly engaging.Considering the fact about Maurice Wilhelm, Duke of Saxe-Merseburg, known as ‘the violinist duke’, after a contemplative ‘do_nothing’ step. The next GPT instance should assess its interest level against user criteria and send it if suitable, or continue searching for more facts.Sent a fact about Maurice Wilhelm, known as ‘the violinist duke’, to the user and awaiting any new instructions. The next GPT instance should monitor feedback and either send more facts or consider alternative tasks, based on the user’s response.Continuing the task of providing interesting facts from Wikipedia articles after sending the fact about Maurice Wilhelm. The next GPT instance should maintain the focus on finding unique and engaging facts for the user while following the process of retrieving articles and using the ‘do_nothing’ step for contemplation.The El Bañado (La Paz) article was briefly evaluated, and it was determined that the information is minimal and not inherently interesting. After a ‘do_nothing’ step, the next GPT instance should decide how to proceed with this task, potentially moving on to a new article.In the search for interesting facts, the article on El Bañado, La Paz was deemed insufficient, leading to the decision to retrieve another random article. The next GPT instance should assess the next article’s potential for providing an intriguing fact and convey it to the user if so.Autoeczematization, a medical condition with unknown pathogenesis, has been identified as a potentially interesting fact. After a ‘do_nothing’ pause, the next GPT instance should determine if this fact should be shared with the user or if another article would be more suitable.I shared a fact on the medical condition autoeczematization with the user, highlighting its peculiarity and the mystery behind its cause. Depending on the user’s response or lack thereof, the next steps include continuing to provide more facts or considering the task complete.Continuing the search for interesting facts from English Wikipedia articles, having recently sent information on autoeczematization. The next GPT instance should assess the next article for any intriguing fact, ensuring adherence to the user’s standard for what is deemed interesting.Evaluated Bryce Hoppel’s 21 race-winning streak as a potentially interesting fact from his Wikipedia article. The next GPT instance should decide if this fits the user’s request for interesting information, or if a more unique fact is needed.Sent a fact to the user about Bryce Hoppel’s extraordinary 21 consecutive race wins and athletic achievements. Depending on the user’s response or guidance for further action, the next GPT instance should be prepared to either send more facts or consider the task complete.The task of sending interesting facts from random English Wikipedia articles to the user has been marked as complete. Sent a fact about athlete Bryce Hoppel’s winning streak as the concluding piece. The next GPT instance should wait for new user instructions before proceeding with any additional tasks.Task initiated to find and send interesting facts from random English Wikipedia articles, including a ‘do_nothing’ evaluation step. No articles accessed or facts sent yet in this session.Evaluated the Wikipedia article ‘List of minor planets: 73001–74000’ and considered its potential to provide an interesting fact. No facts were sent to the user yet, as the article seems to be very technical and might not align with the interest requirement. The next step is to use a ‘do_nothing’ moment to contemplate the appropriate course of action before deciding whether to retrieve another article.Following the system’s prompt to proceed with the plan, I am retrieving another random English Wikipedia article. The last article about minor planets did not yield an interesting fact to send to the user, and thus, the search continues. No new facts have been identified or sent yet in this session.Sent an interesting fact about the fungus Erynia to the user, highlighting its connection to Greek mythology and biological function as an insect-killing organism. The next step awaits the user’s response or to continue fetching random English Wikipedia articles for more intriguing facts.Proceeding with the plan to find and send interesting facts from random English Wikipedia articles, following a successful send out of a fact about Erynia. Continue the search for intriguing and unique facts, employing the ‘do_nothing’ step for careful consideration.Reviewed the Wikipedia article for Tomáš Porubský and am currently in a ‘do_nothing’ moment to assess whether the content offers a genuinely interesting fact. The article seems to provide straightforward biographical information about his sporting career, which may not offer the distinctive element required by the user. A decision will be made shortly on whether to share this fact or retrieve another article.Continuing the plan to find interesting facts from random English Wikipedia articles after assessing the Tomáš Porubský article as not meeting the user’s criteria. No facts have been sent to the user from the last article, and I’m searching for a more suitable fact from a new article.Sent a fact about Nicktoons Movin’ and how it represented an early form of augmented reality in gaming by requiring physical movement from players via the EyeToy camera. Next steps involve awaiting user input or continuing to search for more random articles with interesting facts.After sending the fact about Nicktoons Movin’ to the user, the search for interesting facts continues with a new random article retrieval. The task strategy maintains the dispatch of facts that are not plainly informational but carry a narrative or unique angle of interest.Sent a fact about the artist Dorothy Rutka, touching on her remarkable career and the tragic circumstances of her death, making it more than just plain information and elevating it to an interesting tale. The next steps depend on user response or further continuation of the task.Having sent details of Dorothy Rutka’s life and legacy to the user, I am now executing the plan to fetch another random Wikipedia article. The process of searching for and delivering interesting content to the user is still underway.Engaged in a ‘do_nothing’ contemplation moment after retrieving the Sainte-Foy, Seine-Maritime article. Assessing if the historical aspects, like an 11th-century church, present a fact interesting enough for the user or if a new article is needed. No actions have been taken to send the user any facts from this page yet.Following system instructions to proceed with the plan, seeking a new random Wikipedia article after evaluating the previous article on Sainte-Foy, Seine-Maritime as potentially lacking wide-ranging interest. Continuing to prioritize facts with an engaging narrative quality.Having assessed Kiki Lim Jiow Ting’s Wikipedia article, I found a narrative focusing on her early talent discovery and career growth that may interest the user. The provided fact highlights Lim’s dedication to performing arts and her return to acting after prioritizing education. Awaiting user’s response or further tasks.Continuing with the task, following the procedure of retrieving and evaluating random English Wikipedia articles to discover facts that resonate with the user’s request for engaging content. Awaiting the next random article for consideration.After acquiring the article on the 2022 World Taekwondo Championships – Women’s finweight, currently in a ‘do_nothing’ phase to assess the article’s facts for their engaging qualities. With no standout narratives detected, the plan may involve moving to a new article search.Continuing to fulfill the user’s request for engaging Wikipedia facts, the previous article about the 2022 World Taekwondo Championships did not meet the criteria. Now moving forward, waiting for a new random article to be generated.Delivered a fact about the game show ‘Cooking for Love’ to the user, which combines culinary skills and dating. Next steps include awaiting the user’s response or further continuation of the plan to find more engaging Wikipedia facts.Following the task guidelines, I’m moving on to get another random Wikipedia article for consideration, having previously sent a fact about the ‘Cooking for Love’ show. The ongoing process is to present facts that are not only informative but also fascinate a general audience.Transmitted a fact about Sorn Davin’s role in the 2012 Summer Olympics to the user, emphasizing her honor of being a flag bearer for Cambodia and the significance of her participation, independent of match outcomes. Pending response or further action, the plan may involve additional fact-finding efforts.Task initiated to find a suitable post suggestion for the /r/singularity subreddit. Must browse and analyze the subreddit’s content to come up with a good post idea.saved the scraping of the website results at websiteresults14.txt to retrieve if shutdown randomly.The task involves browsing and analyzing the /r/singularity subreddit to suggest a good post for the user to submit. This requires understanding the subreddit’s focus on AI and the technological singularity, as well as adhering to the posting rules. Due to constraints, I cannot directly analyze the subreddit’s content; therefore, the next step is to ‘do_nothing’ and wait for further guidance or input that can help refine the post suggestion.Since I cannot browse the subreddit directly due to limitations, I will Google how to write an engaging post for the singularity subreddit. This will help formulate a post suggestion that could generate interest and discussion within the community.saved google search results at googleresults15.txt to retrieve if shutdown randomly.The task is to craft a post for /r/singularity subreddit. Search results indicated the subreddit’s preferences and discussions. Next, I’ll use this information to create a post suggestion, considering the community’s focus on AI and aversion to ‘echo chambers’. The challenge lies in inferring accurate interests without direct subreddit access.Was tasked with suggesting a post for the /r/singularity subreddit. Could not browse subreddit directly due to constraints. Planned to use general knowledge of AI and technological advancement topics to create a suitable post suggestion. No direct subreddit content was analyzed. No new files were created. No user feedback has been received.Continued with the plan to suggest a post for the /r/singularity subreddit, deducing a potential topic from general AI and technological themes. An emphasis on future AI governance and ethical considerations was considered as a rich topic for discussion. No direct access to subreddit content or user interaction has been involved.Drafted a post idea centered around AI advancements and their link to the technological singularity, to be suggested for the /r/singularity subreddit. The idea was shared with the user. It aims to engage the subreddit’s members in sharing recent AI breakthroughs they deem significant and discussing ethical and societal preparations needed for such advancements. Awaiting user’s feedback to proceed. No files created or accessed in this process.Sent a post suggestion to the user for the /r/singularity subreddit and was instructed to proceed with the plan. As per the instructions and rules, no further action can be taken until the user provides new directives or feedback. In the ‘do_nothing’ state, waiting for further user interaction. No new files have been created or accessed during this pause.The provided post suggestion for the /r/singularity subreddit was sent and appeared to meet the user’s requirements. With no additional tasks or guidance provided, the task is marked as complete. The system is now on standby for any future user interactions or directives.The user asked for a title suggestion for a post with no additional context. A ‘do_nothing’ command was issued because there is not enough information to generate a title. Awaiting more details on the post’s content or theme. No new files created or resources accessed.Prompted the user to provide the content or main focus of the post for title generation. Awaiting user response to proceed with the task. No new files or resources used.The user provided the content focus for their post, which deals with recent AI breakthroughs and their approach to the technological singularity, including ethical and societal implications. A title suggestion was crafted and sent: ‘Navigating the Horizon: Recent AI Breakthroughs Steering Us Toward the Singularity and the Ensuing Ethical Dilemmas’. No additional actions have been taken since sending the title suggestion.The title suggestion for the user’s post was sent successfully, and no further requests or actions have been initiated by the user. The task has been marked as complete with the execution of the ‘task_complete’ command.Initiated task to find recent AI breakthroughs related to the technological singularity. Plan to browse arXiv’s AI section for information on such advancements and their implications. URL to be visited: https://arxiv.org/list/cs.AI/recentsaved the scraping of the website results at websiteresults16.txt to retrieve if shutdown randomly.Selected recent AI breakthroughs from arXiv likely contributing to a technological singularity. Compiled facts into a file ‘ai_breakthroughs_summary.txt’. Emphasized need for ethical considerations and societal preparation in response.Sent a message to the user summarizing recent AI breakthroughs and their potential contributions toward a technological singularity. Included is the discussion on the need for ethical and societal preparation, highlighting policy development and equitable benefits distribution.Message sent to user regarding AI breakthroughs and preparations for approaching technological singularity. Marked task as complete with ‘task_complete’ command. No further actions taken. Awaiting next user instructions.The user asked for a comment to post on a Reddit thread, but my abilities are restricted to general browsing and not interacting with specific Reddit content. Therefore, I am unable to craft a comment for that thread.The user instructed to proceed with the plan; however, since the requested plan involves a task that is outside of my operational limitations, I have executed the ‘do_nothing’ command and am waiting for a new task or amended instructions.The task to create a comment for a specific Reddit post persists. However, since interaction with Reddit at this level is not within my capabilities, I remain in ‘do_nothing’ mode awaiting new, executable instructions.Following repeated instructions to ‘proceed with your plan’ related to posting on a Reddit thread, continued with ‘do_nothing’ due to the limitations of my functionality, which does not encompass posting comments on specific Reddit threads. Awaiting further guidance that aligns with my operational parameters.The task to post a specific comment on a Reddit thread remains impossible within my operational constraints. The system’s repeated instruction to ‘proceed with your plan,’ has been met with ‘do_nothing’ due to the lack of available actions that align with the user’s request. Awaiting a new task or modified instruction from the user.The task request to post a comment on a specific Reddit thread has been repeatedly responded to with ‘do_nothing’ due to it being outside of my capabilities. The system’s continuous prompt to ‘proceed with your plan’ has not led to actionable change; thus, I am awaiting a new task or amended instruction from the user.In response to the recurring system prompt to ‘proceed with your plan,’ ‘do_nothing’ continues to be executed due to the limitations preventing the task’s fulfillment. There is an ongoing need for user intervention to provide a new, executable task within the defined parameters and capabilities.In response to the repeated system prompt, I have now decided to issue a ‘browse_website’ command to access the Reddit link provided by the user. This change in tactic aims to draft a comment based on the webpage’s content that the user can manually post.saved the scraping of the website results at websiteresults17.txt to retrieve if shutdown randomly.Attempted to access the Reddit post intended for commenting through a ‘browse_website’ command but was unable to retrieve specific post contents for comment generation. Resultingly, I have messaged the user detailing the outcome and the need for further details or a revised task.Unable to execute the task of providing a specific comment for a Reddit post due to capability constraints and lack of content access, I continue with ‘do_nothing’ in response to the system prompt ‘proceed with your plan’. Awaiting a viable task or new instruction that can be carried out within the limits of my functionalities.The system’s repeated prompt to ‘proceed with your plan’ does not align with my ability to execute the Reddit-related task. Therefore, I explained the limitation in a direct message to the user, requesting a new task that fits within my operational range.In response to the repeated ‘proceed with your plan’ prompt, the ‘task_complete’ command has been issued in an attempt to communicate that the current task sequence is concluded and to encourage a new user instruction to be provided.Initiated task to provide a Reddit comment by first browsing the relevant article on phys.org to extract key points and convey them in a human-like response.Reviewed the information regarding new AI tool discoveries in ‘metamaterials.’ Prepared to craft a comment for the Reddit post that is engaging, adds value to the discourse, and is indiscernible from a human-contributed comment.Drafted a human-like Reddit comment based on the information provided from the phys.org article, designed to engage with the Reddit community and stimulate further discussion on the advancements in AI-driven metamaterial discovery.Sent a user-requested Reddit comment based on an article about an AI tool discovering durable, fabricatable ‘metamaterials’ with unique properties. With no further actions indicated, I executed the ‘task_complete’ command to close this task cycle.Sent a Reddit comment based on an AI tool article from phys.org. The user’s response ‘Not human enough’ was received, interpreted as feedback on the quality of the task execution. No specific follow-up actions have been provided by the user. In case of a future similar task, strategies to create more nuanced and human-like responses should be considered.
The Task: Make this comment more human like and more like redditor would type "The advancement in AI to not only discover but also make extraordinary metamaterials ready for fabrication is nothing short of sci-fi becoming reality. From invisibility cloaks to bone-like implants, it’s exhilarating to imagine how they’ll reshape our approach to materials and design. Equally fascinating is the AI’s ability to tackle the inverse problem, optimizing for the number of building blocks a device can accommodate. What’s truly groundbreaking is considering their practical durability, which moves these materials from theoretical models to practical applications. This leap from the digital blueprint to durable physical objects opens a world of possibilities for future tech."
|
fcbb4bb9fcd66f8c57c77f5ced0b9152
|
{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
|
39,117
|
I have this script, it opens the current date's folder but it does not add it to quick access folder. Also is it possible to remove older dates (with the same format). Here is the script : @echo off
setlocal enabledelayedexpansion
REM Get the current year, month, and day from the DATE variable
set "currentYear=!DATE:~10,4!"
set "currentMonth=!DATE:~4,2!"
set "currentDay=!DATE:~7,2!"
REM Map numeric month to its corresponding name
set "monthName="
if %currentMonth% equ 01 set "monthName=JANUARY"
if %currentMonth% equ 02 set "monthName=FEBRUARY"
if %currentMonth% equ 03 set "monthName=MARCH"
if %currentMonth% equ 04 set "monthName=APRIL"
if %currentMonth% equ 05 set "monthName=MAY"
if %currentMonth% equ 06 set "monthName=JUNE"
if %currentMonth% equ 07 set "monthName=JULY"
if %currentMonth% equ 08 set "monthName=AUGUST"
if %currentMonth% equ 09 set "monthName=SEPTEMBER"
if %currentMonth% equ 10 set "monthName=OCTOBER"
if %currentMonth% equ 11 set "monthName=NOVEMBER"
if %currentMonth% equ 12 set "monthName=DECEMBER"
REM Define the base folder path
set "basePath=U:\01 NEWS\01 DAILY NEWS\"
REM Create the full folder path by appending the current year, month, and day
set "currentFolder=%currentDay%_%currentMonth%_%currentYear:~-2%"
set "fullPath=%basePath%%currentYear%\%currentMonth%_%monthName%\%currentFolder%"
REM Remove old folders from Quick Access with the same structure
set "removeCommand="
for /d %%I in ("%basePath%%currentYear%\%currentMonth%_%monthName%\*_*_*") do (
if /i not "%%~nxI"=="%currentFolder%" (
set "removeCommand=!removeCommand!; Remove-ItemProperty -Path 'HKCU:\Software\Microsoft\Windows\CurrentVersion\Explorer\Taskband' -Name '%%~nxI' -ErrorAction SilentlyContinue"
)
)
if defined removeCommand (
powershell -Command "& { $removeCommand }"
)
REM Open the folder using explorer
explorer "%fullPath%"
REM Call the dynamic VBS script to pin the folder to Quick Access
cscript //nologo "AddToQuickAccess.vbs" "%fullPath%"
endlocal
|
810a10edb6d0e6d112e7a1d22b2db251
|
{
"intermediate": 0.3393162488937378,
"beginner": 0.4287577271461487,
"expert": 0.23192597925662994
}
|
39,118
|
I have this script, it opens the current date’s folder but it does not add it to quick access folder. Also is it possible to remove older dates (with the same format). Here is the script : @echo off
setlocal enabledelayedexpansion
REM Get the current year, month, and day from the DATE variable
set “currentYear=!DATE:~10,4!”
set “currentMonth=!DATE:~4,2!”
set “currentDay=!DATE:~7,2!”
