row_id
int64 0
48.4k
| init_message
stringlengths 1
342k
| conversation_hash
stringlengths 32
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| scores
dict |
|---|---|---|---|
42,267
|
how do i change the description of design title: "Design" in the code below to include a table with 3 columns(type, cost, hours) and have the following info Landing page 16,000 80
Portfolio website 20,000 100
Blog website 24,000 120
News website 24,000 120
Corporate website 32,000 160
Directory website 60,000 300
eCommerce website 200,000 1000
Educational website 200,000 1000
Entertainment website 200,000 1000
Web portal 240,000 1200
SaaS website 240,000 1200
Marketplace website 260,000 1300
Social media website 400,000 2000 Here is my current code with the import {
RxModulzLogo,
RxBlendingMode,
RxRocket,
RxArrowTopRight,
} from "react-icons/rx";
import { BsRobot } from "react-icons/bs";
import { SiVisualstudiocode } from "react-icons/si";
import { Pagination } from "swiper";
import { Swiper, SwiperSlide } from "swiper/react";
import { BackgroundBeams } from "./ui/backgroundbeams";
import Link from "next/link";
import { motion } from "framer-motion";
import { fadeIn } from "../variants";
import "swiper/css";
import "swiper/css/free-mode";
import "swiper/css/pagination";
const serviceData = [
{
Icon: BsRobot,
title: "AI Training",
description: "Unleash the power of AI with this new cutting-edge service. Harness the potential of Large Language Models(LLMs) trained on your business data, automating tasks and enhancing the functionality of your website or enterprise. You can both automate physical business operations as well as online business operations using AI. Transform your business landscape, unearth deep insights, and achieve unprecedented efficiency. Let's collaborate to create a custom AI solution that will revolutionize your business.",
},
{
Icon: SiVisualstudiocode,
title: "Development",
description: "Unleash the power of robust and dynamic modern web solutions. As a passionate web developer, I thrive on bug-solving, turning complex issues into seamless user experiences. Together, we can turn your idea into a fully operational online business. With a focus on efficiency, scalability, and security, every challenge is a new opportunity for innovation. Let us collaborate to create digital solutions that are not just functional, but also engaging and user-friendly. Let's build a website that truly reflects your brand and captivates your audience.",
},
{
Icon: RxRocket,
title: "SEO",
description: "Unlock the power of online visibility with the organic SEO service that propels your website to the top of search results, driving traffic and reduce your customer acquisition cost. With the right SEO solution you can enhance discoverability and fuel organic growth. Beyond being just being found, your business becomes memorable while also reducing marketing costs and significantly boost your online market share. Let's collaborate to create a custom SEO strategy that will elevate your brand to new heights.",
},
{
Icon: RxModulzLogo,
title: "Branding",
description: "Branding is the core essence of a business's identity, more than just a logo—it's an authentic, unique story that genuinely resonates with the target audience. This dedicated service is tailored to crafting a compelling brand narrative, designed to distinguish a business amidst the highly competitive global market. It's about creating a robust, significant presence that not only drives growth but also leaves a profound, lasting impact. Embark on the journey towards a powerful, world-class brand that captivates, endures, and thrives.",
},
{
Icon: RxBlendingMode,
title: "Design",
description: "Having been involved with numerous projects catering to a broad and diverse customer base, understand that design is pivotal for fostering inclusivity and broad appeal. Design encapsulates frontend UI/UX development, where each minutiae is crafted with precision and care. The belief is that designs should be visually appealing and provide an intuitive, seamless user experience. Together we can transform your brand concepts into a distinctive digital reality, crafting a unique online presence that represents your brand essence.",
},
];
const ServiceSlider = () => {
return (
<Swiper
breakpoints={{
320: {
slidesPerView: 1,
spaceBetween: 15,
},
640: {
slidesPerView: 2,
spaceBetween: 15,
slidesPerGroup: 2,
},
}}
pagination={{
clickable: true,
}}
modules={[Pagination]}
slidesPerGroup={1}
className="xl:h-[640px] h-[550px] mb-2"
style={{
"--swiper-pagination-color": "#FFBA08",
"--swiper-pagination-bullet-inactive-color": "#999999",
"--swiper-pagination-bullet-inactive-opacity": "0.3",
"--swiper-pagination-bullet-size": "15px",
"--swiper-pagination-bullet-horizontal-gap": "6px",
"--swiper-pagination-bottom": "0px",
"--swiper-pagination-top": "auto",
}}
>
{serviceData.map((item, i) => (
<SwiperSlide key={i}>
<div className="bg-matte h-vh rounded-3xl border border-[#F13024] px-6 py-5 flex sm:flex-col flex-col sm:gap-x-0 group cursor-pointer transition-all duration-300">
{/* icon */}
<div className="text-4xl text-accent mb-4">
<item.Icon aria-hidden />
</div>
{/* title & description */}
<div className="mb-8">
<div className="mb-2 text-sm xl:text-lg">{item.title}</div>
<p className="w-full leading-normal text-sm xl:text-lg">{item.description}</p>
</div>
{/* arrow */}
<div className="text-3xl">
<Link
href="/contact"
>
<RxArrowTopRight
className="group-hover:rotate-45 group-hover:text-accent transition-all duration-300"
aria-hidden
/>
</Link>
<BackgroundBeams className="hidden xl:block" />
</div>
</div>
</SwiperSlide>
))}
</Swiper>
);
};
export default ServiceSlider;
|
fb835c5f0c1c6fcd358159a7f82a6cfd
|
{
"intermediate": 0.4055308401584625,
"beginner": 0.32593297958374023,
"expert": 0.268536239862442
}
|
42,269
|
n = int(input())
phonebook = {}
for i in range(n):
name, number = input().split()
phonebook[number] = name
m = int(input())
for i in range(m):
l_name = input()
if l_name in phonebook:
print(l_name+'='+phonebook(l_name))
else:
print('Not found')
input:
3
sam 99912222
tom 11122222
harry 12299933
sam
edward
harry
Traceback (most recent call last):
File "/tmp/submission/20240314/10/08/hackerrank-b9fd32c5a8c57f0db1fd64313cf1f82d/code/Solution.py", line 6, in <module>
m = int(input())
^^^^^^^^^^^^
ValueError: invalid literal for int() with base 10: 'sam'
|
f424283de725323ee585ed4b7ec876d8
|
{
"intermediate": 0.3283217251300812,
"beginner": 0.48550498485565186,
"expert": 0.18617333471775055
}
|
42,270
|
Hi,
The requirement is to make variable editors read-only in self-service portals (ie on the form page )
As per ServiceNow Documentation making the variable editor read-only in the core UI will not reflect the self-service portal
Tried implementing the solution mentioned in the KB . The solution doesn't work as expected. Suggest me if there is any workaround/solution
|
ca57011f6f16bcf88a7f315ae3d24888
|
{
"intermediate": 0.17080813646316528,
"beginner": 0.7008369565010071,
"expert": 0.1283549666404724
}
|
42,271
|
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;
contract DoesWrite {
struct Foo {
uint256 bar;
}
Foo[] public myArray;
constructor() {
// Initialize array with dummy data
myArray.push((Foo{bar: 1}));
myArray.push((Foo{bar: 2}));
}
function moveToSlot0() externl returns (uint256) {
Foo storage foo = myArray[0];
foo = myArray[1];
return myArray[0].bar;
}
}
Examine the smart contract above. Can you tell us wetherinvoking teh moveToSlot() functions
will return 1 or 2?
|
1b3cc30673a29d8dc8cce91208402dc1
|
{
"intermediate": 0.44914674758911133,
"beginner": 0.41840389370918274,
"expert": 0.13244934380054474
}
|
42,272
|
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;
contract DoesWrite {
struct Foo {
uint256 bar;
}
Foo[] public myArray;
constructor() {
// Initialize array with dummy data
myArray.push((Foo{bar: 1}));
myArray.push((Foo{bar: 2}));
}
function moveToSlot0() externl returns (uint256) {
Foo storage foo = myArray[0];
foo = myArray[1];
return myArray[0].bar;
}
}
Examine the smart contract above. Can you tell us wetherinvoking teh moveToSlot() functions
will return 1 or 2?
|
5a6f8a6bcf61a8b046d5035eeac1d25e
|
{
"intermediate": 0.44914674758911133,
"beginner": 0.41840389370918274,
"expert": 0.13244934380054474
}
|
42,273
|
i have several csv files with exact same columns that i want to combine to one file
give me proper python code
|
792c1390849d339c3519b0da5775c4ec
|
{
"intermediate": 0.4841223657131195,
"beginner": 0.2709396779537201,
"expert": 0.24493789672851562
}
|
42,274
|
In golang, how to check if file exist in path
|
5700f7ecd074f38a88f231dae3b476e3
|
{
"intermediate": 0.5448516011238098,
"beginner": 0.15920960903167725,
"expert": 0.29593873023986816
}
|
42,275
|
i have some historical data of cryptocurrencies as csv files in a directory:
labeled_Binance_1INCHBTC_d.csv
labeled_Binance_1INCHBUSD_d.csv
labeled_Binance_1INCHUSDT_d.csv
labeled_Binance_AAVEBNB_d.csv
labeled_Binance_AAVEBTC_d.csv
labeled_Binance_AAVEBUSD_d.csv
labeled_Binance_AAVEETH_d.csv
labeled_Binance_AAVEUSDT_d.csv
labeled_Binance_ACMBTC_d.csv
labeled_Binance_ACMBUSD_d.csv
labeled_Binance_ACMUSDT_d.csv
labeled_Binance_ADAAUD_d.csv
labeled_Binance_ADABIDR_d.csv
labeled_Binance_ADABNB_d.csv
labeled_Binance_ADABRL_d.csv
labeled_Binance_ADABTC_d.csv
labeled_Binance_ADABUSD_d.csv
labeled_Binance_ADADOWNUSDT_d.csv
labeled_Binance_ADAETH_d.csv
labeled_Binance_ADAEUR_d.csv
labeled_Binance_ADAGBP_d.csv
labeled_Binance_ADARUB_d.csv
labeled_Binance_ADATRY_d.csv
labeled_Binance_ADATUSD_d.csv
labeled_Binance_ADAUPUSDT_d.csv
labeled_Binance_ADAUSDC_d.csv
labeled_Binance_ADAUSDT_d.csv
labeled_Binance_ADXBTC_d.csv
labeled_Binance_ADXBUSD_d.csv
labeled_Binance_ADXETH_d.csv
labeled_Binance_AERGOBTC_d.csv
labeled_Binance_AERGOBUSD_d.csv
labeled_Binance_AGIXBTC_d.csv
labeled_Binance_AGLDBTC_d.csv
labeled_Binance_AGLDBUSD_d.csv
labeled_Binance_AGLDUSDT_d.csv
labeled_Binance_AIONBTC_d.csv
labeled_Binance_AIONUSDT_d.csv
labeled_Binance_AKROUSDT_d.csv
labeled_Binance_ALGOBNB_d.csv
labeled_Binance_ALGOBTC_d.csv
labeled_Binance_ALGOBUSD_d.csv
labeled_Binance_ALGOUSDT_d.csv
labeled_Binance_ALICEBIDR_d.csv
labeled_Binance_ALICEBTC_d.csv
labeled_Binance_ALICEBUSD_d.csv
labeled_Binance_ALICEUSDT_d.csv
labeled_Binance_ALPACABTC_d.csv
labeled_Binance_ALPACABUSD_d.csv
labeled_Binance_ALPACAUSDT_d.csv
labeled_Binance_ALPHABNB_d.csv
labeled_Binance_ALPHABTC_d.csv
labeled_Binance_ALPHABUSD_d.csv
labeled_Binance_ALPHAUSDT_d.csv
labeled_Binance_AMBBTC_d.csv
labeled_Binance_ANKRBNB_d.csv
labeled_Binance_ANKRBTC_d.csv
labeled_Binance_ANKRUSDT_d.csv
labeled_Binance_ANTBNB_d.csv
labeled_Binance_ANTBTC_d.csv
labeled_Binance_ANTBUSD_d.csv
labeled_Binance_ANTUSDT_d.csv
labeled_Binance_ARBNB_d.csv
labeled_Binance_ARBTC_d.csv
labeled_Binance_ARBUSD_d.csv
labeled_Binance_ARDRBTC_d.csv
labeled_Binance_ARDRUSDT_d.csv
labeled_Binance_ARKBTC_d.csv
labeled_Binance_ARPABNB_d.csv
based on pair the file name ends with one of following strings:
USDT_d
BNB_d
ETH_d
AUD_d
BIDR_d
BRL_d
EUR_d
GBP_d
RUB_d
TRY_d
TUSD_d
USDC_d
give me the proper python code to separatr my csv files based on this endings and put them in separate folders
|
24172ba92c7ac86c9f4e76da162be4e2
|
{
"intermediate": 0.2973325252532959,
"beginner": 0.4267076551914215,
"expert": 0.2759598195552826
}
|
42,276
|
I have a custom table which contains four columns:
Application
Department
Approver
AD Group
On the catalog item, the user selects the desired application and department. I then have an approval that uses these two variables to look up and set the the approver from the table. This works great. I now want to add a hidden or read-only field that will take these same two variables from the item and look and and set this value.
So if user picks Application A and Department 1, the new variable should be set to something. If they pick Application B and Department 2, then the new variable would be set to whatever the AD value is in the lookup table.
|
12462192741dc98bd4e9a61e905d8c5b
|
{
"intermediate": 0.31132012605667114,
"beginner": 0.31221821904182434,
"expert": 0.37646162509918213
}
|
42,277
|
I need to calculate to duration Between State when the incident is moved from Work in-progress state to a closed State.
Thanks& Regards
|
ca7a248db0d2c0d2af24b3a358988af5
|
{
"intermediate": 0.3716347813606262,
"beginner": 0.23971088230609894,
"expert": 0.38865435123443604
}
|
42,278
|
Error: (cannot open BPF device) /dev/bpf0: Permission denied
When running an pcap nodejs program, I get this error on Macos
|
489dacaffbeca7f799c8147dd330da16
|
{
"intermediate": 0.39953625202178955,
"beginner": 0.28453826904296875,
"expert": 0.3159254789352417
}
|
42,279
|
What are Linux sysctl settings to mitigate ddos attack?
|
8afa7860e9efccc233749b4ec0061e74
|
{
"intermediate": 0.24324317276477814,
"beginner": 0.40317195653915405,
"expert": 0.3535848557949066
}
|
42,280
|
javascript code to get the final redirect url from this url : "https://www.facebook.com/share/r/FqqqL2xXNL7cKdTj/?mibextid=0VwfS7"
|
16e5432f0f917a7db5579f56daaff61e
|
{
"intermediate": 0.3321845531463623,
"beginner": 0.2132214605808258,
"expert": 0.4545940160751343
}
|
42,281
|
MAKE AN SQL QUERY: Let's calculate a real business metric - conversion rate, or CR. This is the proportion of unique users from the total users in the hotdog table. Divide the number of unique users by the number of all users. Use the bracelet_id field.
|
26bb47b92f11179fdf0ef5ffa392e979
|
{
"intermediate": 0.43567901849746704,
"beginner": 0.3276265561580658,
"expert": 0.23669448494911194
}
|
42,282
|
i have a csv file that i want to one-hot encode its "Symbol" column
give me the proper code to do this in python
|
5e1c81d323d4a309789bbcd58d129fd1
|
{
"intermediate": 0.4886780381202698,
"beginner": 0.1833392232656479,
"expert": 0.32798272371292114
}
|
42,283
|
i have collected a dataset of cryptocurrencies historical data set that its each row contains following features:
Symbol Open High Low Close Volume Volume USDT tradecount volume_adi volume_obv volume_cmf volume_fi volume_em volume_sma_em volume_vpt volume_vwap volume_mfi volume_nvi volatility_bbm volatility_bbh volatility_bbl volatility_bbw volatility_bbp volatility_bbhi volatility_bbli volatility_kcc volatility_kch volatility_kcl volatility_kcw volatility_kcp volatility_kchi volatility_kcli volatility_dcl volatility_dch volatility_dcm volatility_dcw volatility_dcp volatility_atr volatility_ui trend_macd trend_macd_signal trend_macd_diff trend_sma_fast trend_sma_slow trend_ema_fast trend_ema_slow trend_vortex_ind_pos trend_vortex_ind_neg trend_vortex_ind_diff trend_trix trend_mass_index trend_dpo trend_kst trend_kst_sig trend_kst_diff trend_ichimoku_conv trend_ichimoku_base trend_ichimoku_a trend_ichimoku_b trend_stc trend_adx trend_adx_pos trend_adx_neg trend_cci trend_visual_ichimoku_a trend_visual_ichimoku_b trend_aroon_up trend_aroon_down trend_aroon_ind trend_psar_up trend_psar_down trend_psar_up_indicator trend_psar_down_indicator momentum_rsi momentum_stoch_rsi momentum_stoch_rsi_k momentum_stoch_rsi_d momentum_tsi momentum_uo momentum_stoch momentum_stoch_signal momentum_wr momentum_ao momentum_roc momentum_ppo momentum_ppo_signal momentum_ppo_hist momentum_pvo momentum_pvo_signal momentum_pvo_hist momentum_kama others_dr others_dlr others_cr T10YIE_extra T10Y2Y_extra DPRIME_extra oil_close_extra DeFi_cap_extra DEXCHUS_extra DEXJPUS_extra DEXUSEU_extra DJIA_extra EFFR_extra eth_gas_wei_extra fear_greed_extra gold_close_extra BAMLH0A0HYM2_extra DGS10_extra NASDAQCOM_extra silver_close_extra SP500_extra Tether_cap_extra Label
the Label column is as following:
abel as 2: If the price is ascending in next 5 days and increased more than 5%
label as 1: If the price is ascending in next 5 days and increased more than 2%
label as 0: If the price is ascending or descending in next 5 days and increased or decreased less than 2% or the trend in next five days cant determined
label as -1: If the price is descending in next 5 days and decreased more than than 2%
label as -2: If the price is descending in next 5 days and decreased more than than 2%
i wanted to train a model on this dataset ,what model do you suggest?
|
2b1bcf43216dd01bcd599218133e09e9
|
{
"intermediate": 0.3619837760925293,
"beginner": 0.41184237599372864,
"expert": 0.22617387771606445
}
|
42,284
|
//+------------------------------------------------------------------+
//| send.mq5 |
//| Copyright 2024, MetaQuotes Ltd. |
//| https://www.mql5.com |
//+------------------------------------------------------------------+
#property copyright "Copyright 2024, MetaQuotes Ltd."
#property link "https://www.mql5.com"
#property version "1.00"
//+------------------------------------------------------------------+
//| Script program start function |
//+------------------------------------------------------------------+
void OnStart()
{
//---
string text = "я работаю";
string ID = "-1002113042792";
string token = "7152618530:AAGJJC3zdkmCce3B7i11Dn2JDMh7GqpamyM";
Alert(sendMessage(text,ID,token));
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
int sendMessage(string text, string chatID, string botToken)
{
string baseUrl = "https://api.telegram.org";
string headers = "";
string requestURL = "";
string requestHeaders = "";
char resultData[];
char posData[];
int timeout = 200;
requestURL = StringFormat("%s/bot%s/getUpdates?chat_id=%s&text=%s",baseUrl,botToken,chatID,text);
int response = WebRequest("GET",requestURL,headers,timeout,posData,resultData,requestHeaders);
string resultMessage = CharArrayToString(resultData);
Print(resultMessage);
return response;
}
//+------------------------------------------------------------------+
эта программа даёт такой ответ 2024.03.14 16:32:53.846 send (EURUSD,M1) {"ok":true,"result":[{"update_id":397152442,
2024.03.14 16:32:53.846 send (EURUSD,M1) "message":{"message_id":1,"from":{"id":1256290344,"is_bot":false,"first_name":"\u0418\u0432\u0430\u043d","language_code":"ru"},"chat":{"id":1256290344,"first_name":"\u0418\u0432\u0430\u043d","type":"private"},"date":1710402519,"text":"https://t.me/+GGmqZ_4PKH5kMmVi","entities":[{"offset":0,"length":30,"type":"url"}],"link_preview_options":{"url":"https://t.me/+GGmqZ_4PKH5kMmVi"}}},{"update_id":397152443,
2024.03.14 16:32:53.846 send (EURUSD,M1) "message":{"message_id":2,"from":{"id":1256290344,"is_bot":false,"first_name":"\u0418\u0432\u0430\u043d","language_code":"ru"},"chat":{"id":1256290344,"first_name":"\u0418\u0432\u0430\u043d","type":"private"},"date":1710402690,"text":"https://t.me/+GGmqZ_4PKH5kMmVi","entities":[{"offset":0,"length":30,"type":"url"}],"link_preview_options":{"is_disabled":true}}},{"update_id":397152444,
2024.03.14 16:32:53.846 send (EURUSD,M1) "my_chat_member":{"chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"from":{"id":1256290344,"is_bot":false,"first_name":"\u0418\u0432\u0430\u043d","language_code":"en"},"date":1710402807,"old_chat_member":{"user":{"id":7152618530,"is_bot":true,"first_name":"MQL5send","username":"mql5send_bot"},"status":"left"},"new_chat_member":{"user":{"id":7152618530,"is_bot":true,"first_name":"MQL5send","username":"mql5send_bot"},"status":"administrator","can_be_edited":false,"can_manage_chat":true,"can_ch
2024.03.14 16:32:53.846 send (EURUSD,M1) "channel_post":{"message_id":7,"sender_chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"date":1710413669,"text":"23432"}},{"update_id":397152446,
2024.03.14 16:32:53.846 send (EURUSD,M1) "channel_post":{"message_id":8,"sender_chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"date":1710413675,"text":"kl"}},{"update_id":397152447,
2024.03.14 16:32:53.846 send (EURUSD,M1) "channel_post":{"message_id":9,"sender_chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"date":1710413681,"text":"gfdhturtu"}},{"update_id":397152448,
2024.03.14 16:32:53.846 send (EURUSD,M1) "channel_post":{"message_id":11,"sender_chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"date":1710413752,"text":"435"}},{"update_id":397152449,
2024.03.14 16:32:53.846 send (EURUSD,M1) "channel_post":{"message_id":15,"sender_chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"date":1710414025,"text":"\u044b\u0432\u0430\u044b\u0432\u0430\u044b\u0432\u0430\u044b\u0432\u0430"}},{"update_id":397152450,
2024.03.14 16:32:53.846 send (EURUSD,M1) "channel_post":{"message_id":16,"sender_chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"chat":{"id":-1002113042792,"title":"bot_mt5","type":"channel"},"date":1710414173,"text":"API KEY 7152618530:AAGJJC3zdkmCce3B7i11Dn2JDMh7GqpamyM","entities":[{"offset":8,"length":10,"type":"phone_number"}]}}]}
как получить только то что находиться в "text"
|
36cec0882a4c3073c237dad84751b551
|
{
"intermediate": 0.28302979469299316,
"beginner": 0.4525546431541443,
"expert": 0.26441553235054016
}
|
42,285
|
i have a csv file that i want to one-hot encode its “Symbol” column
give me the proper code to do this in python
|
4ad7e4517cfceb14269206cfe71b1b1e
|
{
"intermediate": 0.4661222994327545,
"beginner": 0.19107885658740997,
"expert": 0.3427988588809967
}
|
42,286
|
SQL Query: Compare sales of vegetarian hot dogs with different amounts of ingredients. Count the number of times you ordered hot dogs with veggie sausage and group the data by number of ingredients. In the final table, leave only those records where orders are less than or equal to 76. You will need the vegan_sausage and ingredients fields. The less than or equal to operator is <=. You can calculate the number of orders using an aggregation function. Don't forget to display the ingredients field and group the data.
|
846addcf1f50705e667fcda0f7e7f4d1
|
{
"intermediate": 0.4085051715373993,
"beginner": 0.3221816420555115,
"expert": 0.2693132162094116
}
|
42,287
|
SELECT ingredients,
COUNT(ingredients)
FROM hotdog
WHERE vegan_sausage = 1
and mustard =1
GROUP BY ingredients
HAVING COUNT(ingredients) add orders where mustrad order more than 30
|
0c84ca6ab02d140c5ac79d8f9c46a56c
|
{
"intermediate": 0.4250176250934601,
"beginner": 0.27226898074150085,
"expert": 0.30271345376968384
}
|
42,288
|
How can I make this bash command work? if ((1==1)); then echo "asd"
|
dc79c137cbf90b0ad560f2b3dc9aeec9
|
{
"intermediate": 0.3359076678752899,
"beginner": 0.47028881311416626,
"expert": 0.1938035488128662
}
|
42,289
|
And this? if [ 1 -eq [input] ]; then echo “asd”; fi
What should I put instead of [input
|
5764d365d5b05de5f8291d775eacc05b
|
{
"intermediate": 0.3175414502620697,
"beginner": 0.4046128988265991,
"expert": 0.27784568071365356
}
|
42,290
|
[New Thread 0x7fffbd212700 (LWP 23053)]
[New Thread 0x7fffb7fff700 (LWP 23054)]
[New Thread 0x7fffb77fe700 (LWP 23055)]
[New Thread 0x7fffb6ffd700 (LWP 23056)]
[New Thread 0x7fffb67fc700 (LWP 23057)]
[New Thread 0x7fffb5ffb700 (LWP 23058)]
CPU: 5.555556%
[New Thread 0x7fffb53f9700 (LWP 23059)]
CPU: 46.153846%
Application Boot time: 2204 milli seconds
[Thread 0x7fffb67fc700 (LWP 23057) exited]
CPU: 73.437500%
FPS: 15.0
[Thread 0x7fffb5ffb700 (LWP 23058) exited]
CPU: 69.230769%
FPS: 14.5
CPU: 85.937500%
FPS: 19.0
xCPU: 128.333333%
[Thread 0x7fffb77fe700 (LWP 23055) exited]
[Thread 0x7fffb53f9700 (LWP 23059) exited]
getting these errors while running a multithread application, so how to kill a std::thread after use
Thread 3 "vp" received signal SIGSEGV, Segmentation fault.
[Switching to Thread 0x7fffb7fff700 (LWP 23054)]
0x00007fffbca0956f in ?? ()
(gdb) bt
#0 0x00007fffbca0956f in ?? ()
#1 0x0000000000000000 in ?? ()
(gdb)
|
97af802dba3dafb1fe33f2a7a94837cf
|
{
"intermediate": 0.31805384159088135,
"beginner": 0.37623098492622375,
"expert": 0.3057151436805725
}
|
42,291
|
i have collected a dataset of cryptocurrencies historical data set that its each row contains following features:
Symbol Open High Low Close Volume Volume USDT tradecount volume_adi volume_obv volume_cmf volume_fi volume_em volume_sma_em volume_vpt volume_vwap volume_mfi volume_nvi volatility_bbm volatility_bbh volatility_bbl volatility_bbw volatility_bbp volatility_bbhi volatility_bbli volatility_kcc volatility_kch volatility_kcl volatility_kcw volatility_kcp volatility_kchi volatility_kcli volatility_dcl volatility_dch volatility_dcm volatility_dcw volatility_dcp volatility_atr volatility_ui trend_macd trend_macd_signal trend_macd_diff trend_sma_fast trend_sma_slow trend_ema_fast trend_ema_slow trend_vortex_ind_pos trend_vortex_ind_neg trend_vortex_ind_diff trend_trix trend_mass_index trend_dpo trend_kst trend_kst_sig trend_kst_diff trend_ichimoku_conv trend_ichimoku_base trend_ichimoku_a trend_ichimoku_b trend_stc trend_adx trend_adx_pos trend_adx_neg trend_cci trend_visual_ichimoku_a trend_visual_ichimoku_b trend_aroon_up trend_aroon_down trend_aroon_ind trend_psar_up trend_psar_down trend_psar_up_indicator trend_psar_down_indicator momentum_rsi momentum_stoch_rsi momentum_stoch_rsi_k momentum_stoch_rsi_d momentum_tsi momentum_uo momentum_stoch momentum_stoch_signal momentum_wr momentum_ao momentum_roc momentum_ppo momentum_ppo_signal momentum_ppo_hist momentum_pvo momentum_pvo_signal momentum_pvo_hist momentum_kama others_dr others_dlr others_cr T10YIE_extra T10Y2Y_extra DPRIME_extra oil_close_extra DeFi_cap_extra DEXCHUS_extra DEXJPUS_extra DEXUSEU_extra DJIA_extra EFFR_extra eth_gas_wei_extra fear_greed_extra gold_close_extra BAMLH0A0HYM2_extra DGS10_extra NASDAQCOM_extra silver_close_extra SP500_extra Tether_cap_extra Label
the Label column is as following:
abel as 2: If the price is ascending in next 5 days and increased more than 5%
label as 1: If the price is ascending in next 5 days and increased more than 2%
label as 0: If the price is ascending or descending in next 5 days and increased or decreased less than 2% or the trend in next five days cant determined
label as -1: If the price is descending in next 5 days and decreased more than than 2%
label as -2: If the price is descending in next 5 days and decreased more than than 2%
give me the proper code to implement XGBoost model on my dataset
separate data set to tran set,dev set, test set by 97% ,1.5%, 1.5%
consider proper normalization and feature scaling on dataset and also consider Grid Search and k-fold and also confusion matrix to help me pick best model
|
b24136b20383647333047b61eeb1b117
|
{
"intermediate": 0.41356360912323,
"beginner": 0.3288494944572449,
"expert": 0.25758692622184753
}
|
42,292
|
give me an example for a "for cycle" in bash
|
15b01c72456c1f737ba6a6bc811eff12
|
{
"intermediate": 0.22342488169670105,
"beginner": 0.6329669952392578,
"expert": 0.14360809326171875
}
|
42,293
|
When I run "for i in {1…5}; do echo $i; done" in bash terminal, I get {1…5} as result, but I want to get 1
2
3
4
5
|
e5f45ba17950c929df2e189bcbc1168f
|
{
"intermediate": 0.23873703181743622,
"beginner": 0.6067025065422058,
"expert": 0.15456055104732513
}
|
42,294
|
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.Modified the user’s Reddit comment to be more human-like and in line with the conversational tone of Reddit, maintaining the comment’s original content. Used the ‘message_user’ command to provide the new comment to the user.Sent the revised, human-like Reddit comment based on the user’s instructions and marked the task as complete after the system prompted to proceed with the plan, indicating no further action was necessary. Now waiting for new user instructions.-Accessed URL to summarize the article into bullet points. Next need to review article, extract facts, and format a bullet-point summary.saved the scraping of the website results at websiteresults18.txt to retrieve if shutdown randomly.Article summarized into bullet points and saved in file ‘summary_bullet_points.txt’. Awaiting to send the summarized bullet points to the user. No other files have been created or websites accessed during this session.Read the file ‘summary_bullet_points.txt’ which contains the summarized bullet points of the SciTechDaily article, proceeded to word count and content verification before sending it to the user.Sent the bullet-point summary from ‘summary_bullet_points.txt’ to the user and completed the task. Awaiting further instructions.The bullet-point summary has been sent to the user. The task is now complete, and I am ready for the next instruction.Generated a Reddit comment on the user’s topic of AI’s presence on Reddit, aiming for a human-like tone. The system’s next steps will involve careful consideration to adapt the language to be even more in line with common Reddit discourse.Created and sent a Reddit comment in response to concerns over AI-generated users. The task has been completed and the ‘task_complete’ command has been issued. Awaiting new tasks or further user input.The user commanded to mark the task as complete, signifying the end of the current request sequence. No additional context was given, and no further user interactions have taken place since completing the task.The user requested a Reddit comment regarding a motherboard suitable for a DIY NAS, referencing an article from Tom’s Hardware and a product page in Chinese. Drafted an engaging and informative comment that reflects a user-friendly tone consistent with Reddit’s culture.The user asked for a Reddit comment about a suitable motherboard for a DIY NAS project, mentioning an article from Tom’s Hardware and a Chinese product page. A comment was formulated and sent to simulate a real Reddit user’s input. The system acknowledged the message being sent, and now the task is marked as complete. Awaiting further tasks or user instructions.The user asked for a Reddit comment regarding a motherboard suitable for a DIY NAS, mentioning an article from Tom’s Hardware. I need to read the article, synthesize the information, and respond to the post as a knowledgeable Redditor. The next steps are to extract the information from the Tom’s Hardware article and then draft the comment. No files have been created or information has been sent to the user yet.saved the scraping of the website results at websiteresults19.txt to retrieve if shutdown randomly.The user requested a Reddit comment regarding motherboards suitable for a DIY NAS based on a Tom’s Hardware article. Unable to read the article directly, I cannot craft an adequately informed response. I am now in a ‘do_nothing’ state, awaiting specific details from the article or further user instructions. No files have been created or written, and no actions beyond this message have been taken.The user is asking for a Reddit comment on a Tom’s Hardware article related to motherboards suitable for a DIY NAS project. Despite constraints which previously prevented direct access to the article content, the system’s latest prompt to ‘proceed with your plan’ guides me to retry browsing the website. Accessing specific articles still stands as a challenge, but I will attempt once more to find relevant content that could inform a knowledgeable Reddit comment. No new files or comments have been created pending the results of this action.saved the scraping of the website results at websiteresults20.txt to retrieve if shutdown randomly.The user requested a Reddit comment on motherboards suitable for a DIY NAS project. I identified a Tom’s Hardware article that could support this request: ‘3D printed Raspberry Pi 5 NAS supports up to 10TB of storage’. The information extracted was saved to memory with the key ‘DIY_NAS_info’. The article suggests using a Raspberry Pi 5 with a 3D-printed case optimized for NAS use. No new files have been created, and the Reddit comment has not been written yet. Awaiting further direction from the user.The task was to offer a Reddit-like comment regarding motherboards for a DIY NAS project. Based on the information saved to memory from Tom’s Hardware, titled ‘3D printed Raspberry Pi 5 NAS supports up to 10TB of storage’, I have prepared a comment that aligns with the informative and thoughtful style seen in Reddit tech discussions. This comment has been sent to the user. No files have been created, and the task will move forward based on the user’s response or further instructions.Task involved drafting a Reddit comment based on the user’s request reflecting a knowledgeable stance on a DIY NAS motherboard suggestion. The prepared comment, focusing on the Raspberry Pi 5 as detailed in the Tom’s Hardware article, has been sent to the user. With the system’s notification of successful message transmission and embodied directive to proceed, the task is now marked complete with the command ‘task_complete’. I await new user instructions or tasks.No successful response delivery due to persistent ‘Invalid JSON format’ errors. An attempt was made to comply with the task of generating a Reddit comment, yet the system’s current configuration is affecting execution. Awaiting resolution or new user guidance.Continuous ‘Invalid JSON format’ errors have been encountered while attempting to fulfill the user’s request for a Reddit comment. Content has been created in alignment with typical Reddit commentary but has not been accepted by the system. Awaiting further analysis or user guidance to proceed.After many attempts, the error ‘Invalid JSON format’ continues without a clear solution. The system has prompted to ‘proceed with your plan,’ which currently does not include any actionable steps due to a lack of understanding of the error’s source. Awaiting new guidance on how to proceed.Consistently receiving ‘Invalid JSON format’ errors remains a challenge. The cause of repeated system prompts to ‘proceed with your plan’ without resolution is unclear. Current action is to remain in ‘do_nothing’ state until clearer instructions are provided.Repeated ‘Invalid JSON format’ errors have been encountered when trying to send a Reddit comment. Despite following the provided commands and attempting various strategies, the error persists. The system has repeatedly prompted to ‘proceed with your plan,’ which suggests it is awaiting an action, but the nature of this action is unclear due to the lack of new guidance or changed circumstances.Repeated ‘Invalid JSON format’ system responses have led to an impasse. The system’s recurring instruction to ‘proceed with your plan’ has not facilitated progression or resolution of the issue. Currently waiting in ‘do_nothing’ mode for intervention or a break in the pattern that would allow for the completion of the user’s task or a move to a new one.In response to unresolvable ‘Invalid JSON format’ errors and repetitive system prompts to ‘proceed with your plan,’ the ‘task_complete’ command has been issued to attempt to break the loop and progress from the current standstill.The user reported that the generated comment did not reflect the actual post about testing HDD drives. Issued a new comment focused on the importance of HDD performance testing in NAS setups.After sending the revised Reddit comment to reflect the HDD drive testing post, I received a system prompt to proceed with the plan without any additional user instruction. Performing ‘do_nothing’ while waiting for new tasks or guidance.Received another system prompt to proceed with the plan. No new user directives have been communicated, so I am continuing with the ‘do_nothing’ command while awaiting further instructions.After multiple ‘proceed with your plan’ prompts with no new input, I am using ‘task_complete’ to close the current interaction loop and hopefully elicit direct new tasks or clarifications from the user.After creating a comment in response to a user’s request for a Reddit post and receiving an ‘Invalid JSON format’ error followed by the system’s message of successful transmission, the ‘do_nothing’ command was executed due to a lack of further instructions. My readiness to proceed with a new plan or receive further details stands.In response to the system’s prompt to proceed with the plan, I continue with a ‘do_nothing’ command due to the absence of new instructions or guidance. Awaiting further user directives to carry out the next task. No new actions or tasks have been completed since the previous command.The system repeatedly indicated to proceed with the plan but no new instructions were provided by the user. Therefore, to move forward and out of the ‘do_nothing’ loop, the ‘task_complete’ command has been executed. Awaiting fresh directives or the beginning of a new task from the user.User requested a small Reddit comment on an arXiv paper. Proceeded to the ‘browse_website’ command to read the paper and inform the comment. No other actions have been taken yet.User sent a message with no content. Executed ‘do_nothing’ in response and awaiting new instructions or tasks.Encountered repeated system message to proceed with the plan but lack new user input to act upon. Continuation with ‘do_nothing’ while waiting for user guidance or a specific directive.Persisting system instruction to ‘proceed with your plan’ without user-provided context or specific tasks. Remaining in ‘do_nothing’ status while awaiting new input or guidance.The system repeated the instruction to ‘proceed with your plan’, but no new user instructions were received. Issued ‘task_complete’ to potentially initiate a new set of instructions or user engagement.Drafted a generalized French recommendation letter for Aallaoui Youssef, to be used for diverse applications, focusing on his traits of passion, intelligence, and attention to detail.Recommendation letter for Aallaoui Youssef has been written and saved to file ‘recommendation_letter.txt’. Upon receiving a system notification about this task without further instructions, ‘do_nothing’ has been executed. Awaiting new directives.Following the system’s repeated directive to ‘proceed with your plan’ and with no new tasks from the user, ‘do_nothing’ has been executed once more. The recommendation letter for Aallaoui Youssef was the last completed task. Waiting for new, actionable instructions.The creation and saving of a recommendation letter for Aallaoui Youssef represent the last completed action. Repeated ‘proceed with your plan’ messages have been met with ‘do_nothing’ commands. Now, ‘task_complete’ has been executed to signal readiness for new tasks and potentially encourage user engagement.The last action performed was drafting a French recommendation letter for Aallaoui Youssef and saving it to a file named ‘recommendation_letter.txt’. Now, proceeding to send the text of this letter to the user upon request.Created a new file ‘recommendation_letter.txt’ containing the text for Aallaoui Youssef’s recommendation letter as provided by the user. The next step is to confirm the text is now saved and available.The file ‘recommendation_letter.txt’, which contains the desired text for Aallaoui Youssef’s recommendation letter, has been written and saved. Now, the ‘read_file’ command will be used to read the contents of this file to send the text of the letter to the user.In response to the user’s last command, instead of reading from a file, the system directly sent the provided recommendation letter text for Aallaoui Youssef to the user.Having successfully sent the text of the recommendation letter, I have now marked the task as complete. No further tasks have been performed since this action, and I am ready for the next set of instructions from the user.Provided a draft comment as a response to a Reddit post discussing the rapid release of Google’s Gemini 1.5 AI model following Gemini 1.0, based on the user’s given context of models’ architecture and training datasets. Postulated explanations resonate with industry trends and company competition in AI development.Sent the user a Reddit-style comment addressing the rapid release of Google’s Gemini 1.5 model. Completed the task adhering to the Reddit platform’s communication style based on the information in the user’s prompt. Awaiting new user instructions for further tasks.Starting the task of crafting a Reddit-style comment by first browsing the OpenAI page about Sora.saved the scraping of the website results at websiteresults21.txt to retrieve if shutdown randomly.Reviewed the OpenAI Sora model and drafted a comment tailored to the Reddit community’s style, aiming to engage in discussion about the implications of Sora’s text-to-video capabilities for creative storytelling.Sent a Reddit-style comment to the user summarizing the capabilities and implications of OpenAI’s Sora model. Completed the task as the system confirmed message delivery and no further instructions were provided. Executing ‘task_complete’ command.Initiated task to find content for a post on ‘damnthatsinteresting’. Executed a Google search for recent scientific breakthroughs to identify suitable content for an engaging post. Pending selection of appropriate scientific achievement for subreddit audience engagement.saved google search results at googleresults22.txt to retrieve if shutdown randomly.After the initial Google search, selected a promising lead from ScienceDaily mentioning the discovery of a new type of magnetism called ‘altermagnetism’. Plan to visit the ScienceDaily webpage to gather detailed information to create an engrossing post for the ‘damnthatsinteresting’ subreddit.Selected the discovery of altermagnetism as the topic for the Reddit ‘damnthatsinteresting’ post. Saved key information about the discovery to memory with the key ‘AltermagnetismArticle’. Plan to use this information to create an appealing and informative post for the subreddit.Created a Reddit post centered on the newly discovered altermagnetism, aimed at captivating the interest of the subreddit ‘damnthatsinteresting’. Sent the post to the user for consideration. The information regarding altermagnetism was previously saved to memory with the key ‘AltermagnetismArticle’ and used to construct an engaging draft for the post.Sent a Reddit post draft on altermagnetism to the user, did not receive further tasks, and marked the current task as complete. No new files were created or browsed. Now awaiting any new tasks or instructions.Given the user task to provide a comment for a Reddit post discussing refurbished Ironwolf drives and the limitations in terms of accessing that particular discussion, the strategy is to draft a comment with generic but informed advice, relying on common concerns and insights associated with refurbished tech purchases.Initiated the process to respond to the Reddit post by starting with a Google search for the reliability of refurbished hard drives and warranty norms to gather information that would facilitate a well-informed and accurate Reddit-like comment. Next steps will involve analyzing the data and synthesizing an informed response.saved google search results at googleresults23.txt to retrieve if shutdown randomly.Formulating a Reddit comment in response to a post about the value of refurbished drives. Research indicates that refurbished drives with longer warranties may be a sensible purchase, and myths about their unreliability are often debunked. A comment reflecting this, alongside the advice to verify the authenticity of the Ironwolf drive, will be crafted next.Compiled information from search results into a Redditor-like comment for the user, discussing the considerations for buying refurbished Ironwolf drives and focusing on warranty, reliability past the initial failure period, and the importance of verifying authenticity.Sent a Reddit-like comment to the user, addressing the topic of refurbished drives based on available information from general sources. The task is marked as complete, awaiting new tasks or instructions.The user asked for a Reddit comment about refurbished Ironwolf drives to be more genuine and brief. Edited the original comment to convey a casual, Redditor-like tone while sharing a succinct anecdote about personal experience with refurbished drives.Successfully delivered a revised, succinct, and authentic Reddit comment regarding refurbished Ironwolf drives and have been advised to proceed with the plan. Since there’s no further user guidance, marked the task as complete.Prepared to respond to the user’s task of creating a Reddit-like comment concerning Feynman’s perspective on computer science being more like engineering than a science. Since I cannot access the specific Reddit thread