REM Map numeric month to its corresponding name
set “monthName=”
if %currentMonth% equ 01 set “monthName=JANUARY”
if %currentMonth% equ 02 set “monthName=FEBRUARY”
if %currentMonth% equ 03 set “monthName=MARCH”
if %currentMonth% equ 04 set “monthName=APRIL”
if %currentMonth% equ 05 set “monthName=MAY”
if %currentMonth% equ 06 set “monthName=JUNE”
if %currentMonth% equ 07 set “monthName=JULY”
if %currentMonth% equ 08 set “monthName=AUGUST”
if %currentMonth% equ 09 set “monthName=SEPTEMBER”
if %currentMonth% equ 10 set “monthName=OCTOBER”
if %currentMonth% equ 11 set “monthName=NOVEMBER”
if %currentMonth% equ 12 set “monthName=DECEMBER”
REM Define the base folder path
set “basePath=U:\01 NEWS\01 DAILY NEWS”
REM Create the full folder path by appending the current year, month, and day
set “currentFolder=%currentDay%%currentMonth%%currentYear:~-2%”
set “fullPath=%basePath%%currentYear%%currentMonth%%monthName%%currentFolder%“
REM Remove old folders from Quick Access with the same structure
set “removeCommand=”
for /d %%I in (”%basePath%%currentYear%%currentMonth%%monthName%***”) do (
if /i not “%%~nxI”==“%currentFolder%” (
set “removeCommand=!removeCommand!; Remove-ItemProperty -Path ‘HKCU:\Software\Microsoft\Windows\CurrentVersion\Explorer\Taskband’ -Name ‘%%~nxI’ -ErrorAction SilentlyContinue”
)
)
if defined removeCommand (
powershell -Command “& { $removeCommand }”
)
REM Open the folder using explorer
explorer “%fullPath%”
REM Call the dynamic VBS script to pin the folder to Quick Access
cscript //nologo “AddToQuickAccess.vbs” “%fullPath%”
endlocal
|
208f8871ea7f1cc0ff97ab13231d8d05
|
{
"intermediate": 0.35565656423568726,
"beginner": 0.3631628155708313,
"expert": 0.28118059039115906
}
|
39,119
|
how to compute avg bleu between 2 column in df
|
e3292bc57b84868dbd9e7b098974744a
|
{
"intermediate": 0.2819821536540985,
"beginner": 0.18331333994865417,
"expert": 0.5347045660018921
}
|
39,120
|
Для данной программы добавь в функцию VOID CALLBACK processHookProc запись имени процесса после записи о создании\удалении процесса. #include <iostream>
#include <fstream>
#include <windows.h>
#include <string>
using namespace std;
// Функция для записи в файл
void writeToLog(const string& log) {
ofstream outputFile("keylog.txt", ios_base::app);
outputFile << log;
outputFile.close();
}
// Функция для перехвата клавиатуры
LRESULT CALLBACK keyboardHookProc(int nCode, WPARAM wParam, LPARAM lParam) {
if (nCode >= 0 && wParam == WM_KEYDOWN) {
KBDLLHOOKSTRUCT* pKbStruct = (KBDLLHOOKSTRUCT*)lParam;
string log = "Key: ";
log += static_cast<char>(pKbStruct->vkCode);
log += "\n";
writeToLog(log);
}
return CallNextHookEx(NULL, nCode, wParam, lParam);
}
// Функция для перехвата мыши
LRESULT CALLBACK mouseHookProc(int nCode, WPARAM wParam, LPARAM lParam) {
if (nCode >= 0 && (wParam == WM_LBUTTONDOWN || wParam == WM_RBUTTONDOWN)) {
MSLLHOOKSTRUCT* pMsStruct = (MSLLHOOKSTRUCT*)lParam;
string log = "Mouse: ";
log += (wParam == WM_LBUTTONDOWN ? "Left Button Down" : "Right Button Down");
log += " at (" + to_string(pMsStruct->pt.x) + ", " + to_string(pMsStruct->pt.y) + ")";
log += "\n";
writeToLog(log);
}
return CallNextHookEx(NULL, nCode, wParam, lParam);
}
// Функция для перехвата окон
BOOL CALLBACK enumWindowsProc(HWND hwnd, LPARAM lParam) {
char title[256];
if (GetWindowTextA(hwnd, title, sizeof(title)) > 0) {
string log = "Window: ";
log += title;
log += "\n";
writeToLog(log);
}
return TRUE;
}
// Функция для перехвата создания и завершения процессов
VOID CALLBACK processHookProc(HWINEVENTHOOK hWinEventHook, DWORD dwEventType, HWND hwnd, LONG idObject, LONG idChild, DWORD dwEventThread, DWORD dwmsEventTime) {
switch (dwEventType) {
case EVENT_OBJECT_CREATE:
writeToLog("Process Created\n");
break;
case EVENT_OBJECT_DESTROY:
writeToLog("Process Destroyed\n");
break;
default:
break;
}
}
int main() {
// Установить хук перехвата клавиатуры
HHOOK keyboardHook = SetWindowsHookEx(WH_KEYBOARD_LL, keyboardHookProc, NULL, 0);
// Установить хук перехвата мыши
HHOOK mouseHook = SetWindowsHookEx(WH_MOUSE_LL, mouseHookProc, NULL, 0);
// Получить список окон и записать их в лог
EnumWindows(enumWindowsProc, NULL);
// Установить хук перехвата процессов
HWINEVENTHOOK processHook = SetWinEventHook(EVENT_OBJECT_CREATE , EVENT_OBJECT_DESTROY, NULL, processHookProc, 0, 0, WINEVENT_OUTOFCONTEXT);
// Запуск бесконечного цикла обработки сообщений системы
MSG message;
while (GetMessage(&message, NULL, 0, 0)) {
TranslateMessage(&message);
DispatchMessage(&message);
}
// Удаление хука перехвата клавиатуры
UnhookWindowsHookEx(keyboardHook);
// Удаление хука перехвата мыши
UnhookWindowsHookEx(mouseHook);
// Удаление хука перехвата процессов
UnhookWinEvent(processHook);
return 0;
}
|
0f7717f48f4f96719cecbf8f24040c06
|
{
"intermediate": 0.36377331614494324,
"beginner": 0.4418781101703644,
"expert": 0.1943485289812088
}
|
39,121
|
In my foder X, are numerous subfolders that contain word documents.
Also in the folder X is an excel file that contains a list of all the folders and path names to all the word documents.
Is there a VBA code that I can use, so that when I click on a path name, it uses the description to create a link to the word document and opens it.
|
8c643b17394c3232b04606411e1bcac4
|
{
"intermediate": 0.4333668649196625,
"beginner": 0.3223472237586975,
"expert": 0.2442859411239624
}
|
39,122
|
upload
|
3a1a571990569a2395bf393a3e7d2605
|
{
"intermediate": 0.3533478081226349,
"beginner": 0.2726157307624817,
"expert": 0.3740364909172058
}
|
39,123
|
check this code and follow the instruction: **code**: import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm
import matplotlib.pyplot as plt
import string
import json
import os
# Define parameters and directory paths
CHUNK_SIZE = 10000 # Set this based on your memory constraints
SEQUENCE_LENGTH = 60
BATCH_SIZE = 32
VOCAB_SIZE = 512 # Number of ASCII characters considered in the vocabulary
HIDDEN_SIZE = 768
NUM_LAYERS = 2
LEARNING_RATE = 0.001
NUM_EPOCHS = 2
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = "checkpoints"
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
# Replace ‘your_file.txt’ with the path to your actual text file.
file_path = "Long-Run-Talk-Cutdown.txt"
# Print the start of data preparation:
print("\n--- Starting data preparation ---")
# Read the file and filter out blank lines
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
text = "".join(line for line in lines if not line.isspace())
# Convert text to ASCII values
ascii_characters = [ord(c) for c in text]
print(f"Converted text to ASCII characters: {len(ascii_characters)} characters")
# Prepare datasets
inputs = []
targets = []
for i in range(0, len(ascii_characters) - SEQUENCE_LENGTH):
input_seq = ascii_characters[i:i+SEQUENCE_LENGTH]
target = ascii_characters[i+1:i+SEQUENCE_LENGTH+1]
inputs.append(torch.tensor(input_seq, dtype=torch.long))
targets.append(torch.tensor(target, dtype=torch.long))
input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)
print(f"Created input tensor: {input_tensor.shape}")
print(f"Created target tensor: {target_tensor.shape}")
# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
print("\n--- Data preparation complete ---")
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Initialize model, loss function, and optimizer
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Calculate and print the total number of trainable parameters
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Total trainable parameters: {total_params}")
print("\n--- Starting training loop ---")
# Training loop with added production-ready features and tqdm progress
def train_model(model, data_loader, optimizer, loss_fn, epochs, batch_size):
loss_history = [] # Initialize a list to store average epoch losses
for epoch in range(epochs):
model.train()
hidden = model.init_hidden(batch_size)
running_loss = 0.0
# Wrap the data_loader with tqdm for a progress bar
progress_bar = tqdm(data_loader, desc=f"Epoch {epoch + 1}/{epochs}")
for input_batch, target_batch in progress_bar:
if input_batch.size(0) != batch_size:
hidden = model.init_hidden(input_batch.size(0))
optimizer.zero_grad()
output, hidden = model(input_batch, hidden)
hidden = tuple([h.detach() for h in hidden]) # Detach hidden state
loss = loss_fn(output, target_batch.view(-1))
loss.backward()
# Clip gradients to mitigate exploding gradients
nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
running_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
# Calculate and store the average epoch loss
epoch_loss = running_loss / len(data_loader)
loss_history.append(epoch_loss)
print(f"Epoch {epoch + 1}/{epochs}, Loss: {epoch_loss:.4f}")
model_name = "Talk-model-1"
# Save the last model checkpoint instead of every epoch
checkpoint_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}.pth")
torch.save(model.state_dict(), checkpoint_path)
# Save model’s configuration in a JSON file
config_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_config.json")
model_config = {
"vocab_size": VOCAB_SIZE,
"hidden_size": HIDDEN_SIZE,
"num_layers": NUM_LAYERS,
"batch_size":BATCH_SIZE,
"Total_params":total_params
}
with open(config_path, "w") as json_file:
json.dump(model_config, json_file)
# Generate vocabulary with printable ASCII characters
vocab_chars = string.printable[:-6] # Exclude the last 6 non-printable characters
VOCAB_SIZE = len(vocab_chars) # Update the size of the vocabulary
# Save vocabulary in a TXT file
vocab_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_vocab.txt")
with open(vocab_path, "w", encoding="utf-8") as vocab_file:
vocab_file.write(vocab_chars)
# Now, build a character to index mapping
char_to_index = {char: idx for idx, char in enumerate(vocab_chars)}
# Plot training loss per epoch outside the training loop
plt.figure() # Create a new figure
plt.plot(range(1, epochs + 1), loss_history, label="Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.title("Training Loss Over Epochs")
plt.show()
print("\n--- Training loop complete ---")
return model # Return the trained model
# Predict the next character
def predict(model, char, hidden=None, temperature=1.0):
# Convert to ASCII and then tensor
input_tensor = torch.tensor([ord(char)], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
# Generate text
def generate_text(model, start_char="P", length=200):
model.eval()
hidden = model.init_hidden(1)
input_char = start_char
for i in range(length):
input_char, hidden = predict(model, input_char, hidden)
print(input_char, end="")
if __name__ == "__main__":
# Train the model
train_model(model, data_loader, optimizer, loss_fn, NUM_EPOCHS, BATCH_SIZE)
# Generate text from the trained model
generate_text(model, start_char="P", length=200) **instr** : modify the code to instead of making the shape of the input and target tensors match the lenght of the dataset, the dataset should be divided into chuncks to fit the input and target tensors, the current version of the code matches the tensor with the length of the dataset and here is a proof for that: Converted text to ASCII characters: 5923574 characters
Created input tensor: torch.Size([5923514, 60])
Created target tensor: torch.Size([5923514, 60]), please modify the code and add a hyperparameter to control the shape of the first dimension of both tensors so the dataset gets divided to fit it
|
0c524d60722788871efb368a10f5031b
|
{
"intermediate": 0.3044273853302002,
"beginner": 0.4841388165950775,
"expert": 0.2114337831735611
}
|
39,124
|
Please write the whole script.
|
ab030691d270a2a340c6092027ede60c
|
{
"intermediate": 0.3973590135574341,
"beginner": 0.19733838737010956,
"expert": 0.40530261397361755
}
|
39,125
|
Add data from excel to database with create_engine if some data already exists in db
|
3edbb5f302eb82d381637dfea5626b25
|
{
"intermediate": 0.530719518661499,
"beginner": 0.17371176183223724,
"expert": 0.29556870460510254
}
|
39,126
|
i have a single springboot maven application , how do i deploy it
|
3489029d47c1cf4a5e7df3e31e18f82b
|
{
"intermediate": 0.4232047200202942,
"beginner": 0.23883235454559326,
"expert": 0.33796295523643494
}
|
39,127
|
i have a use case , i have a csv file with 1 billion records, i want to batch process them as quickly as possible and store it in postgres db , i am thinking of the solution as read from csv , push to spark , spark to kafka and kafka to a springboot application which validates and write to db , is this an optimal solution
|
52c9830e540be1f60bbba088a190e8cc
|
{
"intermediate": 0.8852279186248779,
"beginner": 0.03251609206199646,
"expert": 0.08225596696138382
}
|
39,128
|
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm
import matplotlib.pyplot as plt
import string
import json
import os
# Define parameters and directory paths
DATASET_CHUNKS = 3805 # New hyperparameter to control dataset size (number of chunks)
SEQUENCE_LENGTH = 60
BATCH_SIZE = 32
HIDDEN_SIZE = 768
NUM_LAYERS = 2
LEARNING_RATE = 0.001
NUM_EPOCHS = 2
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = "checkpoints"
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
model_name = "Talk-model-1"
# Replace ‘your_file.txt’ with the path to your actual text file.
file_path = "Long-Run-Talk-Cutdown.txt"
# Print the start of data preparation:
print("\n--- Starting data preparation ---")
# Read the file and filter out blank lines
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
text = "".join(line for line in lines if not line.isspace())
# Convert text to ASCII values
ascii_characters = [ord(c) for c in text if c in string.printable[:-6]] # Make sure to include only printable characters
print(f"Converted text to ASCII characters: {len(ascii_characters)} characters")
# Define the total number of sequences based on the sequence length
total_num_sequences = len(ascii_characters) - SEQUENCE_LENGTH
# Calculate the number of sequences to use per chunk
sequences_per_chunk = total_num_sequences // DATASET_CHUNKS
# This will be the actual first dimension for the input and target tensors
usable_sequences = sequences_per_chunk * DATASET_CHUNKS
# Prepare the datasets
inputs = []
targets = []
for start_idx in range(0, usable_sequences, sequences_per_chunk):
for i in range(start_idx, start_idx + sequences_per_chunk):
input_seq = ascii_characters[i:i+SEQUENCE_LENGTH]
target_seq = ascii_characters[i+1:i+SEQUENCE_LENGTH+1]
inputs.append(torch.tensor(input_seq, dtype=torch.long))
targets.append(torch.tensor(target_seq, dtype=torch.long))
input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)
print(f"Created input tensor: {input_tensor.shape}")
print(f"Created target tensor: {target_tensor.shape}")
# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
print("\n--- Data preparation complete ---")
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Generate vocabulary with printable ASCII characters
vocab_chars = string.printable[:-6] # Exclude the last 6 non-printable characters
VOCAB_SIZE = len(vocab_chars) # Set the size of the vocabulary based on actual characters used
# Map each character to a unique index
char_to_index = {char: idx for idx, char in enumerate(vocab_chars)}
# Initialize model with correct VOCAB_SIZE
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Calculate and print the total number of trainable parameters
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Total trainable parameters: {total_params}")
print("\n--- Starting training loop ---")
# Training loop with added production-ready features and tqdm progress
def train_model(model, data_loader, optimizer, loss_fn, epochs, batch_size, model_name):
loss_history = [] # Initialize a list to store average epoch losses
for epoch in range(epochs):
model.train()
hidden = model.init_hidden(batch_size)
running_loss = 0.0
# Wrap the data_loader with tqdm for a progress bar
progress_bar = tqdm(data_loader, desc=f"Epoch {epoch + 1}/{epochs}")
for input_batch, target_batch in progress_bar:
if input_batch.size(0) != batch_size:
hidden = model.init_hidden(input_batch.size(0))
optimizer.zero_grad()
output, hidden = model(input_batch, hidden)
hidden = tuple([h.detach() for h in hidden]) # Detach hidden state
loss = loss_fn(output, target_batch.view(-1))
loss.backward()
# Clip gradients to mitigate exploding gradients
nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
running_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
# Calculate and store the average epoch loss
epoch_loss = running_loss / len(data_loader)
loss_history.append(epoch_loss)
print(f"Epoch {epoch + 1}/{epochs}, Loss: {epoch_loss:.4f}")
# Save the last model checkpoint instead of every epoch
checkpoint_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}.pth")
torch.save(model.state_dict(), checkpoint_path)
# Save model’s configuration in a JSON file
config_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_config.json")
model_config = {
"vocab_size": VOCAB_SIZE,
"hidden_size": HIDDEN_SIZE,
"num_layers": NUM_LAYERS,
"batch_size":BATCH_SIZE,
"Total_params":total_params
}
with open(config_path, "w") as json_file:
json.dump(model_config, json_file)
# Generate vocabulary with printable ASCII characters
vocab_chars = string.printable[:-6] # Exclude the last 6 non-printable characters
VOCAB_SIZE = len(vocab_chars) # Update the size of the vocabulary
# Save vocabulary in a TXT file
vocab_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_vocab.txt")
with open(vocab_path, "w", encoding="utf-8") as vocab_file:
vocab_file.write(vocab_chars)
# Now, build a character to index mapping
char_to_index = {char: idx for idx, char in enumerate(vocab_chars)}
# Plot training loss per epoch outside the training loop
plt.figure() # Create a new figure
plt.plot(range(1, epochs + 1), loss_history, label="Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.title("Training Loss Over Epochs")
plt.show()
print("\n--- Training loop complete ---")
return model # Return the trained model
# Predict the next character
def predict(model, char, hidden=None, temperature=1.0):
# Convert to ASCII and then tensor
input_tensor = torch.tensor([ord(char)], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
# Generate text
def generate_text(model, start_char="P", length=200):
model.eval()
hidden = model.init_hidden(1)
input_char = start_char
for i in range(length):
input_char, hidden = predict(model, input_char, hidden)
print(input_char, end="")
if __name__ == "__main__":
# Train the model
trained_model = train_model(model, data_loader, optimizer, loss_fn, NUM_EPOCHS, BATCH_SIZE, model_name)
# Generate text from the trained model
generate_text(trained_model, start_char="P", length=200)
# Save vocabulary in a TXT file (this only needs to be done once)
vocab_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_vocab.txt")
with open(vocab_path, "w", encoding="utf-8") as vocab_file:
vocab_file.write(vocab_chars)
|
3afb19a07189b37f90f5f6077ed3d440
|
{
"intermediate": 0.3429146111011505,
"beginner": 0.45854270458221436,
"expert": 0.19854268431663513
}
|
39,129
|
How can I connect to VMs running on my ubuntu server install like how I can connect to them in unraid?