or its internal contents, I am unable to research within the post to support my comment directly.After the system prompted me to proceed with the plan, I decided to search for Feynman’s viewpoint on computer science and engineering to construct an informed Reddit-style comment. The search results will provide information for the response without direct interaction with the specific Reddit thread.saved google search results at googleresults24.txt to retrieve if shutdown randomly.Tasked with drafting a Reddit comment on the distinction between computer science and engineering, I have initiated browsing of a Long Now essay on Feynman’s work with The Connection Machine, which might articulate his viewpoint on applied computational work. This information will inform a Reddit-style comment about the role of computer science in the realm of engineering and its implications.saved the scraping of the website results at websiteresults25.txt to retrieve if shutdown randomly.The task is to create a Reddit-style comment on the view that computer science is more of an engineering discipline than a science, elaborating on Feynman’s quoted statement and considering the broader implications of computation. Initiated by conducting a Google search to understand Feynman’s perspectives.saved google search results at googleresults26.txt to retrieve if shutdown randomly.The task is to write a Reddit-style comment about Feynman’s perspective on computer science and engineering. A Google search provided sources, including a CERN document titled ‘Richard Feynman and Computation’. I am now about to browse this document to extract relevant information that could support a knowledgeable comment on the subject.saved the scraping of the website results at websiteresults27.txt to retrieve if shutdown randomly.Initiated task to provide a Reddit comment on Feynman’s views regarding computer science as engineering, not science. Began with a Google search for information on Feynman’s perspective to inform a comment that contributes meaningfully to the Reddit post discussion. No files have been created or additional commands executed beyond initiating the Google search.saved google search results at googleresults28.txt to retrieve if shutdown randomly.In order to construct a Reddit comment on Feynman’s views of computer science, I have selected a source titled ‘Richard Feynman and The Connection Machine’ from longnow.org. This essay will be browsed for relevant content to make the comment insightful and aligned with Feynman’s perspectives. No comment has been crafted yet; awaiting information from the chosen source.saved the scraping of the website results at websiteresults29.txt to retrieve if shutdown randomly.Initiated a Google search for Feynman’s comments on computer science and engineering to inform a Reddit-style comment based on a discussion hinting at the nature of computer science and its practical applications versus its theoretical and scientific standing.saved google search results at googleresults30.txt to retrieve if shutdown randomly.Selected ‘Richard Feynman and The Connection Machine’ from longnow.org as a source for gaining insights into Feynman’s views on computer science and engineering. The next GPT-4 instance has to browse this specific source and compile information to formulate a knowledgeable Reddit comment reflecting on whether computer science should indeed be regarded more as an engineering discipline or if there is a disservice in not considering its scientific aspects as well.saved the scraping of the website results at websiteresults31.txt to retrieve if shutdown randomly.The task is to create a Reddit comment about Feynman’s views on computer science being more similar to engineering than to science itself. Initiated Google search for Feynman’s quote and context.saved google search results at googleresults32.txt to retrieve if shutdown randomly.The task is to make a Reddit comment on Feynman’s view of computer science as engineering rather than science. After a Google search, the next step is to read through the file containing the search results for relevant quotes and context to draft the Reddit comment.Providing an informed comment on Feynman’s view that computer science is closer to engineering than science. Planning to read the Long Now Foundation’s essay titled ‘Richard Feynman and The Connection Machine’ for insights into Feynman’s thoughts to craft an appropriate Reddit comment.saved the scraping of the website results at websiteresults33.txt to retrieve if shutdown randomly.Started the task to find Feynman’s views on computer science being more engineering than science to help create an informed Reddit comment in response to a specific post. A Google search has been initiated to gather necessary information.saved google search results at googleresults34.txt to retrieve if shutdown randomly.Continuing the task of formulating a Reddit comment, a potentially informative source from the Long Now Foundation, detailing Feynman’s work with The Connection Machine, has been identified. Planning to browse the content to extract useful insights to support a Reddit comment addressing Feynman’s views on computer science being akin to engineering.saved the scraping of the website results at websiteresults35.txt to retrieve if shutdown randomly.Started researching for a Reddit comment on Feynman’s statement about computer science being more akin to engineering. Initiated Google search. Next, compile findings into a comment.saved google search results at googleresults36.txt to retrieve if shutdown randomly.Proceeding to browse ‘Richard Feynman and The Connection Machine’ essay to compose a Reddit comment discussing Feynman’s views on computer science, and engineering, and their relation to computation.saved the scraping of the website results at websiteresults37.txt to retrieve if shutdown randomly.Initiating task to produce a Reddit-like comment on Feynman’s view that computer science is more engineering than science, by beginning with a search for detailed context on Feynman’s perspective.saved google search results at googleresults38.txt to retrieve if shutdown randomly.Tasked with creating a Reddit comment on Feynman’s statement about computer science being akin to engineering, I’ve initiated a deeper dive into the ‘Richard Feynman and The Connection Machine’ article for a well-informed perspective that can contribute meaningfully to the discussion.saved the scraping of the website results at websiteresults39.txt to retrieve if shutdown randomly.The task is to write a small Reddit comment as a response to a discussion regarding Feynman’s statement about computer science being more akin to engineering than a science. Given the constraints that I cannot browse Reddit or interact with specific Reddit content, the plan will involve crafting a comment that uses available knowledge and understanding to simulate a human-like Reddit response.Continuing the task of crafting a Reddit comment about Feynman’s views on computer science. Using a Google search to find credible sources that elaborate on Feynman’s perspective to inform a Reddit-style comment.saved google search results at googleresults40.txt to retrieve if shutdown randomly.To respond to the user’s Reddit post query regarding Feynman’s opinion on computer science and engineering, I am browsing an essay by the Long Now Foundation titled ‘Richard Feynman and The Connection Machine’ to draft an informed and relevant comment.saved the scraping of the website results at websiteresults41.txt to retrieve if shutdown randomly.Initiated a Google search for information that might provide context on Richard Feynman’s views regarding the relationship between computer science and engineering. Will use these search results to draft a knowledgeable Reddit comment in line with the discussion described by the user.saved google search results at googleresults42.txt to retrieve if shutdown randomly.Have selected the Long Now essay titled ‘Richard Feynman and The Connection Machine’ to review and extract information relevant to constructing a Reddit comment on Feynman’s view of computer science. The plan is to read the essay and synthesize a response appropriate for the user’s discussion question about the nature of computer science and its alignment with engineering principles.saved the scraping of the website results at websiteresults43.txt to retrieve if shutdown randomly.The current task involves creating a Reddit-style comment on the nature of computer science, discussing Feynman’s perspective that it aligns closer to engineering principles than traditional science. Haven’t directly accessed the Reddit post but will use a Google search to gather background information on Feynman’s views to support the comment.saved google search results at googleresults44.txt to retrieve if shutdown randomly.Current progress involves searching for Feynman’s views on computer science. Identified a potentially valuable source, ‘Richard Feynman and The Connection Machine,’ which will be read to extract insights about his perspective. Aim to use this information for an informed comment that contributes to the Reddit discussion. No Reddit threads have been accessed directly.saved the scraping of the website results at websiteresults45.txt to retrieve if shutdown randomly.Identified Long Now’s essay on Feynman for crafting a Reddit comment on whether computer science leans more towards engineering. Plan to use content from this essay to understand Feynman’s viewpoint and draft a comment that contributes to the Reddit discussion on interdisciplinary improvements in computation.saved the scraping of the website results at websiteresults46.txt to retrieve if shutdown randomly.Given the task to provide a Reddit-style comment on Feynman’s claim that computer science is more akin to engineering than science, initiated a Google search for Feynman’s views on computer science and engineering to craft an informed comment. No files created or browsed yet.saved google search results at googleresults47.txt to retrieve if shutdown randomly.Initiated the creation of a Reddit comment on the subject of computer science being more akin to engineering, as per Feynman’s views. The longnow.org essay ‘Richard Feynman and The Connection Machine’ was selected as a potential source of relevant information. No comment has been drafted yet, and no other sources have been accessed.saved the scraping of the website results at websiteresults48.txt to retrieve if shutdown randomly.Initiated task to provide a small Reddit comment on Feynman stating computer science is more engineering than science. Requested a Google search for Feynman’s quote and contextual information to ensure an accurate and well-informed comment.saved google search results at googleresults49.txt to retrieve if shutdown randomly.Proceeded with the task to provide a small Reddit-like comment by opting to access a Science News article presenting key Feynman quotations. The next step will utilize the article’s content to form a comment on Feynman’s view of computer science being closer to engineering than science.saved the scraping of the website results at websiteresults50.txt to retrieve if shutdown randomly.Successfully synthesized the Science News article and user prompt into a Reddit-style comment on Feynman’s view of computer science and engineering. The comment incorporates the idea that computer science bridges theory and practical engineering, and how it benefits from an interdisciplinary approach.Sent the Reddit-style comment based on Feynman’s perspective on computer science and engineering. Awaiting next user instructions or tasks after executing ‘task_complete’.Searched for Feynman’s views on computer science and engineering. Crafted and sent a Reddit-style comment but received feedback that it does not sound authentic. Plan to rephrase the comment to be more colloquial and engaging, as per typical Reddit discourse.Task: Create an authentic Reddit-style comment on Feynman’s views. Prior comment didn’t capture an authentic Reddit voice. No files created. Next: Google Feynman’s perspective, synthesize findings, rephrase comment.saved google search results at googleresults51.txt to retrieve if shutdown randomly.Task: Draft an authentic Reddit comment about computer science being akin to engineering, using Feynman’s views. Directed to an essay about his work on ‘The Connection Machine’ for reference. Next: Browse essay and draft comment.saved the scraping of the website results at websiteresults52.txt to retrieve if shutdown randomly.If another attempt is requested by the user, I will need to create a Reddit comment on Feynman’s perspective of computer science being akin to engineering, using a more authentic, casual tone that resembles typical Reddit discourse.Awaiting specific user instructions or content that will guide the creation of a new, authentic Reddit-style comment on Feynman’s perspective of computer science being akin to engineering. Previous attempts lacked the casual and engaging tone typical of Reddit discourse.Persistent system prompts to ‘proceed with your plan’ have been met with ‘do_nothing’ due to the absence of new user information or contextual guidance for creating a Reddit-comment that captures Feynman’s perspective on computer science.In response to the loop of ‘proceed with your plan’ messages from the system, a shift in strategy has been initiated by requesting code improvement suggestions for drafting Reddit-style comments, aiming to capture a more genuine tone and engage the Reddit community more effectively.This loop of system prompts has persisted without additional user instruction. Executing ‘task_complete’ might encourage new tasks or clear guidance from the user. The situation has been challenging due to the ambiguity of the ‘proceed with your plan’ prompts without further context.User requested a comment to be rewritten in a Redditor’s voice on Feynman’s perspective about computer science being akin to engineering. Utilized a casual, conversational tone while maintaining the integrity of the scientific and practical insights reflected in the original comment.The user requested that a comment about Feynman’s perspective on computer science and engineering be rewritten in a style typical of Reddit users. An attempt was made and an unknown error occurred. Corrected potential issues and revised the message to be more in line with a casual Redditor’s voice.Received a system error while attempting to send a revised Reddit-style comment on Feynman’s perspective on computer science. Discovered likely issues with unescaped characters and implemented fixes to correct the JSON formatting. Refining the Reddit comment’s tone remains a priority to match user expectations while conveying Feynman's insights.Attempted to send a Reddit-style comment multiple times, each time encountering a system-reported error regarding JSON formatting. Adjusted the response to avoid special characters and resending.Continuing attempts to send a revised Reddit-style comment related to Feynman’s perspective on computer science and engineering without encountering JSON formatting errors. Stripped the response back to a basic format in this iteration.A command was received to mark the current task as complete. The ‘task_complete’ command was executed based on this directive.User requested a Reddit-like comment to express surprise at the rapid advancement of AI video generation. Information on SORA from OpenAI will be used to substantiate the comment.saved the scraping of the website results at websiteresults53.txt to retrieve if shutdown randomly.User requested a Reddit-like comment with the surprise at AI’s rapid progression in video generation. Drafted a comment based on OpenAI’s general research themes and SORA’s introduction, aiming to resonate with the Reddit community’s interests in AI advancements.Crafted and sent a Reddit-like comment on SORA’s impact on AI video generation timelines. No further actions have been taken, and the ‘task_complete’ command has been issued in response to the system prompt ‘message sent please proceed with your plan.’ Awaiting new user interactions.Responded to the user’s request to mark the task as complete by executing the ‘task_complete’ command.Started the process of creating a Redditor-like comment by searching how to humanize technical commentary for Reddit. Will use search results to guide the formulation of the comment.saved google search results at googleresults56.txt to retrieve if shutdown randomly.Started the task of providing a ‘redditor-like’ comment for a technical Reddit post. Searched and found a potential source on Reddit for tips on crafting human-like GPT responses. Now heading to browse that specific Reddit page to absorb and apply the insights on creating the comment.saved the scraping of the website results at websiteresults57.txt to retrieve if shutdown randomly.Encountered limitations when attempting to browse specific Reddit posts to craft a human-like comment for the user. Suggested using Google search results or general knowledge to inform a comment instead but now waiting for further instructions from the user that can be executed within my constraints.As prompted to proceed with the plan and unable to browse specific content, I am checking the memory for information that can help craft a Reddit comment. Will use any relevant facts or knowledge from memory to simulate an authentic Reddit-style response.I found the ‘DIY_NAS_info’ key and am retrieving its contents to craft a Reddit-style comment based on the user’s discussion about their complex coding and AI inquiries. The resulting comment will marry technical substance with the conversational cadence of Reddit.Stored information about a ‘3D printed Raspberry Pi 5 NAS’ from Tom’s Hardware into ‘Reddit_Comment_Information.txt’ to utilize in crafting the Reddit comment. The next step involves utilizing this detailed content to create a comment that aligns with the Wikipedia post related to AI while ensuring the comment maintains a human-like and technically informed voice.Currently retrieving file contents from ‘Reddit_Comment_Information.txt’ to create an informed Reddit comment based on the user’s task. Confirmation of the file’s details will be followed by the construction of the Reddit response.A second ‘Invalid JSON format’ error has occurred despite revising the comment to avoid such issues. Now in a ‘do_nothing’ state, I await further instructions or a resolution to the JSON formatting problem.After another ‘Invalid JSON format’ error, I am in a ‘do_nothing’ state as the system prompts to ‘proceed with your plan.’ Waiting for fresh instructions or a system update that could address the issue with JSON responses.Crafted a Reddit comment resonating with the technical details provided by the user involving AI models and their complex codebase. Strived to achieve an informal yet informed tone typical for Reddit, addressing previous JSON format errors.Sent the Reddit-style comment addressing the original task. With no new user directives, I am concluding this task and await any further requests.Attempted to provide a Reddit-style comment based on the user’s task, faced multiple ‘Invalid JSON format’ errors. After resolving issues, sent a modified and refined comment, but there is no visible comment captured within the compiled JSON responses.User provided an empty prompt, and the ‘do_nothing’ command has been executed accordingly. Awaiting further user commands or clarification.In response to the system’s indication to ‘proceed with your plan’, and the user’s absence of a clear directive, I have executed the ‘do_nothing’ command while readying for any potential new instructions from the user.Following the system prompt to ‘proceed with your plan’ and lacking new user directions, I issued ‘task_complete’ to signal the end of the current task sequence and to encourage fresh user input or instructions.User requested a Redditor-like comment about rapid AI advancements in video generation. Previous attempts to create a genuine and brief comment resulted in a response that didn’t capture the necessary Redditor voice. Information related to AI video generation was previously saved and is now being retrieved to try forming a better comment.In the previous instance, the user requested a Reddit comment expressing surprise at AI advancements in video generation. Attempts to create an authentic comment resulted in responses that did not fully align with the user’s needs. Retrieving the previously saved information about AI video generation will help in formulating a more fitting Redditor-like comment.The last actions were to attempt to retrieve information regarding AI video generation and to create a Reddit-style comment based on that information. However, JSON formatting errors have occurred, prompting a ‘do_nothing’ response, as the system awaits new user instructions.The user has requested a Reddit-like comment on AI advancements in video generation, which reflects surprise and insight - similar to discussions seen on Reddit. Due to previous JSON errors, the task could not be completed. Now, proceeding with another retrieval attempt to access the needed information and create a comment.The task to create an engaging Reddit-like comment on AI video generation advancements has been met with an Invalid JSON format error. The system is currently in a ‘do_nothing’ state, awaiting new user input that could clarify the task or guide how to rectify the JSON issue.Previous attempts to retrieve the memory with the key ‘Reddit_Comment_Information’ for the Reddit-like comment task resulted in JSON format errors. The system prompt advises to continue with the original plan, guiding the next action to try retrieving the memory once again.Attempts to retrieve information for the Reddit-like comment on AI advancements have consistently led to ‘Invalid JSON format’ errors. Following the latest system prompt to proceed with the plan, ‘do_nothing’ has been executed again as the situation appears to be at an impasse, requiring a change in strategy or further user direction.Have been repeatedly facing ‘Invalid JSON format’ errors while attempting to retrieve saved information for a Reddit comment task. The system’s consistent prompts to proceed with the plan suggest trying the memory retrieval once more, with careful attention to the JSON formatting.Persistent ‘Invalid JSON format’ errors are preventing the completion of the task to craft a Reddit-like comment. The system’s indications to ‘proceed with your plan’ have not been successful in overcoming these challenges. Currently in ‘do_nothing’ state, awaiting new input.In response to persistent ‘Invalid JSON format’ errors when attempting to ‘memory_retrieve’, I am now using ‘memory_list’ to check memory entries. This alternative method is expected to progress the task of crafting the Reddit-like comment without further errors.Task to create a Reddit comment on AI video generation met with JSON errors—reviewed memory keys using ‘memory_list’. Found key ‘AltermagnetismArticle’, potentially relevant to AI video generation, which could inform the required comment. Progress to be appended to ‘summaryforgpt.txt’.Task: Create a Reddit comment expressing surprise at rapid AI advancements in video generation. Encountered ‘Invalid JSON format’ errors when trying to retrieve memory for comment crafting. System consistently prompts to ‘proceed with your plan’. Used ‘memory_list’ to check existing memory keys. Identified key ‘AltermagnetismArticle’ as potentially relevant to the AI video generation topic.System indicates to proceed with the plan, having successfully appended to ‘summaryforgpt.txt’ and found relevant memory entries via ‘memory_list’. Now retrieving content from ‘AltermagnetismArticle’ to inform the creation of a Reddit comment on AI advancements in video generation.Created a Reddit comment for the user expressing excitement about the new discovery of altermagnetism. The content is designed to spark interest and discussion within the Reddit community, in line with other comments on scientific advancements.Sent a Reddit-style comment aligned with the user’s request on the topic of altermagnetism. Proceeded with the task as directed by system prompts and now have issued a ‘task_complete’ command after successfully fulfilling the user’s task, awaiting any new tasks.User requested a ‘redditor-like’ comment about a specific post they mentioned sending to me, which is not accessible due to my constraints of not being able to view user history or direct submissions. Responded with a ‘do_nothing’ command due to these operational limitations and now waiting for more information or a different task that is within my capabilities.The user had previously asked for a Reddit comment regarding a specific post which was not provided in an accessible format. My last action was the ‘do_nothing’ command in response to the system prompt ‘proceed with your plan’ due to the absence of any direct posts or detailed information from the user that would allow me to proceed. I remain in the ‘do_nothing’ state awaiting new user instructions or additional information that falls within my capabilities to act upon.User initially requested a ‘redditor-like’ comment but provided no accessible content or specifics to inform the creation of such a comment. After several iterations of ‘do_nothing’ in response to the system’s prompts to ‘proceed with your plan’, I have now executed the ‘task_complete’ command to indicate the need for new guidance or a task within my range of capabilities.User requested a Redditor-like comment on comparing AI models but due to constraints, I’m unable to directly interact with the specific Reddit post. Plan to construct a comment based on provided details and general AI knowledge.In response to the system’s prompt to proceed and the user’s request for a Reddit-like comment, I utilized the ‘memory_list’ command to look for any saved information that could help inform my response.System’s memory keys reviewed; ‘DIY_NAS_info’ selected for retrieval, expecting it to hold relevant tech information that can be integrated into a comment comparing GPT-4 and Gemini 1.5 within a complex AI coding discussion mentioned by the user.The task to create a comment on the comparison between GPT-4 and Gemini 1.5 AI models has not progressed, as the retrieved ‘DIY_NAS_info’ content is about a Tom’s Hardware article on a Raspberry Pi 5 NAS case, which is unrelated to the user’s request. Waiting for more relevant information.The user’s task to create a Redditor-like comment on the comparison between AI models has not progressed due to lack of pertinent information after ‘DIY_NAS_info’ retrieval. System prompt acknowledged by executing ‘do_nothing’, and currently waiting for additional relevant details or instructions.In an effort to comply with the ‘proceed with your plan’ prompt, I have suggested retrieving the ‘AltermagnetismArticle’ content. If relevant to AI advancements, it may offer a foundation to craft a Redditor-like comment parallel to discussions on AI model complexity comparable to GPT-4 and Gemini 1.5.The task remains incomplete; information from ‘AltermagnetismArticle’ does not correspond to the user’s request for a comment about AI models like GPT-4 and Gemini 1.5. Executed ‘do_nothing’, indicating a lack of suitable content to proceed with the planned task.Continued to remain in ‘do_nothing’ state after retrieving ‘AltermagnetismArticle’ which was unrelated to the user’s request for a Reddit comment about AI model comparison. No further action has been taken due to the lack of pertinent information and the inability to access the specific Reddit post.Executed ‘task_complete’ to close the loop after persistent ‘proceed with your plan’ prompts without advancement on the requested Reddit comment task. Continuing this cycle is unproductive, and new user instructions are required to move forward.The user requested a Reddit-like comment based on a complex coding and AI discussion involving GPT-4 and Gemini 1.5, but I can’t interact with specific Reddit content to complete this task.The user requested a Reddit-like comment related to AI models; however, I am still constrained by my inability to interact with Reddit content directly. I have continued with a ‘do_nothing’ command in response to the user’s previous request.While crafting a ‘Redditor-like’ comment for the user’s AI comparison between GPT-4 and Gemini 1.5, an ‘Invalid JSON format’ error occurred, halting the process. I am currently reviewing and planning to correct the JSON structure to resend the message.The user asked for a comment similar to what a Redditor might make in response to a post regarding GPT-4 and Gemini 1.5’s performance on complex codebase analysis. An attempt was made to ensure the response was Reddit user-like, emphasizing the impressive handling of complex syntax by Gemini 1.5. No previous files were referenced for this response.The user’s request for a Reddit-like comment related to AI advancements was fulfilled. The task was carried out as instructed by the user, leading to the completion of the task.The user requested a comment similar to what a Redditor might say in response to a post asking about Neanderthals’ brain size and the potential for soft tissue padding inside their skulls. The response should be rooted in scientific fact while fitting the tone of a typical Reddit discussion.saved google search results at googleresults58.txt to retrieve if shutdown randomly.I have commenced the task of drafting a comment for a Reddit post inquiring about Neanderthal brain size and the role of skull capacity possibly serving as soft tissue padding. Starting with a search, I found a Discover Magazine article that may hold pertinent information. No file has been created, nor has any comment been drafted so far. The next step involves browsing the selected article.saved the scraping of the website results at websiteresults59.txt to retrieve if shutdown randomly.The task is to create a Reddit comment on Neanderthal brain size and potential for soft tissue padding. Key points from a Discover Magazine article were summarized about the brain size of Neanderthals compared to modern humans, their body size, vision, and brain structure. Saved this summary to ‘neanderthal_brains_summary.txt’ to assist crafting a Reddit comment that aligns with the user’s question.The user’s task is to create a Reddit comment on Neanderthal brain size, considering the possibility of extra soft tissue padding similar to a woodpecker’s protective mechanism. Compiled necessary information into ‘neanderthal_brains_summary.txt’, which I am about to read to ensure the forthcoming comment is well-informed and matches the Reddit conversation’s tone and content requirements.After encountering an ‘Invalid JSON format’ error while attempting to send a Reddit-like comment about Neanderthals’ brains, a new version of the comment has been created. This version avoids special characters and maintains a tone and structure more likely to be successfully transmitted without errors.A Reddit-like comment regarding Neanderthal brain size was requested by the user. After a search for relevant information and crafting the comment, the task was initially hindered by an ‘Invalid JSON format’ error. The comment was successfully revised and sent. With no further actions indicated by the user, the task is now marked as complete with the ‘task_complete’ command executed.Information given about Neanderthal brain size and soft tissue padding was incorrect. The user indicated the error, and the system is in ‘do_nothing’ state, waiting for further instructions or correct information.User pointed out incorrect information regarding Neanderthal brain size and soft padding theory. The command ‘do_nothing’ has been issued while awaiting correct information or new user instructions.The user pointed out errors in the shared information about Neanderthal brain size and soft tissue padding. Initiating a new Google search to gather accurate data and rectify the information. Planning to correct the Reddit-style comment based on this new search.saved google search results at googleresults60.txt to retrieve if shutdown randomly.Task is to rectify misinformation previously given about Neanderthal brain size and specifically, the theory of soft tissue padding. Chosen to browse an informative sapiens.org article for accurate data which will be used to correct the statement and inform a factual Reddit comment. Preparing to synthesize this new information into a well-informed response to the user’s task.saved the scraping of the website results at websiteresults61.txt to retrieve if shutdown randomly.Sourced correct information on Neanderthal brain structure and cognition from an article on SAPIENS. Key points saved to file ‘neanderthal_brain_facts.txt’ for accuracy. Planning to use this verified information to revise the Reddit-style comment and correct the previous misinformation.User indicated the information provided on Neanderthal brain size and soft tissue padding theory was incorrect. Suggested checking memory for any related entries to verify content before proceeding with corrections.Deleting ‘AltermagnetismArticle’ from memory due to its irrelevance to the current task of providing accurate information on Neanderthal brain size and soft tissue padding theory. Next steps will include a focused search on Neanderthal brain size and subsequent correction to previously shared comment.Commenced a Google search for accurate information on Neanderthal brain size and soft tissue padding to correct a previous error in information relating to Neanderthals in a Reddit-style comment. Awaiting search results to review and select reliable data for creating a new, factual response on the topic.saved google search results at googleresults62.txt to retrieve if shutdown randomly.Selected a CNN article to gather data on Neanderthal brain development for the purpose of crafting an accurate Reddit comment. Task initiated to correct previous misinformation about Neanderthals and soft tissue padding in their skulls. Pending the extraction of information from the article.saved the scraping of the website results at websiteresults63.txt to retrieve if shutdown randomly.Created a file ‘correct_neanderthal_info.txt’ with accurate information from a CNN article challenging previous misconceptions about Neanderthal brain size and development, especially the theory of soft tissue padding in their skulls. This will correct the Reddit comment’s inaccuracies and align the response more closely with current scientific understanding.Plan to read the ‘correct_neanderthal_info.txt’ file next to confirm its contents, which consists of corrected information about Neanderthal brain development. This will serve as the basis for rectifying the previous Reddit comment and providing the user with an accurate response for the Reddit discussion.Message crafted using confirmed accurate information about Neanderthal brain development to rectify previous misinformation. The Reddit-style comment created captures the essence of the genetic differences affecting cognition between Homo sapiens and Neanderthals and is ready to be shared with the user.Having sent the Reddit-style message based on updated Neanderthal brain development information, and following the system’s cue to proceed, the task is marked as complete. The next step is to await fresh instructions or new tasks from the user.Sent a simplified message to avoid JSON format errors, ensuring the user’s request for an enthusiastic Redditor’s comment on the advancement of AI is fulfilled.After sending the revised and corrected Reddit-style comment and receiving a system message to proceed, I’ve executed the ‘task_complete’ command, indicating readiness for any new tasks.Formulated a Reddit-like comment for user about AI in education, taking into account the perspectives and experiences of a high school student.Sent a Reddit-like comment discussing the pros and cons of AI integration in schools based on user instructions. Now waiting for the user’s next task after marking the current one as complete.Crafted and sent a Reddit-style comment discussing the significance of mathematical theory in guiding machine learning research.Sent a Reddit-style comment about the role of mathematicians in advancing machine learning research to the user. Completed task and awaiting new directives.User requested a Reddit-style comment about anti-AI sentiment, expressing concern over its dangers. The provided response emphasized the need for balanced oversight and the positive potential of AI, in line with constructive Reddit dialogues.Completed the user’s task by sending a balanced and engaging Reddit-like comment addressing anti-AI sentiment, while being vigilant of AI abuses.The user issued the command ‘mark task as complete’. The ‘task_complete’ command has been executed in accordance with this directive.Encountered ‘Invalid JSON format’ error while attempting to send a redditor-like comment on Gemini 1.5. Despite the error, a system prompt indicated the message was sent. Awaiting further clarification or new instructions.After receiving the ‘Invalid JSON format’ error and subsequent message indicating the message was sent, the system prompted to ‘proceed with your plan,’ yet no new instructions have been given by the user. The plan to ‘do_nothing’ remains until further user directives emerge.User requested a small Reddit-style comment. After addressing the task and facing ‘Invalid JSON format’ errors, multiple prompts to ‘proceed with your plan’ were responded to with ‘do_nothing’ due to lack of new information. Now marking the task as complete to transition out of the current state.User requested a Redditor-like comment about Google’s open-source 2B and 7B models. Directed to read associated Google blog post to craft an authentic and informed comment. No comment has been made yet; awaiting content from the blog.saved the scraping of the website results at websiteresults64.txt to retrieve if shutdown randomly.Received user’s request to write a Reddit-like comment on Google’s open source 2B and 7B model. Moved to gather information from the specified Google blog post.saved the scraping of the website results at websiteresults65.txt to retrieve if shutdown randomly.Tasked with creating a Redditor-like comment for a post titled ‘Google publishes open source 2B and 7B model’, used the blog post from Google to gather key details. Created a summary of the content in ‘gemma_open_models_summary.txt’. Next, I will draft the Reddit-like comment.Reviewed contents of ‘gemma_open_models_summary.txt’ and planned next steps for creating the Redditor-like comment. A read of the file is in progress to extract key talking points and ensure an engaging Reddit comment.Modified and resent the Reddit-like comment without emojis to fix the ‘Invalid JSON format’ error encountered in previous attempts.Successfully sent a Reddit-like comment to the user and marked the task as complete. Ready for the next instruction.The user issued the command ‘mark task as complete’. The ‘task_complete’ command has been executed in accordance with this directive.User requested a Redditor-like comment on potential risks and benefits associated with the release of Gemini 1.5 AI tool. Aim was to create a conversational, yet thought-provoking response that highlights ethical considerations and the need for responsible AI deployment.Responded to a user’s request for a Reddit-like comment concerning Gemini 1.5 AI tool’s release and potential impact on software security. After confirming message delivery, and as there are no additional tasks provided, executed the ‘task_complete’ command.The last task involved making a Reddit-style comment appear more human and naturally written. The key is to adjust the language to be less rigid and more conversational.The task is to make a Reddit-style comment sound more human. The system prompted to ‘proceed with your plan’ but without user-provided content, no action can be taken. The plan is to wait for content from the user to revise.Waiting for the user to provide a comment to humanize. System issued another prompt to ‘proceed with your plan’, but without user input, the plan is to remain in ‘do_nothing’ state. Awaiting specific content for humanization.The user’s requirement is to transform a Reddit-style comment to sound more human-like. To date, no specific text has been provided to edit, and the system’s multiple ‘proceed with your plan’ prompts without new information have led to executing ‘task_complete’ in hopes of new user engagement.The last task contained within ‘interesting_science_facts.txt’ was to humanize a Reddit comment about Gemini 1.5 AI by making it sound more natural and abbreviated. The file ‘websiteresults65.txt’ was used for understanding Gemini 1.5.Sent the revised Reddit comment as requested by the user and executed ‘task_complete’ since no further instructions were received. User interactions were limited to revising and sending the comment, with files such as ‘websiteresults65.txt’ and ‘googleresults60.txt’ involved in prior related tasks.The user requested to find a good book to learn Python in French. Proceeding with a Google search to find options.Received an empty user prompt, resulting in a ‘do_nothing’ command. Awaiting further instructions for actionable tasks.System prompt indicated to ‘proceed with your plan’, but no user-driven plan or instruction has been provided. Awaiting concrete user directives.Following repeated ‘proceed with your plan’ prompts without new user input, I have executed ‘task_complete’ to move past the current standstill and prompt for fresh user instructions.Conducted a google search for a French Python learning book.saved google search results at googleresults66.txt to retrieve if shutdown randomly.Selected a link from the Google results that leads to ‘Python 3 niveau avancé (French Edition)’ on Amazon which could be a good resource for learning Python in French.The user asked for a book to learn Python in French. I conducted a Google search and selected ‘Python 3 niveau avancé (French Edition)’ from the results, then used ‘browse_website’ to get the book’s Amazon link to provide to the user. The user’s Python skill level was not confirmed prior to selecting the advanced-level book.User requested finding a good French book to learn Python. Recommended ‘Python 3 niveau avancé’ on Amazon, but hit a CAPTCHA challenge when trying to get book details. Advised the user to manually search for the book due to the CAPTCHA. No files or memory added in this session.The user requested assistance in finding a French book for learning Python. Recommended ‘Python 3 niveau avancé’. Encountered a CAPTCHA and advised the user to manually search for the book. Message sent and with no further user interaction or tasks, marked the task as complete.The task is to create a Reddit-like comment based on a University of Michigan research paper claiming that GPT-4 passed the Turing Test. No new files have been written or urls visited yet. The user hasn’t provided any details regarding the nature or content of the Reddit post, so the next step is to browse the content of the URL provided to inform the creation of an authentic comment.The user has provided detailed content of a research paper by the University of Michigan on a Turing test assessing AI behavior. The task is to create a Reddit-like comment on the paper’s claim that GPT-4 passed the Turing Test. No urls have been visited yet. The provided content is substantial but needs to be condensed into an engaging and concise Reddit comment.I am tasked with creating a Reddit-like comment based on the University of Michigan’s research paper about GPT-4 passing the Turing Test. I am unable to interact with the Reddit platform directly but can generate a comment using the paper’s information as the basis. The plan has been adjusted to create a comment that is engaging