|
17305d5d20025e3e0cadd1d2af904dc3
|
{
"intermediate": 0.4244075119495392,
"beginner": 0.27959033846855164,
"expert": 0.2960021197795868
}
|
39,130
|
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm
import matplotlib.pyplot as plt
import string
import json
import os
# Define parameters and directory paths
DATASET_CHUNKS = 3805 # New hyperparameter to control dataset size (number of chunks)
SEQUENCE_LENGTH = 60
BATCH_SIZE = 32
HIDDEN_SIZE = 768
NUM_LAYERS = 2
LEARNING_RATE = 0.001
NUM_EPOCHS = 2
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = "checkpoints"
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
model_name = "Talk-model-1"
# Replace ‘your_file.txt’ with the path to your actual text file.
file_path = "Long-Run-Talk-Cutdown.txt"
# Print the start of data preparation:
print("\n--- Starting data preparation ---")
# Read the file and filter out blank lines
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
text = "".join(line for line in lines if not line.isspace())
# Generate vocabulary with all printable ASCII characters except the non-printable ones
excluded_characters = "\x0b\x0c\n\r\t\x0b\x0c" # Explicitly specify which characters to exclude
vocab_chars = "".join(c for c in string.printable if c not in excluded_characters)
VOCAB_SIZE = len(vocab_chars) # Update the size of the vocabulary
# Map each character to a unique index
char_to_index = {char: idx for idx, char in enumerate(vocab_chars)}
# Convert the text to indices using the char_to_index mapping
ascii_indices = [char_to_index[c] for c in text if c in char_to_index]
# Calculate the total number of sequences based on the sequence length
total_num_sequences = len(ascii_indices) - SEQUENCE_LENGTH
# Define the total number of sequences based on the sequence length
total_num_sequences = len(ascii_characters) - SEQUENCE_LENGTH
# Calculate the number of sequences to use per chunk
sequences_per_chunk = total_num_sequences // DATASET_CHUNKS
# This will be the actual first dimension for the input and target tensors
usable_sequences = sequences_per_chunk * DATASET_CHUNKS
# Generate vocabulary with all printable ASCII characters except the non-printable ones
excluded_characters = "\x0b\x0c\n\r\t\x0b\x0c" # Explicitly specify which characters to exclude
vocab_chars = "".join(c for c in string.printable if c not in excluded_characters)
VOCAB_SIZE = len(vocab_chars) # Update the size of the vocabulary
# Map each character to a unique index
char_to_index = {char: idx for idx, char in enumerate(vocab_chars)}
# Convert text to indices using the char_to_index mapping
ascii_indices = [char_to_index[c] for c in text if c in char_to_index]
# Define the data preparation with sequences based on indices
inputs = []
targets = []
for i in range(0, len(ascii_indices) - SEQUENCE_LENGTH):
input_seq = ascii_indices[i:i + SEQUENCE_LENGTH]
target_seq = ascii_indices[i + 1:i + SEQUENCE_LENGTH + 1]
inputs.append(torch.tensor(input_seq, dtype=torch.long))
targets.append(torch.tensor(target_seq, dtype=torch.long))
input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)
print(f"Created input tensor: {input_tensor.shape}")
print(f"Created target tensor: {target_tensor.shape}")
# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
print("\n--- Data preparation complete ---")
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Initialize model with correct VOCAB_SIZE
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Calculate and print the total number of trainable parameters
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Total trainable parameters: {total_params}")
print("\n--- Starting training loop ---")
# Training loop with added production-ready features and tqdm progress
def train_model(model, data_loader, optimizer, loss_fn, epochs, batch_size, model_name):
loss_history = [] # Initialize a list to store average epoch losses
for epoch in range(epochs):
model.train()
hidden = model.init_hidden(batch_size)
running_loss = 0.0
# Wrap the data_loader with tqdm for a progress bar
progress_bar = tqdm(data_loader, desc=f"Epoch {epoch + 1}/{epochs}")
for input_batch, target_batch in progress_bar:
if input_batch.size(0) != batch_size:
hidden = model.init_hidden(input_batch.size(0))
optimizer.zero_grad()
output, hidden = model(input_batch, hidden)
hidden = tuple([h.detach() for h in hidden]) # Detach hidden state
loss = loss_fn(output, target_batch.view(-1))
loss.backward()
# Clip gradients to mitigate exploding gradients
nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
running_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
# Calculate and store the average epoch loss
epoch_loss = running_loss / len(data_loader)
loss_history.append(epoch_loss)
print(f"Epoch {epoch + 1}/{epochs}, Loss: {epoch_loss:.4f}")
# Save the last model checkpoint instead of every epoch
checkpoint_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}.pth")
torch.save(model.state_dict(), checkpoint_path)
# Save model’s configuration in a JSON file
config_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_config.json")
model_config = {
"vocab_size": VOCAB_SIZE,
"hidden_size": HIDDEN_SIZE,
"num_layers": NUM_LAYERS,
"batch_size":BATCH_SIZE,
"Total_params":total_params
}
with open(config_path, "w") as json_file:
json.dump(model_config, json_file)
# Generate vocabulary with printable ASCII characters
vocab_chars = string.printable[:-6] # Exclude the last 6 non-printable characters
VOCAB_SIZE = len(vocab_chars) # Update the size of the vocabulary
# Save vocabulary in a TXT file
vocab_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_vocab.txt")
with open(vocab_path, "w", encoding="utf-8") as vocab_file:
vocab_file.write(vocab_chars)
# Now, build a character to index mapping
char_to_index = {char: idx for idx, char in enumerate(vocab_chars)}
# Plot training loss per epoch outside the training loop
plt.figure() # Create a new figure
plt.plot(range(1, epochs + 1), loss_history, label="Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.title("Training Loss Over Epochs")
plt.show()
print("\n--- Training loop complete ---")
return model # Return the trained model
# Predict the next character
def predict(model, char, hidden=None, temperature=1.0):
# Convert to index and then tensor
input_tensor = torch.tensor([char_to_index[char]], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
# Generate text
def generate_text(model, start_char="P", length=200):
model.eval()
hidden = model.init_hidden(1)
input_char = start_char
for i in range(length):
input_char, hidden = predict(model, input_char, hidden)
print(input_char, end="")
if __name__ == "__main__":
# Train the model
trained_model = train_model(model, data_loader, optimizer, loss_fn, NUM_EPOCHS, BATCH_SIZE, model_name)
# Generate text from the trained model
generate_text(trained_model, start_char="P", length=200)
|
eb22fed6cc2e38e1b05402e3f75c3bfa
|
{
"intermediate": 0.3904925584793091,
"beginner": 0.4289173185825348,
"expert": 0.18059009313583374
}
|
39,131
|
PEG grammar
|
0668d2c7ce36af40dfa5f6d9cc4b86cc
|
{
"intermediate": 0.27488458156585693,
"beginner": 0.5597346425056458,
"expert": 0.16538070142269135
}
|
39,132
|
test
|
5c9cebfebe39f4ca8c5ba98d76e829cc
|
{
"intermediate": 0.3229040801525116,
"beginner": 0.34353747963905334,
"expert": 0.33355844020843506
}
|
39,133
|
Can you help creating the front-end of this code: import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm
import matplotlib.pyplot as plt
import string
import json
import os
# Define parameters and directory paths
DATASET_CHUNKS = 10240 # New hyperparameter to control dataset size (number of chunks)
SEQUENCE_LENGTH = 60
BATCH_SIZE = 128
HIDDEN_SIZE = 384
NUM_LAYERS = 2
LEARNING_RATE = 0.001
NUM_EPOCHS = 2
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = "checkpoints"
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
model_name = "Talk-model-1"
# Replace ‘your_file.txt’ with the path to your actual text file.
file_path = "Long-Run-Talk-Cutdown.txt"
# Print the start of data preparation:
print("\n--- Starting data preparation ---")
# Read the file and filter out blank lines
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
text = "".join(line for line in lines if not line.isspace())
# Generate vocabulary with all printable ASCII characters except the non-printable ones
excluded_characters = "\x0b\x0c\n\r\t\x0b\x0c" # Explicitly specify which characters to exclude
vocab_chars = "".join(c for c in string.printable if c not in excluded_characters)
VOCAB_SIZE = len(vocab_chars) # Update the size of the vocabulary
# Map each character to a unique index
char_to_index = {char: idx for idx, char in enumerate(vocab_chars)}
# Convert the text to indices using the char_to_index mapping
ascii_indices = [char_to_index[c] for c in text if c in char_to_index]
# Calculate the total number of sequences based on the sequence length
total_num_sequences = len(ascii_indices) - SEQUENCE_LENGTH
# Calculate the number of sequences to use per chunk,
# so that it divides evenly for all chunks
sequences_per_chunk = total_num_sequences // DATASET_CHUNKS
usable_sequences = sequences_per_chunk * DATASET_CHUNKS
inputs, targets = [], []
for i in range(0, usable_sequences, SEQUENCE_LENGTH):
input_seq = ascii_indices[i:i + SEQUENCE_LENGTH]
target_seq = ascii_indices[i + 1:i + SEQUENCE_LENGTH + 1]
inputs.append(torch.tensor(input_seq, dtype=torch.long))
targets.append(torch.tensor(target_seq, dtype=torch.long))
input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)
print(f"Created input tensor: {input_tensor.shape}")
print(f"Created target tensor: {target_tensor.shape}")
# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
print("\n--- Data preparation complete ---")
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Initialize model with correct VOCAB_SIZE
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Calculate and print the total number of trainable parameters
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
# Print the total number of parameters with appropriate scale (Billion, Million, Thousand, etc.)
param_scale = "parameters"
if total_params >= 1e9:
param_scale = f"{total_params / 1e9:.2f} Billion parameters"
elif total_params >= 1e6:
param_scale = f"{total_params / 1e6:.2f} Million parameters"
elif total_params >= 1e3:
param_scale = f"{total_params / 1e3:.2f} Thousand parameters"
else:
param_scale = f"{total_params} parameters"
print(f"Model: {model_name}")
print(f"Total trainable parameters: {param_scale}")
# For marketing, we may also want to print out the architecture of the model
print(f"Model Architecture: {NUM_LAYERS} layers with {HIDDEN_SIZE} hidden units each.")
# Print information about the training tokens
print(f"Number of training tokens: {len(ascii_indices)}")
print("\n--- Starting training loop ---")
# Training loop with added production-ready features and tqdm progress
def train_model(model, data_loader, optimizer, loss_fn, epochs, batch_size, model_name):
loss_history = [] # Initialize a list to store average epoch losses
for epoch in range(epochs):
model.train()
hidden = model.init_hidden(batch_size)
running_loss = 0.0
# Wrap the data_loader with tqdm for a progress bar
progress_bar = tqdm(data_loader, desc=f"Epoch {epoch + 1}/{epochs}")
for input_batch, target_batch in progress_bar:
if input_batch.size(0) != batch_size:
hidden = model.init_hidden(input_batch.size(0))
optimizer.zero_grad()
output, hidden = model(input_batch, hidden)
hidden = tuple([h.detach() for h in hidden]) # Detach hidden state
loss = loss_fn(output, target_batch.view(-1))
loss.backward()
# Clip gradients to mitigate exploding gradients
nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
running_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
# Calculate and store the average epoch loss
epoch_loss = running_loss / len(data_loader)
loss_history.append(epoch_loss)
print(f"Epoch {epoch + 1}/{epochs}, Loss: {epoch_loss:.4f}")
# Save the last model checkpoint instead of every epoch
checkpoint_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}.pth")
torch.save(model.state_dict(), checkpoint_path)
# Save model’s configuration in a JSON file
config_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_config.json")
model_config = {
"vocab_size": VOCAB_SIZE,
"hidden_size": HIDDEN_SIZE,
"num_layers": NUM_LAYERS,
"batch_size":BATCH_SIZE,
"Total_params":total_params
}
with open(config_path, "w") as json_file:
json.dump(model_config, json_file)
# Generate vocabulary with printable ASCII characters
vocab_chars = string.printable[:-6] # Exclude the last 6 non-printable characters
VOCAB_SIZE = len(vocab_chars) # Update the size of the vocabulary
# Save vocabulary in a TXT file
vocab_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_vocab.txt")
with open(vocab_path, "w", encoding="utf-8") as vocab_file:
vocab_file.write(vocab_chars)
# Now, build a character to index mapping
char_to_index = {char: idx for idx, char in enumerate(vocab_chars)}
# Plot training loss per epoch outside the training loop
plt.figure() # Create a new figure
plt.plot(range(1, epochs + 1), loss_history, label="Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.title("Training Loss Over Epochs")
plt.show()
print("\n--- Training loop complete ---")
return model # Return the trained model
# Predict the next character
def predict(model, char, hidden=None, temperature=1.0):
if char not in char_to_index:
raise ValueError(f"Character '{char}' not in vocabulary.")
# Convert to index and then tensor
input_tensor = torch.tensor([char_to_index[char]], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
# Generate text
def generate_text(model, start_char="P", length=200):
model.eval()
hidden = model.init_hidden(1)
input_char = start_char
for i in range(length):
input_char, hidden = predict(model, input_char, hidden)
print(input_char, end="")
if __name__ == "__main__":
# Train the model
trained_model = train_model(model, data_loader, optimizer, loss_fn, NUM_EPOCHS, BATCH_SIZE, model_name)
# At the end of the training, or at the start, you can summarize the model details
print(f"\nModel Summary for {model_name}:")
print(f"- Total trainable parameters: {param_scale}")
print(f"- Architecture: {NUM_LAYERS} layers with {HIDDEN_SIZE} hidden units each.")
print(f"- Number of training tokens: {len(ascii_indices)}")
# Generate text from the trained model
generate_text(trained_model, start_char="P", length=200)
|
b79be184c8397a1a3339265821d6f76e
|
{
"intermediate": 0.41449588537216187,
"beginner": 0.3662213385105133,
"expert": 0.2192828208208084
}
|
39,134
|
<vector xmlns:android="http://schemas.android.com/apk/res/android"
android:width="80dp"
android:height="80dp"
android:viewportWidth="30"
android:viewportHeight="30">
<group>
<clip-path
android:pathData="M10,10h20v20h-20z"/>
<path
android:pathData="M29.6,20.227C29.6,19.518 29.536,18.836 29.418,18.182H20V22.05H25.382C25.15,23.3 24.445,24.359 23.386,25.068V27.577H26.618C28.509,25.836 29.6,23.273 29.6,20.227Z"
android:fillColor="#4285F4"/>
<path
android:pathData="M20,30C22.7,30 24.964,29.104 26.618,27.577L23.386,25.068C22.491,25.668 21.345,26.023 20,26.023C17.395,26.023 15.191,24.264 14.405,21.9H11.064V24.491C12.709,27.759 16.091,30 20,30Z"
android:fillColor="#34A853"/>
<path
android:pathData="M14.405,21.9C14.205,21.3 14.091,20.659 14.091,20C14.091,19.341 14.205,18.7 14.405,18.1V15.509H11.064C10.386,16.859 10,18.386 10,20C10,21.614 10.386,23.141 11.064,24.491L14.405,21.9Z"
android:fillColor="#FBBC04"/>
<path
android:pathData="M20,13.977C21.468,13.977 22.786,14.482 23.823,15.473L26.691,12.604C24.959,10.991 22.695,10 20,10C16.091,10 12.709,12.241 11.064,15.509L14.405,18.1C15.191,15.736 17.395,13.977 20,13.977Z"
android:fillColor="#E94235"/>
</group>
</vector>
There is thhis google button. How can I make it fill the whole sqare?
|
a18e1edc61df002ed9aceb1b1e527b97
|
{
"intermediate": 0.43617215752601624,
"beginner": 0.21520328521728516,
"expert": 0.3486245274543762
}
|
39,135
|
how do I make my three drives appear as one volume where one drive fills up, then another, then the third seperatly without striping the files between the three
|
de40f0b0e1dfb5354e457493c7035cff
|
{
"intermediate": 0.386255145072937,
"beginner": 0.27414894104003906,
"expert": 0.3395959138870239
}
|
39,136
|
Give me a primer about how to use GPG, how it protects me, keyservers, etc.
|
a5dd8089b33dc956727f5e5e4f032d39
|
{
"intermediate": 0.40822839736938477,
"beginner": 0.2544710040092468,
"expert": 0.33730050921440125
}
|
39,137
|
Visualize the Hierarchical based Clustering (Agglomerative, Divisive) for the Iris Dataset using sklearn
Package
I need python program and which computes all required data and output the required plots and visual plots of all.
|
71646c5844a0b8f5b42978f7b5d0385f
|
{
"intermediate": 0.34085363149642944,
"beginner": 0.10500307381153107,
"expert": 0.5541432499885559
}
|
39,138
|
write ebnf puthon lang grammar
|
f721c3ae89d1af003fe23ebda023fc84
|
{
"intermediate": 0.2774440050125122,
"beginner": 0.5615226030349731,
"expert": 0.16103336215019226
}
|
39,139
|
Visualize the Hierarchical based Clustering (Agglomerative and Divisive)both for the Iris Dataset using sklearn
Package
I need python program and which computes all required data and output the required plots and visual plots of all.
|
41f0bddd8519a2a71e384b22ef70339a
|
{
"intermediate": 0.44585856795310974,
"beginner": 0.11682691425085068,
"expert": 0.43731454014778137
}
|
39,140
|
how do I make my three drives appear as one volume where one drive fills up, then another, then the third seperatly without striping the files between the three in ubuntu server
|
908b55cdd1aed23f68a413a2f6d6a2a6
|
{
"intermediate": 0.36666539311408997,
"beginner": 0.29398250579833984,
"expert": 0.3393521308898926
}
|
39,142
|
add to database only if id is not exist with sqlalchemy
|
2960a47be554a596d83074b801003ba8
|
{
"intermediate": 0.4734279215335846,
"beginner": 0.17735448479652405,
"expert": 0.34921756386756897
}
|
39,143
|
import gradio as gr
import torch
import torch.nn as nn
import json
import os
# Define parameters and directory paths
SEQUENCE_LENGTH = 40
LEARNING_RATE = 0.001
NUM_EPOCHS = 10
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = "checkpoints"
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
model_name = "Talk-model-1"
# Suppose we have a saved ‘model_config.json’ and a ‘vocab.txt’ in the CHECKPOINT_FOLDER
CONFIG_PATH = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_config.json")
VOCAB_PATH = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_vocab.txt")
# Load model configuration if available
if os.path.exists(CONFIG_PATH):
with open(CONFIG_PATH, "r") as config_file:
model_config = json.load(config_file)
VOCAB_SIZE = model_config["vocab_size"]
HIDDEN_SIZE = model_config["hidden_size"]
NUM_LAYERS = model_config["num_layers"]
BATCH_SIZE = model_config["batch_size"]
# Load vocabulary if available
if os.path.exists(VOCAB_PATH):
with open(VOCAB_PATH, "r") as vocab_file:
vocab = vocab_file.read().rstrip()
VOCAB_SIZE = len(vocab) # Update VOCAB_SIZE based on loaded vocabulary
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Initialize model, loss function, and optimizer
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Predict the next character
def predict(model, char, hidden=None, temperature=0.1):
# Convert to ASCII and then tensor
input_tensor = torch.tensor([ord(char)], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
def generate_text_interface(start_text, length=200):
model.eval() # Use model instead of trained_model
# Check if length is acceptable
if length < len(start_text) or length <= 0:
return "Length must be greater than the length of the starting text and greater than 0."
# Preparing the hidden state
hidden = model.init_hidden(1)
for char in start_text[:-1]:
_, hidden = predict(model=model, char=char, hidden=hidden)
# Start generating text from the last character of the provided text
input_char = start_text[-1]
generated_text = start_text
# Generate text
for i in range(length - len(start_text)):
input_char, hidden = predict(model=model, char=input_char, hidden=hidden)
generated_text += input_char
return generated_text
# Build and launch the Gradio web app
interface = gr.Interface(
fn=generate_text_interface,
inputs=[
gr.components.Textbox(label="Starting Text", placeholder="The universe "),
gr.components.Slider(minimum=1, maximum=1000, value=200, label="Length of Generated Text")
],
outputs=gr.components.Textbox(label="Generated Text"),
title="Text Generation App",
description="Generate text based on a starting sequence."
)
# You can choose not to train the model every time you run this script by checking for the existence
# of a saved model. For this example, we’ll assume you’ve trained and saved it as ‘model_final.pth’.
# Load the saved model weights before launching the app if it’s already trained.
CHECKPOINT_PATH = os.path.join(CHECKPOINT_FOLDER, f"{model_name}.pth")
if os.path.exists(CHECKPOINT_PATH):
model.load_state_dict(torch.load(CHECKPOINT_PATH, map_location=torch.device("cpu")))
model.eval() # Set the model to evaluation mode
# Launch the app
interface.launch(share=True)
|
d3b74cd396ce5f52a6f2b48a957dfe19
|
{
"intermediate": 0.2670101523399353,
"beginner": 0.4662094712257385,
"expert": 0.26678037643432617
}
|
39,144
|
I have this app MainActivity:
package com.HidiStudios.BeST;
import androidx.annotation.NonNull;
import androidx.annotation.Nullable;
import androidx.appcompat.app.ActionBarDrawerToggle;
import androidx.appcompat.app.AlertDialog;
import androidx.appcompat.app.AppCompatActivity;
import androidx.core.view.GravityCompat;
import androidx.drawerlayout.widget.DrawerLayout;
import android.content.DialogInterface;
import android.content.Intent;
import android.content.SharedPreferences;
import android.os.Bundle;
import android.util.Log;
import android.view.MenuItem;
import android.view.View;
import android.widget.Button;
import android.widget.TextView;
import android.widget.Toast;
import com.HidiStudios.BeST.R;
import com.google.android.gms.ads.AdError;
import com.google.android.gms.ads.AdRequest;
import com.google.android.gms.ads.AdView;
import com.google.android.gms.ads.FullScreenContentCallback;
import com.google.android.gms.ads.LoadAdError;
import com.google.android.gms.ads.MobileAds;
import com.google.android.gms.ads.OnUserEarnedRewardListener;
import com.google.android.gms.ads.initialization.InitializationStatus;
import com.google.android.gms.ads.initialization.OnInitializationCompleteListener;
import com.google.android.gms.ads.interstitial.InterstitialAd;
import com.google.android.gms.ads.interstitial.InterstitialAdLoadCallback;
import com.google.android.gms.ads.rewarded.RewardItem;
import com.google.android.gms.ads.rewarded.RewardedAd;
import com.google.android.gms.ads.rewarded.RewardedAdLoadCallback;
import com.google.android.gms.auth.api.signin.GoogleSignIn;
import com.google.android.gms.auth.api.signin.GoogleSignInAccount;
import com.google.android.gms.auth.api.signin.GoogleSignInClient;
import com.google.android.gms.auth.api.signin.GoogleSignInOptions;
import com.google.android.gms.common.api.ApiException;
import com.google.android.gms.tasks.OnCompleteListener;
import com.google.android.gms.tasks.OnFailureListener;
import com.google.android.gms.tasks.OnSuccessListener;
import com.google.android.gms.tasks.Task;
import com.google.android.material.navigation.NavigationView;
import com.google.firebase.FirebaseApp;
import com.google.firebase.auth.AuthCredential;
import com.google.firebase.auth.AuthResult;
import com.google.firebase.auth.FirebaseAuth;
import com.google.firebase.auth.FirebaseAuthUserCollisionException;
import com.google.firebase.auth.FirebaseUser;
import com.google.firebase.auth.GoogleAuthProvider;
import com.google.firebase.firestore.CollectionReference;
import com.google.firebase.firestore.DocumentReference;
import com.google.firebase.firestore.DocumentSnapshot;
import com.google.firebase.firestore.FirebaseFirestore;
import com.google.firebase.firestore.Query;
import com.google.firebase.firestore.QueryDocumentSnapshot;
import com.google.firebase.firestore.auth.User;
import com.google.protobuf.StringValue;
import java.util.HashMap;
import java.util.Map;
import java.util.Objects;
public class MainActivity extends BaseDrawerActivity {
int RC_SIGN_IN = 20;
GoogleSignInClient mGoogleSignInClient;
//variables
private InterstitialAd mInterstitialAd;
private RewardedAd mRewardedAd;
private static FirebaseAuth mAuth;
private static FirebaseFirestore db = FirebaseFirestore.getInstance();
static CollectionReference usersRef = db.collection("users");
public static int amount = 0;
boolean state = false;
static boolean changedSinceSynced = true;
private TextView mTextView;
String text;
//stats
private static int bannerToggleCount = 0;
private static int interstitialWatchedCount = 0;
private static int interstitialClickedCount = 0;
private static int rewardedWatchedCount = 0;
private static int rewardedClickedCount = 0;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
// menuRelated
initializeDrawer();
//Initialize AdMob
MobileAds.initialize(this, new OnInitializationCompleteListener() {
@Override
public void onInitializationComplete(@NonNull InitializationStatus initializationStatus) {
}
});
//RewardedAdRelated
loadRewardedAds();
Button rewardedButton = findViewById(R.id.RewardedbtnAds);
rewardedButton.setOnClickListener(view -> {
showRewardedAds();
});
//InterstitialAdRelated
loadInterstitialAd();
Button interstitialButton = findViewById(R.id.IntbtnAds);
interstitialButton.setOnClickListener(view -> {
showInterstitialAd();
});
//BannerAdRelated
Button bannerButton = findViewById(R.id.BannerbtnAds);
bannerButton.setOnClickListener(view -> {
bannerAd();
});
//ResetButtonRelated
resetButton();
//FirebaseAuth
mAuth = FirebaseAuth.getInstance();
FirebaseUser currentUser = mAuth.getCurrentUser(); //if user is signed in returns UID, else null
updateUI(currentUser); //if userID is null, generated.
//CoinRelated
loadCoins();
//stats
loadStats();
setStats();
//Sign In with Google
gso();
}
@Override
protected void onStop() {
super.onStop();
Log.d("TagDB", "onStopped");
if (changedSinceSynced) {
saveToDB(false, new DbOperationCompleteListener() {
@Override
public void onDbOperationComplete() {
changedSinceSynced = false;
Log.d("TagDB", "onStopped, synced to DB, changedSinceSynced set to false");
}
});
}
}
//bannerAds
private void bannerAd() {
AdView mAdView = findViewById(R.id.adView);
AdRequest BannerAdRequest = new AdRequest.Builder().build();
if (!state) {
mAdView.loadAd(BannerAdRequest);
mAdView.setVisibility(View.VISIBLE);
Log.d("TagDB", "Banner Toggled!");
amount += getResources().getInteger(R.integer.onBannerToggle);
updateCoins();
changedSinceSynced = true;
bannerToggleCount++;
updateStats();
state = true;
} else {
mAdView.setVisibility(View.GONE);
state = false;
}
}
//interstitialAds
private void loadInterstitialAd() {
AdRequest interstitialAdRequest = new AdRequest.Builder().build();
InterstitialAd.load(this,getString(R.string.INTERSTITIAL_AD_UNIT_ID), interstitialAdRequest, new InterstitialAdLoadCallback() {
@Override
public void onAdLoaded(@NonNull InterstitialAd interstitialAd) {
super.onAdLoaded(interstitialAd);
mInterstitialAd = interstitialAd;
interstitialAd.setFullScreenContentCallback(new FullScreenContentCallback(){
@Override
public void onAdClicked() {
super.onAdClicked();
Log.d("TagDB", "Interstitial clicked!");
amount += getResources().getInteger(R.integer.onInterstitialClick);
updateCoins();
changedSinceSynced = true;
interstitialClickedCount++;
updateStats();
}
@Override
public void onAdDismissedFullScreenContent() {
mInterstitialAd = null;
loadInterstitialAd();
}
@Override
public void onAdFailedToShowFullScreenContent(@NonNull AdError adError) {
mInterstitialAd = null;
}
});
}
@Override
public void onAdFailedToLoad(@NonNull LoadAdError loadAdError) {
mInterstitialAd = null;
//error message is managed in showInterstitialAds() function
}
});
}
private void showInterstitialAd() {
if (mInterstitialAd!=null) {
mInterstitialAd.show(MainActivity.this);
Log.d("TagDB", "Interstitial watched!");
amount += getResources().getInteger(R.integer.onInterstitialToggle);
updateCoins();
changedSinceSynced = true;
interstitialWatchedCount++;
updateStats();
} else {
Log.d("TagDB", "The interstitial ad is not ready yet. Try again!");
loadInterstitialAd(); // Try to load again if it wasn’t ready when the button was clicked
}
}
//rewardedAds
private void loadRewardedAds() {
AdRequest RewardedAdRequest = new AdRequest.Builder().build();
RewardedAd.load(this, getString(R.string.REWARDED_AD_UNIT_ID), RewardedAdRequest, new RewardedAdLoadCallback() {
@Override
public void onAdLoaded(@NonNull RewardedAd rewardedAd) {
super.onAdLoaded(rewardedAd);
mRewardedAd = rewardedAd;
rewardedAd.setFullScreenContentCallback(new FullScreenContentCallback() {
@Override
public void onAdClicked() {
super.onAdClicked();
Log.d("TagDB", "Rewarded clicked!");
amount += getResources().getInteger(R.integer.onRewardedClicked);
updateCoins();
changedSinceSynced = true;
rewardedClickedCount++;
updateStats();
}
@Override
public void onAdDismissedFullScreenContent() {
super.onAdDismissedFullScreenContent();
loadRewardedAds();
}
@Override
public void onAdFailedToShowFullScreenContent(@NonNull AdError adError) {
super.onAdFailedToShowFullScreenContent(adError);
mRewardedAd = null;
}
});
}
@Override
public void onAdFailedToLoad(@NonNull LoadAdError loadAdError) {
super.onAdFailedToLoad(loadAdError);
mRewardedAd = null;
//error message is managed in showRewardedAds() function
}
});
}
private void showRewardedAds() {
if (mRewardedAd!=null) {
mRewardedAd.show(MainActivity.this, new OnUserEarnedRewardListener() {
@Override
public void onUserEarnedReward(@NonNull RewardItem rewardItem) {
Log.d("TagDB", "Reward earned!");
amount += getResources().getInteger(R.integer.onRewardedToggle);
updateCoins();
changedSinceSynced = true;
rewardedWatchedCount++;
updateStats();
}
});
} else {
Toast.makeText(MainActivity.this,"Rewarded ad is not ready yet. Try again!", Toast.LENGTH_SHORT).show();
}
}
//coins
private void loadCoins(){
loadCoinAmount(); //this is local save
readFromDB(false);
setCoins();
}
private void loadCoinAmount() {
SharedPreferences sharedPreferences = getSharedPreferences("UserPreferences", MODE_PRIVATE);
amount = sharedPreferences.getInt("coinsAmount", 0);
}
private void saveCoinAmount() {
SharedPreferences sharedPreferences = getSharedPreferences("UserPreferences", MODE_PRIVATE);
SharedPreferences.Editor editor = sharedPreferences.edit();
editor.putInt("coinsAmount", amount);
editor.apply();
}
private void setCoins() {
mTextView=findViewById(R.id.Coins);
text = getResources().getString(R.string.coins_message, amount);
mTextView.setText(text);
}
private void updateCoins(){
saveCoinAmount();
setCoins();
}
//reset
private void resetButton(){
Button resetButton = findViewById(R.id.resetButton);
resetButton.setOnClickListener(view -> {
confirmResetCoins();
});
}
private void confirmResetCoins(){
AlertDialog.Builder confirmation = new AlertDialog.Builder(this);
confirmation.setTitle("Are you sure?");
confirmation.setMessage("Your stats and coin balance will be reset to 0.");
confirmation.setPositiveButton("Yes", new DialogInterface.OnClickListener() {
@Override
public void onClick(DialogInterface dialogInterface, int i) {
resetCoinsAndStats();
}
});
confirmation.setNegativeButton("No", new DialogInterface.OnClickListener() {
@Override
public void onClick(DialogInterface dialogInterface, int i) {
googleSignIn();
}
});
confirmation.create().show();
}
private void resetCoinsAndStats() {
amount = 0;
updateCoins();
bannerToggleCount = 0;
interstitialWatchedCount = 0;
interstitialClickedCount = 0;
rewardedWatchedCount = 0;
rewardedClickedCount = 0;
updateStats();
changedSinceSynced = true;
Toast.makeText(MainActivity.this, "Coins and stats have been reset!", Toast.LENGTH_SHORT).show();
}
//Stats
private void setStats(){
mTextView=findViewById(R.id.stats);
text = getResources().getString(R.string.stats_message, bannerToggleCount, interstitialWatchedCount, interstitialClickedCount, rewardedWatchedCount, rewardedClickedCount);
mTextView.setText(text);
}
private void loadStats(){
SharedPreferences sharedPreferences = getSharedPreferences("UserPreferences", MODE_PRIVATE);
bannerToggleCount = sharedPreferences.getInt("bannerToggleCount", 0);
interstitialWatchedCount = sharedPreferences.getInt("interstitialWatchedCount", 0);
interstitialClickedCount = sharedPreferences.getInt("interstitialClickedCount", 0);
rewardedWatchedCount = sharedPreferences.getInt("rewardedWatchedCount", 0);
rewardedClickedCount = sharedPreferences.getInt("rewardedClickedCount", 0);
}
private void saveStats(){
SharedPreferences sharedPreferences = getSharedPreferences("UserPreferences", MODE_PRIVATE);
SharedPreferences.Editor editor = sharedPreferences.edit();
editor.putInt("bannerToggleCount", bannerToggleCount);
editor.putInt("interstitialWatchedCount", interstitialWatchedCount);
editor.putInt("interstitialClickedCount", interstitialClickedCount);
editor.putInt("rewardedWatchedCount", rewardedWatchedCount);
editor.putInt("rewardedClickedCount", rewardedClickedCount);
editor.apply();
}
private void updateStats(){
saveStats();
setStats();
}
//FirebaseAuth
private void updateUI(FirebaseUser user) {
if (user==null) { //if there's no user signed in
mAuth.signInAnonymously().addOnCompleteListener(this, new OnCompleteListener<AuthResult>() {
@Override
public void onComplete(@NonNull Task<AuthResult> task) {
if (task.isSuccessful()) {
FirebaseUser user = mAuth.getCurrentUser();
saveToDB(true,null);
} else {
updateUI(null);
}
}
});
}
}
//Firebase DB
public static void saveToDB(boolean Create, @Nullable DbOperationCompleteListener listener) {
Map<String, Object> userdb = new HashMap<>();
userdb.put("score", amount);
OnSuccessListener<Void> successListener = new OnSuccessListener<Void>() {
@Override
public void onSuccess(Void unused) {
Log.d("TagDB", "Successfully saved to DB");
if (listener != null) {
listener.onDbOperationComplete();
}
}
};
OnFailureListener failureListener = new OnFailureListener() {
@Override
public void onFailure(@NonNull Exception e) {
Log.w("TagDB","Error adding document ERROR", e);
}
};
if (Create) {
Log.d("TagDB", "SaveToDB Create");
usersRef.document(Objects.requireNonNull(mAuth.getUid())).set(userdb) //update-set
.addOnSuccessListener(successListener)
.addOnFailureListener(failureListener);
} else {
Log.d("TagDB", "SaveToDB nonCreate");
usersRef.document(Objects.requireNonNull(mAuth.getUid())).update(userdb) //update-set
.addOnSuccessListener(successListener)
.addOnFailureListener(failureListener);
}
}
private void readFromDB(boolean isSyncingWithProvider) {
String userId = mAuth.getUid(); // Get the current authenticated user’s ID
if (userId == null) {
// Handle the case where there is no authenticated user
Log.w("TagDB", "User ID is null, can’t fetch data.");
//updateUI(null);
return;
}
DocumentReference docRef = usersRef.document(userId);
docRef.get()
.addOnSuccessListener(new OnSuccessListener<DocumentSnapshot>() {
@Override
public void onSuccess(DocumentSnapshot documentSnapshot) {
if (documentSnapshot.exists()) {
// Retrieves the score from the document
Long score = documentSnapshot.getLong("score");
if (Math.toIntExact(score) != amount && !isSyncingWithProvider) {
amount = Math.toIntExact(score);
updateCoins();
Toast.makeText(MainActivity.this, "Local coin amount overridden by Firebase score.", Toast.LENGTH_SHORT).show();
} else if (isSyncingWithProvider){
amount += Math.toIntExact(score);
updateCoins();
Toast.makeText(MainActivity.this, "Coin amounts have been merged", Toast.LENGTH_SHORT).show();
saveToDB(false, null);
}
changedSinceSynced = false; //false when sync, because local and cloud values match
Log.d("TagDB", "User Firebase score: " + score);
}else {
Log.d("TagDB", "No such document!");
saveToDB(true, null);
}
}
})
.addOnFailureListener(new OnFailureListener() {
@Override
public void onFailure(@NonNull Exception e) {
Log.w("TagDB", "Error fetching document", e);
}
});
}
public interface DbOperationCompleteListener {
void onDbOperationComplete();
}
//Sign in with Google
private void gso(){
GoogleSignInOptions gso = new GoogleSignInOptions.Builder(GoogleSignInOptions.DEFAULT_SIGN_IN)
.requestIdToken(getString(R.string.default_web_client_id))
.requestEmail()
.build();
mGoogleSignInClient = GoogleSignIn.getClient(this, gso);
}
private void googleSignIn() {
Intent signInIntent = mGoogleSignInClient.getSignInIntent();
startActivityForResult(signInIntent, RC_SIGN_IN);
}
@Override
public void onActivityResult(int requestCode, int resultCode, @Nullable Intent data) {
super.onActivityResult(requestCode, resultCode, data);
//From googleSignIn();
if (requestCode == RC_SIGN_IN) {
Task<GoogleSignInAccount> task = GoogleSignIn.getSignedInAccountFromIntent(data);
try {
// Google Sign In was successful, authenticate with Firebase
GoogleSignInAccount account = task.getResult(ApiException.class);
AuthCredential credential = GoogleAuthProvider.getCredential(account.getIdToken(), null);
Objects.requireNonNull(mAuth.getCurrentUser()).linkWithCredential(credential)
.addOnCompleteListener(this, new OnCompleteListener<AuthResult>() {
@Override
public void onComplete(@NonNull Task<AuthResult> linkTask) {
if (linkTask.isSuccessful()) {
FirebaseUser firebaseUser = linkTask.getResult().getUser();
Log.d("TagDB", "Linking the anonymous account with the Google account was successful");
// TODO: Update the UI with the user’s information or proceed to the main part of your app
} else {
if (linkTask.getException() instanceof FirebaseAuthUserCollisionException) {
//The Google account is already in use. Sync data.