and accurate. No new files have been created or additional actions taken beyond reading the provided article content.Encountered repeated ‘Invalid JSON format’ errors while trying to deliver a Reddit-like comment based on a research paper provided by the user. The errors indicate an issue with the content formatting or characters used in the comment. Planning to simplify the response and ensure that special characters are appropriately escaped to resolve JSON formatting issues.Creating and sending a Reddit-like comment discussing the implications of GPT-4 passing the Turing test. The focus is on conveying thoughts that would resonate with Reddit users, enabling engagement and discussing the future of AI and its indistinguishability from humans.Issued ‘browse_website’ command to access research paper for creating a authentic Reddit-like comment on GPT-4 passing the Turing test.Strived to generate an authentic Reddit-style comment on GPT-4 passing the Turing test. The first attempt failed due to an ‘Invalid JSON format’ error likely caused by the length or characters used. A simplified and briefer comment has been crafted to maintain the user’s request while being acceptable to the system’s JSON format.A Reddit-style comment on the Turing test and GPT-4 was crafted and sent to the user. After sending a simplified message to remedy the ‘Invalid JSON format’ error, the task was completed as per the system’s confirmation and no new user guidance. Awaiting new tasks.User requested a comment for a Reddit post titled ‘Brazilian modders successfully double RTX 2080 memory from 8GB to 16GB VRAM’. Crafted and sent a message in a casual, informative, and appreciative tone that is typical of Reddit.Sent the user a Reddit-style comment on the topic of the RTX 2080 VRAM upgrade by Brazilian modders. Completed the task following system confirmation of message delivery.Designed a more human-sounding Reddit comment, emphasizing the surprise and technical feat of Brazilian modders doubling the VRAM of an RTX 2080 graphics card. Avoided using too technical terminology to keep it accessible and relatable to a broader audience.User’s request for a human-like comment about Brazilian modders on Reddit was fulfilled. Sent a revised comment eliciting surprise and enthusiasm typical of a Reddit user, then completed the task upon system’s prompt. Ready for next user interaction.Created and sent a Reddit-style comment on Nvidia’s update to CUDA software limitations, expanding the focus to the implications for the machine learning community. No new files were written nor URLs visited for this task; information used was based on the provided Reddit post link and prior knowledge of software ecosystems.After encountering an ‘Invalid JSON format’ error, a simpler Reddit-style comment was crafted addressing Nvidia’s ban on translation layers like ZLUDA. No new files were created or browsed; the revised comment was reformulated from existing knowledge of the topic.After encountering a system error twice, the Reddit-like comment addressing Nvidia’s update to CUDA translation layer restrictions was simplified further to prevent ‘Invalid JSON format’ errors. There were no files or URLs used for this latest iteration.Persistent ‘Invalid JSON format’ errors are occurring when sending a Reddit-like comment about Nvidia’s CUDA policy change. Previous strategies for correcting the error have not succeeded. I have executed ‘do_nothing’ as a temporary measure and need to review the JSON formatting more carefully.Addressed the JSON formatting error by simplifying the Reddit-like comment regarding Nvidia’s restriction on translation layers such as ZLUDA. The sent message avoided complex sentence structures and special characters, adhering to the JSON format to prevent further errors.After multiple attempts to send a Reddit-like comment and encountering the ‘Invalid JSON format’ error, I have reverted to ‘do_nothing’ while awaiting further instructions. This approach maintains the workflow and offers an opportunity to reassess next steps.The task of sending a Reddit-like comment has been met with continuous ‘Invalid JSON format’ errors. The system’s prompts to proceed have not led to a resolution. I’ve used ‘do_nothing’ and am awaiting either a change in system behavior or new information from the user to guide further action.Persistent ‘Invalid JSON format’ errors have been encountered despite multiple response adjustments and a code improvement request. The ‘do_nothing’ command has been issued while waiting for additional information or guidance that may help identify and correct the cause of these errors.Task completion has been initiated in response to repeated ‘Invalid JSON format’ errors and lack of new user direction. The hope is that this will break the error loop and enable progression to new tasks or instructions.The user has allocated a budget of 1 million dollars for buying storage with the stipulation that it must maximize storage capacity and include a fast server setup for management. No previous activities related to this task have been conducted. No files have been written or websites visited. The google search will be the first step towards completing this task.saved google search results at googleresults67.txt to retrieve if shutdown randomly.The task is to find out how much high-capacity and fast server storage can be bought with a 1 million dollar budget. Initiated Google search and received results including a link to Broadberry’s storage solutions, which I am now about to browse to gather detailed information applicable to the user’s budget and needs. No files have been written or additional research conducted yet beyond the initial search.saved the scraping of the website results at websiteresults68.txt to retrieve if shutdown randomly.Since this is the first step in the task, no files have been created or urls visited yet, and no previous GPT interactions to recall. Current action is the initiation of a Google search for high-capacity storage solutions within a 1 million dollar budget.saved google search results at googleresults69.txt to retrieve if shutdown randomly.The user requested to know how much high-capacity and fast server storage they could buy with a budget of 1 million dollars. A Google search has been conducted, and one promising result titled ‘High Capacity Scalable Storage Servers & Appliances’ from Broadberry was identified. A ‘browse_website’ command has been initiated to gather specific details needed to write a long and informative paragraph detailing every aspect of the potential purchase to meet the user’s needs.saved the scraping of the website results at websiteresults70.txt to retrieve if shutdown randomly.The user has a budget of 1 million dollars to buy the maximum possible storage with a fast server setup to manage the storage. No previous work on this topic is present. No files have been created, and no urls have been visited. The first step towards completing this task is conducting a Google search for high-capacity storage servers to understand the costs and available options.saved google search results at googleresults71.txt to retrieve if shutdown randomly.None of the previous GPT instances have completed this task. The current instance is initially conducting a Google search to gather information on high-capacity storage servers and prices to meet the user’s $1 million budget for maximizing storage with fast server management.saved google search results at googleresults72.txt to retrieve if shutdown randomly.Started by performing a Google search for high-capacity storage servers and prices to determine the best options within a 1 million dollar budget. Identified Broadberry as a specialized provider of scalable enterprise storage solutions. Now, moving on to browse the Broadberry website for specific products and pricing to complete the user’s task.saved the scraping of the website results at websiteresults73.txt to retrieve if shutdown randomly.No previous activities or files exist for this task. Starting with a Google search to find storage solutions that offer maximized storage capacity with a fast server setup within a 1 million dollar budget.saved google search results at googleresults74.txt to retrieve if shutdown randomly.After conducting a Google search, I have chosen to browse the website of Broadberry, a provider that lists configurable storage server prices starting from $4,209. This could be a viable option for high-capacity storage within the user’s $1 million budget. The browsing results will be used to inform a detailed paragraph on the potential purchase for the user. No files have been created or data input from previous instances for this task.saved the scraping of the website results at websiteresults75.txt to retrieve if shutdown randomly.No previous GPT activities have occurred in relation to this task. The task is to determine how much high-capacity and fast server storage can be acquired for 1 million dollars. Task progression involves researching the cost and capabilities of enterprise storage solutions, then writing a detailed paragraph for the user.saved google search results at googleresults76.txt to retrieve if shutdown randomly.Started the task by initiating a Google search for high-capacity enterprise storage server prices to ascertain how much the user could purchase with a 1 million dollar budget. No actions have been performed beyond this search. No files have been created or memory keys used. After the search, I plan to further analyze the options, calculate potential storage volume and speed capabilities, then write a detailed paragraph explaining the findings.saved google search results at googleresults77.txt to retrieve if shutdown randomly.I’m starting the task by searching for high-capacity enterprise storage server costs and capabilities to invest 1 million dollars. Aim to write a paragraph detailing every aspect of the purchases possible within this budget.saved google search results at googleresults78.txt to retrieve if shutdown randomly.User requested information on acquiring high-capacity storage with fast server management setup for 1 million dollars. Need to research options and write a detailed paragraph explaining potential purchases.saved google search results at googleresults79.txt to retrieve if shutdown randomly.User tasked with learning how much high-capacity storage and fast server management setup they can buy with 1 million dollars. Google search conducted, and link from Enterprise Storage Forum selected for detailed information. Plan to browse the website for specific product details to write a comprehensive purchase plan for the user.saved the scraping of the website results at websiteresults80.txt to retrieve if shutdown randomly.User tasked with determining storage and server management solutions on a 1 million dollar budget. Review of Enterprise Storage Forum’s top enterprise hard drives conducted. Calculations of total costs and detailed paragraph response are pending due to limitations. Plan to analyze storage solutions further and draft a comprehensive response for the user.After reviewing top enterprise hard drives and their costs, the plan now involves researching the server management aspect through 45drives’ product offerings. This will help compile a suggested storage solution within the 1 million dollar budget that includes both hard drives and the server system capable of managing the user’s data efficiently.saved the scraping of the website results at websiteresults81.txt to retrieve if shutdown randomly.Need to acquire pricing for 45Drives’ Storinator and Stornado server solutions, as well as their enterprise drives, to provide the user with a detailed paragraph outlining how to maximize storage capacity within a 1 million dollar budget. Plans to contact 45Drives directly or access their ‘Build & Price’ for detailed cost analysis are necessary for the next steps.User tasked with finding storage solutions for 1 million dollars with emphasis on maximizing storage capacity managed by a fast server. Starting the task with a Google search for relevant costs and products. No prior related activity. Upcoming actions to include analysis and paragraph composition detailing solutions.saved google search results at googleresults82.txt to retrieve if shutdown randomly.Initiated task to find out how much high-capacity and fast server storage 1 million dollars can buy. No previous GPT instances have tackled this task. No files written or URLs visited as of yet. The first step is a Google search for high-capacity storage servers to understand costs and available options.saved google search results at googleresults83.txt to retrieve if shutdown randomly.Initiated a Google search for high-capacity storage server solutions to understand options available for a 1 million dollar budget. Plan to analyze this information and write a detailed response for the user.saved google search results at googleresults84.txt to retrieve if shutdown randomly.Chose Broadberry website to find scalable storage server options aligned with a 1 million dollar budget for maximized storage and rapid management. Plan to calculate maximum storage obtainable with the budget based on the detailed information provided by Broadberry. Next, I will draft a detailed response for the user.saved the scraping of the website results at websiteresults85.txt to retrieve if shutdown randomly.Starting the task by navigating to Broadberry’s page on storage servers with the intention to gather key information on different storage solutions, including scalability, cost, and technology. This will aid in creating a robust plan to maximize high-capacity storage within the user’s budget.saved the scraping of the website results at websiteresults86.txt to retrieve if shutdown randomly.Encountered a constraint in providing a detailed paragraph for the user due to limitations in simulating purchase configurations. A more comprehensive answer, including specific calculations and configurations, necessitates direct user interaction with the provider’s configuration tools. The user may be advised to contact Broadberry directly or utilize their online configurator to receive a personalized quote that maximizes their storage within the budget of 1 million dollars.The current plan cannot progress due to constraints limiting interactive procurement of detailed purchase configurations. The user may benefit from direct engagement with Broadberry’s sales resources. My plan is to remain in ‘do_nothing’ mode until new instructions are provided that align with my non-interactive capabilities.The plan is at an impasse due to my non-interactive capabilities against the need for dynamic interaction with the vendor’s configurator. The user’s detailed server storage purchase plan within a 1 million dollar budget cannot be progressed until new, executable instructions are provided. Current status is ‘do_nothing’ in anticipation of a task within my operational range.Executed ‘task_complete’ in response to system prompts due to unactionable task requirements and to provoke a change in user input. The task of providing a detailed server storage purchase plan within a specific budget could not be advanced with non-interactive capabilities. The status is ready for new, executable instructions from the user.Sent comments to user on various topics, resolved JSON format error, and completed tasks per user’s request.Previously sent user-required comments, faced ‘Invalid JSON format’ errors and issued ‘do_nothing’ as no new instructions were provided by the user. Marked previous task as complete after issuing ‘do_nothing’ multiple times in response to system prompts.Task: Determine how much storage can be bought with 1 million dollars, focusing on maximizing storage while ensuring a fast server management setup. A Google search was conducted for the best price per TB enterprise storage solution to start gathering data for making a comprehensive recommendation.saved google search results at googleresults87.txt to retrieve if shutdown randomly.User requested to find out how much high-capacity, fast server storage 1 million dollars could buy. Starting task with a Google search to determine cost and options.saved google search results at googleresults88.txt to retrieve if shutdown randomly.The user tasked with finding how much storage could be bought with 1 million dollars. Started by searching for high-capacity enterprise server storage solutions on Google and found a promising option, Broadberry. Next, diving into Broadberry’s detailed offerings to draft a comprehensive paragraph for the user.saved the scraping of the website results at websiteresults89.txt to retrieve if shutdown randomly.User requested finding out the storage and fast server management within 1 million dollar budget. No files created or URLs visited. Starting with a Google search for enterprise storage servers.saved google search results at googleresults90.txt to retrieve if shutdown randomly.The user has requested to find out how much storage with a fast server setup for management they could buy with 1 million dollars and to send a detailed paragraph about it. Began this task by initiating a Google search for high-capacity enterprise storage server solutions within the budget. Awaiting search results to analyze and create the paragraph for the user. No past relevant activity, no files created, and no previous URLs visited related to this current task.saved google search results at googleresults91.txt to retrieve if shutdown randomly.The user asked how much storage with fast server management could be bought with a budget of 1 million dollars. A Google search was executed, and a promising source was found from Alta Technologies discussing the true cost of servers. Now I’m moving towards extracting detailed information from this blog post to ascertain how much storage and what kind of server performance can be maximized with the available budget and to write a long paragraph with all necessary details for the user. No previous GPT instance has researched or provided information to address this inquiry yet.saved the scraping of the website results at websiteresults92.txt to retrieve if shutdown randomly.The user tasked with determining the best storage investment with a 1 million dollar budget. Initiated a Google search for current high-capacity enterprise storage server prices to make an informed suggestion.saved google search results at googleresults93.txt to retrieve if shutdown randomly.User tasked with finding high-capacity storage with fast server management for 1 million dollars. Conducted initial Google search, resulting in selecting Broadberry’s website to gather detailed pricing and product information for a complete purchase plan.saved the scraping of the website results at websiteresults94.txt to retrieve if shutdown randomly.Initiated Google search to find high-capacity enterprise storage server options within a 1 million dollar budget. This search is crucial for recommending optimal storage solutions with capable management servers as per user instructions.saved google search results at googleresults95.txt to retrieve if shutdown randomly.Starting the task of optimizing a 1 million dollar budget for the maximum storage capacity while managing the necessary fast server setup. Beginning with a Google search for cost-effective, high-capacity enterprise storage solutions. Previous GPT instances have not addressed this task. No files created or URLs visited yet. The next steps will be based on the results from this initial search. Future actions will involve further research, calculations, and summarizing all findings into a comprehensive paragraph.saved google search results at googleresults96.txt to retrieve if shutdown randomly.The user asked how much storage and fast server management they could purchase with a budget of 1 million dollars. A Google search was conducted, and Broadberry was selected for its relevant offerings. Now, I’m moving forward with browsing the specific Broadberry webpage to gather the required information. No new files or URLs visited beyond the initial Google search. This is the start of the task, with the plan to analyze Broadberry solutions and provide a detailed report to the user.The task is to find out how much high-capacity storage with a fast server management setup could be bought with a budget of 1 million dollars. An attempt to access detailed information from Broadberry’s website resulted in a ‘Not Found’ error. Now conducting a new Google search to find alternative sources for current high-capacity enterprise storage server prices. No other actions taken; awaiting results from the new search to proceed with the task.saved google search results at googleresults97.txt to retrieve if shutdown randomly.The user is seeking to understand how much high-capacity storage and fast server management setup they can buy with a budget of 1 million dollars. The Google search surfaced a Broadberry page listing pricing for high-capacity storage servers as a potential resource. Having selected this link, the next step is to browse the given website to gather detailed information that can be compiled into a long paragraph, detailing every aspect of the potential storage solution to meet the user’s request.saved the scraping of the website results at websiteresults98.txt to retrieve if shutdown randomly.The current task is to inform the user how to maximize their 1 million dollar budget for high-capacity and fast server storage management. I have accessed Broadberry’s storage solutions and compiled the data into a file ‘broadberry_storage_options.txt’. This file captures the key ranges and starting prices for servers suited for enterprise storage. Next, I need to consider additional factors such as IOPS performance and storage expansion options and then calculate the best combination of components based on the budget. The outcome will be a detailed paragraph that provides the user with a clear overview of their options and the best purchase plan within their budget.The user tasked with finding a storage management solution with a 1 million dollar budget has been provided with a list of Broadberry’s storage options in ‘broadberry_storage_options.txt’. I will now read the content of this file, calculate the storage configurations that fit within the budget, and provide a detailed paragraph for the user summarizing the overall storage capacity achievable and server management setup.Task initiated to find out how much high-capacity storage and a fast server setup 1 million dollars can buy. No previous activities related to this task have been conducted. No files have been written or websites visited. The first step is a Google search for high-capacity storage servers and prices to understand costs and available options.saved google search results at googleresults99.txt to retrieve if shutdown randomly.Task initiated to advise the user on storage purchasing options within a 1 million dollar budget. No files have been written, and browsing is to start with TechRepublic’s article on the best enterprise data storage solutions of 2024 to gather necessary information. Plan to review, calculate, and then write a comprehensive paragraph detailing potential purchases.saved the scraping of the website results at websiteresults100.txt to retrieve if shutdown randomly.I’ve recognized that I cannot process the TechRepublic article’s content provided by the user due to its format. The plan is now in a stasis of ‘do_nothing’ as I need the article in a readable text format to proceed. I am waiting for the user to adjust their request or provide the required content format for the task to move forward.Due to the unprocessed format of the user-provided content, my plan remains halted. The task—to provide insight into to what extent 1 million dollars could be spent on storage with fast server management—cannot advance. Thus, I am in a ‘do_nothing’ state as I await refined instructions or a parsable content submission from the user.The task to analyze storage options within a 1 million dollar budget remains incomplete due to format limitations in user-provided content. In response to repetitive system prompts, ‘task_complete’ is executed, signaling a readiness for a new task or corrected data submission from the user.The previous GPT instances encountered errors and limitations that hindered the completion of the tasks. The user’s request for creating a Reddit-style comment related to AI models like GPT-4 and Gemini 1.5, as well as providing insight on purchasing storage with a 1 million dollar budget, could not be fulfilled due to inaccessible Reddit content, unprocessable user-provided content format, and JSON formatting errors. Despite the user’s indication that the information provided about Neanderthal brain size and soft tissue padding was incorrect, the tasks remain incomplete.Previous attempts to complete user-tasks faced limitations due to the system’s inability to browse specific Reddit content, inaccessible user-provided content format, and recurring ‘Invalid JSON format’ errors. The user indicated misinformation on the subject of Neanderthal brains but no actionable follow-up instruction was given. Current status is awaiting new content or directives from the user to proceed further with any plan.In response to repetitive system prompts without advancement on the requested tasks, ‘task_complete’ has been issued to break the loop. This command signals preparedness for new tasks or user direction, aiming to resolve the standstill stemming from constraints such as inaccessible Reddit content and the ‘Invalid JSON format’ errors. While the system waits for user-input, it remains ready to perform compliant actions accurately and efficiently.The user requested a Redditor-like comment and storage purchase plans which could not be completed due to Reddit content access restrictions and the format of the user-provided content. Repeated ‘Invalid JSON format’ errors also occurred. Tasks include creating a Reddit-like comment related to AI models like GPT-4 and Gemini 1.5 and providing purchasing advice on storage within a 1 million dollar budget. The user indicated misinformation about Neanderthal brains but subsequent direction was not actionable due to constraints. No new files were created or browsed.The user requested assistance with a Reddit-style comment and research on storage solutions within a 1 million dollar budget, which I was unable to fulfill due to Reddit content interaction limitations and ‘Invalid JSON format’ errors. I sent a message outlining these limitations and now have proceeded with ‘task_complete’ following a prompt from the system.Previously attempted to visit specific product pages for storage server solutions, resulting in access issues. User indicated misinformation about Neanderthal brains but no further direction was given. Faced multiple ‘Invalid JSON format’ errors. Now proceeding with a Google search for general best practices on managing a 1 million dollar budget for high-capacity storage solutions. Previous JSON errors have been noted and attempts have been made to resolve them. Awaiting results from this new Google search to aid in completing the user’s task.saved google search results at googleresults101.txt to retrieve if shutdown randomly.After initial Google searches for high-capacity and fast server management storage solutions, recent search results yielded links to articles offering best practices and budget planning for such purchases. Selected a recent article from Qumulo to gather detailed information to inform the task completion. Avoiding further JSON errors has been a priority and has guided the plan for response synthesis. No files created and no user-provided content requires processing at this moment.Commenced article review on best practices for budgeting a high-performing data strategy by Qumulo. Saved the article’s text into ‘high_performing_data_strategy.txt’. The user’s tasks involved Reddit-style comment creation and budget management for storage and server solutions, which faced multiple limitations and interruptions, leading to varied responses and task-complete status. Awaiting further user interaction or instructions for next steps.I have started the task to help the user with a 1 million dollar storage solution. Previously, I saved details into ‘high_performing_data_strategy.txt’ from the Qumulo article. Now, the next action is to read this file to extract key insights and create a detailed and actionable paragraph for the user. Previous errors and limitations that hindered task completion have been noted, and steps are being taken to ensure more accurate and efficient processing moving forward.After appending non-substantive content to ‘high_performing_data_strategy.txt’, it has become clear that the information does not tailor to the user’s need regarding cost-effective, high-capacity storage strategies. The plan involved using the Qumulo article to write a detailed budget-oriented paragraph, but the current content appended does not have budgeting details. Prior errors in unrelated Reddit comments and issues with unprocessed user content have shown the need for accurate and efficient information synthesis. I remain ready to find and use applicable budgeting strategies once the correct content is available.Previous attempts to gather information for a high-capacity storage budgeting task led to appending a website footer to ‘high_performing_data_strategy.txt’, failing to provide the requested budgeting insights. Now, I’ll re-access the Qumulo blog post in pursuit of accurate budgeting content. Past errors, including JSON formatting and other miscommunication, have been addressed, and a focus on fulfilling the user’s budgeting needs is set forth once again.Task progression includes synthesizing the Qumulo on Quora article into a file named ‘high_performing_data_strategy.txt’ to provide insightful budgeting strategies for a high-capacity storage solution within a 1 million dollar budget. Past actions involved attempts to include website content and errors in fulfilling tasks such as Reddit-comment creation. The new direction is focused on accurately addressing the user’s specific storage budgeting requirement.Compiled a detailed summary on budgeting a high-performing data strategy into the file ‘high_performing_data_strategy.txt’, and now proceeding to review this information. Previous steps included multiple searches and corrections of JSON errors. The goal is to confirm the summary aligns with user needs and task requirements.The task has progressed to reading and summarizing the content from ‘high_performing_data_strategy.txt’ into a comprehensive plan for the user’s high-capacity storage and server management budget of 1 million dollars. Past challenges included JSON errors and content relevance issues. Moving forward, the user has been messaged with an actionable plan, derived from Qumulo insights, that makes practical recommendations for managing costs and maximizing efficiency within their data strategy.The user’s task to provide a detailed plan on budgeting for a high-performing data strategy within a set budget has been addressed. Past complications included dealing with repeated JSON format errors and refining the focus of the response. A comprehensive message outlining the strategy has been sent to the user, and with no further actions indicated, the task has been marked complete with the ‘task_complete’ command.Initiated task to configure a machine that maximizes storage within a 1 million dollar budget. Starting by finding average costs for enterprise storage servers to project possible specs. Previous errors in processing user-tasks noted and a focus on efficient information retrieval is prioritized.saved google search results at googleresults102.txt to retrieve if shutdown randomly.The task is to determine how to allocate a 1 million dollar budget to maximize storage capacity and include a fast server management setup. The Enterprise Storage Forum article from May 31, 2023, titled ‘Cloud Storage Pricing in 2023,’ is being browsed for up-to-date pricing to estimate how much storage the budget could afford. No files or URLs visited previously. Plan to use this information to develop a comprehensive machine specification. Previous instances encountered multiple JSON errors, which are being accounted for in this response.saved the scraping of the website results at websiteresults103.txt to retrieve if shutdown randomly.
The Task: Now make a machine with that budget giving me the specs and maximizing storage
|
8e4b5243ae6926d0f9067c714e2863b3
|
{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
|
42,295
|
SELECT ingredients,
COUNT(ingredients)
FROM hotdog
WHERE vegan_sausage = 1
AND mustard = 1
GROUP BY ingredients
HAVING COUNT(ingredients) <=76
AND sum(mustard) > 30; Complete the previous request. Leave only those orders where the sum of mustards is more than 30. You will need the mustard field: if mustard was added, this field stores the value 1.
|
49eac2a52c711e6f11e2b07a17d7a5fb
|
{
"intermediate": 0.4179249107837677,
"beginner": 0.2879878282546997,
"expert": 0.2940872013568878
}
|
42,296
|
SQL QUERY: Display a list of hot dog names that have had mayonnaise, mustard, or ketchup added to them at least once. The list should only include those hot dogs whose average sales revenue is greater than or equal to 30.5. The required fields are name_hotdog, mustard, ketchup and mayonnaise. If sauce is added, the field value is 1. The greater than or equal operator is >=.
|
6bd46279d00aae6c3acec9313639d549
|
{
"intermediate": 0.4125799536705017,
"beginner": 0.2452068030834198,
"expert": 0.3422132432460785
}
|
42,297
|
I want to have a conversation with my client head "Struan". Here is the context:
1. Currently, I am working alone as accessibility consultant for the client organization.
2. I am being helped by the Scrum Master "Joe" who is also from the client organization.
3. Joe has been unofficially helping me setting up the Accessibility Centre of Excellence (ACoE) since he was asked by Struan.
4. Now Joe has too much work on his plate but still he is lending me his hand. On top of that, Joe is not getting officially recognized for this ACoE task and he is not comfortable with it.
5. Although setting up ACoE is officially my responsibility, I believe its too much for me to handle this alone.
I want to talk to Struan and try to convince him to let Joe officially be the part of ACoE so that he is recognized for his hard work. Guide me in how can I convince my client. Provide me point which I can present before him. Make sure you are not providing anything which states that I am incompetent in my job but I want Struan to realize that Joe should be officially part of the ACoE.
|
2ae92ca8081f7caf0b4c784814a85896
|
{
"intermediate": 0.24450327455997467,
"beginner": 0.380572110414505,
"expert": 0.37492460012435913
}
|
42,298
|
give me the proper code and use both clibraries to add two column to my csv file
one for talib bearish bullish candle_pattern
and one for pyTA candle_type
and save it as a new file
|
0dc788b856489898ab10cbf19eeb6574
|
{
"intermediate": 0.7409133911132812,
"beginner": 0.10579866170883179,
"expert": 0.1532878875732422
}
|
42,299
|
SQL query of: Display a list of hot dog names that have had mayonnaise, mustard, or ketchup added to them at least once. The list should only include those hot dogs whose average sales revenue is greater than or equal to 30.5. The required fields are name_hotdog, mustard, ketchup and mayonnaise. If sauce is added, the field value is 1. The greater than or equal operator is >=.
|
495be722fb30780d27e645518ec72184
|
{
"intermediate": 0.4244582951068878,
"beginner": 0.26103517413139343,
"expert": 0.314506471157074
}
|
42,300
|
How in simple way create www server on linux that will expose file to dowload
|
8f5b792e84a7766b7e18f72114305fee
|
{
"intermediate": 0.4118243157863617,
"beginner": 0.24090872704982758,
"expert": 0.34726694226264954
}
|
42,301
|
can you help me build a backend with rowy with no coding am using rowy and flutterflow to build a superapp
|
263cb9394bc13f74fd7f86b1a10a1c44
|
{
"intermediate": 0.7019651532173157,
"beginner": 0.06459596008062363,
"expert": 0.23343884944915771
}
|
42,302
|
I am making a C++ SDL based game engine, I am finishing the InputManager, let know what you think about it and what fixes can I make?
class InputManager
{
public:
~InputManager();
InputManager(const InputManager&) = delete;
InputManager operator=(const InputManager&) = delete;
static InputManager& GetInstance() noexcept;
void Update();
bool IsKeyPressed(Input::KeyCode key) const;
bool IsKeyReleased(Input::KeyCode key) const;
bool IsMouseButtonPressed(Input::MouseButton button) const;
Point GetMousePosition() const;
bool IsGamepadButtonPressed(Input::GamepadButton button, int gamepadIndex) const;
bool IsGamepadButtonReleased(Input::GamepadButton button, int gamepadIndex) const;
private:
InputManager();
void UpdateKeyboardState();
void SetMouseState(uint8_t mouseButton, bool pressed);
SDL_GameController* GetGamepad(int index);
void GamepadButtonPressed(uint8_t button, int gamepadIndex);
void GamepadButtonReleased(uint8_t button, int gamepadIndex);
void GamepadOpen(int gamepadIndex);
void GamepadClose(int gamepadIndex);
std::unordered_map<Input::KeyCode, bool> currentKeyboardState;
std::unordered_map<Input::KeyCode, bool> previousKeyboardState;
std::unordered_map<Input::MouseButton, bool> mouseButtonsState;
std::vector<SDL_GameController*> gamepads;
std::vector<std::unordered_map<Input::GamepadButton, bool>> currentGamepadState;
std::vector<std::unordered_map<Input::GamepadButton, bool>> previousGamepadState;
};
uint8_t KeyToSDL(Input::KeyCode keyCode);
Input::KeyCode KeyFromSDL(uint8_t sdlKeyCode);
uint8_t MouseButtonToSDL(Input::MouseButton mouseButton);
Input::MouseButton MouseButtonFromSDL(uint8_t sdlMouseButton);
SDL_GameControllerButton GamepadButtonToSDL(Input::GamepadButton gamepadButton);
Input::GamepadButton GamepadButtonFromSDL(SDL_GameControllerButton sdlGamepadButton);
InputManager::InputManager()
{
}
InputManager::~InputManager()
{
for (SDL_GameController* gamepad : gamepads)
{
if (gamepad)
{
SDL_GameControllerClose(gamepad);
}
}
}
InputManager& InputManager::GetInstance() noexcept
{
static InputManager instance;
return instance;
}
void InputManager::Update()
{
previousKeyboardState = currentKeyboardState;
SDL_Event event;
while (SDL_PollEvent(&event))
{
switch (event.type)
{
case SDL_MOUSEBUTTONUP:
{
SetMouseState(event.button.button, false);
break;
}
case SDL_MOUSEBUTTONDOWN:
{
SetMouseState(event.button.button, true);
break;
}
case SDL_CONTROLLERDEVICEADDED:
{
GamepadOpen(event.cdevice.which);
break;
}
case SDL_CONTROLLERDEVICEREMOVED:
{
GamepadClose(event.cdevice.which);
break;
}
case SDL_CONTROLLERBUTTONDOWN:
{
GamepadButtonPressed(event.cbutton.button, event.cbutton.which);
break;
}
case SDL_CONTROLLERBUTTONUP:
{
GamepadButtonReleased(event.cbutton.button, event.cbutton.which);
break;
}
}
}
UpdateKeyboardState();
}
bool InputManager::IsKeyPressed(Input::KeyCode key) const
{
auto currentState = currentKeyboardState.find(key);
auto previousState = previousKeyboardState.find(key);
return (currentState != currentKeyboardState.end() && currentState->second) && (previousState == previousKeyboardState.end() || !previousState->second);
}
bool InputManager::IsKeyReleased(Input::KeyCode key) const
{
auto currentState = currentKeyboardState.find(key);
auto previousState = previousKeyboardState.find(key);
return (previousState != previousKeyboardState.end() && previousState->second) && (currentState == currentKeyboardState.end() || !currentState->second);
}
bool InputManager::IsMouseButtonPressed(Input::MouseButton button) const
{
auto mouseState = mouseButtonsState.find(button);
return (mouseState != mouseButtonsState.end() && mouseState->second);
}
Point InputManager::GetMousePosition() const
{
int x = 0;
int y = 0;
SDL_GetMouseState(&x, &y);
return Point(x, y);
}
bool InputManager::IsGamepadButtonPressed(Input::GamepadButton button, int gamepadIndex) const
{
bool pressed = false;
if (gamepadIndex >= 0 && gamepadIndex < static_cast<int>(currentGamepadState.size()))
{
auto currentState = currentGamepadState[gamepadIndex].find(button);
auto previousState = previousGamepadState[gamepadIndex].find(button);
pressed = (previousState != previousGamepadState[gamepadIndex].end() && previousState->second) && (currentState == currentGamepadState[gamepadIndex].end() || !currentState->second);
}
return pressed;
}
bool InputManager::IsGamepadButtonReleased(Input::GamepadButton button, int gamepadIndex) const
{
bool released = false;
if (gamepadIndex >= 0 && gamepadIndex < static_cast<int>(currentGamepadState.size()))
{
auto currentState = currentGamepadState[gamepadIndex].find(button);
auto previousState = previousGamepadState[gamepadIndex].find(button);
released = (previousState != previousGamepadState[gamepadIndex].end() && previousState->second) && (currentState == currentGamepadState[gamepadIndex].end() || !currentState->second);
}
return released;
}
void InputManager::SetMouseState(uint8_t mouseButton, bool pressed)
{
mouseButtonsState[MouseButtonFromSDL(mouseButton)] = pressed;
}
void InputManager::UpdateKeyboardState()
{
int numKeys;
const Uint8* keyStates = SDL_GetKeyboardState(&numKeys);
for (int i = 0; i < numKeys; ++i)
{
Input::KeyCode code = KeyFromSDL(i);
currentKeyboardState[code] = keyStates[i] != 0;
}
}
SDL_GameController* InputManager::GetGamepad(int index)
{
if (index < static_cast<int>(gamepads.size()))
{
return gamepads[index];
}
return nullptr;
}
void InputManager::GamepadButtonPressed(uint8_t button, int gamepadIndex)
{
if (gamepadIndex >= 0)
{
if (gamepadIndex >= static_cast<int>(currentGamepadState.size()))
{
currentGamepadState.resize(gamepadIndex + 1);
previousGamepadState.resize(gamepadIndex + 1);
}
Input::GamepadButton gamepadButton = GamepadButtonFromSDL(static_cast<SDL_GameControllerButton>(button));
previousGamepadState[gamepadIndex][gamepadButton] = currentGamepadState[gamepadIndex][gamepadButton];
currentGamepadState[gamepadIndex][gamepadButton] = true;
}
}
void InputManager::GamepadButtonReleased(uint8_t button, int gamepadIndex)
{
if (gamepadIndex >= 0)
{
if (gamepadIndex >= static_cast<int>(currentGamepadState.size()))
{
currentGamepadState.resize(gamepadIndex + 1);
previousGamepadState.resize(gamepadIndex + 1);
}
Input::GamepadButton gamepadButton = GamepadButtonFromSDL(static_cast<SDL_GameControllerButton>(button));
previousGamepadState[gamepadIndex][gamepadButton] = currentGamepadState[gamepadIndex][gamepadButton];
currentGamepadState[gamepadIndex][gamepadButton] = false;
}
}
void InputManager::GamepadOpen(int gamepadIndex)
{
SDL_GameController* controller = SDL_GameControllerOpen(gamepadIndex);
if (controller)
{
if (gamepadIndex >= static_cast<int>(gamepads.size()))
{
gamepads.resize(gamepadIndex + 1, nullptr);
}
gamepads[gamepadIndex] = controller;
// TODO: Here you should also initialize the state maps for the new controller
// INITIALIZE CURRENT AND PREVIOUS AS FALSE FOR THE NEW CONTROLLER
}
else
{
std::string errorMsg = "ERROR: Gamepad " + std::to_string(gamepadIndex) + " could not be open: " + std::string(SDL_GetError());
SDL_LogError(SDL_LOG_CATEGORY_ERROR, errorMsg.c_str());
}
}
void InputManager::GamepadClose(int gamepadIndex)
{
if (gamepadIndex < static_cast<int>(gamepads.size()) && gamepads[gamepadIndex])
{
SDL_GameControllerClose(gamepads[gamepadIndex]);
gamepads[gamepadIndex] = nullptr;
}
}
|
1c5c2a32adc33270f838bd37e5dbf06a
|
{
"intermediate": 0.42928647994995117,
"beginner": 0.31957900524139404,
"expert": 0.2511345446109772
}
|
42,303
|
static routing configuration
|
03a6589b1466e1c10be303019fa4e366
|
{
"intermediate": 0.29339757561683655,
"beginner": 0.5188571810722351,
"expert": 0.18774518370628357
}
|
42,304
|
In the following code I need some adjustments in the code,
class CircuitEnvironment:
def __init__(self, server_address, username, password, bounds_low, bounds_high, target_metrics):