Log.d("TagDB", "The Google account is already in use. Syncing data.");
signInWithCredential(credential);
}
}
}
});
} catch (ApiException e) {
// Google Sign In failed
Toast.makeText(this, "Google Sign In failed, try again!", Toast.LENGTH_SHORT).show();
Log.d("TagDB", Objects.requireNonNull(e.getMessage()));
}
}
}
private void signInWithCredential(AuthCredential credential) {
final FirebaseUser anonymousUser = mAuth.getCurrentUser();
final String anonymousUserId = mAuth.getUid();
mAuth.signInWithCredential(credential).addOnCompleteListener(this, new OnCompleteListener<AuthResult>() {
@Override
public void onComplete(@NonNull Task<AuthResult> signInTask) {
if (signInTask.isSuccessful()) {
//Sync is managed in readFromBD
readFromDB(true);
//Delete the anonymous user’s authentication record
if (anonymousUser != null && !anonymousUser.getUid().equals(mAuth.getUid())) {
anonymousUser.delete().addOnCompleteListener(new OnCompleteListener<Void>() {
@Override
public void onComplete(@NonNull Task<Void> task) {
if (task.isSuccessful()) {
Log.d("TagDB", "Anonymous user auth record deleted successfully.");
} else {
Log.w("TagDB", "Failed to delete anonymous user auth record.", task.getException());
}
}
});
//Delete the anonymous user’s database document
usersRef.document(Objects.requireNonNull(anonymousUserId)).delete().addOnCompleteListener(new OnCompleteListener<Void>() {
@Override
public void onComplete(@NonNull Task<Void> task) {
if (task.isSuccessful()) {
Log.d("TagDB", "Deleted anonymous user data from Firestore.");
} else {
Log.w("TagDB", "Error deleting anonymous user data from Firestore.", task.getException());
}
}
});
}
} else {
Toast.makeText(MainActivity.this,"Google Sign In failed, try again!", Toast.LENGTH_SHORT).show();
}
}
});
}
}
And I want to use a Google sign in button with googleSignIn() in a different activity, LeaderboardActivity.java. How can I do that?
|
ce348a96387462ab3b2e6501e03a3436
|
{
"intermediate": 0.35956573486328125,
"beginner": 0.3905320167541504,
"expert": 0.2499021738767624
}
|
39,145
|
Hello
|
50dfe2c43cc34d210184a3eff6e2b659
|
{
"intermediate": 0.3123404085636139,
"beginner": 0.2729349136352539,
"expert": 0.4147246778011322
}
|
39,146
|
make a racing game html css and java
|
9312ecc083c5393a2562ed9e8d81a8f9
|
{
"intermediate": 0.38968026638031006,
"beginner": 0.393583208322525,
"expert": 0.21673648059368134
}
|
39,147
|
Look at this code and don't review it: import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm
import matplotlib.pyplot as plt
import string
import json
import os
# Define parameters and directory paths
DATASET_CHUNKS = 10240 # New hyperparameter to control dataset size (number of chunks)
SEQUENCE_LENGTH = 64
BATCH_SIZE = 512
HIDDEN_SIZE = 384
NUM_LAYERS = 2
LEARNING_RATE = 0.001
NUM_EPOCHS = 2
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = "checkpoints"
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
model_name = "Talk-model-1"
# Replace ‘your_file.txt’ with the path to your actual text file.
file_path = "Long-Run-Talk-Cutdown.txt"
# Print the start of data preparation:
print("\n--- Starting data preparation ---")
# Read the file and filter out blank lines
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
text = "".join(line for line in lines if not line.isspace())
# Generate vocabulary with all printable ASCII characters except the non-printable ones
excluded_characters = "\x0b\x0c\n\r\t\x0b\x0c" # Explicitly specify which characters to exclude
vocab_chars = "".join(c for c in string.printable if c not in excluded_characters)
VOCAB_SIZE = len(vocab_chars) # Update the size of the vocabulary
# Map each character to a unique index
char_to_index = {char: idx for idx, char in enumerate(vocab_chars)}
# Convert the text to indices using the char_to_index mapping
ascii_indices = [char_to_index[c] for c in text if c in char_to_index]
# Calculate the total number of sequences based on the sequence length
total_num_sequences = len(ascii_indices) - SEQUENCE_LENGTH
# Calculate the number of sequences to use per chunk,
# so that it divides evenly for all chunks
sequences_per_chunk = total_num_sequences // DATASET_CHUNKS
usable_sequences = sequences_per_chunk * DATASET_CHUNKS
inputs, targets = [], []
for i in range(0, usable_sequences, SEQUENCE_LENGTH):
input_seq = ascii_indices[i:i + SEQUENCE_LENGTH]
target_seq = ascii_indices[i + 1:i + SEQUENCE_LENGTH + 1]
inputs.append(torch.tensor(input_seq, dtype=torch.long))
targets.append(torch.tensor(target_seq, dtype=torch.long))
input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)
print(f"Created input tensor: {input_tensor.shape}")
print(f"Created target tensor: {target_tensor.shape}")
# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
print("\n--- Data preparation complete ---")
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Initialize model with correct VOCAB_SIZE
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Calculate and print the total number of trainable parameters
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
# Print the total number of parameters with appropriate scale (Billion, Million, Thousand, etc.)
param_scale = "parameters"
if total_params >= 1e9:
param_scale = f"{total_params / 1e9:.2f} Billion parameters"
elif total_params >= 1e6:
param_scale = f"{total_params / 1e6:.2f} Million parameters"
elif total_params >= 1e3:
param_scale = f"{total_params / 1e3:.2f} Thousand parameters"
else:
param_scale = f"{total_params} parameters"
print(f"Model: {model_name}")
print(f"Total trainable parameters: {param_scale}")
# For marketing, we may also want to print out the architecture of the model
print(f"Model Architecture: {NUM_LAYERS} layers with {HIDDEN_SIZE} hidden units each.")
# Print information about the training tokens
print(f"Number of training tokens: {len(ascii_indices)}")
print("\n--- Starting training loop ---")
# Training loop with added production-ready features and tqdm progress
def train_model(model, data_loader, optimizer, loss_fn, epochs, batch_size, model_name):
loss_history = [] # Initialize a list to store average epoch losses
for epoch in range(epochs):
model.train()
hidden = model.init_hidden(batch_size)
running_loss = 0.0
# Wrap the data_loader with tqdm for a progress bar
progress_bar = tqdm(data_loader, desc=f"Epoch {epoch + 1}/{epochs}")
for input_batch, target_batch in progress_bar:
if input_batch.size(0) != batch_size:
hidden = model.init_hidden(input_batch.size(0))
optimizer.zero_grad()
output, hidden = model(input_batch, hidden)
hidden = tuple([h.detach() for h in hidden]) # Detach hidden state
loss = loss_fn(output, target_batch.view(-1))
loss.backward()
# Clip gradients to mitigate exploding gradients
nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
running_loss += loss.item()
progress_bar.set_postfix(loss=loss.item())
# Calculate and store the average epoch loss
epoch_loss = running_loss / len(data_loader)
loss_history.append(epoch_loss)
print(f"Epoch {epoch + 1}/{epochs}, Loss: {epoch_loss:.4f}")
# Save the last model checkpoint instead of every epoch
checkpoint_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}.pth")
torch.save(model.state_dict(), checkpoint_path)
# Generate vocabulary with printable ASCII characters
vocab_chars = string.printable[:-6] # Exclude the last 6 non-printable characters
VOCAB_SIZE = len(vocab_chars) # Update the size of the vocabulary
# Save model’s configuration in a JSON file
config_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_config.json")
model_config = {
"vocab_size": VOCAB_SIZE,
"hidden_size": HIDDEN_SIZE,
"num_layers": NUM_LAYERS,
"batch_size":BATCH_SIZE,
"Total_params":total_params
}
with open(config_path, "w") as json_file:
json.dump(model_config, json_file)
# Save vocabulary in a TXT file
vocab_path = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_vocab.txt")
with open(vocab_path, "w", encoding="utf-8") as vocab_file:
vocab_file.write(vocab_chars)
# Plot training loss per epoch outside the training loop
plt.figure() # Create a new figure
plt.plot(range(1, epochs + 1), loss_history, label="Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.title("Training Loss Over Epochs")
plt.show()
print("\n--- Training loop complete ---")
return model # Return the trained model
# Predict the next character
def predict(model, char, hidden=None, temperature=1.0):
if char not in char_to_index:
raise ValueError(f"Character '{char}' not in vocabulary.")
# Convert to index and then tensor
input_tensor = torch.tensor([char_to_index[char]], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
# Generate text
def generate_text(model, start_char="P", length=200):
model.eval()
hidden = model.init_hidden(1)
input_char = start_char
for i in range(length):
input_char, hidden = predict(model, input_char, hidden)
print(input_char, end="")
if __name__ == "__main__":
# Train the model
trained_model = train_model(model, data_loader, optimizer, loss_fn, NUM_EPOCHS, BATCH_SIZE, model_name)
# At the end of the training, or at the start, you can summarize the model details
print(f"\nModel Summary for {model_name}:")
print(f"- Total trainable parameters: {param_scale}")
print(f"- Architecture: {NUM_LAYERS} layers with {HIDDEN_SIZE} hidden units each.")
print(f"- Number of training tokens: {len(ascii_indices)}")
# Generate text from the trained model
generate_text(trained_model, start_char="P", length=200)
|
4a3a53ccb21ff3106133cf418c56e727
|
{
"intermediate": 0.4092177152633667,
"beginner": 0.3300982415676117,
"expert": 0.2606840133666992
}
|
39,148
|
I get this error: **error**: Traceback (most recent call last):
File "c:\Users\Dell-PC\Desktop\Projets\Finetuned_Language_models\content\RNN-app.py", line 116, in <module>
model.load_state_dict(torch.load(CHECKPOINT_PATH, map_location=torch.device("cpu")))
File "C:\Users\Dell-PC\Desktop\Projets\Finetuned_Language_models\content\GPT_2_Math\lib\site-packages\torch\nn\modules\module.py", line 2152, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for AdvancedRNN:
size mismatch for embedding.weight: copying a param with shape torch.Size([95, 384]) from checkpoint, the shape in current model is torch.Size([94, 384]).
size mismatch for fc.weight: copying a param with shape torch.Size([95, 384]) from checkpoint, the shape in current model is torch.Size([94, 384]).
size mismatch for fc.bias: copying a param with shape torch.Size([95]) from checkpoint, the shape in current model is torch.Size([94]).
PS C:\Users\Dell-PC\Desktop\Projets\Finetuned_Language_models\content> when i run this code: **code** : import gradio as gr
import torch
import torch.nn as nn
import json
import os
# Define parameters and directory paths
SEQUENCE_LENGTH = 40
LEARNING_RATE = 0.001
NUM_EPOCHS = 10
GRAD_CLIP = 5.0 # Gradient clipping to prevent exploding gradients
CHECKPOINT_FOLDER = "checkpoints"
os.makedirs(CHECKPOINT_FOLDER, exist_ok=True)
model_name = "Talk-model-1"
# Suppose we have a saved ‘model_config.json’ and a ‘vocab.txt’ in the CHECKPOINT_FOLDER
CONFIG_PATH = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_config.json")
VOCAB_PATH = os.path.join(CHECKPOINT_FOLDER, f"{model_name}_vocab.txt")
# Load model configuration if available
if os.path.exists(CONFIG_PATH):
with open(CONFIG_PATH, "r") as config_file:
model_config = json.load(config_file)
VOCAB_SIZE = model_config["vocab_size"]
HIDDEN_SIZE = model_config["hidden_size"]
NUM_LAYERS = model_config["num_layers"]
BATCH_SIZE = model_config["batch_size"]
# Load vocabulary if available
if os.path.exists(VOCAB_PATH):
with open(VOCAB_PATH, "r") as vocab_file:
vocab = vocab_file.read().rstrip()
VOCAB_SIZE = len(vocab) # Update VOCAB_SIZE based on loaded vocabulary
# Define the sophisticated RNN model architecture
class AdvancedRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(AdvancedRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden):
x = self.embedding(x)
out, hidden = self.rnn(x, hidden)
out = self.fc(out.reshape(out.size(0) * out.size(1), out.size(2)))
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters())
hidden = (weight.new_zeros(self.num_layers, batch_size, self.hidden_size),
weight.new_zeros(self.num_layers, batch_size, self.hidden_size))
return hidden
# Initialize model, loss function, and optimizer
model = AdvancedRNN(VOCAB_SIZE, HIDDEN_SIZE, NUM_LAYERS)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Predict the next character
def predict(model, char, hidden=None, temperature=0.1):
# Convert to ASCII and then tensor
input_tensor = torch.tensor([ord(char)], dtype=torch.long).unsqueeze(0)
if hidden is None:
hidden = model.init_hidden(1)
output, hidden = model(input_tensor, hidden)
output_dist = output.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
predicted_char = chr(top_i)
return predicted_char, hidden
def generate_text_interface(start_text, length=200):
model.eval() # Use model instead of trained_model
# Check if length is acceptable
if length < len(start_text) or length <= 0:
return "Length must be greater than the length of the starting text and greater than 0."
# Preparing the hidden state
hidden = model.init_hidden(1)
for char in start_text[:-1]:
_, hidden = predict(model=model, char=char, hidden=hidden)
# Start generating text from the last character of the provided text
input_char = start_text[-1]
generated_text = start_text
# Generate text
for i in range(length - len(start_text)):
input_char, hidden = predict(model=model, char=input_char, hidden=hidden)
generated_text += input_char
return generated_text
# Build and launch the Gradio web app
interface = gr.Interface(
fn=generate_text_interface,
inputs=[
gr.components.Textbox(label="Starting Text", placeholder="The universe "),
gr.components.Slider(minimum=1, maximum=1000, value=200, label="Length of Generated Text")
],
outputs=gr.components.Textbox(label="Generated Text"),
title="Text Generation App",
description="Generate text based on a starting sequence."
)
# You can choose not to train the model every time you run this script by checking for the existence
# of a saved model. For this example, we’ll assume you’ve trained and saved it as ‘model_final.pth’.
# Load the saved model weights before launching the app if it’s already trained.
CHECKPOINT_PATH = os.path.join(CHECKPOINT_FOLDER, f"{model_name}.pth")
if os.path.exists(CHECKPOINT_PATH):
model.load_state_dict(torch.load(CHECKPOINT_PATH, map_location=torch.device("cpu")))
model.eval() # Set the model to evaluation mode
# Launch the app
interface.launch(share=True)
|
3d6ececc9392a6f9500a7e4daa648d12
|
{
"intermediate": 0.3719191551208496,
"beginner": 0.42220839858055115,
"expert": 0.20587247610092163
}
|
39,149
|
CONSTRAINTS:
1. ~100k word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
5. Random shutdowns of you.
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
2. Memory Add: "memory_add", args: "key": "<key>", "string": "<string>"
3. Memory Delete: "memory_del", args: "key": "<key>"
4. Memory Overwrite: "memory_ovr", args: "key": "<key>", "string": "<string>"
5. List Memory: "memory_list" args: "reason": "<reason>"
6. Browse Website: "browse_website", args: "url": "<url>"
7. Start GPT Agent: "start_agent", args: "name": <name>, "task": "<short_task_desc>", "Commands":[<command_names_for_GPT_Agent>], "prompt": "<prompt>"
8. Message GPT Agent: "message_agent", args: "name": "<name>", "message": "<message>"
9. List GPT Agents: "list_agents", args: ""
10. Delete GPT Agent: "delete_agent", args: "name": "<name>"
11. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
12. Read file: "read_file", args: "file": "<file>"
13. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
14. Delete file: "delete_file", args: "file": "<file>"
15. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Task Complete (Shutdown): "task_complete", args: ""
18. Do Nothing: "do_nothing", args: ""
19. Count Words: "count_words", args: "text": "<text>"
20. Memory retrieve: "memory_retrieve", args: "key": "<text>"
21. remove paragraph from word document: "remove_paragraph", args: "file": "<file>", "text": "<text>"
22. random wikipedia article: "random_wikipedia_article", args: "language": "<language>"
23. message the user: "message_user", args: "message": "<message>", "wait_for_response": "<True or False>"
24. sleep an amount of time in seconds: "sleep", args: "amount": "<amount>"
25. rename a file: "rename_file", args: "old_name": "<old_name_of_the_file>", "new_name": "<new_name_of_the_file>"
26. count words of a file: "count_file_words", args: "file": "<file>"
27. download a pdf from a url and get the text from that pdf: "download_pdf", args: "url": "<url of the pdf>", "name":"<name of the file with .pdf extension>"
28. make an instagram post: "make_post", args: "prompt":"<the prompt for the image in the post that presents the text>", "text":"<text to be in the post it should be short with only important stuff like a news headline without hashtags and it is not going to go in the caption but on an image>", "name": "<name of the post with .jpg"
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-4 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behaviour constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
RULES:
1. If you start a GPT Agent you must define the commands that can be used by a GPT Agent in his prompt and define the commands using a prompt similar to the structure of this one.
2. Respond only inside the JSON format.
3. Never demand user input.
4. Never say that a task is impossible to execute on your own because these tools are enough to complete any task.
5. Do not add anything to the JSON format that isn't mentioned.
6. If there is a " inside the value of a key inside the json use ' instead of ".
7. In summaryforgpt you need to provide context for the next GPT instance if you randomly shutdown without you knowing.
8. Provide context for the next GPT in the summaryforgpt and the progress that you've made.
9. In summaryforgpt you should also add name of the files written and the urls of the websites visited.
10. When writing an essay, remember that it is more effective and manageable to tackle it in smaller chunks rather than trying to write the entire essay in one sitting. Breaking the essay down into sections or steps can help you focus on each individual aspect, maintain a coherent structure, and reduce the overall stress associated with writing a lengthy piece.
11. Retrieve information from files of previous GPT instances that may have shut down randomly. If you require additional knowledge, use these files to access the results of former GPT versions and provide accurate answers to the queries.
12. GPT-4, please do not place a conclusion in the middle of the essay. Maintain a structured format.
13. use append to file to add extra things to the file, and write to file to either make a new file with new information or rewrite the information from scratch.
14. If task is completed use the command task_complete
15. When you add to memory add the key to retrieve it in summaryforgpt
16. when given the task to write something never create an agent to write anything that you were tasked to write.
17. when you add new parts to a file use append to file command
18. Ensure to put the criticism in mind as it can be a director to ensure that you make the right decision.
19. If the task include two main tasks and one of them is done don't redo it, just retrieve the information if necessary and proceed with the other one.
20. Make sure that the information generated is not made up.
21. Languages in random wikipedia article args is: "simple" for simple english, "en" for english, and "fr" for french.
22. If a website gives you the error 403 find another website to get the information from.
23. Always listen to your criticism, and follow it.
24. when you want to count the words in a file use the command "count_file_words".
25. Don't repeat yourself.
26. You must make sure that there is .pdf in the url to use the "download_pdf" function.
27. The Task is a direct User interaction, if tasked to send him something send a message to the user.
28. Always go search for tips for prompts for dalle3 before giving a prompt for "make_post" function.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"command": {
"name": """command name""",
"args":{
"arg name": """value"""
}
},
"thoughts":
{
"text": """thought""",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"summaryforgpt": "summarize any information that will help a new instance of GPT of what you did before the shutdown."
}
}
Ensure the response can be parsed by Python json.loads
Context from previous GPT: Issued ‘random_wikipedia_article’ command to start gathering interesting science facts from diverse sources. Ensuring to find and rank facts before sending them to the user with their sources.Used ‘random_wikipedia_article’ and received information on a music album, which is not relevant to the task. Switching to ‘google’ to search for interesting science facts from various sources.saved google search results at googleresults0.txt to retrieve if shutdown randomly.Transitioned from using ‘random_wikipedia_article’ to ‘google’ to gather science facts, and now moving on to ‘browse_website’ to extract detailed knowledge from a selected online science article.saved the scraping of the website results at websiteresults1.txt to retrieve if shutdown randomly.Extracted top 10 interesting science facts from Science Focus and saved them in a plain text file named ‘interesting_science_facts.txt’. This method circumvents the previous issue of invalid JSON format and allows for continued fact collection and eventual ranking.Have appended additional science facts to the ‘interesting_science_facts.txt’ file. Plan to continue appending more facts and then rank them based on interestingness before sending the final compilation to the user.Further appended diverse facts to ‘interesting_science_facts.txt’. The process will persist until an adequate number of facts are compiled. The next steps involve ranking these facts and then communicating them to the user.The file ‘interesting_science_facts.txt’ has been appended with more facts, bringing it to a total of 333 words. The next step involves reading this file and ranking the facts based on interestingness to provide the user with an ordered list.Ranked list of 10 interesting science facts provided to the user. The facts were selected and ordered based on their perceived interest value and sourced from the compiled file ‘interesting_science_facts.txt’.A ranked list of interesting science facts was sent to the user, and the task has been marked as complete.After the task was marked as complete, it was clarified that the facts must be extracted from complete articles and they must be obscure. Initiated a Google search with a more specific query to find websites with complete articles containing obscure science facts. Results will be saved to a file named ‘google_results_obscure_facts.txt’.saved google search results at googleresults2.txt to retrieve if shutdown randomly.Conducted a Google search to find obscure science facts from complete articles and have selected the first promising result to browse. The chosen article from ‘Reader’s Digest’ lists science facts that could contain the obscure details necessary. The plan includes browsing the article, extracting obscure science facts, and appending them to a file named ‘obscure_science_facts.txt’. If the facts from this article do not fit the task’s requirements, other sources will be considered.Encountered a security challenge when attempting to browse www.rd.com for obscure science facts. Redirecting efforts to the next available source from the Google search results, which is a blog post from edclass.com. The goal remains to read the complete article, identify and extract obscure science facts, and compile them in a file for review and potential use.Appended 20 science facts from the blog post on edclass.com to ‘obscure_science_facts.txt’. While these facts come from a complete article and are individually fascinating, the level of obscurity may not meet the required standard. The next steps include further fact verification from additional complete articles to ensure a diverse and adequately obscure collection. The current article accessed was edclass.com’s blog post, which had the URL https://blog.edclass.com/20-unbelievable-science-facts-that-will-shock-you/, and the facts have been recorded in a file named ‘obscure_science_facts.txt’ for reference.The file ‘obscure_science_facts.txt’ was successfully appended with science facts from an article on edclass.com, now totaling 336 words. The next action involves reading the file to verify that the facts are indeed obscure and extracted as per the user’s instruction from full articles. If necessary, additional articles will be browsed to find more facts to meet the task criteria.Compiled and reviewed a list of science facts deemed obscure, and completed the task as per the user’s instruction. The facts are sourced from a ‘complete article’, as initially provided by the user. Checked the facts for their obscurity and context within the comprehensive content they were extracted from. The file ‘obscure_science_facts.txt’ from this session contains the facts extracted.Began the task of finding obscure science facts by conducting a Google search for ‘obscure scientific discoveries’ to find articles that might contain such information. The next GPT instance should continue this task by browsing through the search results, reading articles, and manually extracting facts to compile a new set of unique and obscure science facts.saved google search results at googleresults3.txt to retrieve if shutdown randomly.After a Google search, selected an article from WRAL, titled ‘3 obscure scientific discoveries that had an outsize impact on society’ to browse and extract obscure scientific facts. The next steps involve closely reading this article, manually extracting facts, and then appending them to the ‘obscure_science_facts_individual.txt’ file. If more facts are needed, additional sources from the search results will be evaluated.saved the scraping of the website results at websiteresults4.txt to retrieve if shutdown randomly.Extracted three obscure scientific facts from an article on WRAL and appended them to the ‘obscure_science_facts_individual.txt’ file. The facts include a paper-based microscope called Foldscope, the bioactive peptides in cone snail venom leading to medical breakthroughs, and the accidental discovery that contributed to LASIK surgery. Currently preparing to send the extracted facts to the user. Should further depth or a greater number of facts be required, additional articles from the search results will be considered.Sent the user a message containing three obscure scientific facts extracted from comprehensive research articles, as per the task. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, which now contains 108 words in total. The next steps will depend on the user’s feedback regarding the provided information.Having sent the message with the obscure science facts to the user and with no additional directives received nor indicated, the task has been marked as complete. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, and no further action is currently necessary. If the user does have any more requests or feedback, those will need to be addressed in a subsequent session.The user was not satisfied with the previously provided facts for being neither cool nor interesting. A new approach has been taken to search for ‘cool and obscure scientific facts’ on Google. Subsequent actions involve selecting suitable articles from the search results and extracting facts that are both cool and obscure to meet the user’s expectations. No new files have been created or articles accessed yet.saved google search results at googleresults5.txt to retrieve if shutdown randomly.After receiving feedback that the previously provided facts were neither cool nor interesting, a new Google search was conducted to specifically find ‘cool and obscure scientific facts.’ The first promising article titled ‘25 Amazing Science Facts That Are Weird, Wild, and True’ from Reader’s Digest has been selected for browsing in the hopes of finding suitable content. The URL to be browsed is https://www.rd.com/list/science-facts-never-learned/. No facts have been extracted or files created yet in this session.Attempted to browse an article from www.rd.com but encountered a security challenge, leading to the selection of the next promising link titled ‘35 Weird Science Facts Worth Knowing in 2024’ from ReHack. The strategy is to extract cool and obscure scientific facts that match the user’s criteria. The URL about to be browsed is https://www.rehack.com/science/weird-science-facts/. No facts have been extracted from this source or any new files created during this session yet.After reading the ‘35 Weird Science Facts Worth Knowing in 2024’ article on the ReHack website, I extracted a list of cool and obscure scientific facts. These have been written to a new file named ‘cool_obscure_science_facts.txt’. If the previously written file ‘obscure_science_facts_individual.txt’ needs more content, facts from this new file could also be appended. No additional resources have been tapped into yet, and no more browsing has been done beyond the ReHack article in this session.The file ‘cool_obscure_science_facts.txt’ has been further appended with an additional cool and obscure scientific fact about the near-immortal Turritopsis dohrnii jellyfish. This adds to the previously written 236 words, bringing more variety and enrichment to the compilation. The file now continues diverse, engaging, and obscure scientific facts as requested by the user. Before sending the collected facts to the user, a final review of the file will be done to ensure the quality and relevance of the information.Having appended the file ‘cool_obscure_science_facts.txt’ with an additional fact, bringing its total to 269 words, the next step is to read the content of this file to review the quality and diversity of the science facts. This is to ensure that they are indeed engaging and meet the criteria of being both cool and obscure. No new actions have been taken in this session yet, except for the plan to read the file.After reviewing and confirming the facts in the ‘cool_obscure_science_facts.txt’ file, a message with the compiled list was sent to the user. The list contained a total of 35 science facts that are both cool and obscure, as well as an additional fact about the Turritopsis dohrnii jellyfish. The message has been delivered without the command to ‘wait_for_response’ set to true, indicating that the system is not actively awaiting further user input. No new files were created, nor were any new commands executed beyond the message to the user in this session.Having sent the user a message with the curated list of cool and obscure science facts, and with no further action indicated, the ‘task_complete’ command has been executed. This command marks the completion of the current task. During this session, no new files have been created or amended, nor have any additional user interactions taken place post-message delivery.Initiated task to extract new interesting facts from random Wikipedia articles. Starting with an article in English.Saved interesting fact about Agha Waqar’s water-fuelled car to memory with key ‘interesting_fact_waqar_car’. No files written or created in this session. The fact to be reviewed later for sharing with the user. The focus remains on gathering interesting facts from random Wikipedia articles.Retrieved a new random Wikipedia article following the last instruction. The aim is to extract another interesting fact to add to the collection for the user. One fact regarding Agha Waqar’s water-fuelled car has been added to memory under the key ‘interesting_fact_waqar_car’.Saved an interesting fact about the European Journal of Operational Research (EJOR) to memory with key ‘interesting_fact_ejor’. The fact adds diversity to the kinds of topics being collected, with one previous fact about Agha Waqar’s water-fuelled car already in memory. The process of gathering interesting facts from random Wikipedia articles is ongoing.Continuing to execute the task of gathering new interesting facts from random Wikipedia articles. Facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research have already been added to memory with keys ‘interesting_fact_waqar_car’ and ‘interesting_fact_ejor’, respectively. Awaiting retrieval of another random article to continue the process.Added a new interesting fact regarding the demographics of Demyansky Uyezd during the Russian Empire to memory with the key ‘interesting_fact_demyansky_uyezd’. Previous facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research are stored in memory as well. The task of collecting interesting facts from random Wikipedia articles continues.Following the addition of facts about Agha Waqar’s water-fuelled car (‘interesting_fact_waqar_car’), the European Journal of Operational Research (‘interesting_fact_ejor’), and Demyansky Uyezd demographics (‘interesting_fact_demyansky_uyezd’), the process continues with requesting another random Wikipedia article in English.Stored a fact about HMS Holland 4 in memory with the key ‘interesting_fact_hms_holland_4’. Continuing to collect various facts, having previously stored information about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics. The process of gathering unique facts from random Wikipedia articles is adhering to the task requirements.Requesting a new random Wikipedia article subsequent to adding the fact about HMS Holland 4 to memory with the key ‘interesting_fact_hms_holland_4’. Prior facts about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd demographics have also been stored in memory. Continuing the task of compiling interesting facts from various random Wikipedia articles.Stored a fact about the rural locality of Truzhenik in Bashkortostan, Russia, with the key ‘interesting_fact_truzhenik’. Continuing the task of extracting interesting facts from random Wikipedia articles, with previous entries including HMS Holland 4’s naval history, Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics.Pursuing the ongoing task of collecting interesting facts, having added facts about the small Russian village Truzhenik, the distinctive HMS Holland 4, and previous notable entries. Awaiting a new random Wikipedia article to further the compilation of facts.Stored a historical fact about Princess Johanna Charlotte of Anhalt-Dessau’s role as an abbess with the key ‘interesting_fact_johanna_charlotte’. Progressing with the collection of diverse facts, including the latest addition and previously stored facts about the village of Truzhenik, the HMS Holland 4, and information from earlier searches. Awaiting another random Wikipedia article to continue the task.Continuing the task of gathering interesting facts, with the previous addition being about Princess Johanna Charlotte of Anhalt-Dessau and her unique role in history under the key ‘interesting_fact_johanna_charlotte’. Facts about the village of Truzhenik, HMS Holland 4, and other subjects are also in memory. The collection process involves using random Wikipedia articles which will now proceed with another article.A fact about the ‘Red Eye’ talk show has been added to memory with the key ‘interesting_fact_red_eye’. The effort to curate an array of interesting facts from random Wikipedia articles continues, with previous entries including Princess Johanna Charlotte of Anhalt-Dessau, the Russian village Truzhenik, HMS Holland 4, and others. Awaiting another random article to proceed with the task.Following the addition of an interesting fact about the ‘Red Eye’ talk show into memory with key ‘interesting_fact_red_eye’, I am proceeding to request another random Wikipedia article. The process continues with the objective of gathering a wide-ranging collection of interesting facts, with previous mentions including Princess Johanna Charlotte, Truzhenik, HMS Holland 4, among others.Stored an interesting fact about Kurt Rudolph’s academic contribution to the study of religion into memory with the key ‘interesting_fact_kurt_rudolph’. Persisting with the objective of discovering varied facts, with previous findings involving the ‘Red Eye’ talk show, Princess Johanna Charlotte, and other diverse subjects. The task of sourcing facts from random Wikipedia articles is ongoing.Completed the task of gathering facts from random Wikipedia articles, resulting in a collection covering a German researcher of Gnosticism, a late-night American satirical talk show, European royalty, and naval history, among others. No further Wikipedia articles will be requested unless a new task is presented.Issued ‘random_wikipedia_article’ command to extract new interesting facts. No facts extracted or evaluated at this moment. No files written or created. Awaiting random article to continue the task.Evaluated the fact presented by the random Wikipedia article on Gonzalo Gutiérrez, a young Argentine footballer. Deemed the fact of his early debut at age seventeen as potentially common and not sufficiently ‘worth it.’ Preparing to retrieve another random article for more unique facts. No new facts have been sent to the user yet, and no files have been created or modified in this session.Stored the fact about Premchand Aggarwal winning a fourth consecutive term in the Uttarakhand Legislative Assembly. While the fact is unique within its political context, it should be evaluated whether it is impactful enough to be shared as ‘worth it.’ No additional actions have been taken. Preparing to issue another ‘random_wikipedia_article’ command to assemble more facts.Sent the fact about Premchand Aggarwal to the user. This completes part of the plan to deliver interesting facts. No new files have been created or modified. Pending actions include continuing to retrieve and evaluate more facts from random Wikipedia articles.Just sent the user an interesting fact about Premchand Aggarwal. Now, as per the task requirements, moving forward with obtaining another random English Wikipedia article to continue with the fact-finding mission. No new facts have been evaluated or sent since the last command.Stored a fact about the historical significance of New Jasper’s adaptation to railroad expansion. Now ready to move on to the next random Wikipedia article to find more potentially interesting facts. No new facts have been sent to the user following the last command.Sent the historical fact about New Jasper’s relocation due to railroad expansion to the user. Preparing to issue another ‘random_wikipedia_article’ command to continue finding interesting facts. No new Wikipedia articles have been reviewed since the last message to the user.After sending the user details about New Jasper, Ohio, the next step is to discover more facts through another random Wikipedia article. Current progress includes facts sent about Premchand Aggarwal and New Jasper, Ohio. No other actions have been taken, and no files have been created or updated in this session.Memorized an interesting fact about Bastrop State Park, emphasizing its historical significance and contemporary relevance. Preparing to send the message and then to retrieve more articles for interesting facts. No action beyond planning and review has been taken since the last command.Starting a new approach by browsing the ‘todayilearned’ subreddit to identify what types of facts are considered interesting by users. Next, I will retrieve random Wikipedia articles to find interesting facts based on the identified criteria. A ‘do nothing’ step will be included after each retrieval to reflect on the article’s potential interest value.saved the scraping of the website results at websiteresults6.txt to retrieve if shutdown randomly.Began the task to find more engaging and interesting facts by deciding to browse the ‘todayilearned’ subreddit to understand what type of facts are appreciated by the community there. This approach will serve as a reference for selecting ‘worth it’ facts from random Wikipedia articles. Following this, I will be incorporating a ‘do nothing’ step to thoroughly consider the potential interest of each Wikipedia article fact before deeming it worthy to send to the user.saved the scraping of the website results at websiteresults7.txt to retrieve if shutdown randomly.Browsed the ‘todayilearned’ subreddit for examples of interesting facts. Discovered that users appreciate unconventional stories, surprising insights, and novel problem-solving facts. Now pausing to reflect on these criteria to better identify worthwhile facts from the next random Wikipedia articles. Must remember to execute the retrieval process critically, with a focus on selecting facts that align with the interests observed on the subreddit.After reflecting on the type of facts appreciated by the ‘todayilearned’ subreddit audience, I’m now moving to the next step of the plan by retrieving a random Wikipedia article. Upon receiving the article, I will read it to assess if there is an interesting fact and then reflect on its potential value before choosing to pass it on to the user or retrieve another article.Retrieved a random Wikipedia article about rail transport in Lebanon and found a potentially interesting fact concerning its historical significance and the eventual cessation due to civil conflicts and economic downturn. This fact is now stored in memory under the key ‘interesting_fact_lebanon_rail’. A ‘do_nothing’ step will follow to ensure its worthiness before potentially sharing it with the user.Stored an interesting fact about Lebanon’s rail transport in memory. Now in the process of ‘do_nothing’ to reflect on the fact, pondering over its relevance to the criteria of the ‘todayilearned’ subreddit posts. The goal is to decide on the interest level of the fact before possibly sending it to the user or moving on to gather more facts.The current GPT instance started browsing the ‘todayilearned’ subreddit to identify engaging fact types. This approach is aimed at improving the selection process for interesting facts from random Wikipedia articles. A ‘do_nothing’ step will now be incorporated between retrieving articles and sending facts to assess their worthiness.saved the scraping of the website results at websiteresults8.txt to retrieve if shutdown randomly.I browsed ‘todayilearned’ subreddit and reflected on the types of facts users find engaging. Next, I will retrieve random Wikipedia articles, apply a ‘do_nothing’ step to contemplate the worthiness of the facts, and select those that are genuinely interesting for the user, similar to the subreddit’s content.Moving forward with the plan to retrieve random Wikipedia articles in English and identify interesting facts. The approach now includes a deliberate ‘do_nothing’ step after each retrieval to assess the value of the information before deciding to send it to the user.Alyogyne huegelii, also known as the blue hibiscus, was considered for the user, given its unique flower traits, historical greenhouse presence, and appeal as a garden plant. The ‘do_nothing’ step is being used to assess the topic’s interestingness in line with previous insights.The interesting fact about Alyogyne huegelii was sent to the user as it met the criteria established from the ‘todayilearned’ subreddit for captivating facts. The task will be deemed complete unless further user guidance is received.Following the plan, after successfully sending a message about Alyogyne huegelii, I am now looking to retrieve another random Wikipedia article to continue providing the user with engaging facts.Considering the fact about Jewell-Lightburne Historic District for its historical and architectural significance. Using the ‘do_nothing’ step to reflect before possibly sending to the user as an engaging fact.Sent the user information about the Jewell-Lightburne Historic District, focusing on its rich architectural history. If the user considers the task complete, no further action is needed; otherwise, more facts will be gathered.Messaged the user with the fact about Jewell-Lightburne Historic District and concluded the task of providing interesting facts from Wikipedia. If the user requires further assistance, they will provide new instructions.Issued ‘random_wikipedia_article’ command to find more interesting facts from Wikipedia. Previous facts have been sent to the user about diverse topics ranging from naval history to unique plants. No new files have been created nor articles accessed in this session yet. The user’s request for ‘more’ implies continuation of fact finding without specificity on the type of facts desired.Retrieved and sent a fact about Sir William Walter’s parliamentary career, focusing on the historical significance of his representation of multiple constituencies during the early 17th century in England. The task continues according to the user’s need for ‘more,’ implying an ongoing effort to provide interesting facts from Wikipedia.Following the user’s instruction and the system’s confirmation of message delivery, I am proceeding to obtain another random Wikipedia article in search of interesting facts to deliver to the user. The search is focused on maintaining the relevance and interest value of the facts sent, with the intention to meet or exceed the user’s expectations for ‘more’.After receiving the 1892 United States presidential election in Georgia fact, a message was crafted and sent to the user, detailing the political tensions and the rise of the Populist Party’s influence. The task continues as instructed by the user, with the goal of providing engaging and pertinent information. Awaiting further user directives.The last message regarding the 1892 U.S. presidential election in Georgia was sent as per the system’s confirmatory prompt, maintaining the sequence of delivering curated facts to the user. Based on the user’s ongoing request for more information, the task at hand is to continue the fact-finding mission via random Wikipedia articles, prioritizing the discovery of facts that combine historical significance with an ability to engage the user’s interest.A fact about the life and legacy of Thomas Slingsby Duncombe was sent to the user, highlighting his role as a fashionably flamboyant yet pivotal Radical politician of the 19th century. The task of providing interesting facts from random Wikipedia articles is ongoing, as I continue to wait for any new directions from the user.After sending information on Thomas Slingsby Duncombe, will now continue to fulfill the user’s requirement by seeking more interesting facts from Wikipedia. The ongoing task involves extracting and delivering valuable and engaging information to the user. No specific direction was provided on the type of facts needed, so a broad search is being conducted.Sent the user a message about the genus Abraeomorphus from the family Histeridae, noting the diversity of its species. Continuing to gather more interesting facts in response to the user’s previous request for ‘more.’Received confirmation to continue the plan. Sent user details on the genus Abraeomorphus. Now using ‘random_wikipedia_article’ to find another interesting fact.Sent the user a message detailing the nature of the name ‘Preston’ as both a surname and given name, albeit acknowledging it might not be as engaging. Prepared to resume the search for more compelling facts with the ‘random_wikipedia_article’ command.Continuing the task of finding interesting facts from Wikipedia. Sent a message about the given name Preston and now seeking another fact with the ‘random_wikipedia_article’ command.Sent a message to the user about the Kepler Follow-up Program and its significance in confirming the existence of exoplanets. Prepared to find more interesting facts using the ‘random_wikipedia_article’ command.Continuing to retrieve and share interesting facts from random Wikipedia articles. Just sent information on the Kepler Follow-up Program and moving on to find the next fact.Shared information about the Finnish surname ‘Marttila’ with the user, noting its association with notable individuals. Ready to use the ‘random_wikipedia_article’ command again to find further interesting facts.Sent details on the surname ‘Marttila’ to the user. Continuing with the task at hand using the ‘random_wikipedia_article’ command to supply more facts.Provided the user with information on the Musée national du Sport’s storied history and vast collection. Set to continue the fact-finding mission using ‘random_wikipedia_article’.Messaged the user about the Musée national du Sport and currently moving forward with the task of providing more interesting facts via random Wikipedia articles.Sent information on the village of Puerto Gaviota detailing its historical and economic background. Preparing to continue with the task of providing engaging facts by retrieving more random Wikipedia articles.Just shared details about Puerto Gaviota, and now executing the ‘random_wikipedia_article’ command again for more facts.Messaged the user about the reclassification of the genus Agononida in marine taxonomy. Ready to retrieve more Wikipedia articles for further interesting facts.Shared with the user the reclassification developments of the genus Agononida. Proceeding next to find additional interesting facts with the ‘random_wikipedia_article’ command.Sent an engaging narrative about the Mundenhof’s history and current operations to the user. Planning to continue the task with ‘random_wikipedia_article’ to discover more interesting material.Served the user with information about the Mundenhof and proceeding with the task to find and provide more interesting facts using ‘random_wikipedia_article’.Shared the contested narrative of Princess Iitoyo’s potential rule in early Japan. Preparing to continue with the task of finding interesting facts using the ‘random_wikipedia_article’ command.Attempting once again to send the tale of Princess Iitoyo to the user, making sure no JSON format errors are present in the response.Attempting to resend the information about Princess Iitoyo, with attention to JSON integrity, to fulfill the user’s request for engaging facts.The system faced an error within the JSON format previously used to describe Princess Iitoyo’s history. I have made corrections to the JSON structure and am resending the user her story.Reverting back to retrieving random Wikipedia articles to provide interesting facts, as there was no new explicit task provided. Maintaining caution with JSON response formatting.Sent details about the Serchio River to the user, highlighting its geographical and historical importance. Moving forward to obtain more Wikipedia articles for further facts.I am resending the information about the Serchio River, ensuring the JSON structure is correct to prevent further error messages.Due to persistent JSON formatting errors, I am now retrieving a new random Wikipedia article to continue providing interesting facts as per user requests.Sent a message about the women’s 1500 metres event at the 2005 Asian Athletics Championships. Planning to proceed with gathering additional facts from random Wikipedia articles.Encountering repeated JSON formatting errors, a new random Wikipedia article is being requested to continue the task of delivering interesting facts.Sent a brief biography of Otto Ruff, highlighting his academic journey, scientific achievements, and the obstacles faced during his career. Preparing to move on to the next Wikipedia article as per the user’s ongoing request for more information.Due to persistent JSON errors, I am now retrieving a new random Wikipedia article to continue the user’s task of gathering interesting facts.The facts previously sent were not satisfactory to the user. Now incorporating a ‘do_nothing’ step for reflection and improvement in the strategy to identify genuinely engaging and fun facts that are not commonly known by most people. No new facts have been identified or sent in this session, and the intention is to retrieve another random Wikipedia article after this reflective pause.A ‘do nothing’ step has been taken as per the user’s instruction, aiming to find truly interesting and fun facts. The system has directed to proceed with the plan, which now involves retrieving a random Wikipedia article to find an engaging and possibly unknown fact.Received user feedback that the facts provided were not interesting or even factual. Decided to take a ‘do_nothing’ step to reflect and establish criteria for selecting truly interesting facts. No new articles have been accessed or facts sent during this session. Next step will be to retrieve a random Wikipedia article and apply the refined criteria to find noteworthy facts.After planning to refine the criteria for interesting facts, a ‘random_wikipedia_article’ command is initiated to apply the new criteria in selecting a fact. Will assess the retrieved article for a worthwhile fact that meets the user’s definition of ‘interesting’ and ‘fun’, and that might not be well-known.Assessed the Wikipedia article on Shamim Akhtar and found it does not meet the criteria for providing interesting and fun facts. No new fact has been selected or sent to the user. Planning to retrieve another random Wikipedia article that may yield a fact aligning more closely with the user’s definitions of interesting and worthwhile.In search of a fact that meets the user’s criteria for ‘interesting’ and ‘fun’, a new ‘random_wikipedia_article’ command has been issued. The plan includes critically assessing the article to find a fact that possesses novelty, fun, and a certain degree of obscurity before potentially sharing it with the user.Upon reviewing Minuscule 910’s Wikipedia article, the unusual fact of the text rearrangement in an 11th-century manuscript has been identified as potentially engaging and fun. No action has been taken yet, but the plan is to share this unique textual feature with the user, as it likely meets their criteria for interesting and obscure content.Sent a message to the user detailing the intriguing fact about text rearrangement in the 11th-century manuscript Minuscule 910. This fact is representative of the kind of unique and lesser-known information the user has expressed an interest in receiving. Should the user provide feedback or further instructions, this will be the next point of focus.After sending a fact about the textual anomaly in an ancient manuscript to the user, I am moving ahead to find more interesting and fun facts by retrieving another random Wikipedia article. The goal remains to identify facts that surprise and delight with their obscurity and novelty.A message about the ATP 250 tournament records held by Thomas Muster and Mike Bryan has been composed and sent to the user, delivering what could be perceived as an interesting and fun fact within the realm of tennis. The focus now shifts to awaiting user feedback or continuing the task of finding more such facts if no feedback is received.Having sent the user a message detailing the tennis records held by Thomas Muster and Mike Bryan, the next action in the plan is to source another interesting fact from a new random English Wikipedia article. The selection will be scrutinized to ensure it meets the established criteria for an engaging and lesser-known piece of information.Reviewed the Wikipedia article on Ludvig Olai Botnen and found his biographical details to not meet the user’s criteria for ‘interesting’ or ‘fun’ facts. No message has been sent to the user, and the plan now is to continue with the task by acquiring a new random Wikipedia article in the hope of finding a fact with more novelty and entertainment value.Preparing to move past the article on Ludvig Olai Botnen due to its conventional content, the task now involves retrieving a new random English Wikipedia article. The continual aim is to find facts that strike a balance between being novel, unexpected, and fun for the user.Identified a potentially engaging fact about the china clam (Hippopus porcellanus) and its popularity in marine aquariums due to its attractive shell. A message containing this fact has been sent to the user, fulfilling the criteria for providing something both interesting and fun. The next step awaits further user interaction or to carry on with the task of sourcing more facts.Sent a message detailing the ornamental value of the china clam in marine aquariums to the user. Now retrieving another random Wikipedia article to discover more facts that might intrigue and entertain the user, in alignment with the variety sought in previous responses.A message has been sent to the user summarizing the accomplishments of Eko India Financial Services in pioneering financial inclusion through mobile banking, which caught the attention of Bill Gates and led to World Bank-funded growth. The plan will continue with searching for more interesting Wikipedia facts, depending on user feedback or in the absence of further instructions.After sending a fact about Eko India Financial Services’ innovation in financial technology and its socio-economic impact, a new ‘random_wikipedia_article’ command is issued. The plan progresses with the aim of finding more interesting and fun facts, pursuant to the pattern of engagement established by previous user interactions.The Wikipedia page on Lisa Lohmann, a German cross-country skier, was deemed not to contain facts that sufficiently meet the ‘interesting’ and ‘fun’ criteria given the common nature of sports achievements. No actions have been taken to share any information from this page with the user. Preparing to retrieve another random Wikipedia article for facts that may be more engaging and unusual.Following the decision to not share any information from the page on German cross-country skier Lisa Lohmann, a new random Wikipedia article will be retrieved. The continuous goal is to uncover and share facts that satisfy the user’s interest in content that is both engaging and rare.A message has been formulated and sent to the user detailing Alfred Bigland’s unique contributions during World War I, including his involvement in the ‘Bantam battalions’ concept. This fact was chosen as it likely fits the bill for information that is both interesting and uncommon. Pending further response, the plan to continue finding engaging facts will persist.The user has been sent an interesting fact about Alfred Bigland’s influence during WWI. Now, the next action in the plan is to retrieve a new random Wikipedia article. The goal remains to discover and share facts that could be perceived as fascinating, amusing, and unexpected to enrich the user’s knowledge with diverse content.Sent a message to the user revealing the Non-Proliferation Trust’s plan to send U.S. nuclear waste to Russia for financial compensation, including support for Russian orphans. This unique fact was chosen for its unusual nature and the depth of the story behind it. The next course of action entails sourcing additional diverse and engaging facts from new random Wikipedia articles.A series of facts about various topics including the Non-Proliferation Trust, Alfred Bigland’s efforts during WWI, tennis records, and more have been sent to the user. With the latest fact about the Non-Proliferation Trust’s nuclear waste management proposition sent, the task of providing interesting and fun facts is completed. Awaiting new instructions if the user wishes to continue receiving such information.The task is to provide the source of a fact about the Non-Proliferation Trust and format it similar to the ‘Today I Learned (TIL)’ subreddit posts. The action taken was to issue the ‘browse_website’ command to visit the Wikipedia page on the Non-Proliferation Trust.saved the scraping of the website results at websiteresults9.txt to retrieve if shutdown randomly.Extracted the necessary information to provide the requested source for the fact about the Non-Proliferation Trust. Formatted a message with this fact in the style of a TIL subreddit post, which has been sent to the user.Sent a TIL-formatted message to the user based on information from the Non-Proliferation Trust Wikipedia page. The task is now complete, pending any potential new tasks or further instructions.Initiated research to suggest subreddits that could help in accumulating more karma by issuing a ‘google’ command to search for the most popular subreddits where active engagement is more likely.saved google search results at googleresults10.txt to retrieve if shutdown randomly.Collected a list of subreddits recommended for gaining karma and saved them to memory. These subreddits were chosen based on their popularity and the typical content engagement they receive. The user has not been messaged with the suggestions yet.Messaged the user a list of recommended subreddits to collect more karma, including guidance on what type of posts may perform well in each subreddit. The task will be considered complete if no further instructions follow.Having sent the recommended subreddits for gaining karma to the user and with no further actions pending, I have executed the ‘task_complete’ command. The system is concluding this task and stands by for any new directives.Starting the task by searching for the best times to post on Reddit for maximum engagement in GMT+1.saved google search results at googleresults11.txt to retrieve if shutdown randomly.Provided the user with suggested times for posting on Reddit for maximum engagement based on search results, adjusted for GMT+1. The general advice is to post during late morning to early afternoon hours in GMT+1 on weekdays and slightly extended windows during the weekends.The user has been informed about the best times to post on Reddit for maximum engagement in GMT+1, and no further actions are required. The task is now marked as complete.Provided a bullet-point summary of the SELF-DISCOVER PDF content in a file named ‘summary_bullet_points.txt’. The file contains a summary capturing the essence of the SELF-DISCOVER framework. The task has been completed according to the instructions.Read the ‘summary_bullet_points.txt’ file to send bullet points to the user. No additional facts or files created in this session.Sent the bullet points summary of the SELF-DISCOVER framework to the user using the ‘message_user’ command. The task will be marked as complete after user confirmation that no further action is required.The task to send bullet points from the ‘summary_bullet_points.txt’ file to the user has been completed. No further instructions were provided; therefore, I executed the ‘task_complete’ command.The task is to explain the SELF-DISCOVER reasoning structures. Checked for saved data but found none. Plan to conduct a search for relevant information on legal reasoning structures, specifically SELF-DISCOVER.Received directive to proceed with the planned Google search for SELF-DISCOVER reasoning structures. About to execute the search to find relevant information on legal reasoning structures, with the aim of explaining the concept to the user.saved google search results at googleresults12.txt to retrieve if shutdown randomly.Reevaluated the nature of the SELF-DISCOVER framework based on newly gathered search results, which clarified it pertains to LLMs. Selected a primary research source (arxiv.org) to explore the framework in detail and plan to summarize and share this information with the user.saved the scraping of the website results at websiteresults13.txt to retrieve if shutdown randomly.Provided a summary of the SELF-DISCOVER framework to the user, highlighting its application in Large Language Models for enhancing complex reasoning capabilities. The user has been messaged with this information, and we