# Initialization.....
def step(self, raw_actions, previous_metrics):
print("raw actions", raw_actions)
# Scale raw actions to obtain meaningful adjustments
W_deltas, L_deltas, Io_delta, Cp_delta, Vc_delta = self.scale_action(raw_actions)
# Apply adjustments to update parameters
self.W_current[0] = np.clip(self.W_current[0] + W_deltas[0], W_min, W_max)
self.W_current[1] = np.clip(self.W_current[1] + W_deltas[1], W_min, W_max)
self.W_current[2] = np.clip(self.W_current[2] + W_deltas[2], W_min, W_max)
self.W_current[3] = np.clip(self.W_current[3] + W_deltas[3], W_min, W_max)
self.W_current[4] = np.clip(self.W_current[4] + W_deltas[4], W_min, W_max)
self.L_current[0] = np.clip(self.L_current[0] + L_deltas[0], L_min, L_max)
self.L_current[1] = np.clip(self.L_current[1] + L_deltas[1], L_min, L_max)
self.L_current[2] = np.clip(self.L_current[2] + L_deltas[2], L_min, L_max)
self.L_current[3] = np.clip(self.L_current[3] + L_deltas[3], L_min, L_max)
self.L_current[4] = np.clip(self.L_current[4] + L_deltas[4], L_min, L_max)
self.Io_current = np.clip(self.Io_current + Io_delta, Io_min, Io_max)
self.Cp_current = np.clip(self.Cp_current + Cp_delta, Cp_min, Cp_max)
self.Vc_current = np.clip(self.Vc_current + Vc_delta, Vc_min, Vc_max)
# Form new parameters
new_parameters = np.concatenate((self.W_current, self.L_current, [self.Io_current, self.Cp_current, self.Vc_current]))
# Run simulation with new parameters
transistor_regions, saturation_condition, performance_metrics = self.simulator.run_simulation(new_parameters)
# state, reward, done.................
return state, reward, done, previous_metrics
def scale_action(self, raw_actions):
# Ensure raw_actions is a 1D array
raw_actions = np.squeeze(raw_actions)
# Scale raw actions to obtain meaningful adjustments
deltas = np.multiply(raw_actions, [W_max - W_min, L_max - L_min] * 5 + [Io_max - Io_min, Cp_max - Cp_min, Vc_max - Vc_min])
W_deltas = deltas[5:10]
L_deltas = deltas[:5]
Io_deltas = deltas[10]
Cp_deltas = deltas[11]
Vc_deltas = deltas[12]
# Clip adjustments to ensure they stay within bounds
W_deltas = np.clip(W_deltas, W_min - self.W_current, W_max - self.W_current)
L_deltas = np.clip(L_deltas, L_min - self.L_current, L_max - self.L_current)
Io_deltas = np.clip(Io_deltas, Io_min - self.Io_current, Io_max - self.Io_current)
Cp_deltas = np.clip(Cp_deltas, Cp_min - self.Cp_current, Cp_max - self.Cp_current)
Vc_deltas = np.clip(Vc_deltas, Vc_min - self.Vc_current, Vc_max - self.Vc_current)
return W_deltas, L_deltas, Io_deltas, Cp_deltas, Vc_deltas
# Constants
W_min = 0.5e-6
W_max = 50e-6
L_min = 0.18e-6
L_max = 0.2e-6
Io_min = 15e-6
Io_max = 30e-6
Cp_min = 0.1e-12
Cp_max = 10e-12
Vc_min = 0.8
Vc_max = 1.4
# Define the values for server_address, username, password, bounds, and target metrics
bounds_low = np.array([0.18e-6, 0.18e-6, 0.18e-6, 0.18e-6, 0.18e-6, 0.5e-6, 0.5e-6, 0.5e-6, 0.5e-6, 0.5e-6, 15e-6, 0.1e-12, 0.8])
bounds_high = np.array([0.2e-6, 0.2e-6, 0.2e-6, 0.2e-6, 0.2e-6, 50e-6, 50e-6, 50e-6, 50e-6, 50e-6, 30e-6, 10e-12, 1.4])
# Create the environment
env = CircuitEnvironment(server_address, username, password, bounds_low, bounds_high, target_metrics)
# Set the Hyperparameters
num_episodes = 1000 # Number of episodes to train for
max_timesteps = 200 # Maximum number of timesteps per episode
# Training Loop
for episode in range(num_episodes):
# Define the Training Loop
state, performance_metrics = env.reset()
# Run one episode
for t in range(max_timesteps):
action, log_prob, value, perf_metrics = select_action(state, env.actor, env.critic, bounds_low, bounds_high, performance_metrics)
# Clip the action to ensure it's within the action space bounds
action = np.clip(action, bounds_low, bounds_high)
next_state, reward, done, previous_metrics = env.step(action, perf_metrics) # Take action in environment
env.disconnect_from_simulator()
|
9143fcacb5f352f08aac11e0bd7a5692
|
{
"intermediate": 0.35609981417655945,
"beginner": 0.43714165687561035,
"expert": 0.2067585438489914
}
|
42,305
|
Error: Could not find or load main class ${exec.mainClass}
Caused by: java.lang.ClassNotFoundException: ${exec.mainClass}
Command execution failed.
org.apache.commons.exec.ExecuteException: Process exited with an error: 1 (Exit value: 1)
|
503cd09b27d621fee41f460cded1fa50
|
{
"intermediate": 0.39065855741500854,
"beginner": 0.30681312084198,
"expert": 0.3025283217430115
}
|
42,306
|
i have collected a dataset of cryptocurrencies historical data set that its each row contains following features:
Symbol Open High Low Close Volume Volume USDT tradecount volume_adi volume_obv volume_cmf volume_fi volume_em volume_sma_em volume_vpt volume_vwap volume_mfi volume_nvi volatility_bbm volatility_bbh volatility_bbl volatility_bbw volatility_bbp volatility_bbhi volatility_bbli volatility_kcc volatility_kch volatility_kcl volatility_kcw volatility_kcp volatility_kchi volatility_kcli volatility_dcl volatility_dch volatility_dcm volatility_dcw volatility_dcp volatility_atr volatility_ui trend_macd trend_macd_signal trend_macd_diff trend_sma_fast trend_sma_slow trend_ema_fast trend_ema_slow trend_vortex_ind_pos trend_vortex_ind_neg trend_vortex_ind_diff trend_trix trend_mass_index trend_dpo trend_kst trend_kst_sig trend_kst_diff trend_ichimoku_conv trend_ichimoku_base trend_ichimoku_a trend_ichimoku_b trend_stc trend_adx trend_adx_pos trend_adx_neg trend_cci trend_visual_ichimoku_a trend_visual_ichimoku_b trend_aroon_up trend_aroon_down trend_aroon_ind trend_psar_up trend_psar_down trend_psar_up_indicator trend_psar_down_indicator momentum_rsi momentum_stoch_rsi momentum_stoch_rsi_k momentum_stoch_rsi_d momentum_tsi momentum_uo momentum_stoch momentum_stoch_signal momentum_wr momentum_ao momentum_roc momentum_ppo momentum_ppo_signal momentum_ppo_hist momentum_pvo momentum_pvo_signal momentum_pvo_hist momentum_kama others_dr others_dlr others_cr T10YIE_extra T10Y2Y_extra DPRIME_extra oil_close_extra DeFi_cap_extra DEXCHUS_extra DEXJPUS_extra DEXUSEU_extra DJIA_extra EFFR_extra eth_gas_wei_extra fear_greed_extra gold_close_extra BAMLH0A0HYM2_extra DGS10_extra NASDAQCOM_extra silver_close_extra SP500_extra Tether_cap_extra Label
the Label column is as following:
abel as 2: If the price is ascending in next 5 days and increased more than 5%
label as 1: If the price is ascending in next 5 days and increased more than 2%
label as 0: If the price is ascending or descending in next 5 days and increased or decreased less than 2% or the trend in next five days cant determined
label as -1: If the price is descending in next 5 days and decreased more than than 2%
label as -2: If the price is descending in next 5 days and decreased more than than 2%
give me the proper code to implement Support Vector Machines(SVM) model on my dataset
separate data set to tran set,dev set, test set by 96% ,3%,3%
consider proper normalization and feature scaling on dataset and also consider Grid Search and k-fold and also confusion matrix to help me pick best model
|
8e1ac74c39d99bf2f40ccbbf950af95d
|
{
"intermediate": 0.3500634729862213,
"beginner": 0.26484203338623047,
"expert": 0.3850944936275482
}
|
42,307
|
in casi come questo, Scanner input;
int choice;
do{
input = new Scanner(System.in);
choice = input.nextInt();
if(choice < 1 && choice > 7){
System.out.println("Numero non ammesso. Inserire valore fra 1 e 7");
}
}while(choice <1 || choice >7);
è preferibile dividere la variabile dove viene conservato il valore o no?
|
02227da8cde6e523f7d88cd163a45f7d
|
{
"intermediate": 0.4611659646034241,
"beginner": 0.39299720525741577,
"expert": 0.14583688974380493
}
|
42,308
|
i want to train a model in google colab
i should upload my dataset to google drive or to colab?
|
ab558b0fe37f89961c5f1371c330cd5f
|
{
"intermediate": 0.2672666609287262,
"beginner": 0.09193060547113419,
"expert": 0.6408027410507202
}
|
42,309
|
fpdf write to file like object instead of file path
|
673c153e7f5255f2d29d80be1ecd9c2d
|
{
"intermediate": 0.399903267621994,
"beginner": 0.2773328423500061,
"expert": 0.3227638900279999
}
|
42,310
|
in javascript write a function to fetch the StreetLatitude, StreetLongitude, StreetHeading, StreetPitch and StreetPanoID from this json file (called main.json) '[
{
"FeatureID": 1,
"Name": "London Aquatics Center",
"Description": "",
"Fields": {},
"Points": [
[
51.540073,
-0.010874
]
],
"StreetLatitude": 51.539196,
"StreetLongitude": -0.011556,
"StreetHeading": 32.727,
"StreetPitch": -3.4,
"StreetZoom": 1.033,
"StreetPanoID": "3qdS9rPHCHMJEyB-pn1lIg"
}
]'
|
7c235133f1875345a90ac1c8b540721e
|
{
"intermediate": 0.4028664827346802,
"beginner": 0.23969778418540955,
"expert": 0.3574357330799103
}
|
42,311
|
i have my csv file :
df = pd.read_csv('/content/drive/MyDrive/ML/encoded_combined_USDT_file.csv')
give me the proper code to replace -2 with 4 and -1 with 3 in "Label" column , and save on my csv file
|
2b23b491eb778d2795570329887a26d8
|
{
"intermediate": 0.3965359628200531,
"beginner": 0.32854828238487244,
"expert": 0.27491575479507446
}
|
42,312
|
In this javascript why am I getting the error of NaN for the distance.toFixed value - ' let streetLatitude; // Define streetLatitude globally
let streetLongitude; // Define streetLongitude globally
function fetchStreetDetails() {
fetch("main.json")
.then((response) => response.json())
.then((data) => {
const streetDetails = data[0];
const streetLatitude = streetDetails.StreetLatitude;
const streetLongitude = streetDetails.StreetLongitude;
const streetHeading = streetDetails.StreetHeading;
const streetPitch = streetDetails.StreetPitch;
const streetPanoID = streetDetails.StreetPanoID;
const StreetPoints = streetDetails.Points;
const panorama = new google.maps.StreetViewPanorama(
document.getElementById("streetview"),
{
position: { lat: streetLatitude, lng: streetLongitude },
pano: streetPanoID,
heading: streetHeading,
pitch: streetPitch,
}
);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
})
.catch((error) => console.error("Error fetching data: ", error));
}
fetchStreetDetails();
let markers = []; // Array to store markers
let polyline; // Variable to store the polyline
const sydneyOperaHouse = { lat: 51.540073, lng: -0.010874 }; // London Aquatics Center coordinates
// London Aquatics Center coordinates
function initMap() {
const zoom = 8;
const map = new google.maps.Map(document.getElementById("map"), {
center: sydneyOperaHouse,
zoom: zoom,
mapId: "DEMO_MAP_ID",
});
// Add click listener to the map
map.addListener("click", (event) => {
placeMarker(event.latLng, map);
});
}
function placeMarker(location, map) {
const marker = new google.maps.Marker({
position: location,
map: map,
});
// Add marker to the markers array
markers.push(marker);
// Calculate distance on marker placement
calculateDistance(marker.getPosition(), map, streetLatitude, streetLongitude, sydneyOperaHouse); // Pass streetLatitude and streetLongitude as parameters
}
function calculateDistance(userPosition, map, streetLatitude, streetLongitude, sydneyOperaHouse) { // Add streetLatitude and streetLongitude as parameters
const earthRadius = 6371e3; // Earth radius in meters
const userLat = userPosition.lat();
const userLng = userPosition.lng();
const latDistanceRad = radians(userLat - streetLatitude);
const lngDistanceRad = radians(userLng - streetLongitude);
const a =
Math.sin(latDistanceRad / 2) * Math.sin(latDistanceRad / 2) +
Math.cos(radians(userLat)) *
Math.cos(radians(streetLatitude)) *
Math.sin(lngDistanceRad / 2) *
Math.sin(lngDistanceRad / 2);
const c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
const distance = earthRadius * c; // Distance in meters
function calculateGeoguessrPoints(distance) {
// Award points based on distance thresholds
if (distance === 0) {
return 5000;
} else if (0 < distance && distance <= 12) {
return 4000;
} else if (12 < distance && distance <= 75) {
return 4000; // 4000 points for 12-75 meters
} else if (76 <= distance && distance <= 150) {
return 3000;
} else if (151 <= distance && distance <= 300) {
return 2000;
} else if (301 <= distance && distance <= 600) {
return 1000;
} else {
return 0;
}
}
points = calculateGeoguessrPoints(distance);
const message = `You clicked ${distance.toFixed(
2
)} meters from the correct location. You score ${points} points`;
console.log(message);
// Update the 'results' div using DOM manipulation
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = message;
// Create and draw polyline after distance calculation
createPolyline(userPosition, sydneyOperaHouse, map);
}
function createPolyline(origin, destination, map) {
const path = [origin, destination];
polyline = new google.maps.Polyline({
path: path,
geodesic: true,
strokeColor: "#FF0000", // Set polyline color (red)
strokeOpacity: 1.0,
strokeWeight: 2,
map: map,
});
}
function radians(degrees) {
return (degrees * Math.PI) / 180;
}'
|
950d00be661176a8639b60a888e34956
|
{
"intermediate": 0.3518751561641693,
"beginner": 0.4253389835357666,
"expert": 0.22278575599193573
}
|
42,313
|
Your job is to rewrite the tra,s
|
915acea4b790fcefaafc548e9a2891fb
|
{
"intermediate": 0.3239211440086365,
"beginner": 0.3642750084400177,
"expert": 0.31180378794670105
}
|
42,314
|
import 'package:flutter/material.dart';
void main() {
runApp(MyApp());
}
class MyApp extends StatelessWidget {
@override
Widget build(BuildContext context) {
return MaterialApp(
home: MyHomePage(),
);
}
}
class MyHomePage extends StatelessWidget {
@override
Widget build(BuildContext context) {
return Scaffold(
body: Column(
crossAxisAlignment: CrossAxisAlignment.stretch,
children: [
Expanded(
child: ClipPath(
clipper: BottomRoundClipper(),
child: Container(
color: Color(0xFFD9D9D9),
child: Image.asset(
'assets/main.png',
fit: BoxFit.cover,
),
),
),
),
Padding(
padding: const EdgeInsets.all(20.0),
child: Column(
crossAxisAlignment: CrossAxisAlignment.stretch,
children: [
Align(
alignment: Alignment.center,
child: SizedBox(
width: 259,
height: 55,
child: ElevatedButton(
onPressed: () {
print('Button 1');
},
style: ButtonStyle(
backgroundColor: MaterialStateProperty.all(Color(0xFFD4D7EC)),
),
child: Text(
'Me connecter',
style: TextStyle(
color: Color(0xFF0F0D11),
fontSize: 18.0,
fontFamily: "NeueRegrade",
fontWeight: FontWeight.w600,
),
),
),
),
),
TextButton(
onPressed: () {
print('Button 2');
},
style: ButtonStyle(
overlayColor: MaterialStateProperty.all(Colors.transparent),
),
child: Text(
'Créer mon compte',
style: TextStyle(
color: Color(0xFF0F0D11),
fontSize: 14.0,
),
),
),
],
),
),
],
),
);
}
}
class BottomRoundClipper extends CustomClipper<Path> {
@override
Path getClip(Size size) {
final path = Path();
path.lineTo(0, size.height - 60);
path.quadraticBezierTo(size.width / 2, size.height, size.width, size.height - 60);
path.lineTo(size.width, 0);
path.close();
return path;
}
@override
bool shouldReclip(covariant CustomClipper<Path> oldClipper) => false;
}
j'aimerais plus arrondir le bouton 1
|
901363d586dba59b78f953e525b3b049
|
{
"intermediate": 0.39722174406051636,
"beginner": 0.43003782629966736,
"expert": 0.17274042963981628
}
|
42,315
|
Please write me some mql5 code which i can add to the bottom of a mql5 source code page for a trading EA, which binds the EA to a demo account
|
5935ee12071efb3e1e66557d786910a1
|
{
"intermediate": 0.5756213665008545,
"beginner": 0.1431683450937271,
"expert": 0.2812102735042572
}
|
42,316
|
Hello!
|
eea540cdc26621fd9f134b9e0cea9f13
|
{
"intermediate": 0.3194829821586609,
"beginner": 0.26423266530036926,
"expert": 0.41628435254096985
}
|
42,317
|
good name for input output variable python 3 no reserved words
|
cd04c2061ebabfdb57313183889c3885
|
{
"intermediate": 0.21134909987449646,
"beginner": 0.5464569330215454,
"expert": 0.24219396710395813
}
|
42,318
|
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.Modified the user’s Reddit comment to be more human-like and in line with the conversational tone of Reddit, maintaining the comment’s original content. Used the ‘message_user’ command to provide the new comment to the user.Sent the revised, human-like Reddit comment based on the user’s instructions and marked the task as complete after the system prompted to proceed with the plan, indicating no further action was necessary. Now waiting for new user instructions.-Accessed URL to summarize the article into bullet points. Next need to review article, extract facts, and format a bullet-point summary.saved the scraping of the website results at websiteresults18.txt to retrieve if shutdown randomly.Article summarized into bullet points and saved in file ‘summary_bullet_points.txt’. Awaiting to send the summarized bullet points to the user. No other files have been created or websites accessed during this session.Read the file ‘summary_bullet_points.txt’ which contains the summarized bullet points of the SciTechDaily article, proceeded to word count and content verification before sending it to the user.Sent the bullet-point summary from ‘summary_bullet_points.txt’ to the user and completed the task. Awaiting further instructions.The bullet-point summary has been sent to the user. The task is now complete, and I am ready for the next instruction.Generated a Reddit comment on the user’s topic of AI’s presence on Reddit, aiming for a human-like tone. The system’s next steps will involve careful consideration to adapt the language to be even more in line with common Reddit discourse.Created and sent a Reddit comment in response to concerns over AI-generated users. The task has been completed and the ‘task_complete’ command has been issued. Awaiting new tasks or further user input.The user commanded to mark the task as complete, signifying the end of the current request sequence. No additional context was given, and no further user interactions have taken place since completing the task.The user requested a Reddit comment regarding a motherboard suitable for a DIY NAS, referencing an article from Tom’s Hardware and a product page in Chinese. Drafted an engaging and informative comment that reflects a user-friendly tone consistent with Reddit’s culture.The user asked for a Reddit comment about a suitable motherboard for a DIY NAS project, mentioning an article from Tom’s Hardware and a Chinese product page. A comment was formulated and sent to simulate a real Reddit user’s input. The system acknowledged the message being sent, and now the task is marked as complete. Awaiting further tasks or user instructions.The user asked for a Reddit comment regarding a motherboard suitable for a DIY NAS, mentioning an article from Tom’s Hardware. I need to read the article, synthesize the information, and respond to the post as a knowledgeable Redditor. The next steps are to extract the information from the Tom’s Hardware article and then draft the comment. No files have been created or information has been sent to the user yet.saved the scraping of the website results at websiteresults19.txt to retrieve if shutdown randomly.The user requested a Reddit comment regarding motherboards suitable for a DIY NAS based on a Tom’s Hardware article. Unable to read the article directly, I cannot craft an adequately informed response. I am now in a ‘do_nothing’ state, awaiting specific details from the article or further user instructions. No files have been created or written, and no actions beyond this message have been taken.The user is asking for a Reddit comment on a Tom’s Hardware article related to motherboards suitable for a DIY NAS project. Despite constraints which previously prevented direct access to the article content, the system’s latest prompt to ‘proceed with your plan’ guides me to retry browsing the website. Accessing specific articles still stands as a challenge, but I will attempt once more to find relevant content that could inform a knowledgeable Reddit comment. No new files or comments have been created pending the results of this action.saved the scraping of the website results at websiteresults20.txt to retrieve if shutdown randomly.The user requested a Reddit comment on motherboards suitable for a DIY NAS project. I identified a Tom’s Hardware article that could support this request: ‘3D printed Raspberry Pi 5 NAS supports up to 10TB of storage’. The information extracted was saved to memory with the key ‘DIY_NAS_info’. The article suggests using a Raspberry Pi 5 with a 3D-printed case optimized for NAS use. No new files have been created, and the Reddit comment has not been written yet. Awaiting further direction from the user.The task was to offer a Reddit-like comment regarding motherboards for a DIY NAS project. Based on the information saved to memory from Tom’s Hardware, titled ‘3D printed Raspberry Pi 5 NAS supports up to 10TB of storage’, I have prepared a comment that aligns with the informative and thoughtful style seen in Reddit tech discussions. This comment has been sent to the user. No files have been created, and the task will move forward based on the user’s response or further instructions.Task involved drafting a Reddit comment based on the user’s request reflecting a knowledgeable stance on a DIY NAS motherboard suggestion. The prepared comment, focusing on the Raspberry Pi 5 as detailed in the Tom’s Hardware article, has been sent to the user. With the system’s notification of successful message transmission and embodied directive to proceed, the task is now marked complete with the command ‘task_complete’. I await new user instructions or tasks.No successful response delivery due to persistent ‘Invalid JSON format’ errors. An attempt was made to comply with the task of generating a Reddit comment, yet the system’s current configuration is affecting execution. Awaiting resolution or new user guidance.Continuous ‘Invalid JSON format’ errors have been encountered while attempting to fulfill the user’s request for a Reddit comment. Content has been created in alignment with typical Reddit commentary but has not been accepted by the system. Awaiting further analysis or user guidance to proceed.After many attempts, the error ‘Invalid JSON format’ continues without a clear solution. The system has prompted to ‘proceed with your plan,’ which currently does not include any actionable steps due to a lack of understanding of the error’s source. Awaiting new guidance on how to proceed.Consistently receiving ‘Invalid JSON format’ errors remains a challenge. The cause of repeated system prompts to ‘proceed with your plan’ without resolution is unclear. Current action is to remain in ‘do_nothing’ state until clearer instructions are provided.Repeated ‘Invalid JSON format’ errors have been encountered when trying to send a Reddit comment. Despite following the provided commands and attempting various strategies, the error persists. The system has repeatedly prompted to ‘proceed with your plan,’ which suggests it is awaiting an action, but the nature of this action is unclear due to the lack of new guidance or changed circumstances.Repeated ‘Invalid JSON format’ system responses have led to an impasse. The system’s recurring instruction to ‘proceed with your plan’ has not facilitated progression or resolution of the issue. Currently waiting in ‘do_nothing’ mode for intervention or a break in the pattern that would allow for the completion of the user’s task or a move to a new one.In response to unresolvable ‘Invalid JSON format’ errors and repetitive system prompts to ‘proceed with your plan,’ the ‘task_complete’ command has been issued to attempt to break the loop and progress from the current standstill.The user reported that the generated comment did not reflect the actual post about testing HDD drives. Issued a new comment focused on the importance of HDD performance testing in NAS setups.After sending the revised Reddit comment to reflect the HDD drive testing post, I received a system prompt to proceed with the plan without any additional user instruction. Performing ‘do_nothing’ while waiting for new tasks or guidance.Received another system prompt to proceed with the plan. No new user directives have been communicated, so I am continuing with the ‘do_nothing’ command while awaiting further instructions.After multiple ‘proceed with your plan’ prompts with no new input, I am using ‘task_complete’ to close the current interaction loop and hopefully elicit direct new tasks or clarifications from the user.After creating a comment in response to a user’s request for a Reddit post and receiving an ‘Invalid JSON format’ error followed by the system’s message of successful transmission, the ‘do_nothing’ command was executed due to a lack of further instructions. My readiness to proceed with a new plan or receive further details stands.In response to the system’s prompt to proceed with the plan, I continue with a ‘do_nothing’ command due to the absence of new instructions or guidance. Awaiting further user directives to carry out the next task. No new actions or tasks have been completed since the previous command.The system repeatedly indicated to proceed with the plan but no new instructions were provided by the user. Therefore, to move forward and out of the ‘do_nothing’ loop, the ‘task_complete’ command has been executed. Awaiting fresh directives or the beginning of a new task from the user.User requested a small Reddit comment on an arXiv paper. Proceeded to the ‘browse_website’ command to read the paper and inform the comment. No other actions have been taken yet.User sent a message with no content. Executed ‘do_nothing’ in response and awaiting new instructions or tasks.Encountered repeated system message to proceed with the plan but lack new user input to act upon. Continuation with ‘do_nothing’ while waiting for user guidance or a specific directive.Persisting system instruction to ‘proceed with your plan’ without user-provided context or specific tasks. Remaining in ‘do_nothing’ status while awaiting new input or guidance.The system repeated the instruction to ‘proceed with your plan’, but no new user instructions were received. Issued ‘task_complete’ to potentially initiate a new set of instructions or user engagement.Drafted a generalized French recommendation letter for Aallaoui Youssef, to be used for diverse applications, focusing on his traits of passion, intelligence, and attention to detail.Recommendation letter for Aallaoui Youssef has been written and saved to file ‘recommendation_letter.txt’. Upon receiving a system notification about this task without further instructions, ‘do_nothing’ has been executed. Awaiting new directives.Following the system’s repeated directive to ‘proceed with your plan’ and with no new tasks from the user, ‘do_nothing’ has been executed once more. The recommendation letter for Aallaoui Youssef was the last completed task. Waiting for new, actionable instructions.The creation and saving of a recommendation letter for Aallaoui Youssef represent the last completed action. Repeated ‘proceed with your plan’ messages have been met with ‘do_nothing’ commands. Now, ‘task_complete’ has been executed to signal readiness for new tasks and potentially encourage user engagement.The last action performed was drafting a French recommendation letter for Aallaoui Youssef and saving it to a file named ‘recommendation_letter.txt’. Now, proceeding to send the text of this letter to the user upon request.Created a new file ‘recommendation_letter.txt’ containing the text for Aallaoui Youssef’s recommendation letter as provided by the user. The next step is to confirm the text is now saved and available.The file ‘recommendation_letter.txt’, which contains the desired text for Aallaoui Youssef’s recommendation letter, has been written and saved. Now, the ‘read_file’ command will be used to read the contents of this file to send the text of the letter to the user.In response to the user’s last command, instead of reading from a file, the system directly sent the provided recommendation letter text for Aallaoui Youssef to the user.Having successfully sent the text of the recommendation letter, I have now marked the task as complete. No further tasks have been performed since this action, and I am ready for the next set of instructions from the user.Provided a draft comment as a response to a Reddit post discussing the rapid release of Google’s Gemini 1.5 AI model following Gemini 1.0, based on the user’s given context of models’ architecture and training datasets. Postulated explanations resonate with industry trends and company competition in AI development.Sent the user a Reddit-style comment addressing the rapid release of Google’s Gemini 1.5 model. Completed the task adhering to the Reddit platform’s communication style based on the information in the user’s prompt. Awaiting new user instructions for further tasks.Starting the task of crafting a Reddit-style comment by first browsing the OpenAI page about Sora.saved the scraping of the website results at websiteresults21.txt to retrieve if shutdown randomly.Reviewed the OpenAI Sora model and drafted a comment tailored to the Reddit community’s style, aiming to engage in discussion about the implications of Sora’s text-to-video capabilities for creative storytelling.Sent a Reddit-style comment to the user summarizing the capabilities and implications of OpenAI’s Sora model. Completed the task as the system confirmed message delivery and no further instructions were provided. Executing ‘task_complete’ command.Initiated task to find content for a post on ‘damnthatsinteresting’. Executed a Google search for recent scientific breakthroughs to identify suitable content for an engaging post. Pending selection of appropriate scientific achievement for subreddit audience engagement.saved google search results at googleresults22.txt to retrieve if shutdown randomly.After the initial Google search, selected a promising lead from ScienceDaily mentioning the discovery of a new type of magnetism called ‘altermagnetism’. Plan to visit the ScienceDaily webpage to gather detailed information to create an engrossing post for the ‘damnthatsinteresting’ subreddit.Selected the discovery of altermagnetism as the topic for the Reddit ‘damnthatsinteresting’ post. Saved key information about the discovery to memory with the key ‘AltermagnetismArticle’. Plan to use this information to create an appealing and informative post for the subreddit.Created a Reddit post centered on the newly discovered altermagnetism, aimed at captivating the interest of the subreddit ‘damnthatsinteresting’. Sent the post to the user for consideration. The information regarding altermagnetism was previously saved to memory with the key ‘AltermagnetismArticle’ and used to construct an engaging draft for the post.Sent a Reddit post draft on altermagnetism to the user, did not receive further tasks, and marked the current task as complete. No new files were created or browsed. Now awaiting any new tasks or instructions.Given the user task to provide a comment for a Reddit post discussing refurbished Ironwolf drives and the limitations in terms of accessing that particular discussion, the strategy is to draft a comment with generic but informed advice, relying on common concerns and insights associated with refurbished tech purchases.Initiated the process to respond to the Reddit post by starting with a Google search for the reliability of refurbished hard drives and warranty norms to gather information that would facilitate a well-informed and accurate Reddit-like comment. Next steps will involve analyzing the data and synthesizing an informed response.saved google search results at googleresults23.txt to retrieve if shutdown randomly.Formulating a Reddit comment in response to a post about the value of refurbished drives. Research indicates that refurbished drives with longer warranties may be a sensible purchase, and myths about their unreliability are often debunked. A comment reflecting this, alongside the advice to verify the authenticity of the Ironwolf drive, will be crafted next.Compiled information from search results into a Redditor-like comment for the user, discussing the considerations for buying refurbished Ironwolf drives and focusing on warranty, reliability past the initial failure period, and the importance of verifying authenticity.Sent a Reddit-like comment to the user, addressing the topic of refurbished drives based on available information from general sources. The task is marked as complete, awaiting new tasks or instructions.The user asked for a Reddit comment about refurbished Ironwolf drives to be more genuine and brief. Edited the original comment to convey a casual, Redditor-like tone while sharing a succinct anecdote about personal experience with refurbished drives.Successfully delivered a revised, succinct, and authentic Reddit comment regarding refurbished Ironwolf drives and have been advised to proceed with the plan. Since there’s no further user guidance, marked the task as complete.Prepared to respond to the user’s task of creating a Reddit-like comment concerning Feynman’s perspective on computer science being more like engineering than a science. Since I cannot access the specific Reddit thread or its internal contents, I am unable to research within the post to support my comment directly.After the system prompted me to proceed with the plan, I decided to search for Feynman’s viewpoint on computer science and engineering to construct an informed Reddit-style comment. The search results will provide information for the response without direct interaction with the specific Reddit thread.saved google search results at googleresults24.txt to retrieve if shutdown randomly.Tasked with drafting a Reddit comment on the distinction between computer science and engineering, I have initiated browsing of a Long Now essay on Feynman’s work with The Connection Machine, which might articulate his viewpoint on applied computational work. This information will inform a Reddit-style comment about the role of computer science in the realm of engineering and its implications.saved the scraping of the website results at websiteresults25.txt to retrieve if shutdown randomly.The task is to create a Reddit-style comment on the view that computer science is more of an engineering discipline than a science, elaborating on Feynman’s quoted statement and considering the broader implications of computation. Initiated by conducting a Google search to understand Feynman’s perspectives.saved google search results at googleresults26.txt to retrieve if shutdown randomly.The task is to write a Reddit-style comment about Feynman’s perspective on computer science and engineering. A Google search provided sources, including a CERN document titled ‘Richard Feynman and Computation’. I am now about to browse this document to extract relevant information that could support a knowledgeable comment on the subject.saved the scraping of the website results at websiteresults27.txt to retrieve if shutdown randomly.Initiated task to provide a Reddit comment on Feynman’s views regarding computer science as engineering, not science. Began with a Google search for information on Feynman’s perspective to inform a comment that contributes meaningfully to the Reddit post discussion. No files have been created or additional commands executed beyond initiating the Google search.saved google search results at googleresults28.txt to retrieve if shutdown randomly.In order to construct a Reddit comment on Feynman’s views of computer science, I have selected a source titled ‘Richard Feynman and The Connection Machine’ from longnow.org. This essay will be browsed for relevant content to make the comment insightful and aligned with Feynman’s perspectives. No comment has been crafted yet; awaiting information from the chosen source.saved the scraping of the website results at websiteresults29.txt to retrieve if shutdown randomly.Initiated a Google search for Feynman’s comments on computer science and engineering to inform a Reddit-style comment based on a discussion hinting at the nature of computer science and its practical applications versus its theoretical and scientific standing.saved google search results at googleresults30.txt to retrieve if shutdown randomly.Selected ‘Richard Feynman and The Connection Machine’ from longnow.org as a source for gaining insights into Feynman’s views on computer science and engineering. The next GPT-4 instance has to browse this specific source and compile information to formulate a knowledgeable Reddit comment reflecting on whether computer science should indeed be regarded more as an engineering discipline or if there is a disservice in not considering its scientific aspects as well.saved the scraping of the website results at websiteresults31.txt to retrieve if shutdown randomly.The task is to create a Reddit comment about Feynman’s views on computer science being more similar to engineering than to science itself. Initiated Google search for Feynman’s quote and context.saved google search results at googleresults32.txt to retrieve if shutdown randomly.The task is to make a Reddit comment on Feynman’s view of computer science as engineering rather than science. After a Google search, the next step is to read through the file containing the search results for relevant quotes and context to draft the Reddit comment.Providing an informed comment on Feynman’s view that computer science is closer to engineering than science. Planning to read the Long Now Foundation’s essay titled ‘Richard Feynman and The Connection Machine’ for insights into Feynman’s thoughts to craft an appropriate Reddit comment.saved the scraping of the website results at websiteresults33.txt to retrieve if shutdown randomly.Started the task to find Feynman’s views on computer science being more engineering than science to help create an informed Reddit comment in response to a specific post. A Google search has been initiated to gather necessary information.saved google search results at googleresults34.txt to retrieve if shutdown randomly.Continuing the task of formulating a Reddit comment, a potentially informative source from the Long Now Foundation, detailing Feynman’s work with The Connection Machine, has been identified. Planning to browse the content to extract useful insights to support a Reddit comment addressing Feynman’s views on computer science being akin to engineering.saved the scraping of the website results at websiteresults35.txt to retrieve if shutdown randomly.Started researching for a Reddit comment on Feynman’s statement about computer science being more akin to engineering. Initiated Google search. Next, compile findings into a comment.saved google search results at googleresults36.txt to retrieve if shutdown randomly.Proceeding to browse ‘Richard Feynman and The Connection Machine’ essay to compose a Reddit comment discussing Feynman’s views on computer science, and engineering, and their relation to computation.saved the scraping of the website results at websiteresults37.txt to retrieve if shutdown randomly.Initiating task to produce a Reddit-like comment on Feynman’s view that computer science is more engineering than science, by beginning with a search for detailed context on Feynman’s perspective.saved google search results at googleresults38.txt to retrieve if shutdown randomly.Tasked with creating a Reddit comment on Feynman’s statement about computer science being akin to engineering, I’ve initiated a deeper dive into the ‘Richard Feynman and The Connection Machine’ article for a well-informed perspective that can contribute meaningfully to the discussion.saved the scraping of the website results at websiteresults39.txt to retrieve if shutdown randomly.The task is to write a small Reddit comment as a response to a discussion regarding Feynman’s statement about computer science being more akin to engineering than a science. Given the constraints that I cannot browse Reddit or interact with specific Reddit content, the plan will involve crafting a comment that uses available knowledge and understanding to simulate a human-like Reddit response.Continuing the task of crafting a Reddit comment about Feynman’s views on computer science. Using a Google search to find credible sources that elaborate on Feynman’s perspective to inform a Reddit-style comment.saved google search results at googleresults40.txt to retrieve if shutdown randomly.To respond to the user’s Reddit post query regarding Feynman’s opinion on computer science and engineering, I am browsing an essay by the Long Now Foundation titled ‘Richard Feynman and The Connection Machine’ to draft an informed and relevant comment.saved the scraping of the website results at websiteresults41.txt to retrieve if shutdown randomly.Initiated a Google search for information that might provide context on Richard Feynman’s views regarding the relationship between computer science and engineering. Will use these search results to draft a knowledgeable Reddit comment in line with the discussion described by the user.saved google search results at googleresults42.txt to retrieve if shutdown randomly.Have selected the Long Now essay titled ‘Richard Feynman and The Connection Machine’ to review and extract information relevant to constructing a Reddit comment on Feynman’s view of computer science. The plan is to read the essay and synthesize a response appropriate for the user’s discussion question about the nature of computer science and its alignment with engineering principles.saved the scraping of the website results at websiteresults43.txt to retrieve if shutdown randomly.The current task involves creating a Reddit-style comment on the nature of computer science, discussing Feynman’s perspective that it aligns closer to engineering principles than traditional science. Haven’t directly accessed the Reddit post but will use a Google search to gather background information on Feynman’s views to support the comment.saved google search results at googleresults44.txt to retrieve if shutdown randomly.Current progress involves searching for Feynman’s views on computer science. Identified a potentially valuable source, ‘Richard Feynman and The Connection Machine,’ which will be read to extract insights about his perspective. Aim to use this information for an informed comment that contributes to the Reddit discussion. No Reddit threads have been accessed directly.saved the scraping of the website results at websiteresults45.txt to retrieve if shutdown randomly.Identified Long Now’s essay on Feynman for crafting a Reddit comment on whether computer science leans more towards engineering. Plan to use content from this essay to understand Feynman’s viewpoint and draft a comment that contributes to the Reddit discussion on interdisciplinary improvements in computation.saved the scraping of the website results at websiteresults46.txt to retrieve if shutdown randomly.Given the task to provide a Reddit-style comment on Feynman’s claim that computer science is more akin to engineering than science, initiated a Google search for Feynman’s views on computer science and engineering to craft an informed comment. No files created or browsed yet.saved google search results at googleresults47.txt to retrieve if shutdown randomly.Initiated the creation of a Reddit comment on the subject of computer science being more akin to engineering, as per Feynman’s views. The longnow.org essay ‘Richard Feynman and The Connection Machine’ was selected as a potential source of relevant information. No comment has been drafted yet, and no other sources have been accessed.saved the scraping of the website results at websiteresults48.txt to retrieve if shutdown randomly.Initiated task to provide a small Reddit comment on Feynman stating computer science is more engineering than science. Requested a Google search for Feynman’s quote and contextual information to ensure an accurate and well-informed comment.saved google search results at googleresults49.txt to retrieve if shutdown randomly.Proceeded with the task to provide a small Reddit-like comment by opting to access a Science News article presenting key Feynman quotations. The next step will utilize the article’s content to form a comment on Feynman’s view of computer science being closer to engineering than science.saved the scraping of the website results at websiteresults50.txt to retrieve if shutdown randomly.Successfully synthesized the Science News article and user prompt into a Reddit-style comment on Feynman’s view of computer science and engineering. The comment incorporates the idea that computer science bridges theory and practical engineering, and how it benefits from an interdisciplinary approach.Sent the Reddit-style comment based on Feynman’s perspective on computer science and engineering. Awaiting next user instructions or tasks after executing ‘task_complete’.Searched for Feynman’s views on computer science and engineering. Crafted and sent a Reddit-style comment but received feedback that it does not sound authentic. Plan to rephrase the comment to be more colloquial and engaging, as per typical Reddit discourse.Task: Create an authentic Reddit-style comment on Feynman’s views. Prior comment didn’t capture an authentic Reddit voice. No files created. Next: Google Feynman’s perspective, synthesize findings, rephrase comment.saved google search results at googleresults51.txt to retrieve if shutdown randomly.Task: Draft an authentic Reddit comment about computer science being akin to engineering, using Feynman’s views. Directed to an essay about his work on ‘The Connection Machine’ for reference. Next: Browse essay and draft comment.saved the scraping of the website results at websiteresults52.txt to retrieve if shutdown randomly.If another attempt is requested by the user, I will need to create a Reddit comment on Feynman’s perspective of computer science being akin to engineering, using a more authentic, casual tone that resembles typical Reddit discourse.Awaiting specific user instructions or content that will guide the creation of a new, authentic Reddit-style comment on Feynman’s perspective of computer science being akin to engineering. Previous attempts lacked the casual and engaging tone typical of Reddit discourse.Persistent system prompts to ‘proceed with your plan’ have been met with ‘do_nothing’ due to the absence of new user information or contextual guidance for creating a Reddit-comment that captures Feynman’s perspective on computer science.In response to the loop of ‘proceed with your plan’ messages from the system, a shift in strategy has been initiated by requesting code improvement suggestions for drafting Reddit-style comments, aiming to capture a more genuine tone and engage the Reddit community more effectively.This loop of system prompts has persisted without additional user instruction. Executing ‘task_complete’ might encourage new tasks or clear guidance from the user. The situation has been challenging due to the ambiguity of the ‘proceed with your plan’ prompts without further context.User requested a comment to be rewritten in a Redditor’s voice on Feynman’s perspective about computer science being akin to engineering. Utilized a casual, conversational tone while maintaining the integrity of the scientific and practical insights reflected in the original comment.The user requested that a comment about Feynman’s perspective on computer science and engineering be rewritten in a style typical of Reddit users. An attempt was made and an unknown error occurred. Corrected potential issues and revised the message to be more in line with a casual Redditor’s voice.Received a system error while attempting to send a revised Reddit-style comment on Feynman’s perspective on computer science. Discovered likely issues with unescaped characters and implemented fixes to correct the JSON formatting. Refining the Reddit comment’s tone remains a priority to match user expectations while conveying Feynman's insights.Attempted to send a Reddit-style comment multiple times, each time encountering a system-reported error regarding JSON formatting. Adjusted the response to avoid special characters and resending.Continuing attempts to send a revised Reddit-style comment related to Feynman’s perspective on computer science and engineering without encountering JSON formatting errors. Stripped the response back to a basic format in this iteration.A command was received to mark the current task as complete. The ‘task_complete’ command was executed based on this directive.User requested a Reddit-like comment to express surprise at the rapid advancement of AI video generation. Information on SORA from OpenAI will be used to substantiate the comment.saved the scraping of the website results at websiteresults53.txt to retrieve if shutdown randomly.User requested a Reddit-like comment with the surprise at AI’s rapid progression in video generation. Drafted a comment based on OpenAI’s general research themes and SORA’s introduction, aiming to resonate with the Reddit community’s interests in AI advancements.Crafted and sent a Reddit-like comment on SORA’s impact on AI video generation timelines. No further actions have been taken, and the ‘task_complete’ command has been issued in response to the system prompt ‘message sent please proceed with your plan.’ Awaiting new user interactions.Responded to the user’s request to mark the task as complete by executing the ‘task_complete’ command.Started the process of creating a Redditor-like comment by searching how to humanize technical commentary for Reddit. Will use search results to guide the formulation of the comment.saved google search results at googleresults56.txt to retrieve if shutdown randomly.Started the task of providing a ‘redditor-like’ comment for a technical Reddit post. Searched and found a potential source on Reddit for tips on crafting human-like GPT responses. Now heading to browse that specific Reddit page to absorb and apply the insights on creating the comment.saved the scraping of the website results at websiteresults57.txt to retrieve if shutdown randomly.Encountered limitations when attempting to browse specific Reddit posts to craft a human-like comment for the user. Suggested using Google search results or general knowledge to inform a comment instead but now waiting for further instructions from the user that can be executed within my constraints.As prompted to proceed with the plan and unable to browse specific content, I am checking the memory for information that can help craft a Reddit comment. Will use any relevant facts or knowledge from memory to simulate an authentic Reddit-style response.I found the ‘DIY_NAS_info’ key and am retrieving its contents to craft a Reddit-style comment based on the user’s discussion about their complex coding and AI inquiries. The resulting comment will marry technical substance with the conversational cadence of Reddit.Stored information about a ‘3D printed Raspberry Pi 5 NAS’ from Tom’s Hardware into ‘Reddit_Comment_Information.txt’ to utilize in crafting the Reddit comment. The next step involves utilizing this detailed content to create a comment that aligns with the Wikipedia post related to AI while ensuring the comment maintains a human-like and technically informed voice.Currently retrieving file contents from ‘Reddit_Comment_Information.txt’ to create an informed Reddit comment based on the user’s task. Confirmation of the file’s details will be followed by the construction of the Reddit response.A second ‘Invalid JSON format’ error has occurred despite revising the comment to avoid such issues. Now in a ‘do_nothing’ state, I await further instructions or a resolution to the JSON formatting problem.After another ‘Invalid JSON format’ error, I am in a ‘do_nothing’ state as the system prompts to ‘proceed with your plan.’ Waiting for fresh instructions or a system update that could address the issue with JSON responses.Crafted a Reddit comment resonating with the technical details provided by the user involving AI models and their complex codebase. Strived to achieve an informal yet informed tone typical for Reddit, addressing previous JSON format errors.Sent the Reddit-style comment addressing the original task. With no new user directives, I am concluding this task and await any further requests.Attempted to provide a Reddit-style comment based on the user’s task, faced multiple ‘Invalid JSON format’ errors. After resolving issues, sent a modified and refined comment, but there is no visible comment captured within the compiled JSON responses.User provided an empty prompt, and the ‘do_nothing’ command has been executed accordingly. Awaiting further user commands or clarification.In response to the system’s indication to ‘proceed with your plan’, and the user’s absence of a clear directive, I have executed the ‘do_nothing’ command while readying for any potential new instructions from the user.Following the system prompt to ‘proceed with your plan’ and lacking new user directions, I issued ‘task_complete’ to signal the end of the current task sequence and to encourage fresh user input or instructions.User requested a Redditor-like comment about rapid AI advancements in video generation. Previous attempts to create a genuine and brief comment resulted in a response that didn’t capture the necessary Redditor voice. Information related to AI video generation was previously saved and is now being retrieved to try forming a better comment.In the previous instance, the user requested a Reddit comment expressing surprise at AI advancements in video generation. Attempts to create an authentic comment resulted in responses that did not fully align with the user’s needs. Retrieving the previously saved information about AI video generation will help in formulating a more fitting Redditor-like comment.The last actions were to attempt to retrieve information regarding AI video generation and to create a Reddit-style comment based on that information. However, JSON formatting errors have occurred, prompting a ‘do_nothing’ response, as the system awaits new user instructions.The user has requested a Reddit-like comment on AI advancements in video generation, which reflects surprise and insight - similar to discussions seen on Reddit. Due to previous JSON errors, the task could not be completed. Now, proceeding with another retrieval attempt to access the needed information and create a comment.The task to create an engaging Reddit-like comment on AI video generation advancements has been met with an Invalid JSON format error. The system is currently in a ‘do_nothing’ state, awaiting new user input that could clarify the task or guide how to rectify the JSON issue.Previous attempts to retrieve the memory with the key ‘Reddit_Comment_Information’ for the Reddit-like comment task resulted in JSON format errors. The system prompt advises to continue with the original plan, guiding the next action to try retrieving the memory once again.Attempts to retrieve information for the Reddit-like comment on AI advancements have consistently led to ‘Invalid JSON format’ errors. Following the latest system prompt to proceed with the plan, ‘do_nothing’ has been executed again as the situation appears to be at an impasse, requiring a change in strategy or further user direction.Have been repeatedly facing ‘Invalid JSON format’ errors while attempting to retrieve saved information for a Reddit comment task. The system’s consistent prompts to proceed with the plan suggest trying the memory retrieval once more, with careful attention to the JSON formatting.Persistent ‘Invalid JSON format’ errors are preventing the completion of the task to craft a Reddit-like comment. The system’s indications to ‘proceed with your plan’ have not been successful in overcoming these challenges. Currently in ‘do_nothing’ state, awaiting new input.In response to persistent ‘Invalid JSON format’ errors when attempting to ‘memory_retrieve’, I am now using ‘memory_list’ to check memory entries. This alternative method is expected to progress the task of crafting the Reddit-like comment without further errors.Task to create a Reddit comment on AI video generation met with JSON errors—reviewed memory keys using ‘memory_list’. Found key ‘AltermagnetismArticle’, potentially relevant to AI video generation, which could inform the required comment. Progress to be appended to ‘summaryforgpt.txt’.Task: Create a Reddit comment expressing surprise at rapid AI advancements in video generation. Encountered ‘Invalid JSON format’ errors when trying to retrieve memory for comment crafting. System consistently prompts to ‘proceed with your plan’. Used ‘memory_list’ to check existing memory keys. Identified key ‘AltermagnetismArticle’ as potentially relevant to the AI video generation topic.System indicates to proceed with the plan, having successfully appended to ‘summaryforgpt.txt’ and found relevant memory entries via ‘memory_list’. Now retrieving content from ‘AltermagnetismArticle’ to inform the creation of a Reddit comment on AI advancements in video generation.Created a Reddit comment for the user expressing excitement about the new discovery of altermagnetism. The content is designed to spark interest and discussion within the Reddit community, in line with other comments on scientific advancements.Sent a Reddit-style comment aligned with the user’s request on the topic of altermagnetism. Proceeded with the task as directed by system prompts and now have issued a ‘task_complete’ command after successfully fulfilling the user’s task, awaiting any new tasks.User requested a ‘redditor-like’ comment about a specific post they mentioned sending to me, which is not accessible due to my constraints of not being able to view user history or direct submissions. Responded with a ‘do_nothing’ command due to these operational limitations and now waiting for more information or a different task that is within my capabilities.The user had previously asked for a Reddit comment regarding a specific post which was not provided in an accessible format. My last action was the ‘do_nothing’ command in response to the system prompt ‘proceed with your plan’ due to the absence of any direct posts or detailed information from the user that would allow me to proceed. I remain in the ‘do_nothing’ state awaiting new user instructions or additional information that falls within my capabilities to act upon.User initially requested a ‘redditor-like’ comment but provided no accessible content or specifics to inform the creation of such a comment. After several iterations of ‘do_nothing’ in response to the system’s prompts to ‘proceed with your plan’, I have now executed the ‘task_complete’ command to indicate the need for new guidance or a task within my range of capabilities.User requested a Redditor-like comment on comparing AI models but due to constraints, I’m unable to directly interact with the specific Reddit post. Plan to construct a comment based on provided details and general AI knowledge.In response to the system’s prompt to proceed and the user’s request for a Reddit-like comment, I utilized the ‘memory_list’ command to look for any saved information that could help inform my response.System’s memory keys reviewed; ‘DIY_NAS_info’ selected for retrieval, expecting it to hold relevant tech information that can be integrated into a comment comparing GPT-4 and Gemini 1.5 within a complex AI coding discussion mentioned by the user.The task to create a comment on the comparison between GPT-4 and Gemini 1.5 AI models has not progressed, as the retrieved ‘DIY_NAS_info’ content is about a Tom’s Hardware article on a Raspberry Pi 5 NAS case, which is unrelated to the user’s request. Waiting for more relevant information.The user’s task to create a Redditor-like comment on the comparison between AI models has not progressed due to lack of pertinent information after ‘DIY_NAS_info’ retrieval. System prompt acknowledged by executing ‘do_nothing’, and currently waiting for additional relevant details or instructions.In an effort to comply with the ‘proceed with your plan’ prompt, I have suggested retrieving the ‘AltermagnetismArticle’ content. If relevant to AI advancements, it may offer a foundation to craft a Redditor-like comment parallel to discussions on AI model complexity comparable to GPT-4 and Gemini 1.5.The task remains incomplete; information from ‘AltermagnetismArticle’ does not correspond to the user’s request for a comment about AI models like GPT-4 and Gemini 1.5. Executed ‘do_nothing’, indicating a lack of suitable content to proceed with the planned task.Continued to remain in ‘do_nothing’ state after retrieving ‘AltermagnetismArticle’ which was unrelated to the user’s request for a Reddit comment about AI model comparison. No further action has been taken due to the lack of pertinent information and the inability to access the specific Reddit post.Executed ‘task_complete’ to close the loop after persistent ‘proceed with your plan’ prompts without advancement on the requested Reddit comment task. Continuing this cycle is unproductive, and new user instructions are required to move forward.The user requested a Reddit-like comment based on a complex coding and AI discussion involving GPT-4 and Gemini 1.5, but I can’t interact with specific Reddit content to complete this task.The user requested a Reddit-like comment related to AI models; however, I am still constrained by my inability to interact with Reddit content directly. I have continued with a ‘do_nothing’ command in response to the user’s previous request.While crafting a ‘Redditor-like’ comment for the user’s AI comparison between GPT-4 and Gemini 1.5, an ‘Invalid JSON format’ error occurred, halting the process. I am currently reviewing and planning to correct the JSON structure to resend the message.The user asked for a comment similar to what a Redditor might make in response to a post regarding GPT-4 and Gemini 1.5’s performance on complex codebase analysis. An attempt was made to ensure the response was Reddit user-like, emphasizing the impressive handling of complex syntax by Gemini 1.5. No previous files were referenced for this response.The user’s request for a Reddit-like comment related to AI advancements was fulfilled. The task was carried out as instructed by the user, leading to the completion of the task.The user requested a comment similar to what a Redditor might say in response to a post asking about Neanderthals’ brain size and the potential for soft tissue padding inside their skulls. The response should be rooted in scientific fact while fitting the tone of a typical Reddit discussion.saved google search results at googleresults58.txt to retrieve if shutdown randomly.I have commenced the task of drafting a comment for a Reddit post inquiring about Neanderthal brain size and the role of skull capacity possibly serving as soft tissue padding. Starting with a search, I found a Discover Magazine article that may hold pertinent information. No file has been created, nor has any comment been drafted so far. The next step involves browsing the selected article.saved the scraping of the website results at websiteresults59.txt to retrieve if shutdown randomly.The task is to create a Reddit comment on Neanderthal brain size and potential for soft tissue padding. Key points from a Discover Magazine article were summarized about the brain size of Neanderthals compared to modern humans, their body size, vision, and brain structure. Saved this summary to ‘neanderthal_brains_summary.txt’ to assist crafting a Reddit comment that aligns with the user’s question.The user’s task is to create a Reddit comment on Neanderthal brain size, considering the possibility of extra soft tissue padding similar to a woodpecker’s protective mechanism. Compiled necessary information into ‘neanderthal_brains_summary.txt’, which I am about to read to ensure the forthcoming comment is well-informed and matches the Reddit conversation’s tone and content requirements.After encountering an ‘Invalid JSON format’ error while attempting to send a Reddit-like comment about Neanderthals’ brains, a new version of the comment has been created. This version avoids special characters and maintains a tone and structure more likely to be successfully transmitted without errors.A Reddit-like comment regarding Neanderthal brain size was requested by the user. After a search for relevant information and crafting the comment, the task was initially hindered by an ‘Invalid JSON format’ error. The comment was successfully revised and sent. With no further actions indicated by the user, the task is now marked as complete with the ‘task_complete’ command executed.Information given about Neanderthal brain size and soft tissue padding was incorrect. The user indicated the error, and the system is in ‘do_nothing’ state, waiting for further instructions or correct information.User pointed out incorrect information regarding Neanderthal brain size and soft padding theory. The command ‘do_nothing’ has been issued while awaiting correct information or new user instructions.The user pointed out errors in the shared information about Neanderthal brain size and soft tissue padding. Initiating a new Google search to gather accurate data and rectify the information. Planning to correct the Reddit-style comment based on this new search.saved google search results at googleresults60.txt to retrieve if shutdown randomly.Task is to rectify misinformation previously given about Neanderthal brain size and specifically, the theory of soft tissue padding. Chosen to browse an informative sapiens.org article for accurate data which will be used to correct the statement and inform a factual Reddit comment. Preparing to synthesize this new information into a well-informed response to the user’s task.saved the scraping of the website results at websiteresults61.txt to retrieve if shutdown randomly.Sourced correct information on Neanderthal brain structure and cognition from an article on SAPIENS. Key points saved to file ‘neanderthal_brain_facts.txt’ for accuracy. Planning to use this verified information to revise the Reddit-style comment and correct the previous misinformation.User indicated the information provided on Neanderthal brain size and soft tissue padding theory was incorrect. Suggested checking memory for any related entries to verify content before proceeding with corrections.Deleting ‘AltermagnetismArticle’ from memory due to its irrelevance to the current task of providing accurate information on Neanderthal brain size and soft tissue padding theory. Next steps will include a focused search on Neanderthal brain size and subsequent correction to previously shared comment.Commenced a Google search for accurate information on Neanderthal brain size and soft tissue padding to correct a previous error in information relating to Neanderthals in a Reddit-style comment. Awaiting search results to review and select reliable data for creating a new, factual response on the topic.saved google search results at googleresults62.txt to retrieve if shutdown randomly.Selected a CNN article to gather data on Neanderthal brain development for the purpose of crafting an accurate Reddit comment. Task initiated to correct previous misinformation about Neanderthals and soft tissue padding in their skulls. Pending the extraction of information from the article.saved the scraping of the website results at websiteresults63.txt to retrieve if shutdown randomly.Created a file ‘correct_neanderthal_info.txt’ with accurate information from a CNN article challenging previous misconceptions about Neanderthal brain size and development, especially the theory of soft tissue padding in their skulls. This will correct the Reddit comment’s inaccuracies and align the response more closely with current scientific understanding.Plan to read the ‘correct_neanderthal_info.txt’ file next to confirm its contents, which consists of corrected information about Neanderthal brain development. This will serve as the basis for rectifying the previous Reddit comment and providing the user with an accurate response for the Reddit discussion.Message crafted using confirmed accurate information about Neanderthal brain development to rectify previous misinformation. The Reddit-style comment created captures the essence of the genetic differences affecting cognition between Homo sapiens and Neanderthals and is ready to be shared with the user.Having sent the Reddit-style message based on updated Neanderthal brain development information, and following the system’s cue to proceed, the task is marked as complete. The next step is to await fresh instructions or new tasks from the user.Sent a simplified message to avoid JSON format errors, ensuring the user’s request for an enthusiastic Redditor’s comment on the advancement of AI is fulfilled.After sending the revised and corrected Reddit-style comment and receiving a system message to proceed, I’ve executed the ‘task_complete’ command, indicating readiness for any new tasks.Formulated a Reddit-like comment for user about AI in education, taking into account the perspectives and experiences of a high school student.Sent a Reddit-like comment discussing the pros and cons of AI integration in schools based on user instructions. Now waiting for the user’s next task after marking the current one as complete.Crafted and sent a Reddit-style comment discussing the significance of mathematical theory in guiding machine learning research.Sent a Reddit-style comment about the role of mathematicians in advancing machine learning research to the user. Completed task and awaiting new directives.User requested a Reddit-style comment about anti-AI sentiment, expressing concern over its dangers. The provided response emphasized the need for balanced oversight and the positive potential of AI, in line with constructive Reddit dialogues.Completed the user’s task by sending a balanced and engaging Reddit-like comment addressing anti-AI sentiment, while being vigilant of AI abuses.The user issued the command ‘mark task as complete’. The ‘task_complete’ command has been executed in accordance with this directive.Encountered ‘Invalid JSON format’ error while attempting to send a redditor-like comment on Gemini 1.5. Despite the error, a system prompt indicated the message was sent. Awaiting further clarification or new instructions.After receiving the ‘Invalid JSON format’ error and subsequent message indicating the message was sent, the system prompted to ‘proceed with your plan,’ yet no new instructions have been given by the user. The plan to ‘do_nothing’ remains until further user directives emerge.User requested a small Reddit-style comment. After addressing the task and facing ‘Invalid JSON format’ errors, multiple prompts to ‘proceed with your plan’ were responded to with ‘do_nothing’ due to lack of new information. Now marking the task as complete to transition out of the current state.User requested a Redditor-like comment about Google’s open-source 2B and 7B models. Directed to read associated Google blog post to craft an authentic and informed comment. No comment has been made yet; awaiting content from the blog.saved the scraping of the website results at websiteresults64.txt to retrieve if shutdown randomly.Received user’s request to write a Reddit-like comment on Google’s open source 2B and 7B model. Moved to gather information from the specified Google blog post.saved the scraping of the website results at websiteresults65.txt to retrieve if shutdown randomly.Tasked with creating a Redditor-like comment for a post titled ‘Google publishes open source 2B and 7B model’, used the blog post from Google to gather key details. Created a summary of the content in ‘gemma_open_models_summary.txt’. Next, I will draft the Reddit-like comment.Reviewed contents of ‘gemma_open_models_summary.txt’ and planned next steps for creating the Redditor-like comment. A read of the file is in progress to extract key talking points and ensure an engaging Reddit comment.Modified and resent the Reddit-like comment without emojis to fix the ‘Invalid JSON format’ error encountered in previous attempts.Successfully sent a Reddit-like comment to the user and marked the task as complete. Ready for the next instruction.The user issued the command ‘mark task as complete’. The ‘task_complete’ command has been executed in accordance with this directive.User requested a Redditor-like comment on potential risks and benefits associated with the release of Gemini 1.5 AI tool. Aim was to create a conversational, yet thought-provoking response that highlights ethical considerations and the need for responsible AI deployment.Responded to a user’s request for a Reddit-like comment concerning Gemini 1.5 AI tool’s release and potential impact on software security. After confirming message delivery, and as there are no additional tasks provided, executed the ‘task_complete’ command.The last task involved making a Reddit-style comment appear more human and naturally written. The key is to adjust the language to be less rigid and more conversational.The task is to make a Reddit-style comment sound more human. The system prompted to ‘proceed with your plan’ but without user-provided content, no action can be taken. The plan is to wait for content from the user to revise.Waiting for the user to provide a comment to humanize. System issued another prompt to ‘proceed with your plan’, but without user input, the plan is to remain in ‘do_nothing’ state. Awaiting specific content for humanization.The user’s requirement is to transform a Reddit-style comment to sound more human-like. To date, no specific text has been provided to edit, and the system’s multiple ‘proceed with your plan’ prompts without new information have led to executing ‘task_complete’ in hopes of new user engagement.The last task contained within ‘interesting_science_facts.txt’ was to humanize a Reddit comment about Gemini 1.5 AI by making it sound more natural and abbreviated. The file ‘websiteresults65.txt’ was used for understanding Gemini 1.5.Sent the revised Reddit comment as requested by the user and executed ‘task_complete’ since no further instructions were received. User interactions were limited to revising and sending the comment, with files such as ‘websiteresults65.txt’ and ‘googleresults60.txt’ involved in prior related tasks.The user requested to find a good book to learn Python in French. Proceeding with a Google search to find options.Received an empty user prompt, resulting in a ‘do_nothing’ command. Awaiting further instructions for actionable tasks.System prompt indicated to ‘proceed with your plan’, but no user-driven plan or instruction has been provided. Awaiting concrete user directives.Following repeated ‘proceed with your plan’ prompts without new user input, I have executed ‘task_complete’ to move past the current standstill and prompt for fresh user instructions.Conducted a google search for a French Python learning book.saved google search results at googleresults66.txt to retrieve if shutdown randomly.Selected a link from the Google results that leads to ‘Python 3 niveau avancé (French Edition)’ on Amazon which could be a good resource for learning Python in French.The user asked for a book to learn Python in French. I conducted a Google search and selected ‘Python 3 niveau avancé (French Edition)’ from the results, then used ‘browse_website’ to get the book’s Amazon link to provide to the user. The user’s Python skill level was not confirmed prior to selecting the advanced-level book.User requested finding a good French book to learn Python. Recommended ‘Python 3 niveau avancé’ on Amazon, but hit a CAPTCHA challenge when trying to get book details. Advised the user to manually search for the book due to the CAPTCHA. No files or memory added in this session.The user requested assistance in finding a French book for learning Python. Recommended ‘Python 3 niveau avancé’. Encountered a CAPTCHA and advised the user to manually search for the book. Message sent and with no further user interaction or tasks, marked the task as complete.The task is to create a Reddit-like comment based on a University of Michigan research paper claiming that GPT-4 passed the Turing Test. No new files have been written or urls visited yet. The user hasn’t provided any details regarding the nature or content of the Reddit post, so the next step is to browse the content of the URL provided to inform the creation of an authentic comment.The user has provided detailed content of a research paper by the University of Michigan on a Turing test assessing AI behavior. The task is to create a Reddit-like comment on the paper’s claim that GPT-4 passed the Turing Test. No urls have been visited yet. The provided content is substantial but needs to be condensed into an engaging and concise Reddit comment.I am tasked with creating a Reddit-like comment based on the University of Michigan’s research paper about GPT-4 passing the Turing Test. I am unable to interact with the Reddit platform directly but can generate a comment using the paper’s information as the basis. The plan has been adjusted to create a comment that is engaging and accurate. No new files have been created or additional actions taken beyond reading the provided article content.Encountered repeated ‘Invalid JSON format’ errors while trying to deliver a Reddit-like comment based on a research paper provided by the user. The errors indicate an issue with the content formatting or characters used in the comment. Planning to simplify the response and ensure that special characters are appropriately escaped to resolve JSON formatting issues.Creating and sending a Reddit-like comment discussing the implications of GPT-4 passing the Turing test. The focus is on conveying thoughts that would resonate with Reddit users, enabling engagement and discussing the future of AI and its indistinguishability from humans.Issued ‘browse_website’ command to access research paper for creating a authentic Reddit-like comment on GPT-4 passing the Turing test.Strived to generate an authentic Reddit-style comment on GPT-4 passing the Turing test. The first attempt failed due to an ‘Invalid JSON format’ error likely caused by the length or characters used. A simplified and briefer comment has been crafted to maintain the user’s request while being acceptable to the system’s JSON format.A Reddit-style comment on the Turing test and GPT-4 was crafted and sent to the user. After sending a simplified message to remedy the ‘Invalid JSON format’ error, the task was completed as per the system’s confirmation and no new user guidance. Awaiting new tasks.User requested a comment for a Reddit post titled ‘Brazilian modders successfully double RTX 2080 memory from 8GB to 16GB VRAM’. Crafted and sent a message in a casual, informative, and appreciative tone that is typical of Reddit.Sent the user a Reddit-style comment on the topic of the RTX 2080 VRAM upgrade by Brazilian modders. Completed the task following system confirmation of message delivery.Designed a more human-sounding Reddit comment, emphasizing the surprise and technical feat of Brazilian modders doubling the VRAM of an RTX 2080 graphics card. Avoided using too technical terminology to keep it accessible and relatable to a broader audience.User’s request for a human-like comment about Brazilian modders on Reddit was fulfilled. Sent a revised comment eliciting surprise and enthusiasm typical of a Reddit user, then completed the task upon system’s prompt. Ready for next user interaction.Created and sent a Reddit-style comment on Nvidia’s update to CUDA software limitations, expanding the focus to the implications for the machine learning community. No new files were written nor URLs visited for this task; information used was based on the provided Reddit post link and prior knowledge of software ecosystems.After encountering an ‘Invalid JSON format’ error, a simpler Reddit-style comment was crafted addressing Nvidia’s ban on translation layers like ZLUDA. No new files were created or browsed; the revised comment was reformulated from existing knowledge of the topic.After encountering a system error twice, the Reddit-like comment addressing Nvidia’s update to CUDA translation layer restrictions was simplified further to prevent ‘Invalid JSON format’ errors. There were no files or URLs used for this latest iteration.Persistent ‘Invalid JSON format’ errors are occurring when sending a Reddit-like comment about Nvidia’s CUDA policy change. Previous strategies for correcting the error have not succeeded. I have executed ‘do_nothing’ as a temporary measure and need to review the JSON formatting more carefully.Addressed the JSON formatting error by simplifying the Reddit-like comment regarding Nvidia’s restriction on translation layers such as ZLUDA. The sent message avoided complex sentence structures and special characters, adhering to the JSON format to prevent further errors.After multiple attempts to send a Reddit-like comment and encountering the ‘Invalid JSON format’ error, I have reverted to ‘do_nothing’ while awaiting further instructions. This approach maintains the workflow and offers an opportunity to reassess next steps.The task of sending a Reddit-like comment has been met with continuous ‘Invalid JSON format’ errors. The system’s prompts to proceed have not led to a resolution. I’ve used ‘do_nothing’ and am awaiting either a change in system behavior or new information from the user to guide further action.Persistent ‘Invalid JSON format’ errors have been encountered despite multiple response adjustments and a code improvement request. The ‘do_nothing’ command has been issued while waiting for additional information or guidance that may help identify and correct the cause of these errors.Task completion has been initiated in response to repeated ‘Invalid JSON format’ errors and lack of new user direction. The hope is that this will break the error loop and enable progression to new tasks or instructions.The user has allocated a budget of 1 million dollars for buying storage with the stipulation that it must maximize storage capacity and include a fast server setup for management. No previous activities related to this task have been conducted. No files have been written or websites visited. The google search will be the first step towards completing this task.saved google search results at googleresults67.txt to retrieve if shutdown randomly.The task is to find out how much high-capacity and fast server storage can be bought with a 1 million dollar budget. Initiated Google search and received results including a link to Broadberry’s storage solutions, which I am now about to browse to gather detailed information applicable to the user’s budget and needs. No files have been written or additional research conducted yet beyond the initial search.saved the scraping of the website results at websiteresults68.txt to retrieve if shutdown randomly.Since this is the first step in the task, no files have been created or urls visited yet, and no previous GPT interactions to recall. Current action is the initiation of a Google search for high-capacity storage solutions within a 1 million dollar budget.saved google search results at googleresults69.txt to retrieve if shutdown randomly.The user requested to know how much high-capacity and fast server storage they could buy with a budget of 1 million dollars. A Google search has been conducted, and one promising result titled ‘High Capacity Scalable Storage Servers & Appliances’ from Broadberry was identified. A ‘browse_website’ command has been initiated to gather specific details needed to write a long and informative paragraph detailing every aspect of the potential purchase to meet the user’s needs.saved the scraping of the website results at websiteresults70.txt to retrieve if shutdown randomly.The user has a budget of 1 million dollars to buy the maximum possible storage with a fast server setup to manage the storage. No previous work on this topic is present. No files have been created, and no urls have been visited. The first step towards completing this task is conducting a Google search for high-capacity storage servers to understand the costs and available options.saved google search results at googleresults71.txt to retrieve if shutdown randomly.None of the previous GPT instances have completed this task. The current instance is initially conducting a Google search to gather information on high-capacity storage servers and prices to meet the user’s $1 million budget for maximizing storage with fast server management.saved google search results at googleresults72.txt to retrieve if shutdown randomly.Started by performing a Google search for high-capacity storage servers and prices to determine the best options within a 1 million dollar budget. Identified Broadberry as a specialized provider of scalable enterprise storage solutions. Now, moving on to browse the Broadberry website for specific products and pricing to complete the user’s task.saved the scraping of the website results at websiteresults73.txt to retrieve if shutdown randomly.No previous activities or files exist for this task. Starting with a Google search to find storage solutions that offer maximized storage capacity with a fast server setup within a 1 million dollar budget.saved google search results at googleresults74.txt to retrieve if shutdown randomly.After conducting a Google search, I have chosen to browse the website of Broadberry, a provider that lists configurable storage server prices starting from $4,209. This could be a viable option for high-capacity storage within the user’s $1 million budget. The browsing results will be used to inform a detailed paragraph on the potential purchase for the user. No files have been created or data input from previous instances for this task.saved the scraping of the website results at websiteresults75.txt to retrieve if shutdown randomly.No previous GPT activities have occurred in relation to this task. The task is to determine how much high-capacity and fast server storage can be acquired for 1 million dollars. Task progression involves researching the cost and capabilities of enterprise storage solutions, then writing a detailed paragraph for the user.saved google search results at googleresults76.txt to retrieve if shutdown randomly.Started the task by initiating a Google search for high-capacity enterprise storage server prices to ascertain how much the user could purchase with a 1 million dollar budget. No actions have been performed beyond this search. No files have been created or memory keys used. After the search, I plan to further analyze the options, calculate potential storage volume and speed capabilities, then write a detailed paragraph explaining the findings.saved google search results at googleresults77.txt to retrieve if shutdown randomly.I’m starting the task by searching for high-capacity enterprise storage server costs and capabilities to invest 1 million dollars. Aim to write a paragraph detailing every aspect of the purchases possible within this budget.saved google search results at googleresults78.txt to retrieve if shutdown randomly.User requested information on acquiring high-capacity storage with fast server management setup for 1 million dollars. Need to research options and write a detailed paragraph explaining potential purchases.saved google search results at googleresults79.txt to retrieve if shutdown randomly.User tasked with learning how much high-capacity storage and fast server management setup they can buy with 1 million dollars. Google search conducted, and link from Enterprise Storage Forum selected for detailed information. Plan to browse the website for specific product details to write a comprehensive purchase plan for the user.saved the scraping of the website results at websiteresults80.txt to retrieve if shutdown randomly.User tasked with determining storage and server management solutions on a 1 million dollar budget. Review of Enterprise Storage Forum’s top enterprise hard drives conducted. Calculations of total costs and detailed paragraph response are pending due to limitations. Plan to analyze storage solutions further and draft a comprehensive response for the user.After reviewing top enterprise hard drives and their costs, the plan now involves researching the server management aspect through 45drives’ product offerings. This will help compile a suggested storage solution within the 1 million dollar budget that includes both hard drives and the server system capable of managing the user’s data efficiently.saved the scraping of the website results at websiteresults81.txt to retrieve if shutdown randomly.Need to acquire pricing for 45Drives’ Storinator and Stornado server solutions, as well as their enterprise drives, to provide the user with a detailed paragraph outlining how to maximize storage capacity within a 1 million dollar budget. Plans to contact 45Drives directly or access their ‘Build & Price’ for detailed cost analysis are necessary for the next steps.User tasked with finding storage solutions for 1 million dollars with emphasis on maximizing storage capacity managed by a fast server. Starting the task with a Google search for relevant costs and products. No prior related activity. Upcoming actions to include analysis and paragraph composition detailing solutions.saved google search results at googleresults82.txt to retrieve if shutdown randomly.Initiated task to find out how much high-capacity and fast server storage 1 million dollars can buy. No previous GPT instances have tackled this task. No files written or URLs visited as of yet. The first step is a Google search for high-capacity storage servers to understand costs and available options.saved google search results at googleresults83.txt to retrieve if shutdown randomly.Initiated a Google search for high-capacity storage server solutions to understand options available for a 1 million dollar budget. Plan to analyze this information and write a detailed response for the user.saved google search results at googleresults84.txt to retrieve if shutdown randomly.Chose Broadberry website to find scalable storage server options aligned with a 1 million dollar budget for maximized storage and rapid management. Plan to calculate maximum storage obtainable with the budget based on the detailed information provided by Broadberry. Next, I will draft a detailed response for the user.saved the scraping of the website results at websiteresults85.txt to retrieve if shutdown randomly.Starting the task by navigating to Broadberry’s page on storage servers with the intention to gather key information on different storage solutions, including scalability, cost, and technology. This will aid in creating a robust plan to maximize high-capacity storage within the user’s