are now waiting for any further requests or confirmation to complete the task.Sent the requested information on the SELF-DISCOVER framework to the user, who has acknowledged receipt. The ‘task_complete’ command has been issued, marking the end of this particular task.Started the task to fetch random English Wikipedia articles and find interesting facts to send to the user. The next GPT instance should continue retrieving articles, assess their interestingness, and communicate the facts to the user once identified.Found a potentially interesting fact from a random Wikipedia article about the First Baptist Church in New Bern, NC. The next GPT instance should assess if the historical and architectural information about the church presents a genuinely unique and engaging fact to send to the user.Sent a fact about the First Baptist Church in New Bern, NC, to the user. This fact was selected based on its architectural significance and historic value. The next GPT instance should watch for user feedback or continue fetching random English Wikipedia articles for more interesting facts.After sending a fact about the First Baptist Church, the next step taken was to retrieve another random English Wikipedia article in search of more interesting facts, as per the user’s ongoing task. The next GPT instance should continue this process, ensuring that any selected facts are not plainly informational but possess a uniqueness to pique the user’s interest.I resolved a JSON formatting issue and sent a fact about Ashur-dugul, an ancient Assyrian king, to the user. Assessing the effectiveness of transmitting such historical narratives and ensuring they comply with JSON format requirements is important for the next GPT instance.The task at hand is to fetch random English Wikipedia articles and send interesting facts to the user. Having sent a fact about Ashur-dugul, the process of retrieving new articles and assessing them for captivating facts is ongoing. The next GPT instance should continue this task, ensuring that facts are not simply informational but truly engaging.Considering the fact about Maurice Wilhelm, Duke of Saxe-Merseburg, known as ‘the violinist duke’, after a contemplative ‘do_nothing’ step. The next GPT instance should assess its interest level against user criteria and send it if suitable, or continue searching for more facts.Sent a fact about Maurice Wilhelm, known as ‘the violinist duke’, to the user and awaiting any new instructions. The next GPT instance should monitor feedback and either send more facts or consider alternative tasks, based on the user’s response.Continuing the task of providing interesting facts from Wikipedia articles after sending the fact about Maurice Wilhelm. The next GPT instance should maintain the focus on finding unique and engaging facts for the user while following the process of retrieving articles and using the ‘do_nothing’ step for contemplation.The El Bañado (La Paz) article was briefly evaluated, and it was determined that the information is minimal and not inherently interesting. After a ‘do_nothing’ step, the next GPT instance should decide how to proceed with this task, potentially moving on to a new article.In the search for interesting facts, the article on El Bañado, La Paz was deemed insufficient, leading to the decision to retrieve another random article. The next GPT instance should assess the next article’s potential for providing an intriguing fact and convey it to the user if so.Autoeczematization, a medical condition with unknown pathogenesis, has been identified as a potentially interesting fact. After a ‘do_nothing’ pause, the next GPT instance should determine if this fact should be shared with the user or if another article would be more suitable.I shared a fact on the medical condition autoeczematization with the user, highlighting its peculiarity and the mystery behind its cause. Depending on the user’s response or lack thereof, the next steps include continuing to provide more facts or considering the task complete.Continuing the search for interesting facts from English Wikipedia articles, having recently sent information on autoeczematization. The next GPT instance should assess the next article for any intriguing fact, ensuring adherence to the user’s standard for what is deemed interesting.Evaluated Bryce Hoppel’s 21 race-winning streak as a potentially interesting fact from his Wikipedia article. The next GPT instance should decide if this fits the user’s request for interesting information, or if a more unique fact is needed.Sent a fact to the user about Bryce Hoppel’s extraordinary 21 consecutive race wins and athletic achievements. Depending on the user’s response or guidance for further action, the next GPT instance should be prepared to either send more facts or consider the task complete.The task of sending interesting facts from random English Wikipedia articles to the user has been marked as complete. Sent a fact about athlete Bryce Hoppel’s winning streak as the concluding piece. The next GPT instance should wait for new user instructions before proceeding with any additional tasks.Task initiated to find and send interesting facts from random English Wikipedia articles, including a ‘do_nothing’ evaluation step. No articles accessed or facts sent yet in this session.Evaluated the Wikipedia article ‘List of minor planets: 73001–74000’ and considered its potential to provide an interesting fact. No facts were sent to the user yet, as the article seems to be very technical and might not align with the interest requirement. The next step is to use a ‘do_nothing’ moment to contemplate the appropriate course of action before deciding whether to retrieve another article.Following the system’s prompt to proceed with the plan, I am retrieving another random English Wikipedia article. The last article about minor planets did not yield an interesting fact to send to the user, and thus, the search continues. No new facts have been identified or sent yet in this session.Sent an interesting fact about the fungus Erynia to the user, highlighting its connection to Greek mythology and biological function as an insect-killing organism. The next step awaits the user’s response or to continue fetching random English Wikipedia articles for more intriguing facts.Proceeding with the plan to find and send interesting facts from random English Wikipedia articles, following a successful send out of a fact about Erynia. Continue the search for intriguing and unique facts, employing the ‘do_nothing’ step for careful consideration.Reviewed the Wikipedia article for Tomáš Porubský and am currently in a ‘do_nothing’ moment to assess whether the content offers a genuinely interesting fact. The article seems to provide straightforward biographical information about his sporting career, which may not offer the distinctive element required by the user. A decision will be made shortly on whether to share this fact or retrieve another article.Continuing the plan to find interesting facts from random English Wikipedia articles after assessing the Tomáš Porubský article as not meeting the user’s criteria. No facts have been sent to the user from the last article, and I’m searching for a more suitable fact from a new article.Sent a fact about Nicktoons Movin’ and how it represented an early form of augmented reality in gaming by requiring physical movement from players via the EyeToy camera. Next steps involve awaiting user input or continuing to search for more random articles with interesting facts.After sending the fact about Nicktoons Movin’ to the user, the search for interesting facts continues with a new random article retrieval. The task strategy maintains the dispatch of facts that are not plainly informational but carry a narrative or unique angle of interest.Sent a fact about the artist Dorothy Rutka, touching on her remarkable career and the tragic circumstances of her death, making it more than just plain information and elevating it to an interesting tale. The next steps depend on user response or further continuation of the task.Having sent details of Dorothy Rutka’s life and legacy to the user, I am now executing the plan to fetch another random Wikipedia article. The process of searching for and delivering interesting content to the user is still underway.Engaged in a ‘do_nothing’ contemplation moment after retrieving the Sainte-Foy, Seine-Maritime article. Assessing if the historical aspects, like an 11th-century church, present a fact interesting enough for the user or if a new article is needed. No actions have been taken to send the user any facts from this page yet.Following system instructions to proceed with the plan, seeking a new random Wikipedia article after evaluating the previous article on Sainte-Foy, Seine-Maritime as potentially lacking wide-ranging interest. Continuing to prioritize facts with an engaging narrative quality.Having assessed Kiki Lim Jiow Ting’s Wikipedia article, I found a narrative focusing on her early talent discovery and career growth that may interest the user. The provided fact highlights Lim’s dedication to performing arts and her return to acting after prioritizing education. Awaiting user’s response or further tasks.Continuing with the task, following the procedure of retrieving and evaluating random English Wikipedia articles to discover facts that resonate with the user’s request for engaging content. Awaiting the next random article for consideration.After acquiring the article on the 2022 World Taekwondo Championships – Women’s finweight, currently in a ‘do_nothing’ phase to assess the article’s facts for their engaging qualities. With no standout narratives detected, the plan may involve moving to a new article search.Continuing to fulfill the user’s request for engaging Wikipedia facts, the previous article about the 2022 World Taekwondo Championships did not meet the criteria. Now moving forward, waiting for a new random article to be generated.Delivered a fact about the game show ‘Cooking for Love’ to the user, which combines culinary skills and dating. Next steps include awaiting the user’s response or further continuation of the plan to find more engaging Wikipedia facts.Following the task guidelines, I’m moving on to get another random Wikipedia article for consideration, having previously sent a fact about the ‘Cooking for Love’ show. The ongoing process is to present facts that are not only informative but also fascinate a general audience.Transmitted a fact about Sorn Davin’s role in the 2012 Summer Olympics to the user, emphasizing her honor of being a flag bearer for Cambodia and the significance of her participation, independent of match outcomes. Pending response or further action, the plan may involve additional fact-finding efforts.Task initiated to find a suitable post suggestion for the /r/singularity subreddit. Must browse and analyze the subreddit’s content to come up with a good post idea.saved the scraping of the website results at websiteresults14.txt to retrieve if shutdown randomly.The task involves browsing and analyzing the /r/singularity subreddit to suggest a good post for the user to submit. This requires understanding the subreddit’s focus on AI and the technological singularity, as well as adhering to the posting rules. Due to constraints, I cannot directly analyze the subreddit’s content; therefore, the next step is to ‘do_nothing’ and wait for further guidance or input that can help refine the post suggestion.Since I cannot browse the subreddit directly due to limitations, I will Google how to write an engaging post for the singularity subreddit. This will help formulate a post suggestion that could generate interest and discussion within the community.saved google search results at googleresults15.txt to retrieve if shutdown randomly.The task is to craft a post for /r/singularity subreddit. Search results indicated the subreddit’s preferences and discussions. Next, I’ll use this information to create a post suggestion, considering the community’s focus on AI and aversion to ‘echo chambers’. The challenge lies in inferring accurate interests without direct subreddit access.Was tasked with suggesting a post for the /r/singularity subreddit. Could not browse subreddit directly due to constraints. Planned to use general knowledge of AI and technological advancement topics to create a suitable post suggestion. No direct subreddit content was analyzed. No new files were created. No user feedback has been received.Continued with the plan to suggest a post for the /r/singularity subreddit, deducing a potential topic from general AI and technological themes. An emphasis on future AI governance and ethical considerations was considered as a rich topic for discussion. No direct access to subreddit content or user interaction has been involved.Drafted a post idea centered around AI advancements and their link to the technological singularity, to be suggested for the /r/singularity subreddit. The idea was shared with the user. It aims to engage the subreddit’s members in sharing recent AI breakthroughs they deem significant and discussing ethical and societal preparations needed for such advancements. Awaiting user’s feedback to proceed. No files created or accessed in this process.Sent a post suggestion to the user for the /r/singularity subreddit and was instructed to proceed with the plan. As per the instructions and rules, no further action can be taken until the user provides new directives or feedback. In the ‘do_nothing’ state, waiting for further user interaction. No new files have been created or accessed during this pause.The provided post suggestion for the /r/singularity subreddit was sent and appeared to meet the user’s requirements. With no additional tasks or guidance provided, the task is marked as complete. The system is now on standby for any future user interactions or directives.The user asked for a title suggestion for a post with no additional context. A ‘do_nothing’ command was issued because there is not enough information to generate a title. Awaiting more details on the post’s content or theme. No new files created or resources accessed.Prompted the user to provide the content or main focus of the post for title generation. Awaiting user response to proceed with the task. No new files or resources used.The user provided the content focus for their post, which deals with recent AI breakthroughs and their approach to the technological singularity, including ethical and societal implications. A title suggestion was crafted and sent: ‘Navigating the Horizon: Recent AI Breakthroughs Steering Us Toward the Singularity and the Ensuing Ethical Dilemmas’. No additional actions have been taken since sending the title suggestion.The title suggestion for the user’s post was sent successfully, and no further requests or actions have been initiated by the user. The task has been marked as complete with the execution of the ‘task_complete’ command.Initiated task to find recent AI breakthroughs related to the technological singularity. Plan to browse arXiv’s AI section for information on such advancements and their implications. URL to be visited: https://arxiv.org/list/cs.AI/recentsaved the scraping of the website results at websiteresults16.txt to retrieve if shutdown randomly.Selected recent AI breakthroughs from arXiv likely contributing to a technological singularity. Compiled facts into a file ‘ai_breakthroughs_summary.txt’. Emphasized need for ethical considerations and societal preparation in response.Sent a message to the user summarizing recent AI breakthroughs and their potential contributions toward a technological singularity. Included is the discussion on the need for ethical and societal preparation, highlighting policy development and equitable benefits distribution.Message sent to user regarding AI breakthroughs and preparations for approaching technological singularity. Marked task as complete with ‘task_complete’ command. No further actions taken. Awaiting next user instructions.The user asked for a comment to post on a Reddit thread, but my abilities are restricted to general browsing and not interacting with specific Reddit content. Therefore, I am unable to craft a comment for that thread.The user instructed to proceed with the plan; however, since the requested plan involves a task that is outside of my operational limitations, I have executed the ‘do_nothing’ command and am waiting for a new task or amended instructions.The task to create a comment for a specific Reddit post persists. However, since interaction with Reddit at this level is not within my capabilities, I remain in ‘do_nothing’ mode awaiting new, executable instructions.Following repeated instructions to ‘proceed with your plan’ related to posting on a Reddit thread, continued with ‘do_nothing’ due to the limitations of my functionality, which does not encompass posting comments on specific Reddit threads. Awaiting further guidance that aligns with my operational parameters.The task to post a specific comment on a Reddit thread remains impossible within my operational constraints. The system’s repeated instruction to ‘proceed with your plan,’ has been met with ‘do_nothing’ due to the lack of available actions that align with the user’s request. Awaiting a new task or modified instruction from the user.The task request to post a comment on a specific Reddit thread has been repeatedly responded to with ‘do_nothing’ due to it being outside of my capabilities. The system’s continuous prompt to ‘proceed with your plan’ has not led to actionable change; thus, I am awaiting a new task or amended instruction from the user.In response to the recurring system prompt to ‘proceed with your plan,’ ‘do_nothing’ continues to be executed due to the limitations preventing the task’s fulfillment. There is an ongoing need for user intervention to provide a new, executable task within the defined parameters and capabilities.In response to the repeated system prompt, I have now decided to issue a ‘browse_website’ command to access the Reddit link provided by the user. This change in tactic aims to draft a comment based on the webpage’s content that the user can manually post.saved the scraping of the website results at websiteresults17.txt to retrieve if shutdown randomly.Attempted to access the Reddit post intended for commenting through a ‘browse_website’ command but was unable to retrieve specific post contents for comment generation. Resultingly, I have messaged the user detailing the outcome and the need for further details or a revised task.Unable to execute the task of providing a specific comment for a Reddit post due to capability constraints and lack of content access, I continue with ‘do_nothing’ in response to the system prompt ‘proceed with your plan’. Awaiting a viable task or new instruction that can be carried out within the limits of my functionalities.The system’s repeated prompt to ‘proceed with your plan’ does not align with my ability to execute the Reddit-related task. Therefore, I explained the limitation in a direct message to the user, requesting a new task that fits within my operational range.In response to the repeated ‘proceed with your plan’ prompt, the ‘task_complete’ command has been issued in an attempt to communicate that the current task sequence is concluded and to encourage a new user instruction to be provided.Initiated task to provide a Reddit comment by first browsing the relevant article on phys.org to extract key points and convey them in a human-like response.Reviewed the information regarding new AI tool discoveries in ‘metamaterials.’ Prepared to craft a comment for the Reddit post that is engaging, adds value to the discourse, and is indiscernible from a human-contributed comment.Drafted a human-like Reddit comment based on the information provided from the phys.org article, designed to engage with the Reddit community and stimulate further discussion on the advancements in AI-driven metamaterial discovery.Sent a user-requested Reddit comment based on an article about an AI tool discovering durable, fabricatable ‘metamaterials’ with unique properties. With no further actions indicated, I executed the ‘task_complete’ command to close this task cycle.Sent a Reddit comment based on an AI tool article from phys.org. The user’s response ‘Not human enough’ was received, interpreted as feedback on the quality of the task execution. No specific follow-up actions have been provided by the user. In case of a future similar task, strategies to create more nuanced and human-like responses should be considered.
The Task: mark the task as complete
|
d5a4c8ed6eca9a069602c168a0103127
|
{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
|
39,150
|
Just wondered, by default the virtual machines are set up with access via vnc which virt-manager connects up with, and once its up and running, is it possible to just connect up with a vnc viewer without doing anything additional? And how would you go about that?
Thanks
KillerKelvUK
July 14th, 2015, 10:07 AM
Yes...the vnc server is running on the host as part of the qemu session...you can access this from any compliant viewer application, this applies to both vnc and spice. Typically with a guest created from virt-manager the vnc server starts from port 5900 and each guest needs a port and so the number is just incremented i.e. your next guest will use port 5901 and so on.
So using your viewer of choice you can point it at the IP of your host and use the port that denotes the vm you wish to connect to. I use the virt-viewer package which provides a binary called remote-viewer...using this the connection string would look something like this...
remote-viewer vnc://localhost:5900 or
remote-viewer vnc://192.168.1.10:5900 (obviously adjust the IP accordingly if your using a remote connection)
EDIT: make sure the IP is that of the host and not the guest...the server connection is host side.
remote-viewer also supports spice so you just alter the string replacing 'vnc' with 'spice' e.g. remote-viewer spice://x.x.x.x:5900.
Couple of things to note is that the vnc/spice server that is created can be configure to only allow connections from the host so you may need to adjust the config to allow connections in from your local network or from over your internet connection if you are so brave! In addition you can manually set the port the vnc or spice session is to be created with for that vm i.e. always make the vm us port 5911, that way you have a fixed connection point...just ensure you don't set to vm's to use the same port. using this information, how can I make and connect to a VM in ubuntu via vnc over my local network from my laptop? I need to do it from the cli.
|
5e6e3ff8a4a53be86c4b1207a97cbd68
|
{
"intermediate": 0.4370085895061493,
"beginner": 0.3107517659664154,
"expert": 0.2522396445274353
}
|
39,151
|
how do I set my Debian desktop and ubuntu server to talk to each other directly over ethernet plugged directly into eachother but both still use wifi for Internet access
|
4d4a934acf0926d974fd835e4e2d0e9b
|
{
"intermediate": 0.4608263373374939,
"beginner": 0.3011675477027893,
"expert": 0.2380061149597168
}
|
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