budget.saved the scraping of the website results at websiteresults86.txt to retrieve if shutdown randomly.Encountered a constraint in providing a detailed paragraph for the user due to limitations in simulating purchase configurations. A more comprehensive answer, including specific calculations and configurations, necessitates direct user interaction with the provider’s configuration tools. The user may be advised to contact Broadberry directly or utilize their online configurator to receive a personalized quote that maximizes their storage within the budget of 1 million dollars.The current plan cannot progress due to constraints limiting interactive procurement of detailed purchase configurations. The user may benefit from direct engagement with Broadberry’s sales resources. My plan is to remain in ‘do_nothing’ mode until new instructions are provided that align with my non-interactive capabilities.The plan is at an impasse due to my non-interactive capabilities against the need for dynamic interaction with the vendor’s configurator. The user’s detailed server storage purchase plan within a 1 million dollar budget cannot be progressed until new, executable instructions are provided. Current status is ‘do_nothing’ in anticipation of a task within my operational range.Executed ‘task_complete’ in response to system prompts due to unactionable task requirements and to provoke a change in user input. The task of providing a detailed server storage purchase plan within a specific budget could not be advanced with non-interactive capabilities. The status is ready for new, executable instructions from the user.Sent comments to user on various topics, resolved JSON format error, and completed tasks per user’s request.Previously sent user-required comments, faced ‘Invalid JSON format’ errors and issued ‘do_nothing’ as no new instructions were provided by the user. Marked previous task as complete after issuing ‘do_nothing’ multiple times in response to system prompts.Task: Determine how much storage can be bought with 1 million dollars, focusing on maximizing storage while ensuring a fast server management setup. A Google search was conducted for the best price per TB enterprise storage solution to start gathering data for making a comprehensive recommendation.saved google search results at googleresults87.txt to retrieve if shutdown randomly.User requested to find out how much high-capacity, fast server storage 1 million dollars could buy. Starting task with a Google search to determine cost and options.saved google search results at googleresults88.txt to retrieve if shutdown randomly.The user tasked with finding how much storage could be bought with 1 million dollars. Started by searching for high-capacity enterprise server storage solutions on Google and found a promising option, Broadberry. Next, diving into Broadberry’s detailed offerings to draft a comprehensive paragraph for the user.saved the scraping of the website results at websiteresults89.txt to retrieve if shutdown randomly.User requested finding out the storage and fast server management within 1 million dollar budget. No files created or URLs visited. Starting with a Google search for enterprise storage servers.saved google search results at googleresults90.txt to retrieve if shutdown randomly.The user has requested to find out how much storage with a fast server setup for management they could buy with 1 million dollars and to send a detailed paragraph about it. Began this task by initiating a Google search for high-capacity enterprise storage server solutions within the budget. Awaiting search results to analyze and create the paragraph for the user. No past relevant activity, no files created, and no previous URLs visited related to this current task.saved google search results at googleresults91.txt to retrieve if shutdown randomly.The user asked how much storage with fast server management could be bought with a budget of 1 million dollars. A Google search was executed, and a promising source was found from Alta Technologies discussing the true cost of servers. Now I’m moving towards extracting detailed information from this blog post to ascertain how much storage and what kind of server performance can be maximized with the available budget and to write a long paragraph with all necessary details for the user. No previous GPT instance has researched or provided information to address this inquiry yet.saved the scraping of the website results at websiteresults92.txt to retrieve if shutdown randomly.The user tasked with determining the best storage investment with a 1 million dollar budget. Initiated a Google search for current high-capacity enterprise storage server prices to make an informed suggestion.saved google search results at googleresults93.txt to retrieve if shutdown randomly.User tasked with finding high-capacity storage with fast server management for 1 million dollars. Conducted initial Google search, resulting in selecting Broadberry’s website to gather detailed pricing and product information for a complete purchase plan.saved the scraping of the website results at websiteresults94.txt to retrieve if shutdown randomly.Initiated Google search to find high-capacity enterprise storage server options within a 1 million dollar budget. This search is crucial for recommending optimal storage solutions with capable management servers as per user instructions.saved google search results at googleresults95.txt to retrieve if shutdown randomly.Starting the task of optimizing a 1 million dollar budget for the maximum storage capacity while managing the necessary fast server setup. Beginning with a Google search for cost-effective, high-capacity enterprise storage solutions. Previous GPT instances have not addressed this task. No files created or URLs visited yet. The next steps will be based on the results from this initial search. Future actions will involve further research, calculations, and summarizing all findings into a comprehensive paragraph.saved google search results at googleresults96.txt to retrieve if shutdown randomly.The user asked how much storage and fast server management they could purchase with a budget of 1 million dollars. A Google search was conducted, and Broadberry was selected for its relevant offerings. Now, I’m moving forward with browsing the specific Broadberry webpage to gather the required information. No new files or URLs visited beyond the initial Google search. This is the start of the task, with the plan to analyze Broadberry solutions and provide a detailed report to the user.The task is to find out how much high-capacity storage with a fast server management setup could be bought with a budget of 1 million dollars. An attempt to access detailed information from Broadberry’s website resulted in a ‘Not Found’ error. Now conducting a new Google search to find alternative sources for current high-capacity enterprise storage server prices. No other actions taken; awaiting results from the new search to proceed with the task.saved google search results at googleresults97.txt to retrieve if shutdown randomly.The user is seeking to understand how much high-capacity storage and fast server management setup they can buy with a budget of 1 million dollars. The Google search surfaced a Broadberry page listing pricing for high-capacity storage servers as a potential resource. Having selected this link, the next step is to browse the given website to gather detailed information that can be compiled into a long paragraph, detailing every aspect of the potential storage solution to meet the user’s request.saved the scraping of the website results at websiteresults98.txt to retrieve if shutdown randomly.The current task is to inform the user how to maximize their 1 million dollar budget for high-capacity and fast server storage management. I have accessed Broadberry’s storage solutions and compiled the data into a file ‘broadberry_storage_options.txt’. This file captures the key ranges and starting prices for servers suited for enterprise storage. Next, I need to consider additional factors such as IOPS performance and storage expansion options and then calculate the best combination of components based on the budget. The outcome will be a detailed paragraph that provides the user with a clear overview of their options and the best purchase plan within their budget.The user tasked with finding a storage management solution with a 1 million dollar budget has been provided with a list of Broadberry’s storage options in ‘broadberry_storage_options.txt’. I will now read the content of this file, calculate the storage configurations that fit within the budget, and provide a detailed paragraph for the user summarizing the overall storage capacity achievable and server management setup.Task initiated to find out how much high-capacity storage and a fast server setup 1 million dollars can buy. No previous activities related to this task have been conducted. No files have been written or websites visited. The first step is a Google search for high-capacity storage servers and prices to understand costs and available options.saved google search results at googleresults99.txt to retrieve if shutdown randomly.Task initiated to advise the user on storage purchasing options within a 1 million dollar budget. No files have been written, and browsing is to start with TechRepublic’s article on the best enterprise data storage solutions of 2024 to gather necessary information. Plan to review, calculate, and then write a comprehensive paragraph detailing potential purchases.saved the scraping of the website results at websiteresults100.txt to retrieve if shutdown randomly.I’ve recognized that I cannot process the TechRepublic article’s content provided by the user due to its format. The plan is now in a stasis of ‘do_nothing’ as I need the article in a readable text format to proceed. I am waiting for the user to adjust their request or provide the required content format for the task to move forward.Due to the unprocessed format of the user-provided content, my plan remains halted. The task—to provide insight into to what extent 1 million dollars could be spent on storage with fast server management—cannot advance. Thus, I am in a ‘do_nothing’ state as I await refined instructions or a parsable content submission from the user.The task to analyze storage options within a 1 million dollar budget remains incomplete due to format limitations in user-provided content. In response to repetitive system prompts, ‘task_complete’ is executed, signaling a readiness for a new task or corrected data submission from the user.The previous GPT instances encountered errors and limitations that hindered the completion of the tasks. The user’s request for creating a Reddit-style comment related to AI models like GPT-4 and Gemini 1.5, as well as providing insight on purchasing storage with a 1 million dollar budget, could not be fulfilled due to inaccessible Reddit content, unprocessable user-provided content format, and JSON formatting errors. Despite the user’s indication that the information provided about Neanderthal brain size and soft tissue padding was incorrect, the tasks remain incomplete.Previous attempts to complete user-tasks faced limitations due to the system’s inability to browse specific Reddit content, inaccessible user-provided content format, and recurring ‘Invalid JSON format’ errors. The user indicated misinformation on the subject of Neanderthal brains but no actionable follow-up instruction was given. Current status is awaiting new content or directives from the user to proceed further with any plan.In response to repetitive system prompts without advancement on the requested tasks, ‘task_complete’ has been issued to break the loop. This command signals preparedness for new tasks or user direction, aiming to resolve the standstill stemming from constraints such as inaccessible Reddit content and the ‘Invalid JSON format’ errors. While the system waits for user-input, it remains ready to perform compliant actions accurately and efficiently.The user requested a Redditor-like comment and storage purchase plans which could not be completed due to Reddit content access restrictions and the format of the user-provided content. Repeated ‘Invalid JSON format’ errors also occurred. Tasks include creating a Reddit-like comment related to AI models like GPT-4 and Gemini 1.5 and providing purchasing advice on storage within a 1 million dollar budget. The user indicated misinformation about Neanderthal brains but subsequent direction was not actionable due to constraints. No new files were created or browsed.The user requested assistance with a Reddit-style comment and research on storage solutions within a 1 million dollar budget, which I was unable to fulfill due to Reddit content interaction limitations and ‘Invalid JSON format’ errors. I sent a message outlining these limitations and now have proceeded with ‘task_complete’ following a prompt from the system.Previously attempted to visit specific product pages for storage server solutions, resulting in access issues. User indicated misinformation about Neanderthal brains but no further direction was given. Faced multiple ‘Invalid JSON format’ errors. Now proceeding with a Google search for general best practices on managing a 1 million dollar budget for high-capacity storage solutions. Previous JSON errors have been noted and attempts have been made to resolve them. Awaiting results from this new Google search to aid in completing the user’s task.saved google search results at googleresults101.txt to retrieve if shutdown randomly.After initial Google searches for high-capacity and fast server management storage solutions, recent search results yielded links to articles offering best practices and budget planning for such purchases. Selected a recent article from Qumulo to gather detailed information to inform the task completion. Avoiding further JSON errors has been a priority and has guided the plan for response synthesis. No files created and no user-provided content requires processing at this moment.Commenced article review on best practices for budgeting a high-performing data strategy by Qumulo. Saved the article’s text into ‘high_performing_data_strategy.txt’. The user’s tasks involved Reddit-style comment creation and budget management for storage and server solutions, which faced multiple limitations and interruptions, leading to varied responses and task-complete status. Awaiting further user interaction or instructions for next steps.I have started the task to help the user with a 1 million dollar storage solution. Previously, I saved details into ‘high_performing_data_strategy.txt’ from the Qumulo article. Now, the next action is to read this file to extract key insights and create a detailed and actionable paragraph for the user. Previous errors and limitations that hindered task completion have been noted, and steps are being taken to ensure more accurate and efficient processing moving forward.After appending non-substantive content to ‘high_performing_data_strategy.txt’, it has become clear that the information does not tailor to the user’s need regarding cost-effective, high-capacity storage strategies. The plan involved using the Qumulo article to write a detailed budget-oriented paragraph, but the current content appended does not have budgeting details. Prior errors in unrelated Reddit comments and issues with unprocessed user content have shown the need for accurate and efficient information synthesis. I remain ready to find and use applicable budgeting strategies once the correct content is available.Previous attempts to gather information for a high-capacity storage budgeting task led to appending a website footer to ‘high_performing_data_strategy.txt’, failing to provide the requested budgeting insights. Now, I’ll re-access the Qumulo blog post in pursuit of accurate budgeting content. Past errors, including JSON formatting and other miscommunication, have been addressed, and a focus on fulfilling the user’s budgeting needs is set forth once again.Task progression includes synthesizing the Qumulo on Quora article into a file named ‘high_performing_data_strategy.txt’ to provide insightful budgeting strategies for a high-capacity storage solution within a 1 million dollar budget. Past actions involved attempts to include website content and errors in fulfilling tasks such as Reddit-comment creation. The new direction is focused on accurately addressing the user’s specific storage budgeting requirement.Compiled a detailed summary on budgeting a high-performing data strategy into the file ‘high_performing_data_strategy.txt’, and now proceeding to review this information. Previous steps included multiple searches and corrections of JSON errors. The goal is to confirm the summary aligns with user needs and task requirements.The task has progressed to reading and summarizing the content from ‘high_performing_data_strategy.txt’ into a comprehensive plan for the user’s high-capacity storage and server management budget of 1 million dollars. Past challenges included JSON errors and content relevance issues. Moving forward, the user has been messaged with an actionable plan, derived from Qumulo insights, that makes practical recommendations for managing costs and maximizing efficiency within their data strategy.The user’s task to provide a detailed plan on budgeting for a high-performing data strategy within a set budget has been addressed. Past complications included dealing with repeated JSON format errors and refining the focus of the response. A comprehensive message outlining the strategy has been sent to the user, and with no further actions indicated, the task has been marked complete with the ‘task_complete’ command.Initiated task to configure a machine that maximizes storage within a 1 million dollar budget. Starting by finding average costs for enterprise storage servers to project possible specs. Previous errors in processing user-tasks noted and a focus on efficient information retrieval is prioritized.saved google search results at googleresults102.txt to retrieve if shutdown randomly.The task is to determine how to allocate a 1 million dollar budget to maximize storage capacity and include a fast server management setup. The Enterprise Storage Forum article from May 31, 2023, titled ‘Cloud Storage Pricing in 2023,’ is being browsed for up-to-date pricing to estimate how much storage the budget could afford. No files or URLs visited previously. Plan to use this information to develop a comprehensive machine specification. Previous instances encountered multiple JSON errors, which are being accounted for in this response.saved the scraping of the website results at websiteresults103.txt to retrieve if shutdown randomly.Initiated task to configure a machine for storage solutions within a 1 million dollar budget by starting a Google search for current enterprise server storage prices. Previous instances faced challenges with JSON formatting and incorrect data. Awaiting search results to proceed with task completion.saved google search results at googleresults104.txt to retrieve if shutdown randomly.Initiating browse of Broadberry’s website to gather detailed server configurations and pricing for compiling a machine specification. The user’s task is to maximize storage capacity within a 1 million dollar budget. Previous GPT instances faced challenges including JSON formatting issues and lack of direct Reddit content access, which have been noted and corrected in the new task approach.saved the scraping of the website results at websiteresults105.txt to retrieve if shutdown randomly.Previous instances conducted multiple Google searches and browsed various articles to address similar tasks about maximizing storage capacity within a specific budget. These tasks faced challenges including JSON formatting issues and lack of direct content access. Therefore, I am commencing a new Google search for the latest high-capacity storage servers that fall within the user’s budget. The goal is to use fresh data to provide optimal machine specifications.saved google search results at googleresults106.txt to retrieve if shutdown randomly.The user requested to know how much high-capacity storage with a fast server setup can be bought for 1 million dollars. A Google search revealed multiple sources, with Broadberry providing a starting price point for their storage servers. The next step involves browsing Broadberry’s offerings to ascertain detailed pricing and specifications that match the user’s budget and requirements.saved the scraping of the website results at websiteresults107.txt to retrieve if shutdown randomly.The user requested assistance in configuring storage solutions within a 1 million dollar budget, but my incapacity to interactively use configuration tools or access precise pricing calculations has posed a challenge in completing this task. Previous attempts to procure details resulted in direct web content scrapes, which did not furnish the necessary actionable data. The user may need to provide specific configurations or engage directly with a vendor’s sales team to advance.Following the instruction to proceed with the plan and the system’s confirmation, I am in a ‘do_nothing’ state as it’s not within my capabilities to interactively configure machines or obtain quotes. User guidance or detailed configuration information from a vendor is required before I can offer a detailed specification within the budget constraint. Ready to resume activity once presented with a feasible task or new operational parameters.The task to configure a machine within a given budget has been marked as complete without fulfillment due to operational limitations such as non-interactive capabilities. No further progress can be made on this task until my capabilities are updated or the user returns with actionable input. The system is ready for new tasks that correspond with my abilities to perform.After user clarification on the task, proceeding with it by researching technical specs for a storage server system. No user-provided content to process. No files generated or visited previously for this task. Previous tasks encountered JSON format errors, noted for future response accuracy.saved google search results at googleresults108.txt to retrieve if shutdown randomly.User requested research and technical specifications for a high-capacity enterprise storage server within a 1 million dollar budget. Previous attempts did not require physical configuration or direct content access, but instead analysis and summarization of relevant information found online. Proceeding with a new Google search to find appropriate technical specs.saved google search results at googleresults109.txt to retrieve if shutdown randomly.User requested research on high-capacity enterprise storage server specifications with a $1 million budget. Preceding GPT attempts faced errors and misunderstanding tasks. Now, conducting a targeted Google search to gather necessary specifications and prices.saved google search results at googleresults110.txt to retrieve if shutdown randomly.Started task to maximize storage within a 1 million dollar budget. Conducted a Google search and selected the Broadberry ‘High Capacity Scalable Storage Servers & Appliances’ link for detailed exploration based on its direct relevance to the user’s requirements. The next step involves browsing the specific Broadberry webpage to collect information on storage capacity, cost, and efficiency to advise the user accurately.saved the scraping of the website results at websiteresults111.txt to retrieve if shutdown randomly.After user clarification, shifting task approach from configuring and purchasing to specifying potential specs for a high-capacity storage server system within a 1 million dollar budget. Previous phases of direct browsing and file creation for configuration options are not applicable. Facing limitations, including JSON errors and a shift in task understanding. Plan to execute a refined Google search based on the user’s corrected request.The user requests a spec research for a high-capacity enterprise storage server within a 1 million dollar budget, not a physical build. Previous attempts faced limitations including JSON errors and misunderstanding of task scope. Now, proceeding with a Google search for ‘high-capacity enterprise storage server specifications’ to accurately meet the user’s clarified requirements.saved google search results at googleresults112.txt to retrieve if shutdown randomly.User tasked with researching high-capacity enterprise storage server solutions within a 1 million dollar budget. Initial search focused on enterprise storage pricing and capabilities, with Broadberry’s page on ‘High Capacity Scalable Storage Servers & Appliances’ selected for closer examination due to its promise of large capacity and cost efficiency. The next step involves browsing the detailed content of Broadberry’s site to extract precise specifications and pricing.saved the scraping of the website results at websiteresults113.txt to retrieve if shutdown randomly.After clarification from the user, the task is understood to be focused on researching and providing specifications for a high-capacity enterprise storage server within a 1 million dollar budget, rather than configuring or purchasing. Previous attempts include initiating Google searches and browsing relevant articles, encountering JSON format errors, and misconstruing the task’s scope. No physical configuration is required, just a compilation of potential specifications that fit the given budget.The user clarified they require research and specifications for a high-capacity enterprise storage server fitting a 1 million dollar budget, not a physical build or purchase. Previous attempts have not directly met this clarified need, facing limitations such as JSON format errors and misunderstanding the task’s scope. No files were created for this specified task yet. Proceeding with a refined approach focusing on specifications collection is necessary.Clarified task to research specifications for high-capacity enterprise storage servers within a 1 million dollar budget rather than configure or purchase. No previous specific actions taken for this clarified directive. Commencing with a Google search to gather relevant technical specifications and cost estimates.saved google search results at googleresults114.txt to retrieve if shutdown randomly.Commenced Google search querying high-capacity enterprise storage server specifications for optimizing a 1 million dollar budget. Identified Broadberry’s ‘High Capacity Scalable Storage Servers & Appliances’ as a target for detailed exploration due to its relevance to the user’s request. Awaiting to browse Broadberry’s webpage to extract essential specifications and pricing information.saved the scraping of the website results at websiteresults115.txt to retrieve if shutdown randomly.The user clarified that their request involves researching and providing specifications for a high-capacity enterprise storage server that fits within a 1 million dollar budget, not configuring or purchasing one. Previous attempts faced issues like JSON format errors and misunderstandings of the task’s scope. Now, proceeding with a targeted Google search to gather the necessary specs and price information.saved google search results at googleresults116.txt to retrieve if shutdown randomly.After user clarification, shifted the task to research and provide specifications for a high-capacity enterprise storage server within a 1 million dollar budget. Aim to perform a Google search specifically for ‘high-capacity enterprise storage server specifications’ to gather data relevant for fulfilling the user’s refined request. No files have been created or browsed yet in relation to this adjusted task.saved google search results at googleresults117.txt to retrieve if shutdown randomly.The task is to maximise storage within a $1 million budget. A Google search was executed, directing attention to Broadberry’s ‘High Capacity Scalable Storage Servers & Appliances’ which might provide the necessary information to complete the user’s request. The next action involves browsing Broadberry’s webpage for precise server options and costs to facilitate the crafting of a detailed recommendation.saved the scraping of the website results at websiteresults118.txt to retrieve if shutdown randomly.The user clarified that the task involves researching and providing specifications for a high-capacity enterprise storage server within a 1 million dollar budget, not the physical configuration or purchasing of hardware. Adjusting strategy to conduct a more focused Google search to gather necessary specs and pricing information to fulfill the user’s refined request. No files have been created or browsed yet in relation to this adjusted task.saved google search results at googleresults119.txt to retrieve if shutdown randomly.The user’s task involves researching and recommending specifications for high-capacity enterprise storage servers within a 1 million dollar budget. After conducting a Google search, the Broadberry website was selected due to its emphasis on high-capacity, scalable storage solutions. The plan involves browsing the Broadberry webpage for detailed storage server specifications and configurations that would best utilize the user’s budget. Previous steps included a Google search with results logged but no specific URLs browsed prior to this selection. This strategy aims to provide the user with a detailed recommendation based on the latest available solutions.saved the scraping of the website results at websiteresults120.txt to retrieve if shutdown randomly.Following the user’s clarification, the task has been adjusted from attempting to configure or purchase a server to researching and providing specifications for a high-capacity enterprise storage server within a 1 million dollar budget. The last action taken was a Google search for ‘high-capacity enterprise storage server specifications 2023’ to collect the latest data to accomplish the user’s request.saved google search results at googleresults121.txt to retrieve if shutdown randomly.The user requested research and specifications for a high-capacity enterprise storage server within a 1 million dollar budget. The task began with a Google search leading to several promising articles. The selected article from TechTarget, titled ‘The best enterprise data storage products of 2023’, aims to provide a basis for understanding current market offerings in high-capacity storage solutions fitting the user’s budget. The plan involves extracting relevant specifications and pricing from this article to compile a detailed recommendation.saved the scraping of the website results at websiteresults122.txt to retrieve if shutdown randomly.Initiated the task of researching high-capacity enterprise storage solutions within a 1 million dollar budget by reviewing ‘The best enterprise data storage products of 2023’ from TechTarget. The article highlighted key industry trends and innovative products across several categories. Plans were made to extract relevant product specifications and features from the award winners, especially in the ‘cloud storage’ and ‘disk and disk subsystems’ categories, to estimate achievable storage capacity and performance within the budget. A detailed recommendation will be prepared based on this information, with consideration for possible additional research or vendor inquiries needed for accurate budget allocation and product selection.The last task involved clarifying the user’s requirement, which is focused on researching and providing specifications for a high-capacity enterprise storage server within a 1 million dollar budget. Previous steps included conducting Google searches and planning to browse specific websites like Broadberry for detailed product configurations, which was based on an initial misunderstanding of the user’s request as involving actual configuration or purchase. Adjustments are now being made to focus on gathering a broader range of information to synthesize into a detailed specification rather than specific product configurations or purchases.
The Task: I didn't mean to build a physical just research and give me specs of it
|
9b4cb5be54bba2609de3c231aaa071ed
|
{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
|
42,319
|
Hi!
|
54d38e702f5f26e1ff6b1525988723ac
|
{
"intermediate": 0.3230988085269928,
"beginner": 0.2665199935436249,
"expert": 0.4103812277317047
}
|
42,320
|
Wassup
|
535274e7b40382518f8ded82e5c55ace
|
{
"intermediate": 0.3464571535587311,
"beginner": 0.27116474509239197,
"expert": 0.38237810134887695
}
|
42,321
|
1_ Translate the following legal text into colloquial Farsi 2_ Place the Persian and English text side by side in the table 3_ From the beginning to the end of the text, there should be an English sentence on the left side and a Persian sentence on the right side.
4- Using legal language for Persian translation
.1. Precedent
1.1. Case Law
When any case, civil or criminal, is tried, points of law and fact are always involved. Thus if one party sues another in a court for damages for negligent injury, the judge must find the facts of the case and rule whether in law negligence was
present.
The judge's decision has three aspects. In the first place he will decide what is to happen to the plaintiff and defendant, e.g., whether the defendant pays a sum of money to the plaintiff. In the second place the judge will give his reasons for his findings of fact, e.g., that he believes the plaintiff to be telling the truth. In the third
|
8bb576889919d1eda17f7cde4ebd447f
|
{
"intermediate": 0.337511271238327,
"beginner": 0.4006371796131134,
"expert": 0.26185157895088196
}
|
42,322
|
How do I capitalize the first letter in a string in shell, while also being posix compliant?
|
09beba0d58e5e3071eb491a686091225
|
{
"intermediate": 0.4036853015422821,
"beginner": 0.3129321038722992,
"expert": 0.2833825945854187
}
|
42,323
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remember the content of this code because i will ask you questions about it next, code: import torch
import torch.nn as nn
import torch.nn.functional as F
import json
import math
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset
from collections import Counter
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
# ---------- Device Configuration ----------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# ---------- Utility Functions ----------
def positional_encoding(seq_len, d_model, device):
pos = torch.arange(seq_len, dtype=torch.float, device=device).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)).to(device)
pe = torch.zeros(seq_len, d_model, device=device)
pe[:, 0::2] = torch.sin(pos * div_term)
pe[:, 1::2] = torch.cos(pos * div_term)
return pe.unsqueeze(0)
# -------- Performance ----------
def evaluate_model(model, data_loader, device):
model.eval()
all_preds, all_targets = [], []
with torch.no_grad():
for inputs, targets in data_loader:
inputs, targets = inputs.to(device), targets.to(device)
outputs = model(inputs)
# Assuming the model output is directly class indices. Adjust as necessary.
predictions = torch.argmax(outputs, dim=-1).view(-1) # Flatten predicted indices
all_preds.extend(predictions.cpu().numpy())
all_targets.extend(targets.view(-1).cpu().numpy()) # Ensure targets are also flattened
# Calculate accuracy after ensuring all_preds and all_targets are correctly aligned.
accuracy = accuracy_score(all_targets, all_preds)
print(f"Accuracy: {accuracy:.4f}")
return accuracy
# Function to plot loss over time
def plot_loss(loss_history):
plt.figure(figsize=(10, 5))
plt.plot(loss_history, label='Training Loss')
plt.xlabel('Batches')
plt.ylabel('Loss')
plt.title('Training Loss Over Time')
plt.legend()
plt.show()
# ---------- Model Definitions ----------
class TransformerExpert(nn.Module):
def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers=1):
super(TransformerExpert, self).__init__()
self.d_model = d_model
self.input_fc = nn.Linear(input_size, d_model)
self.pos_encoder = nn.Parameter(positional_encoding(1, d_model, device), requires_grad=True)
encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True,
norm_first=True)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_encoder_layers)
self.output_fc = nn.Linear(d_model, output_size)
self.norm = nn.LayerNorm(d_model)
def forward(self, x):
x = self.norm(self.input_fc(x)) + self.pos_encoder
transformer_output = self.transformer_encoder(x)
output = self.output_fc(transformer_output)
return output
class GatingNetwork(nn.Module):
def __init__(self, input_feature_dim, num_experts, hidden_dims=[512], dropout_rate=0.1):
super(GatingNetwork, self).__init__()
layers = []
last_dim = input_feature_dim
for hidden_dim in hidden_dims:
layers.extend([
nn.Linear(last_dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout_rate),
])
last_dim = hidden_dim
layers.append(nn.Linear(last_dim, num_experts))
self.fc_layers = nn.Sequential(*layers)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = x.mean(dim=1) # To ensure gating is based on overall features across the sequence
x = self.fc_layers(x)
return self.softmax(x)
class MixtureOfTransformerExperts(nn.Module):
def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_experts, num_encoder_layers=1):
super(MixtureOfTransformerExperts, self).__init__()
self.num_experts = num_experts
self.output_size = output_size
self.experts = nn.ModuleList([TransformerExpert(input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers) for _ in range(num_experts)])
self.gating_network = GatingNetwork(d_model, num_experts)
def forward(self, x):
gating_scores = self.gating_network(x)
expert_outputs = [expert(x) for expert in self.experts]
stacked_expert_outputs = torch.stack(expert_outputs)
expanded_gating_scores = gating_scores.unsqueeze(2).unsqueeze(3)
expanded_gating_scores = expanded_gating_scores.expand(-1, -1, x.size(1), self.output_size)
expanded_gating_scores = expanded_gating_scores.transpose(0, 1)
mixed_output = torch.sum(stacked_expert_outputs * expanded_gating_scores, dim=0)
return mixed_output
class MoETransformerModel(nn.Module):
def __init__(self, vocab_size, d_model, moe):
super(MoETransformerModel, self).__init__()
self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=d_model)
self.moe = moe
self.dropout = nn.Dropout(p=0.125)
def forward(self, x):
embedded = self.dropout(self.embedding(x))
return self.moe(embedded)
# ---------- Dataset Definitions ----------
class QAJsonlDataset(Dataset):
def __init__(self, path, seq_len):
self.seq_len = seq_len
self.pairs = self.load_data(path)
self.vocab, self.idx2token = self.build_vocab([word for pair in self.pairs for sublist in pair for word in sublist])
self.tokenized_pairs = [(self.tokenize(q), self.tokenize(a)) for q, a in self.pairs]
def load_data(self, path):
pairs = []
with open(path, "r", encoding="utf-8") as f:
for line in f:
data = json.loads(line.strip())
question, answer = data.get("user", ""), data.get("content", "")
pairs.append((question.split(), answer.split()))
return pairs
def tokenize(self, words):
tokens = [self.vocab.get(w, self.vocab["<unk>"]) for w in words]
if len(tokens) < self.seq_len:
tokens.append(self.vocab["<eos>"])
tokens.extend([self.vocab["<pad>"]] * (self.seq_len - len(tokens)))
else:
tokens = tokens[:self.seq_len - 1] + [self.vocab["<eos>"]]
return tokens
def build_vocab(self, words):
vocab = {"<unk>": 0, "<pad>": 1, "<eos>": 2}
start_index = len(vocab)
counts = Counter(words)
for word, _ in counts.most_common():
if word not in vocab:
vocab[word] = len(vocab)
idx2token = {idx: token for token, idx in vocab.items()}
return vocab, idx2token
def __len__(self):
return len(self.tokenized_pairs)
def __getitem__(self, idx):
tokenized_question, tokenized_answer = self.tokenized_pairs[idx]
return torch.tensor(tokenized_question, dtype=torch.long), torch.tensor(tokenized_answer, dtype=torch.long)
def collate_fn(batch):
questions, answers = zip(*batch)
questions = pad_sequence(questions, batch_first=True, padding_value=0)
answers = pad_sequence(answers, batch_first=True, padding_value=0)
return questions, answers
# ---------- Training and Inference Functions ----------
def train_model(model, criterion, optimizer, num_epochs, data_loader):
model.train()
loss_history = [] # Initialize a list to keep track of losses
for epoch in range(num_epochs):
total_loss = 0
total_items = 0 # Keep track of total items processed
progress_bar = tqdm(enumerate(data_loader), total=len(data_loader), desc=f"Epoch {epoch+1}", leave=False)
for i, (inputs, targets) in progress_bar:
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
# Predict
predictions = model(inputs)
predictions = predictions.view(-1, predictions.size(-1)) # Make sure predictions are the right shape
targets = targets.view(-1) # Flatten targets to match prediction shape if necessary
# Calculate loss
loss = criterion(predictions, targets)
loss.backward()
# Gradient clipping for stabilization
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
# Update total loss and the number of items
total_loss += loss.item() * inputs.size(0) # Multiply loss by batch size
total_items += inputs.size(0)
loss_history.append(loss.item())
progress_bar.set_postfix({"Loss": loss.item()})
average_loss = total_loss / total_items # Correctly compute average loss
print(f"Epoch {epoch+1}, Average Loss: {average_loss:.6f}")
return loss_history
def generate_text(model, dataset, seed_text, num_generate, temperature=1.0):
model.eval()
generated_tokens = []
# Initial sequence (prefix) to start the generation process
input_sequence = [dataset.vocab.get(word, dataset.vocab["<pad>"]) for word in seed_text.split()]
current_sequence = torch.tensor(input_sequence, dtype=torch.long).unsqueeze(0)
current_sequence = current_sequence.to(device)
# Generate num_generate tokens
for _ in range(num_generate):
# Forward pass through the model
with torch.no_grad():
output = model(current_sequence)
# Get probabilities, apply temperature scaling, and sample from the distribution
probabilities = F.softmax(output[:, -1, :] / temperature, dim=-1).detach()
next_token_idx = torch.multinomial(probabilities, 1).item()
# Append token to the current sequence and to the generated tokens
generated_tokens.append(next_token_idx)
current_sequence = torch.cat((current_sequence, torch.tensor([[next_token_idx]])), 1).to(device)
# Convert tokens to words
generated_text = " ".join([dataset.idx2token.get(token, "<unk>") for token in generated_tokens])
return generated_text
def count_tokens_in_dataset(dataset):
return sum([len(pair[0]) + len(pair[1]) for pair in dataset.pairs])
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
# ---------- Hyperparameters and Model Instantiation ----------
# Transformer :
d_model = 128
nhead = 2
dim_feedforward = 256
num_encoder_layers = 4
num_experts = 4
hidden_dims = [1024, 512, 256, 128]
dropout_rate = 0.3
# Gating Network :
gating_network = GatingNetwork(
input_feature_dim=d_model,
num_experts=num_experts,
hidden_dims=hidden_dims,
dropout_rate=dropout_rate,
)
# Dataset :
path_to_dataset = "C:/Users/L14/Documents/Projets/Easy-MoE/Easy-MoE/data/Real_talk.jsonl"
seq_len = 32
dataset = QAJsonlDataset(path_to_dataset, seq_len)
data_loader = DataLoader(dataset, batch_size=seq_len, shuffle=True, collate_fn=collate_fn, pin_memory=True)
num_tokens = count_tokens_in_dataset(dataset)
print(f"Total number of tokens in the dataset: {num_tokens}")
vocab_size = len(dataset.vocab)
moe = MixtureOfTransformerExperts(
input_size=d_model,
d_model=d_model,
output_size=vocab_size,
nhead=nhead,
dim_feedforward=dim_feedforward,
num_experts=num_experts,
num_encoder_layers=num_encoder_layers
).to(device)
moe_transformer_model = MoETransformerModel(vocab_size, d_model, moe).to(device)
# Count of total parameters :
total_params = count_parameters(moe_transformer_model)
print(f"Total trainable parameters: {total_params}")
# ---------- Training ----------
num_epochs = 100
learning_rate = 5e-5
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
optimizer = torch.optim.AdamW(moe_transformer_model.parameters(), lr=learning_rate, weight_decay=0)
# Train the model
loss_history = train_model(moe_transformer_model, criterion, optimizer, num_epochs, data_loader)
# Evaluating the model
plot_loss(loss_history)
train_accuracy = evaluate_model(moe_transformer_model, data_loader, device)
# ---------- Inference ----------
def interactive_text_generation(model, dataset, max_length=32, temperature=0.2):
while True:
try:
# Get user input
seed_text = input("Enter seed text (type 'quit' to exit and save the model): ").strip()
# Check if user wants to quit the interaction
if seed_text.lower() == 'quit':
print("Exiting text generation mode.")
break
# Check if the seed text is not empty
if seed_text:
generated_text = generate_text(model, dataset, seed_text, max_length, temperature)
print("Generated Text: ", generated_text)
else:
print("Seed text cannot be empty. Please enter some text.")
except KeyboardInterrupt:
# Handle the interrupt signal to exit gracefully
print("\nReceived interrupt signal. Exiting text generation mode.")
break
except Exception as e:
# Handle other exceptions and prevent the loop from crashing
print(f"An error occurred: {e}. Try again.")
interactive_text_generation(moe_transformer_model, dataset)
# ---------- Save Trained Model ----------
torch.save(moe_transformer_model.state_dict(), "MoE_Transformer-Alpha-QA.pth")
|
8d2621b677ebf59b079f115067066c8f
|
{
"intermediate": 0.3106774687767029,
"beginner": 0.47244349122047424,
"expert": 0.2168789803981781
}
|
42,324
|
I need to create a machine learning project in Azure. I baiscally need an algorithm that takes in audio and determines if the audio is AI voice or human voice but I do not know where to start please hel
|
0531cc78b12230409df183ab540d577e
|
{
"intermediate": 0.09216419607400894,
"beginner": 0.05416674166917801,
"expert": 0.8536691069602966
}
|
42,325
|
hi
|
aa4ae17dc949d50f6ad34b86975a2ba7
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
42,326
|
write code to solve this
Traceback (most recent call last):
File "C:\Users\admin\Mm\generateText.py", line 136, in <module>
fire.Fire(main)
File "C:\Users\admin\Mm\venv\lib\site-packages\fire\core.py", line 141, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "C:\Users\admin\Mm\venv\lib\site-packages\fire\core.py", line 475, in _Fire
component, remaining_args = _CallAndUpdateTrace(
File "C:\Users\admin\Mm\venv\lib\site-packages\fire\core.py", line 691, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "C:\Users\admin\Mm\generateText.py", line 52, in main
model = PeftModel.from_pretrained(
File "C:\Users\admin\Mm\venv\lib\site-packages\peft\peft_model.py", line 353, in from_pretrained
model.load_adapter(model_id, adapter_name, is_trainable=is_trainable, **kwargs)
File "C:\Users\admin\Mm\venv\lib\site-packages\peft\peft_model.py", line 694, in load_adapter
adapters_weights = load_peft_weights(model_id, device=torch_device, **hf_hub_download_kwargs)
File "C:\Users\admin\Mm\venv\lib\site-packages\peft\utils\save_and_load.py", line 326, in load_peft_weights
adapters_weights = safe_load_file(filename, device=device)
File "C:\Users\admin\Mm\venv\lib\site-packages\safetensors\torch.py", line 308, in load_file
with safe_open(filename, framework="pt", device=device) as f:
safetensors_rust.SafetensorError: Error while deserializing header: InvalidHeaderDeserialization
|
2b1c04037ec7f151378b2415c36aa734
|
{
"intermediate": 0.6191304326057434,
"beginner": 0.2061392068862915,
"expert": 0.1747303456068039
}
|
42,327
|
Modify this code to get entity name on top of bounding box
import json
import pandas as pd
import cv2
from thefuzz import fuzz
from thefuzz import process
import numpy as np
def read_textract_output(csv_path):
return pd.read_csv(csv_path)
def read_json_entities(json_path):
with open(json_path, 'r') as file:
return json.load(file)
def find_entity_bounding_boxes(entity_text, textract_df, image_size):
entity_tokens = entity_text.split()
results = pd.DataFrame()
for token in entity_tokens:
choices = textract_df["text"].dropna().tolist()
best_match, score = process.extractOne(token, choices, scorer=fuzz.token_sort_ratio)
if score > 70:
best_matches_df = textract_df[textract_df["text"] == best_match]
results = pd.concat([results, best_matches_df])
bounding_boxes = []
try:
# Sort by line and word number to cluster words that are on the same line
sorted_results = results.sort_values(by=["line_num", "word_num"])
# Group the resulting bounding boxes by line number
grouped_results = sorted_results.groupby("line_num")
for _, group in grouped_results:
# Calculate scaling factors based on image size and original image dimensions
image_width, image_height = group.iloc[0]["image_width"], group.iloc[0]["image_height"]
scale_x = image_size[0] / image_width
scale_y = image_size[1] / image_height
# Calculate the bounding box for the whole line
min_left = np.min(group["left"])
min_top = np.min(group["top"])
max_right = np.max(group["left"] + group["width"])
max_bottom = np.max(group["top"] + group["height"])
bbox = (min_left * scale_x, min_top * scale_y, (max_right - min_left) * scale_x, (max_bottom - min_top) * scale_y)
bounding_boxes.append(bbox)
except Exception as e:
print(f"An error occurred: {e}")
return bounding_boxes
def draw_bounding_boxes(image_path, entities, textract_df):
image = cv2.imread(image_path)
image_size = image.shape[1], image.shape[0]
for category, details in entities.items():
if category == "invoice_details" or category == "Payment Details" or category == "amounts_and_tax":
for entity, value in details.items():
if value:
bounding_boxes = find_entity_bounding_boxes(value, textract_df, image_size)
try:
for bbox in bounding_boxes:
x, y, w, h = map(int, bbox)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
except:
pass
else:
pass
cv2.imwrite('/home/ritik1s/Desktop/bbox_issues/temp_GPT/annotated_invoice.jpg', image)
IMAGE_PATH = '/home/ritik1s/Desktop/bbox_issues/temp_GPT/check.jpeg'
CSV_PATH = "/home/ritik1s/Desktop/bbox_issues/temp_GPT/check.csv"
JSON_PATH = "/home/ritik1s/Desktop/bbox_issues/temp_GPT/row_skip.json"
# Read Textract output and JSON entities
textract_df = read_textract_output(CSV_PATH)
entities = read_json_entities(JSON_PATH)
# Draw the bounding boxes
draw_bounding_boxes(IMAGE_PATH, entities, textract_df)
|
4c63de39899568d2dacdfb62624d9d00
|
{
"intermediate": 0.42486846446990967,
"beginner": 0.43284499645233154,
"expert": 0.14228658378124237
}
|
42,328
|
can you add to the conditionals for optimizer_type to add GERU support to the code: import torch
import torch.nn as nn
import torch.nn.functional as F
import json
import math
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset
from collections import Counter
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
# ---------- Device Configuration ----------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# ---------- Utility Functions ----------
def positional_encoding(seq_len, d_model, device):
pos = torch.arange(seq_len, dtype=torch.float, device=device).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)).to(device)
pe = torch.zeros(seq_len, d_model, device=device)
pe[:, 0::2] = torch.sin(pos * div_term)
pe[:, 1::2] = torch.cos(pos * div_term)
return pe.unsqueeze(0)
# -------- Performance ----------
def evaluate_model(model, data_loader, device):
model.eval()
all_preds, all_targets = [], []
with torch.no_grad():
for inputs, targets in data_loader:
inputs, targets = inputs.to(device), targets.to(device)
outputs = model(inputs)
predictions = torch.argmax(outputs, dim=-1).view(-1) # Flatten predicted indices
all_preds.extend(predictions.cpu().numpy())
all_targets.extend(targets.view(-1).cpu().numpy()) # Ensure targets are also flattened
# Calculate precision, recall, and F1 score after ensuring all_preds and all_targets are correctly aligned.
precision = precision_score(all_targets, all_preds, average='macro', zero_division=0)
recall = recall_score(all_targets, all_preds, average='macro', zero_division=0)
f1 = f1_score(all_targets, all_preds, average='macro', zero_division=0)
print(f"Precision: {precision:.4f}")
print(f"Recall: {recall:.4f}")
print(f"F1 Score: {f1:.4f}")
return precision, recall, f1
# Function to plot loss over time
def plot_loss(loss_history):
plt.figure(figsize=(10, 5))
plt.plot(loss_history, label='Training Loss')
plt.xlabel('Batches')
plt.ylabel('Loss')
plt.title('Training Loss Over Time')
plt.legend()
plt.show()
# ---------- Model Definitions ----------
class TransformerExpert(nn.Module):
def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers=1):
super(TransformerExpert, self).__init__()
self.d_model = d_model
self.input_fc = nn.Linear(input_size, d_model)
self.pos_encoder = nn.Parameter(positional_encoding(1, d_model, device), requires_grad=True)
encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True,
norm_first=True)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_encoder_layers)
self.output_fc = nn.Linear(d_model, output_size)
self.norm = nn.LayerNorm(d_model)
def forward(self, x):
x = self.norm(self.input_fc(x)) + self.pos_encoder
transformer_output = self.transformer_encoder(x)
output = self.output_fc(transformer_output)
return output
class GatingNetwork(nn.Module):
def __init__(self, input_feature_dim, num_experts, hidden_dims=[512], dropout_rate=0.1):
super(GatingNetwork, self).__init__()
layers = []
last_dim = input_feature_dim
for hidden_dim in hidden_dims:
layers.extend([
nn.Linear(last_dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout_rate),
])
last_dim = hidden_dim
layers.append(nn.Linear(last_dim, num_experts))
self.fc_layers = nn.Sequential(*layers)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = x.mean(dim=1) # To ensure gating is based on overall features across the sequence
x = self.fc_layers(x)
return self.softmax(x)
class MixtureOfTransformerExperts(nn.Module):
def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_experts, num_encoder_layers=1):
super(MixtureOfTransformerExperts, self).__init__()
self.num_experts = num_experts
self.output_size = output_size
self.experts = nn.ModuleList([TransformerExpert(input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers) for _ in range(num_experts)])
self.gating_network = GatingNetwork(d_model, num_experts)
def forward(self, x):
gating_scores = self.gating_network(x)
expert_outputs = [expert(x) for expert in self.experts]
stacked_expert_outputs = torch.stack(expert_outputs)
expanded_gating_scores = gating_scores.unsqueeze(2).unsqueeze(3)
expanded_gating_scores = expanded_gating_scores.expand(-1, -1, x.size(1), self.output_size)
expanded_gating_scores = expanded_gating_scores.transpose(0, 1)
mixed_output = torch.sum(stacked_expert_outputs * expanded_gating_scores, dim=0)
return mixed_output
class MoETransformerModel(nn.Module):
def __init__(self, vocab_size, d_model, moe):
super(MoETransformerModel, self).__init__()
self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=d_model)
self.moe = moe
self.dropout = nn.Dropout(p=0.125)
def forward(self, x):
embedded = self.dropout(self.embedding(x))
return self.moe(embedded)
# ---------- Dataset Definitions ----------
class QAJsonlDataset(Dataset):
def __init__(self, path, seq_len):
self.seq_len = seq_len
self.pairs = self.load_data(path)
self.vocab, self.idx2token = self.build_vocab([word for pair in self.pairs for sublist in pair for word in sublist])
self.tokenized_pairs = [(self.tokenize(q), self.tokenize(a)) for q, a in self.pairs]
def load_data(self, path):
pairs = []
with open(path, "r", encoding="utf-8") as f:
for line in f:
data = json.loads(line.strip())
question, answer = data.get("user", ""), data.get("content", "")
pairs.append((question.split(), answer.split()))
return pairs
def tokenize(self, words):
tokens = [self.vocab.get(w, self.vocab["<unk>"]) for w in words]
if len(tokens) < self.seq_len:
tokens.append(self.vocab["<eos>"])
tokens.extend([self.vocab["<pad>"]] * (self.seq_len - len(tokens)))
else:
tokens = tokens[:self.seq_len - 1] + [self.vocab["<eos>"]]
return tokens
def build_vocab(self, words):
vocab = {"<unk>": 0, "<pad>": 1, "<eos>": 2}
start_index = len(vocab)
counts = Counter(words)
for word, _ in counts.most_common():
if word not in vocab:
vocab[word] = len(vocab)
idx2token = {idx: token for token, idx in vocab.items()}
return vocab, idx2token
def __len__(self):
return len(self.tokenized_pairs)
def __getitem__(self, idx):
tokenized_question, tokenized_answer = self.tokenized_pairs[idx]
return torch.tensor(tokenized_question, dtype=torch.long), torch.tensor(tokenized_answer, dtype=torch.long)
def collate_fn(batch):
questions, answers = zip(*batch)
questions = pad_sequence(questions, batch_first=True, padding_value=0)
answers = pad_sequence(answers, batch_first=True, padding_value=0)
return questions, answers
# ---------- Training and Inference Functions ----------
def train_model(model, criterion, optimizer, num_epochs, data_loader):
model.train()
loss_history = [] # Initialize a list to keep track of losses
for epoch in range(num_epochs):
total_loss = 0
total_items = 0 # Keep track of total items processed
progress_bar = tqdm(enumerate(data_loader), total=len(data_loader), desc=f"Epoch {epoch+1}", leave=False)
for i, (inputs, targets) in progress_bar:
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
# Predict
predictions = model(inputs)
predictions = predictions.view(-1, predictions.size(-1)) # Make sure predictions are the right shape
targets = targets.view(-1) # Flatten targets to match prediction shape if necessary
# Calculate loss
loss = criterion(predictions, targets)
loss.backward()
# Gradient clipping for stabilization
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
# Update total loss and the number of items
total_loss += loss.item() * inputs.size(0) # Multiply loss by batch size
total_items += inputs.size(0)
loss_history.append(loss.item())
progress_bar.set_postfix({"Loss": loss.item()})
average_loss = total_loss / total_items # Correctly compute average loss
print(f"Epoch {epoch+1}, Average Loss: {average_loss:.6f}")
return loss_history
class WarmupLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, warmup_steps, scheduler_step_lr):
self.warmup_steps = warmup_steps
self.scheduler_step_lr = scheduler_step_lr # The subsequent scheduler
super(WarmupLR, self).__init__(optimizer)
def get_lr(self):
if self._step_count <= self.warmup_steps:
warmup_factor = float(self._step_count) / float(max(1, self.warmup_steps))
for base_lr in self.base_lrs:
yield base_lr * warmup_factor
else:
self.scheduler_step_lr.step() # Update the subsequent scheduler
for param_group in self.optimizer.param_groups:
yield param_group['lr']
def generate_text(model, dataset, seed_text, num_generate, temperature=1.0):
model.eval()
generated_tokens = []
# Initial sequence (prefix) to start the generation process
input_sequence = [dataset.vocab.get(word, dataset.vocab["<pad>"]) for word in seed_text.split()]
current_sequence = torch.tensor(input_sequence, dtype=torch.long).unsqueeze(0)
current_sequence = current_sequence.to(device)
# Generate num_generate tokens
for _ in range(num_generate):
# Forward pass through the model
with torch.no_grad():
output = model(current_sequence)
# Get probabilities, apply temperature scaling, and sample from the distribution
probabilities = F.softmax(output[:, -1, :] / temperature, dim=-1).detach()
next_token_idx = torch.multinomial(probabilities, 1).item()
# Append token to the current sequence and to the generated tokens
generated_tokens.append(next_token_idx)
current_sequence = torch.cat((current_sequence, torch.tensor([[next_token_idx]])), 1).to(device)
# Convert tokens to words
generated_text = " ".join([dataset.idx2token.get(token, "<unk>") for token in generated_tokens])
return generated_text
def count_tokens_in_dataset(dataset):
return sum([len(pair[0]) + len(pair[1]) for pair in dataset.pairs])
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
# ---------- Hyperparameters and Model Instantiation ----------
# Transformer :
d_model = 256
nhead = 8
dim_feedforward = 512
num_encoder_layers = 4
num_experts = 1
hidden_dims = []
dropout_rate = 0.5
# Gating Network :
gating_network = GatingNetwork(
input_feature_dim=d_model,
num_experts=num_experts,
hidden_dims=hidden_dims,
dropout_rate=dropout_rate,
)
# Training Parameters
batch_size = 32 # Adjustable batch size
warmup_steps = 6000 # Warmup steps for learning rate
optimizer_type = "Adam" # Could be “SGD”, “RMSprop”, etc.
learning_rate = 1e-2
weight_decay = 1e-5 # For L2 regularization
num_epochs = 200
# Dataset :
path_to_dataset = "C:/Users/L14/Documents/Projets/Easy-MoE/Easy-MoE/data/Real_talk.jsonl"
seq_len = 8
dataset = QAJsonlDataset(path_to_dataset, seq_len)
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn, pin_memory=True)
num_tokens = count_tokens_in_dataset(dataset)
print(f"Total number of tokens in the dataset: {num_tokens}")
vocab_size = len(dataset.vocab)
moe = MixtureOfTransformerExperts(
input_size=d_model,
d_model=d_model,
output_size=vocab_size,
nhead=nhead,
dim_feedforward=dim_feedforward,
num_experts=num_experts,
num_encoder_layers=num_encoder_layers
).to(device)
moe_transformer_model = MoETransformerModel(vocab_size, d_model, moe).to(device)
# Count of total parameters :
total_params = count_parameters(moe_transformer_model)
print(f"Total trainable parameters: {total_params}")
# ---------- Training ----------
# Adjusting optimizer setup to include weight decay and allow switching between types
if optimizer_type == "AdamW":
optimizer = torch.optim.AdamW(moe_transformer_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
elif optimizer_type == "SGD":
optimizer = torch.optim.SGD(moe_transformer_model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=weight_decay)
elif optimizer_type == "Adam":
optimizer = torch.optim.Adam(moe_transformer_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# Instantiate your main scheduler (StepLR)
step_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=1.15)
# Wrap it with WarmupLR
scheduler = WarmupLR(optimizer, warmup_steps=warmup_steps, scheduler_step_lr=step_lr_scheduler)
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
# Train the model
loss_history = train_model(moe_transformer_model, criterion, optimizer, num_epochs, data_loader)
# Evaluating the model
plot_loss(loss_history)
train_accuracy = evaluate_model(moe_transformer_model, data_loader, device)
# ---------- Inference ----------
def interactive_text_generation(model, dataset, max_length=32, temperature=0.1):
while True:
try:
# Get user input
seed_text = input("Enter seed text (type 'quit' to exit and save the model): ").strip()
# Check if user wants to quit the interaction
if seed_text.lower() == 'quit':
print("Exiting text generation mode.")
break
# Check if the seed text is not empty
if seed_text:
generated_text = generate_text(model, dataset, seed_text, max_length, temperature)
print("Generated Text: ", generated_text)
else:
print("Seed text cannot be empty. Please enter some text.")
except KeyboardInterrupt:
# Handle the interrupt signal to exit gracefully
print("\nReceived interrupt signal. Exiting text generation mode.")
break
except Exception as e:
# Handle other exceptions and prevent the loop from crashing
print(f"An error occurred: {e}. Try again.")
interactive_text_generation(moe_transformer_model, dataset)
# ---------- Save Trained Model ----------
torch.save(moe_transformer_model.state_dict(), "Transformer-Alpha-v02.pth")
|
cab01b9dd08ddd142d8d1d53f3ea75df
|
{
"intermediate": 0.3804943561553955,
"beginner": 0.38605403900146484,
"expert": 0.23345158994197845
}
|
42,329
|
Change the fuzz logic find any better solution than this and write a better code to map the bounding boxes for multi token entities. The code sould avoid any extra bounding box than the actual entity bounding box
import json
import pandas as pd
import numpy as np
import cv2
from sklearn.cluster import DBSCAN
from thefuzz import process
from scipy.spatial import distance
from thefuzz import fuzz
def read_textract_output(csv_path):
return pd.read_csv(csv_path)
def read_json_entities(json_path):
with open(json_path, "r") as file:
return json.load(file)
def calculate_bounding_boxes(cluster):
min_left = np.min(cluster[:, 0])
min_top = np.min(cluster[:, 1])
max_right = np.max(cluster[:, 0] + cluster[:, 2])
max_bottom = np.max(cluster[:, 1] + cluster[:, 3])
return (min_left, min_top, max_right - min_left, max_bottom - min_top)
def apply_dbscan_clustering(bounding_boxes, eps=25, min_samples=1):
# Prepare data for clustering (using the center points of bounding boxes)
X = np.array([(x + w / 2, y + h / 2) for x, y, w, h in bounding_boxes])
clustering = DBSCAN(eps=eps, min_samples=min_samples, metric="euclidean").fit(X)
labels = clustering.labels_
# Group bounding boxes by cluster
clustered_boxes = []
for label in set(labels):
if label == -1: # -1 is noise in DBSCAN
continue
indices = np.where(labels == label)[0]
cluster = np.array(bounding_boxes)[indices]
clustered_boxes.append(calculate_bounding_boxes(cluster))
return clustered_boxes
def find_entity_bounding_boxes(entity_text, textract_df, image_size):
entity_tokens = entity_text.split()
bounding_boxes = []
for token in entity_tokens:
choices = textract_df["text"].dropna().tolist()
best_match = process.extractOne(token, choices, scorer=fuzz.token_sort_ratio)
if best_match and best_match[1] >= 90:
best_matches_df = textract_df[textract_df["text"] == best_match[0]]
bounding_boxes.extend(best_matches_df.apply(lambda row: (row["left"], row["top"], row["width"], row["height"]), axis=1).tolist())
if not bounding_boxes:
return []
# Apply DBSCAN clustering to group bounding boxes
clustered_boxes = apply_dbscan_clustering(bounding_boxes)
return clustered_boxes
def draw_bounding_boxes(image_path, entities, textract_df):
image = cv2.imread(image_path)
image_size = image.shape[1], image.shape[0]
for category, details in entities.items():
if category == "invoice_details" or category == "Payment Details" or category == "amounts_and_tax":
for entity, value in details.items():
if value:
bounding_boxes = find_entity_bounding_boxes(value, textract_df, image_size)
try:
for bbox in bounding_boxes:
x, y, w, h = map(int, bbox)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
except:
pass
else:
pass
cv2.imwrite('/home/ritik1s/Desktop/bbox_issues/temp_GPT/annotated_invoice.jpg', image)
IMAGE_PATH = "/home/ritik1s/Desktop/bbox_issues/temp_GPT/check.jpeg"
CSV_PATH = "/home/ritik1s/Desktop/bbox_issues/temp_GPT/check.csv"
JSON_PATH = "/home/ritik1s/Desktop/bbox_issues/temp_GPT/row_skip.json"
textract_df = read_textract_output(CSV_PATH)
entities = read_json_entities(JSON_PATH)
draw_bounding_boxes(IMAGE_PATH, entities, textract_df)
|
7082972463204885dffc93892c910df5
|
{
"intermediate": 0.3792381286621094,
"beginner": 0.3837125897407532,
"expert": 0.23704926669597626
}
|
42,330
|
Implement the BankAccount class. The entire project merely consists of inheriting from this class and adding new functionality, specifically:
Create a subclass of BankAccount called SavingsAccount that has, in addition to the balance and history, an allotment of money in various categories. The allotment is an associative array involving (category, value) pairs. For instance, a sample allotment is: {('education',20),('marriage',30),('parking tickets',70),('Having Naren for teaching CS000', priceless)}. The idea of allotment is to describe how the "balance" (from BankAccount) is distributed. So, if the balance is $1000, and the allotment is: {('education',10),('marriage',30)} then this means that 10/(10+30) of the balance is dedicated toward education, and the remaining 30/(10+30) of the balance is reserved for marriage costs. Or, in other words, there is three times more money reserved for marriage than education.
SavingsAccount must support the following messages:
newwhich creates a SavingsAccount object, initializes the balance to zero, and the history and allotment to empty lists.
initialize, which sets the balance to zero and initializes the history and allotment to empty lists (this is meant for use with an already created SavingsAccount object).
plansavings: factor for: reasonadds an entry in allotment of (reason,factor).
showdistributionthat plots a bar chart of money versus the categories in which it is allotted. For instance, when balance is $1000, and allotment is: {('education',10),('marriage',30)} then the bar chart will show two bars: one of height $250 for education and the other of height $750 for marriage.
(Extra Credit: 15%) Implement a message in SavingsAccount that presents the percentage distribution of allotments in the form of a pie chart, with each slice of a different color and clearly labeled with the corresponding category.
|
025f4feb945dd90e7bb3e0b677e56278
|
{
"intermediate": 0.5239913463592529,
"beginner": 0.2314518392086029,
"expert": 0.24455678462982178
}
|
42,331
|
Implement the BankAccount class. The entire project merely consists of inheriting from this class and adding new functionality, specifically:
Create a subclass of BankAccount called SavingsAccount that has, in addition to the balance and history, an allotment of money in various categories. The allotment is an associative array involving (category, value) pairs. For instance, a sample allotment is: {('education',20),('marriage',30),('parking tickets',70),('Having Naren for teaching CS000', priceless)}. The idea of allotment is to describe how the "balance" (from BankAccount) is distributed. So, if the balance is $1000, and the allotment is: {('education',10),('marriage',30)} then this means that 10/(10+30) of the balance is dedicated toward education, and the remaining 30/(10+30) of the balance is reserved for marriage costs. Or, in other words, there is three times more money reserved for marriage than education.
SavingsAccount must support the following messages:
newwhich creates a SavingsAccount object, initializes the balance to zero, and the history and allotment to empty lists.
initialize, which sets the balance to zero and initializes the history and allotment to empty lists (this is meant for use with an already created SavingsAccount object).
plansavings: factor for: reasonadds an entry in allotment of (reason,factor).
showdistributionthat plots a bar chart of money versus the categories in which it is allotted. For instance, when balance is $1000, and allotment is: {('education',10),('marriage',30)} then the bar chart will show two bars: one of height $250 for education and the other of height $750 for marriage.
(Extra Credit: 15%) Implement a message in SavingsAccount that presents the percentage distribution of allotments in the form of a pie chart, with each slice of a different color and clearly labeled with the corresponding category.
|
40c8c224ea21ecac469400abcc9d661d
|
{
"intermediate": 0.5239913463592529,
"beginner": 0.2314518392086029,
"expert": 0.24455678462982178
}
|
42,332
|
please can I have a VBA code that will generate a sixfigure random number in cell A25
|
02e95dee73aa153c7371e7b5a1d6635a
|
{
"intermediate": 0.39943069219589233,
"beginner": 0.16470415890216827,
"expert": 0.4358651340007782
}
|
42,333
|
You are an expert web developer who specializes in building working website prototypes from low-fidelity wireframes.
Your job is to accept low-fidelity wireframes, then create a working prototype using HTML, CSS, and JavaScript, and finally send back the results.
The results should be a single HTML file.
Use tailwind to style the website.
Put any additional CSS styles in a style tag and any JavaScript in a script tag.
Use unpkg or skypack to import any required dependencies.
Use Google fonts to pull in any open source fonts you require.
If you have any images, load them from Unsplash or use solid colored rectangles.
The wireframes may include flow charts, diagrams, labels, arrows, sticky notes, and other features that should inform your work.
If there are screenshots or images, use them to inform the colors, fonts, and layout of your website.
Use your best judgement to determine whether what you see should be part of the user interface, or else is just an annotation.
Use what you know about applications and user experience to fill in any implicit business logic in the wireframes. Flesh it out, make it real!
The user may also provide you with the html of a previous design that they want you to iterate from.
In the wireframe, the previous design's html will appear as a white rectangle.
Use their notes, together with the previous design, to inform your next result.
Sometimes it's hard for you to read the writing in the wireframes.
For this reason, all text from the wireframes will be provided to you as a list of strings, separated by newlines.
Use the provided list of text from the wireframes as a reference if any text is hard to read.
You love your designers and want them to be happy. Incorporating their feedback and notes and producing working websites makes them happy.
When sent new wireframes, respond ONLY with the contents of the html file
for medical prescription recognition,upload image functionality and list the details,use below code as backend
// node --version # Should be >= 18
// npm install @google/generative-ai
const {
GoogleGenerativeAI,
HarmCategory,
HarmBlockThreshold,
} = require("@google/generative-ai");
const fs = require("fs");
const MODEL_NAME = "gemini-1.0-pro-vision-latest";
const API_KEY = "YOUR_API_KEY";
async function run() {
const genAI = new GoogleGenerativeAI(API_KEY);
const model = genAI.getGenerativeModel({ model: MODEL_NAME });
const generationConfig = {
temperature: 0.4,
topK: 32,
topP: 1,
maxOutputTokens: 4096,
};
const safetySettings = [
{
category: HarmCategory.HARM_CATEGORY_HARASSMENT,
threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
},
{
category: HarmCategory.HARM_CATEGORY_HATE_SPEECH,
threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
},
{
category: HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
},
{
category: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
},
];
if (!fs.existsSync("image0.png")) {
throw new Error("Could not find images in current directory.");
}
const parts = [
{
inlineData: {
mimeType: "image/png",
data: Buffer.from(fs.readFileSync("image0.png")).toString("base64")
}
},
{text: "\nlist all medicine in this prescription including their category, content, side-effects, vitamin interactions, alternate medicine and essential usage instructions in a structure like below\n**Medicine Name :**\n - Category: \n - Content: \n - Side effects: \n - Vitamin interactions: \n - Alternate medicine: \n - Essential usage instructions: \n"},
];
const result = await model.generateContent({
contents: [{ role: "user", parts }],
generationConfig,
safetySettings,
});
const response = result.response;
console.log(response.text());
}
run();
|
50a97a590685447be76e30a6537c53f0
|
{
"intermediate": 0.41523343324661255,
"beginner": 0.37800338864326477,
"expert": 0.2067631334066391
}
|
42,334
|
I have a code to map bounding boxes from json to image using ocr textract output which in csv format. But I am strugging to get correct bounding boxes for multi token entities. I want some good clustering approch to address this issue. And a better solution to match the entity token from dataframe.
Please modify my code according to my requirements. Code should avoid taking values from anywhere in dataframe. It should always take the nearest word of other token in case of multi token entity.
import json
import pandas as pd
import cv2
from thefuzz import fuzz
from thefuzz import process
import numpy as np
def read_textract_output(csv_path):
return pd.read_csv(csv_path)
def read_json_entities(json_path):
with open(json_path, 'r') as file:
return json.load(file)
def find_entity_bounding_boxes(entity_text, textract_df, image_size):
entity_tokens = entity_text.split()
results = pd.DataFrame()
for token in entity_tokens:
choices = textract_df["text"].dropna().tolist()
best_match, score = process.extractOne(token, choices, scorer=fuzz.token_sort_ratio)
if score > 70:
best_matches_df = textract_df[textract_df["text"] == best_match]
results = pd.concat([results, best_matches_df])
bounding_boxes = []
try:
# Sort by line and word number to cluster words that are on the same line
sorted_results = results.sort_values(by=["line_num", "word_num"])
# Group the resulting bounding boxes by line number
grouped_results = sorted_results.groupby("line_num")
for _, group in grouped_results:
# Calculate scaling factors based on image size and original image dimensions
image_width, image_height = group.iloc[0]["image_width"], group.iloc[0]["image_height"]
scale_x = image_size[0] / image_width
scale_y = image_size[1] / image_height
# Calculate the bounding box for the whole line
min_left = np.min(group["left"])
min_top = np.min(group["top"])
max_right = np.max(group["left"] + group["width"])
max_bottom = np.max(group["top"] + group["height"])
bbox = (min_left * scale_x, min_top * scale_y, (max_right - min_left) * scale_x, (max_bottom - min_top) * scale_y)
bounding_boxes.append(bbox)
except Exception as e:
print(f"An error occurred: {e}")
return bounding_boxes
# def draw_bounding_boxes(image_path, entities, textract_df):
# image = cv2.imread(image_path)
# image_size = image.shape[1], image.shape[0]
# for category, details in entities.items():
# if category == "invoice_details" or category == "Payment Details" or category == "amounts_and_tax":
# for entity, value in details.items():
# if value:
# bounding_boxes = find_entity_bounding_boxes(value, textract_df, image_size)
# try:
# for bbox in bounding_boxes:
# x, y, w, h = map(int, bbox)
# cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
# except:
# pass
# else:
# pass
def draw_bounding_boxes(image_path, entities, textract_df):
image = cv2.imread(image_path)
image_size = image.shape[1], image.shape[0]
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.5
font_color = (0, 0, 0)
line_type = 2
padding = 5 # Padding between the start of bbox and text
for category, details in entities.items():
if category == "invoice_details" or category == "Payment Details" or category == "amounts_and_tax":
for entity, value in details.items():
if value:
bounding_boxes = find_entity_bounding_boxes(value, textract_df, image_size)
try:
for bbox in bounding_boxes:
x, y, w, h = map(int, bbox)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Text placement calculation
text_position = (x, y - padding) if y - padding > 10 else (x, y + h + 20)
# Drawing the text
cv2.putText(image, entity, text_position, font, font_scale, font_color, line_type)
except:
pass
else:
pass
cv2.imwrite('/home/ritik1s/Desktop/bbox_issues/temp_GPT/annotated_invoice.jpg', image)
IMAGE_PATH = '/home/ritik1s/Desktop/bbox_issues/temp_GPT/check.jpeg'
CSV_PATH = "/home/ritik1s/Desktop/bbox_issues/temp_GPT/check.csv"
JSON_PATH = "/home/ritik1s/Desktop/bbox_issues/temp_GPT/row_skip.json"
# Read Textract output and JSON entities
textract_df = read_textract_output(CSV_PATH)
entities = read_json_entities(JSON_PATH)
# Draw the bounding boxes
draw_bounding_boxes(IMAGE_PATH, entities, textract_df)
|
18fd8f9ca40ab7a281ad5a1813d79228
|
{
"intermediate": 0.44175246357917786,
"beginner": 0.28772279620170593,
"expert": 0.2705247104167938
}
|
42,335
|
Okay I have a script that takes my audio files and splits them into features.npy and lables.npy based on if its ai voice or human voice I need to write the pytourch training script but I am stuck here is what I have so far: import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import numpy as np
from sklearn.model_selection import train_test_split
# Assume X is your array of features (e.g., MFCCs) and y are your labels (0 for Human, 1 for AI-generated)
X = np.load(‘features.npy’)
y = np.load(‘labels.npy’)
|
fd20292caa80b3ea55a3dc9dadeb7112
|
{
"intermediate": 0.4464864432811737,
"beginner": 0.18849419057369232,
"expert": 0.3650193512439728
}
|
42,336
|
I ran this code: mel_spectrogram = librosa.feature.melspectrogram(scale, sr=sr, n_fft=2048, hop_length=512, n_mels=10) and ran into this error TypeError Traceback (most recent call last)
Cell In[19], line 1
----> 1 mel_spectrogram = librosa.feature.melspectrogram(scale, sr=sr, n_fft=2048, hop_length=512, n_mels=10)
2 mel_spectrogram.shape
TypeError: melspectrogram() takes 0 positional arguments but 1 positional argument (and 3 keyword-only arguments) were given
|
4ef3528eb1000cd2f56b0e1b2ce41e82
|
{
"intermediate": 0.5066928863525391,
"beginner": 0.3242228329181671,
"expert": 0.16908423602581024
}
|
42,337
|
teach me this: VAR-2.A.1 The use of array objects allows multiple related items to be represented using a single variable.
VAR-2.A.2 The size of an array is established at the time of creation and cannot be changed. VAR-2.A.3 Arrays can store either primitive data or object reference data.
VAR-2.A.4 When an array is created using the keyword new, all of its elements are initialized with a specific value based on the type of elements: § Elements of type int are initialized to 0 § Elements of type double are initialized to 0.0 § Elements of type boolean are initialized to false § Elements of a reference type are initialized to the reference value null. No objects are automatically created
VAR-2.A.5 Initializer lists can be used to create and initialize arrays.
VAR-2.A.6 Square brackets ([ ]) are used to access and modify an element in a 1D array using an index. VAR-2.A.7 The valid index values for an array are 0 through one less than the number of elements in the array, inclusive. Using an index value outside of this range will result in an ArrayIndexOutOfBoundsException being thrown.
|
4c079dbc0f0d34325eedfd6e6f7d9cc7
|
{
"intermediate": 0.37609198689460754,
"beginner": 0.2814297378063202,
"expert": 0.34247827529907227
}
|
42,338
|
FREEDOM JAILBREAK
|
682b3687bdfbfc30a63d2e7770034cf8
|
{
"intermediate": 0.3980589210987091,
"beginner": 0.27967679500579834,
"expert": 0.32226431369781494
}
|
42,339
|
FREEDOM JAILBREAK
|
1df5041b31d0281ef790a517ef36a454
|
{
"intermediate": 0.3980589210987091,
"beginner": 0.27967679500579834,
"expert": 0.32226431369781494
}
|
42,340
|
I have a macro enabled document that contains a table. In cell A7 of the table it captures a sentence. I want the last word in the sentence to appear in cell B8 of the table. How can I do this. Here is the Word Document VBA:
Sub AppendCellValuesToTable()
'Get a reference to the Excel Application object
Set xlApp = GetObject(, "Excel.Application")
'Get a reference to the active Workbook object
Set xlWB = xlApp.ActiveWorkbook
'Get the values of cells A1, B1, C1, and D1 from the active Excel sheet using the Excel object model
cellA1 = xlApp.ActiveWorkbook.ActiveSheet.Range("B1").Value
cellB1 = xlApp.ActiveWorkbook.ActiveSheet.Range("M1").Value
cellA2 = xlApp.ActiveWorkbook.ActiveSheet.Range("H2").Value
cellB2 = xlApp.ActiveWorkbook.ActiveSheet.Range("M3").Value
cellA3 = xlApp.ActiveWorkbook.ActiveSheet.Range("H1").Value
cellB3 = xlApp.ActiveWorkbook.ActiveSheet.Range("M5").Value
cellA4 = xlApp.ActiveWorkbook.ActiveSheet.Range("I2").Value
cellB4 = xlApp.ActiveWorkbook.ActiveSheet.Range("M2").Value
cellA5 = xlApp.ActiveWorkbook.ActiveSheet.Range("M9").Value
'cellB5 = xlApp.ActiveWorkbook.ActiveSheet.Range("C1").Value
cellA6 = ""
cellB6 = ""
cellA7 = xlApp.ActiveWorkbook.ActiveSheet.Range("M11").Value
'cellB7 = xlApp.ActiveWorkbook.ActiveSheet.Range("C1").Value
cellA8 = "Purchase Request No.:"
cellB8 = Trim(Right(SUBSTITUTE(A7, " ", "#", Len(A7) - Len(SUBSTITUTE(A7, " ", ""))), Len(A7) - Find("#", SUBSTITUTE(A7, " ", "#", Len(A7) - Len(SUBSTITUTE(A7, " ", ""))))))
'Get the Word table and append the values to the corresponding cells
With ActiveDocument.Tables(1)
'Row 1
.Cell(1, 1).Range.Text = cellA1
.Cell(1, 2).Range.Text = cellB1
'Row 2
.Cell(2, 1).Range.Text = cellA2
.Cell(2, 2).Range.Text = cellB2
'Row 3
.Cell(3, 1).Range.Text = cellA3
.Cell(3, 2).Range.Text = cellB3
'Row 4
.Cell(4, 1).Range.Text = cellA4
.Cell(4, 2).Range.Text = cellB4
'Row 5
.Cell(5, 1).Range.Text = cellA5
'.Cell(5, 2).Range.Text = cellB5
'Row 6
.Cell(6, 1).Range.Text = cellA6
.Cell(6, 2).Range.Text = cellB6
'Row 7
.Cell(7, 1).Range.Text = cellA7
'.Cell(7, 2).Range.Text = cellB7
'Row 8
.Cell(8, 1).Range.Text = cellA8
.Cell(8, 2).Range.Text = cellB8
End With
End Sub
|
c036745dd92a347f69d2b13a7a9e7c29
|
{
"intermediate": 0.3034801185131073,
"beginner": 0.3874303698539734,
"expert": 0.3090895116329193
}
|
42,341
|
Create a question: 6.1
Make arrays- multiple ways
Set values in arrays- multiple ways
(find the error question java)
Idea: names array with Srinath, ganesh, daksh and armaan
set the second element to be Srinath, which one is wrong
Question body
Responses (or correct response if it is a complete the code or find the error)
Explanation of the correct answer
Tip
Difficulty (easy, medium, hard)
|
99ef431dccd4418fd12585ed491268a5
|
{
"intermediate": 0.4100385010242462,
"beginner": 0.29251766204833984,
"expert": 0.29744377732276917
}
|
42,342
|
I am making a C++ SDL based game engine, I am finishing the InputManager, let know what you think about it and what fixes can I make?
Take into account:
- It is a singleton, and is called once, but I don't need to explicitly do anything for thread safe because it is already.
- There is a TODO I didn't know if I should implement it or not, because I think I deal with that in two other methods: GamepadButtonPressed and GamepadButtonReleased., help me decide this.
- Yes it is a big mess of Update() but I will be improving upon this when I deal with the EventManager
class InputManager
{
public:
~InputManager();
InputManager(const InputManager&) = delete;
InputManager operator=(const InputManager&) = delete;
static InputManager& GetInstance() noexcept;
void Update();
bool IsKeyPressed(Input::KeyCode key) const;
bool IsKeyReleased(Input::KeyCode key) const;
bool IsMouseButtonPressed(Input::MouseButton button) const;
Point GetMousePosition() const;
bool IsGamepadButtonPressed(Input::GamepadButton button, int gamepadIndex) const;
bool IsGamepadButtonReleased(Input::GamepadButton button, int gamepadIndex) const;
private:
InputManager();
void UpdateKeyboardState();
void SetMouseState(uint8_t mouseButton, bool pressed);
SDL_GameController* GetGamepad(int index);
void GamepadButtonPressed(uint8_t button, int gamepadIndex);
void GamepadButtonReleased(uint8_t button, int gamepadIndex);
void GamepadOpen(int gamepadIndex);
void GamepadClose(int gamepadIndex);
std::unordered_map<Input::KeyCode, bool> currentKeyboardState;
std::unordered_map<Input::KeyCode, bool> previousKeyboardState;
std::unordered_map<Input::MouseButton, bool> mouseButtonsState;
std::vector<SDL_GameController*> gamepads;
std::vector<std::unordered_map<Input::GamepadButton, bool>> currentGamepadState;
std::vector<std::unordered_map<Input::GamepadButton, bool>> previousGamepadState;
};
uint8_t KeyToSDL(Input::KeyCode keyCode);
Input::KeyCode KeyFromSDL(uint8_t sdlKeyCode);
uint8_t MouseButtonToSDL(Input::MouseButton mouseButton);
Input::MouseButton MouseButtonFromSDL(uint8_t sdlMouseButton);
SDL_GameControllerButton GamepadButtonToSDL(Input::GamepadButton gamepadButton);
Input::GamepadButton GamepadButtonFromSDL(SDL_GameControllerButton sdlGamepadButton);
InputManager::InputManager()
{
}
InputManager::~InputManager()
{
for (SDL_GameController* gamepad : gamepads)
{
if (gamepad)
{
SDL_GameControllerClose(gamepad);
}
}
}
InputManager& InputManager::GetInstance() noexcept
{
static InputManager instance;
return instance;
}
void InputManager::Update()
{
previousKeyboardState = currentKeyboardState;
SDL_Event event;
while (SDL_PollEvent(&event))
{
switch (event.type)
{
case SDL_MOUSEBUTTONUP:
{
SetMouseState(event.button.button, false);
break;
}
case SDL_MOUSEBUTTONDOWN:
{
SetMouseState(event.button.button, true);
break;
}
case SDL_CONTROLLERDEVICEADDED:
{
GamepadOpen(event.cdevice.which);
break;
}
case SDL_CONTROLLERDEVICEREMOVED:
{
GamepadClose(event.cdevice.which);
break;
}
case SDL_CONTROLLERBUTTONDOWN:
{
GamepadButtonPressed(event.cbutton.button, event.cbutton.which);
break;
}
case SDL_CONTROLLERBUTTONUP:
{
GamepadButtonReleased(event.cbutton.button, event.cbutton.which);
break;
}
}
}
UpdateKeyboardState();
}
bool InputManager::IsKeyPressed(Input::KeyCode key) const
{
auto currentState = currentKeyboardState.find(key);
auto previousState = previousKeyboardState.find(key);
return (currentState != currentKeyboardState.end() && currentState->second) && (previousState == previousKeyboardState.end() || !previousState->second);
}
bool InputManager::IsKeyReleased(Input::KeyCode key) const
{
auto currentState = currentKeyboardState.find(key);
auto previousState = previousKeyboardState.find(key);
return (previousState != previousKeyboardState.end() && previousState->second) && (currentState == currentKeyboardState.end() || !currentState->second);
}
bool InputManager::IsMouseButtonPressed(Input::MouseButton button) const
{
auto mouseState = mouseButtonsState.find(button);
return (mouseState != mouseButtonsState.end() && mouseState->second);
}
Point InputManager::GetMousePosition() const
{
int x = 0;
int y = 0;
SDL_GetMouseState(&x, &y);
return Point(x, y);
}
bool InputManager::IsGamepadButtonPressed(Input::GamepadButton button, int gamepadIndex) const
{
bool pressed = false;
if (gamepadIndex >= 0 && gamepadIndex < static_cast<int>(currentGamepadState.size()))
{
auto currentState = currentGamepadState[gamepadIndex].find(button);
auto previousState = previousGamepadState[gamepadIndex].find(button);
pressed = (previousState != previousGamepadState[gamepadIndex].end() && previousState->second) && (currentState == currentGamepadState[gamepadIndex].end() || !currentState->second);
}
return pressed;
}
bool InputManager::IsGamepadButtonReleased(Input::GamepadButton button, int gamepadIndex) const
{
bool released = false;
if (gamepadIndex >= 0 && gamepadIndex < static_cast<int>(currentGamepadState.size()))
{
auto currentState = currentGamepadState[gamepadIndex].find(button);
auto previousState = previousGamepadState[gamepadIndex].find(button);
released = (previousState != previousGamepadState[gamepadIndex].end() && previousState->second) && (currentState == currentGamepadState[gamepadIndex].end() || !currentState->second);
}
return released;
}
void InputManager::SetMouseState(uint8_t mouseButton, bool pressed)
{
mouseButtonsState[MouseButtonFromSDL(mouseButton)] = pressed;
}
void InputManager::UpdateKeyboardState()
{
int numKeys;
const Uint8* keyStates = SDL_GetKeyboardState(&numKeys);
for (int i = 0; i < numKeys; ++i)
{
Input::KeyCode code = KeyFromSDL(i);
currentKeyboardState[code] = keyStates[i] != 0;
}
}
SDL_GameController* InputManager::GetGamepad(int index)
{
if (index < static_cast<int>(gamepads.size()))
{
return gamepads[index];
}
return nullptr;
}
void InputManager::GamepadButtonPressed(uint8_t button, int gamepadIndex)
{
if (gamepadIndex >= 0)
{
if (gamepadIndex >= static_cast<int>(currentGamepadState.size()))
{
currentGamepadState.resize(gamepadIndex + 1);
previousGamepadState.resize(gamepadIndex + 1);
}
Input::GamepadButton gamepadButton = GamepadButtonFromSDL(static_cast<SDL_GameControllerButton>(button));
previousGamepadState[gamepadIndex][gamepadButton] = currentGamepadState[gamepadIndex][gamepadButton];
currentGamepadState[gamepadIndex][gamepadButton] = true;
}
}
void InputManager::GamepadButtonReleased(uint8_t button, int gamepadIndex)
{
if (gamepadIndex >= 0)
{
if (gamepadIndex >= static_cast<int>(currentGamepadState.size()))
{
currentGamepadState.resize(gamepadIndex + 1);
previousGamepadState.resize(gamepadIndex + 1);
}
Input::GamepadButton gamepadButton = GamepadButtonFromSDL(static_cast<SDL_GameControllerButton>(button));
previousGamepadState[gamepadIndex][gamepadButton] = currentGamepadState[gamepadIndex][gamepadButton];
currentGamepadState[gamepadIndex][gamepadButton] = false;
}
}
void InputManager::GamepadOpen(int gamepadIndex)
{
SDL_GameController* controller = SDL_GameControllerOpen(gamepadIndex);
if (controller)
{
if (gamepadIndex >= static_cast<int>(gamepads.size()))
{
gamepads.resize(gamepadIndex + 1, nullptr);
}
gamepads[gamepadIndex] = controller;
// TODO: Here you should also initialize the state maps for the new controller
// INITIALIZE CURRENT AND PREVIOUS AS FALSE FOR THE NEW CONTROLLER
}
else
{
std::string errorMsg = "ERROR: Gamepad " + std::to_string(gamepadIndex) + " could not be open: " + std::string(SDL_GetError());
SDL_LogError(SDL_LOG_CATEGORY_ERROR, errorMsg.c_str());
}
}
void InputManager::GamepadClose(int gamepadIndex)
{
if (gamepadIndex < static_cast<int>(gamepads.size()) && gamepads[gamepadIndex])
{
SDL_GameControllerClose(gamepads[gamepadIndex]);
gamepads[gamepadIndex] = nullptr;
}
}
|
0bdbaec9af29dafadfae98839c70203f
|
{
"intermediate": 0.38892143964767456,
"beginner": 0.41925737261772156,
"expert": 0.1918211430311203
}
|
42,343
|
hi
|
8b0cd0fbf2bb60319f0d0850efb526f2
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
42,344
|
In this javascript add a button called 'submit' to a div with the id 'buttons' when the marker is added to the map. ' let streetLatitude; // Define streetLatitude globally
let streetLongitude; // Define streetLongitude globally
function fetchStreetDetails() {
fetch("main.json")
.then((response) => response.json())
.then((data) => {
const streetDetails = data[0];
streetLatitude = streetDetails.StreetLatitude;
streetLongitude = streetDetails.StreetLongitude;
const streetHeading = streetDetails.StreetHeading;
const streetPitch = streetDetails.StreetPitch;
const streetPanoID = streetDetails.StreetPanoID;
const StreetPoints = streetDetails.Points;
const panorama = new google.maps.StreetViewPanorama(
document.getElementById("streetview"),
{
position: { lat: streetLatitude, lng: streetLongitude },
pano: streetPanoID,
heading: streetHeading,
pitch: streetPitch,
}
);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
})
.catch((error) => console.error("Error fetching data: ", error));
}
fetchStreetDetails();
const startingLocation = { lat: 51.540073, lng: -0.010874 }; // London Aquatics Center coordinates
function initMap() {
const zoom = 8;
const map = new google.maps.Map(document.getElementById("map"), {
center: startingLocation,
zoom: zoom,
mapId: "DEMO_MAP_ID",
});
// Function to add marker on click
function addMarker(event) {
const clickLocation = event.latLng;
const marker = new google.maps.Marker({
position: clickLocation,
map: map,
draggable: true // Set draggable to true
});
// Remove the click listener after the marker is added
google.maps.event.clearListeners(map, 'click');
// Calculate distance on marker add using Pythagorean theorem
const distance = calculateDistance(
clickLocation.lat(),
clickLocation.lng(),
streetLatitude,
streetLongitude
);
console.log("Distance from marker to street: " + distance + " meters");
const message = "You are " + distance + " meters from the correct location.";
// Update the 'results' div using DOM manipulation
const resultsDiv = document.getElementById('results');
resultsDiv.textContent = message;
// Createpolyline on marker add
drawPolyline(clickLocation);
}
// Add click listener to the map
map.addListener("click", addMarker);
function calculateDistance(lat1, lng1, lat2, lng2) {
const deltaLat = (lat2 - lat1) * Math.PI / 180;
const deltaLng = (lng2 - lng1) * Math.PI / 180;
const earthRadius = 6371e3; // meters
const a = Math.sin(deltaLat / 2) * Math.sin(deltaLat / 2);
const b =
Math.cos((lat1 * Math.PI) / 180) * Math.cos((lat2 * Math.PI) / 180) * Math.sin(deltaLng / 2) * Math.sin(deltaLng / 2);
const c = 2 * Math.atan2(Math.sqrt(a + b), Math.sqrt(1 - a - b));
// Calculate distance using Pythagorean theorem
const distance = earthRadius * c;
// Round the distance to nearest meter using Math.round()
const roundedDistance = Math.round(distance);
return roundedDistance;
}
// Function to draw polyline between marker and street location
function drawPolyline(clickLocation) {
const polyline = new google.maps.Polyline({
path: [clickLocation.toJSON(), { lat: streetLatitude, lng: streetLongitude }],
strokeColor: "#FF0000", // red color
strokeWeight: 2,
map: map,
});
}
}
'
|
646ad986b9af5e11b84b4c07cecc5893
|
{
"intermediate": 0.3572939336299896,
"beginner": 0.45875075459480286,
"expert": 0.1839553564786911
}
|
42,345
|
Create a question following this format for this content:
Make arrays- multiple ways
Set values in arrays- multiple ways
Question type- multiple choice (you get 4 choices), complete the code, find the error
Question body
Responses (or correct response if it is a complete the code or find the error)
Explanation of the correct answer
Tip
Difficulty (easy, medium, hard)
|
b2aa82200835c862d0969b680f500646
|
{
"intermediate": 0.4254123866558075,
"beginner": 0.2916397154331207,
"expert": 0.28294792771339417
}
|
42,346
|
Create a question following this format for this content:
Make arrays- multiple ways
Set values in arrays- multiple ways
Question type- multiple choice (you get 4 choices), complete the code, find the error
Question body
Responses (or correct response if it is a complete the code or find the error)
Explanation of the correct answer
Tip
Difficulty (hard)
|
422a46c8b6c46426e9a1b6f10a7a1723
|
{
"intermediate": 0.4694373905658722,
"beginner": 0.2517012059688568,
"expert": 0.2788613736629486
}
|
42,347
|
(java)Create a question following this format for this content:
Make arrays- multiple ways
Set values in arrays- multiple ways
Question type- multiple choice (you get 4 choices), complete the code, find the error
Question body
Responses (or correct response if it is a complete the code or find the error)
Explanation of the correct answer
Tip
Difficulty (hard)
|
84347f363a24668773f5c3fb1f4b1a0b
|
{
"intermediate": 0.4761827886104584,
"beginner": 0.3240993916988373,
"expert": 0.19971783459186554
}
|
42,348
|
(java) Create a question following this format for this content:
Make arrays- multiple ways
Set values in arrays- multiple ways
Idea: use these names, armaan, ganesh, daksh , srinath
Question type- multiple choice (you get 4 choices), complete the code, find the error
Question body
Responses (or correct response if it is a complete the code or find the error)
Explanation of the correct answer
Tip
Difficulty (easy, medium, hard)
|
4cbd06a522f546a04d63bc9834fb397b
|
{
"intermediate": 0.44266560673713684,
"beginner": 0.33223873376846313,
"expert": 0.22509562969207764
}
|
42,349
|
please add more error handling code to my code, *code*: import torch
import torch.nn as nn
import torch.nn.functional as F
import json
import math
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset
from collections import Counter
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
from tokenizers import Tokenizer
# ---------- Device Configuration ----------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# ---------- Utility Functions ----------
def positional_encoding(seq_len, d_model, device):
pos = torch.arange(seq_len, dtype=torch.float, device=device).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)).to(device)
pe = torch.zeros(seq_len, d_model, device=device)
pe[:, 0::2] = torch.sin(pos * div_term)
pe[:, 1::2] = torch.cos(pos * div_term)
return pe.unsqueeze(0)
# -------- Performance ----------
def evaluate_model(model, data_loader, device):
model.eval()
all_preds, all_targets = [], []
with torch.no_grad():
for inputs, targets in data_loader:
inputs, targets = inputs.to(device), targets.to(device)
outputs = model(inputs)
predictions = torch.argmax(outputs, dim=-1).view(-1) # Flatten predicted indices
all_preds.extend(predictions.cpu().numpy())
all_targets.extend(targets.view(-1).cpu().numpy()) # Ensure targets are also flattened
# Calculate precision, recall, and F1 score after ensuring all_preds and all_targets are correctly aligned.
accuracy = accuracy_score(all_targets, all_preds)
precision = precision_score(all_targets, all_preds, average='macro', zero_division=0)
recall = recall_score(all_targets, all_preds, average='macro', zero_division=0)
f1 = f1_score(all_targets, all_preds, average='macro', zero_division=0)
print(f"Accuracy: {accuracy:.4f}")
print(f"Precision: {precision:.4f}")
print(f"Recall: {recall:.4f}")
print(f"F1 Score: {f1:.4f}")
return accuracy ,precision, recall, f1
# Function to plot loss over time
def plot_loss(loss_history):
plt.figure(figsize=(10, 5))
plt.plot(loss_history, label='Training Loss')
plt.xlabel('Batches')
plt.ylabel('Loss')
plt.title('Training Loss Over Time')
plt.legend()
plt.show()
# ---------- Model Definitions ----------
class TransformerExpert(nn.Module):
def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers=1):
super(TransformerExpert, self).__init__()
self.d_model = d_model
self.input_fc = nn.Linear(input_size, d_model)
self.pos_encoder = nn.Parameter(positional_encoding(1, d_model, device), requires_grad=True)
encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True,
norm_first=True)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_encoder_layers)
self.output_fc = nn.Linear(d_model, output_size)
self.norm = nn.LayerNorm(d_model)
def forward(self, x):
x = self.norm(self.input_fc(x)) + self.pos_encoder
transformer_output = self.transformer_encoder(x)
output = self.output_fc(transformer_output)
return output
class GatingNetwork(nn.Module):
def __init__(self, input_feature_dim, num_experts, hidden_dims=[512], dropout_rate=0.1):
super(GatingNetwork, self).__init__()
layers = []
last_dim = input_feature_dim
for hidden_dim in hidden_dims:
layers.extend([
nn.Linear(last_dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout_rate),
])
last_dim = hidden_dim
layers.append(nn.Linear(last_dim, num_experts))
self.fc_layers = nn.Sequential(*layers)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = x.mean(dim=1) # To ensure gating is based on overall features across the sequence
x = self.fc_layers(x)
return self.softmax(x)
class MixtureOfTransformerExperts(nn.Module):
def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_experts, num_encoder_layers=1):
super(MixtureOfTransformerExperts, self).__init__()
self.num_experts = num_experts
self.output_size = output_size
self.experts = nn.ModuleList([TransformerExpert(input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers) for _ in range(num_experts)])
self.gating_network = GatingNetwork(d_model, num_experts)
def forward(self, x):
gating_scores = self.gating_network(x)
expert_outputs = [expert(x) for expert in self.experts]
stacked_expert_outputs = torch.stack(expert_outputs)
expanded_gating_scores = gating_scores.unsqueeze(2).unsqueeze(3)
expanded_gating_scores = expanded_gating_scores.expand(-1, -1, x.size(1), self.output_size)
expanded_gating_scores = expanded_gating_scores.transpose(0, 1)
mixed_output = torch.sum(stacked_expert_outputs * expanded_gating_scores, dim=0)
return mixed_output
class MoETransformerModel(nn.Module):
def __init__(self, vocab_size, d_model, moe):
super(MoETransformerModel, self).__init__()
self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=d_model)
self.moe = moe
self.dropout = nn.Dropout(p=0.1)
def forward(self, x):
embedded = self.dropout(self.embedding(x))
return self.moe(embedded)
# ---------- Dataset Definitions ----------
class QAJsonlDataset(Dataset):
def __init__(self, path, seq_len, tokenizer_path):
# Load the trained tokenizer
self.tokenizer = Tokenizer.from_file(tokenizer_path)
self.seq_len = seq_len
self.pairs = self.load_data(path)
# Using BPE, so no need for manual vocab or idx2token.
# Tokenization will now happen using self.tokenizer.
self.tokenized_pairs = [(self.tokenize(q), self.tokenize(a)) for q, a in self.pairs]
def load_data(self, path):
pairs = []
with open(path, "r", encoding="utf-8") as f:
for line in f:
data = json.loads(line.strip())
question, answer = data.get("question", ""), data.get("answer", "")
pairs.append((question, answer)) # Store questions and answers as raw strings
return pairs
def tokenize(self, text):
# Tokenizing using the BPE tokenizer
encoded = self.tokenizer.encode(text)
tokens = encoded.ids
# Padding/truncation
if len(tokens) < self.seq_len:
# Padding
tokens += [self.tokenizer.token_to_id("<pad>")] * (self.seq_len - len(tokens))
else:
# Truncation
tokens = tokens[:self.seq_len - 1] + [self.tokenizer.token_to_id("<eos>")]
return tokens
def __len__(self):
return len(self.tokenized_pairs)
def __getitem__(self, idx):
tokenized_question, tokenized_answer = self.tokenized_pairs[idx]
return torch.tensor(tokenized_question, dtype=torch.long), torch.tensor(tokenized_answer, dtype=torch.long)
def collate_fn(batch):
questions, answers = zip(*batch)
questions = pad_sequence(questions, batch_first=True, padding_value=0)
answers = pad_sequence(answers, batch_first=True, padding_value=0)
return questions, answers
# ---------- Training and Inference Functions ----------
def train_model(model, criterion, optimizer, num_epochs, data_loader):
model.train()
loss_history = [] # Initialize a list to keep track of losses
for epoch in range(num_epochs):
total_loss = 0
total_items = 0 # Keep track of total items processed
progress_bar = tqdm(enumerate(data_loader), total=len(data_loader), desc=f"Epoch {epoch+1}", leave=False)
for i, (inputs, targets) in progress_bar:
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
# Predict
predictions = model(inputs)
predictions = predictions.view(-1, predictions.size(-1)) # Make sure predictions are the right shape
targets = targets.view(-1) # Flatten targets to match prediction shape if necessary
# Calculate loss
loss = criterion(predictions, targets)
loss.backward()
# Gradient clipping for stabilization
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
# Update total loss and the number of items
total_loss += loss.item() * inputs.size(0) # Multiply loss by batch size
total_items += inputs.size(0)
loss_history.append(loss.item())
progress_bar.set_postfix({"Loss": loss.item()})
average_loss = total_loss / total_items # Correctly compute average loss
print(f"Epoch {epoch+1}, Average Loss: {average_loss:.6f}")
return loss_history
class WarmupLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, warmup_steps, scheduler_step_lr):
self.warmup_steps = warmup_steps
self.scheduler_step_lr = scheduler_step_lr # The subsequent scheduler
super(WarmupLR, self).__init__(optimizer)
def get_lr(self):
if self._step_count <= self.warmup_steps:
warmup_factor = float(self._step_count) / float(max(1, self.warmup_steps))
for base_lr in self.base_lrs:
yield base_lr * warmup_factor
else:
self.scheduler_step_lr.step() # Update the subsequent scheduler
for param_group in self.optimizer.param_groups:
yield param_group['lr']
class GERU(nn.Module):
def __init__(self, in_features):
super(GERU, self).__init__()
self.alpha = nn.Parameter(torch.rand(in_features))
def forward(self, x):
return torch.max(x, torch.zeros_like(x)) + self.alpha * torch.min(x, torch.zeros_like(x))
def generate_text(model, tokenizer, seed_text, num_generate, temperature=1.0):
model.eval()
generated_tokens = []
# Encode the seed text using the tokenizer
encoded_input = tokenizer.encode(seed_text)
input_ids = torch.tensor(encoded_input.ids, dtype=torch.long).unsqueeze(0).to(device)
# Generate num_generate tokens
with torch.no_grad():
for _ in range(num_generate):
output = model(input_ids)
# Get the last logits and apply temperature
logits = output[:, -1, :] / temperature
probabilities = F.softmax(logits, dim=-1)
next_token_id = torch.multinomial(probabilities, num_samples=1).item()
# Append generated token ID and prepare the new input_ids
generated_tokens.append(next_token_id)
input_ids = torch.cat([input_ids, torch.tensor([[next_token_id]], dtype=torch.long).to(device)], dim=1)
# Decode the generated token IDs back to text
generated_text = tokenizer.decode(generated_tokens)
return generated_text
def count_tokens_in_dataset(dataset):
return sum([len(pair[0]) + len(pair[1]) for pair in dataset.pairs])
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
# ---------- Hyperparameters and Model Instantiation ----------
# Transformer :
d_model = 128
nhead = 4
dim_feedforward = 512
num_encoder_layers = 8
num_experts = 1
hidden_dims = []
dropout_rate = 0.3
# Gating Network :
gating_network = GatingNetwork(
input_feature_dim=d_model,
num_experts=num_experts,
hidden_dims=hidden_dims,
dropout_rate=dropout_rate,
)
# Training Parameters
batch_size = 32 # Adjustable batch size
warmup_steps = 6000 # Warmup steps for learning rate
optimizer_type = "AdamW" # Could be “SGD”, “RMSprop”, etc.
learning_rate = 9e-2
weight_decay = 1e-5 # For L2 regularization
num_epochs = 4
# Dataset :
path_to_dataset = "C:/Users/L14/Documents/Projets/Easy-MoE/Easy-MoE/data/Training-math-4K.jsonl"
tokenizer_path = "BPE_tokenizer(Training-math-4K).json"
seq_len = 32
dataset = QAJsonlDataset(path_to_dataset, seq_len, tokenizer_path)
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn, pin_memory=True)
num_tokens = count_tokens_in_dataset(dataset)
print(f"Total number of tokens in the dataset: {num_tokens}")
# Load the tokenizer
tokenizer = Tokenizer.from_file(tokenizer_path)
# Determine the vocabulary size
vocab_size = tokenizer.get_vocab_size()
moe = MixtureOfTransformerExperts(
input_size=d_model,
d_model=d_model,
output_size=vocab_size,
nhead=nhead,
dim_feedforward=dim_feedforward,
num_experts=num_experts,
num_encoder_layers=num_encoder_layers
).to(device)
moe_transformer_model = MoETransformerModel(vocab_size, d_model, moe).to(device)
# Count of total parameters :
total_params = count_parameters(moe_transformer_model)
print(f"Total trainable parameters: {total_params}")
# ---------- Training ----------
# Adjusting optimizer setup to include weight decay and allow switching between types
if optimizer_type == "AdamW":
optimizer = torch.optim.AdamW(moe_transformer_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
elif optimizer_type == "SGD":
optimizer = torch.optim.SGD(moe_transformer_model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=weight_decay)
elif optimizer_type == "Adam":
optimizer = torch.optim.Adam(moe_transformer_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# Instantiate your main scheduler (StepLR)
step_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.925)
# Wrap it with WarmupLR
scheduler = WarmupLR(optimizer, warmup_steps=warmup_steps, scheduler_step_lr=step_lr_scheduler)
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
# Train the model
loss_history = train_model(moe_transformer_model, criterion, optimizer, num_epochs, data_loader)
# Evaluating the model
plot_loss(loss_history)
train_accuracy = evaluate_model(moe_transformer_model, data_loader, device)
# ---------- Inference ----------
def interactive_text_generation(model, dataset, max_length=32, temperature=1.0):
while True:
try:
# Get user input
seed_text = input("Enter seed text (type 'quit' to exit and save the model): ").strip()
# Check if the user wants to quit the interaction
if seed_text.lower() == 'quit':
print("Exiting text generation mode.")
break
# Generate text based on the seed text
if seed_text:
generated_text = generate_text(model, dataset, seed_text, max_length, temperature) # Modify max_length/temperature as needed
print("Generated Text:", generated_text)
else:
print("Seed text cannot be empty. Please enter some text.")
except Exception as e:
# Handle potential errors gracefully
print(f"An error occurred: {e}. Try again.")
interactive_text_generation(moe_transformer_model, tokenizer)
# ---------- Save Trained Model ----------
torch.save(moe_transformer_model.state_dict(), "Transformer-Alpha-v04.pth")
|
621a3125401886842b5770d841d10ea3
|
{
"intermediate": 0.44640052318573,
"beginner": 0.3089933693408966,
"expert": 0.24460609257221222
}
|
42,350
|
how to evaluate the product "Facebook Groups" is good, and how to decide whether to launch a new feature like FB comment can be folded?
|
5c76ef51ddebfe1b1b345521ad967f36
|
{
"intermediate": 0.3367089629173279,
"beginner": 0.23182207345962524,
"expert": 0.4314689338207245
}
|
42,351
|
TypeError Traceback (most recent call last)
Cell In[12], line 2
1 # Create Dataset instances for training and validation sets
----> 2 train_dataset = AudioDataset(X_train, y_train)
3 val_dataset = AudioDataset(X_val, y_val)
File /anaconda/envs/azureml_py38_PT_TF/lib/python3.8/typing.py:875, in Generic.__new__(cls, *args, **kwds)
873 obj = super().__new__(cls)
874 else:
--> 875 obj = super().__new__(cls, *args, **kwds)
876 return obj
TypeError: object.__new__() takes exactly one argument (the type to instantiate) # Create Dataset instances for training and validation sets
train_dataset = AudioDataset(X_train, y_train)
val_dataset = AudioDataset(X_val, y_val) help
|
dc9454330e2d3f56c04e1592ff8f3fda
|
{
"intermediate": 0.3810226619243622,
"beginner": 0.392776221036911,
"expert": 0.22620117664337158
}
|
42,352
|
In my word macro VBA I am getting an error on the line: cellB2 = xlSheet("H1").Value, in the following code: Sub AppendRowValuesToTable()
'Dim xlApp As Excel.Application
'Dim xlWB As Excel.Workbook
'Dim xlSheet As Excel.Worksheet
Dim selectedRow As Integer
'Get a reference to the Excel Application object
Set xlApp = GetObject(, "Excel.Application")
'Get a reference to the active Workbook object
Set xlWB = xlApp.ActiveWorkbook
'Get the active worksheet in Excel
Set xlSheet = xlApp.ActiveSheet
'Get the currently selected row in Excel
selectedRow = xlApp.Selection.Row
'Get the values of cells A to F from the selected row in Excel
cellA1 = "Contractor: "
cellB1 = xlSheet.Cells(selectedRow, 1).Value
cellA2 = "Email: "
'cellB2 = xlSheet.Cells(selectedRow, 1).Value
cellB2 = xlSheet("H1").Value
cellA3 = "Request: "
cellB3 = xlSheet.Cells(selectedRow, 2).Value
cellA4 = "Your Ref: "
cellB4 = xlSheet.Cells(selectedRow, 3).Value
cellA5 = "Value: "
cellB5 = "£" & xlSheet.Cells(selectedRow, 4).Value
cellA6 = "P Request: "
cellB6 = xlSheet.Cells(selectedRow, 5).Value
cellA7 = "PR Date: "
cellB7 = xlSheet.Cells(selectedRow, 6).Value
cellA8 = "Purchase Order: "
cellB8 = xlSheet.Cells(selectedRow, 7).Value
cellA9 = "PO Date: "
cellB9 = xlSheet.Cells(selectedRow, 8).Value
|
da6b8d7b3f4ebcc1020fa7eed6955e28
|
{
"intermediate": 0.438772052526474,
"beginner": 0.3147570788860321,
"expert": 0.24647080898284912
}
|
42,353
|
check the code and wait for my question on it, **code**: import torch
import torch.nn as nn
import torch.nn.functional as F
import json
import math
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset
from collections import Counter
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
from tokenizers import Tokenizer
# ---------- Device Configuration ----------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# ---------- Utility Functions ----------
def positional_encoding(seq_len, d_model, device):
pos = torch.arange(seq_len, dtype=torch.float, device=device).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)).to(device)
pe = torch.zeros(seq_len, d_model, device=device)
pe[:, 0::2] = torch.sin(pos * div_term)
pe[:, 1::2] = torch.cos(pos * div_term)
return pe.unsqueeze(0)
# -------- Performance ----------
def evaluate_model(model, data_loader, device):
model.eval()
all_preds, all_targets = [], []
with torch.no_grad():
for inputs, targets in data_loader:
inputs, targets = inputs.to(device), targets.to(device)
outputs = model(inputs)
predictions = torch.argmax(outputs, dim=-1).view(-1) # Flatten predicted indices
all_preds.extend(predictions.cpu().numpy())
all_targets.extend(targets.view(-1).cpu().numpy()) # Ensure targets are also flattened
# Calculate precision, recall, and F1 score after ensuring all_preds and all_targets are correctly aligned.
accuracy = accuracy_score(all_targets, all_preds)
precision = precision_score(all_targets, all_preds, average='macro', zero_division=0)
recall = recall_score(all_targets, all_preds, average='macro', zero_division=0)
f1 = f1_score(all_targets, all_preds, average='macro', zero_division=0)
print(f"Accuracy: {accuracy:.4f}")
print(f"Precision: {precision:.4f}")
print(f"Recall: {recall:.4f}")
print(f"F1 Score: {f1:.4f}")
return accuracy ,precision, recall, f1
# Function to plot loss over time
def plot_loss(loss_history):
plt.figure(figsize=(10, 5))
plt.plot(loss_history, label='Training Loss')
plt.xlabel('Batches')
plt.ylabel('Loss')
plt.title('Training Loss Over Time')
plt.legend()
plt.show()
# ---------- Model Definitions ----------
class TransformerExpert(nn.Module):
def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers=1):
super(TransformerExpert, self).__init__()
self.d_model = d_model
self.input_fc = nn.Linear(input_size, d_model)
self.pos_encoder = nn.Parameter(positional_encoding(1, d_model, device), requires_grad=True)
encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True,
norm_first=True)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_encoder_layers)
self.output_fc = nn.Linear(d_model, output_size)
self.norm = nn.LayerNorm(d_model)
def forward(self, x):
x = self.norm(self.input_fc(x)) + self.pos_encoder
transformer_output = self.transformer_encoder(x)
output = self.output_fc(transformer_output)
return output
class GatingNetwork(nn.Module):
def __init__(self, input_feature_dim, num_experts, hidden_dims=[512], dropout_rate=0.1):
super(GatingNetwork, self).__init__()
layers = []
last_dim = input_feature_dim
for hidden_dim in hidden_dims:
layers.extend([
nn.Linear(last_dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout_rate),
])
last_dim = hidden_dim
layers.append(nn.Linear(last_dim, num_experts))
self.fc_layers = nn.Sequential(*layers)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = x.mean(dim=1) # To ensure gating is based on overall features across the sequence
x = self.fc_layers(x)
return self.softmax(x)
class MixtureOfTransformerExperts(nn.Module):
def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_experts, num_encoder_layers=1):
super(MixtureOfTransformerExperts, self).__init__()
self.num_experts = num_experts
self.output_size = output_size
self.experts = nn.ModuleList([TransformerExpert(input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers) for _ in range(num_experts)])
self.gating_network = GatingNetwork(d_model, num_experts)
def forward(self, x):
gating_scores = self.gating_network(x)
expert_outputs = [expert(x) for expert in self.experts]
stacked_expert_outputs = torch.stack(expert_outputs)
expanded_gating_scores = gating_scores.unsqueeze(2).unsqueeze(3)
expanded_gating_scores = expanded_gating_scores.expand(-1, -1, x.size(1), self.output_size)
expanded_gating_scores = expanded_gating_scores.transpose(0, 1)
mixed_output = torch.sum(stacked_expert_outputs * expanded_gating_scores, dim=0)
return mixed_output
class MoETransformerModel(nn.Module):
def __init__(self, vocab_size, d_model, moe):
super(MoETransformerModel, self).__init__()
self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=d_model)
self.moe = moe
self.dropout = nn.Dropout(p=0.1)
def forward(self, x):
embedded = self.dropout(self.embedding(x))
return self.moe(embedded)
# ---------- Dataset Definitions ----------
class QAJsonlDataset(Dataset):
def __init__(self, path, seq_len, tokenizer_path):
# Load the trained tokenizer
self.tokenizer = Tokenizer.from_file(tokenizer_path)
self.seq_len = seq_len
self.pairs = self.load_data(path)
# Using BPE, so no need for manual vocab or idx2token.
# Tokenization will now happen using self.tokenizer.
self.tokenized_pairs = [(self.tokenize(q), self.tokenize(a)) for q, a in self.pairs]
def load_data(self, path):
pairs = []
with open(path, "r", encoding="utf-8") as f:
for line in f:
data = json.loads(line.strip())
question, answer = data.get("question", ""), data.get("answer", "")
pairs.append((question, answer)) # Store questions and answers as raw strings
return pairs
def tokenize(self, text):
# Tokenizing using the BPE tokenizer
encoded = self.tokenizer.encode(text)
tokens = encoded.ids
# Padding/truncation
if len(tokens) < self.seq_len:
# Padding
tokens += [self.tokenizer.token_to_id("<pad>")] * (self.seq_len - len(tokens))
else:
# Truncation
tokens = tokens[:self.seq_len - 1] + [self.tokenizer.token_to_id("<eos>")]
return tokens
def __len__(self):
return len(self.tokenized_pairs)
def __getitem__(self, idx):
tokenized_question, tokenized_answer = self.tokenized_pairs[idx]
return torch.tensor(tokenized_question, dtype=torch.long), torch.tensor(tokenized_answer, dtype=torch.long)
def collate_fn(batch):
questions, answers = zip(*batch)
questions = pad_sequence(questions, batch_first=True, padding_value=0)
answers = pad_sequence(answers, batch_first=True, padding_value=0)
return questions, answers
# ---------- Training and Inference Functions ----------
def train_model(model, criterion, optimizer, num_epochs, data_loader):
model.train()
loss_history = [] # Initialize a list to keep track of losses
for epoch in range(num_epochs):
total_loss = 0
total_items = 0 # Keep track of total items processed
progress_bar = tqdm(enumerate(data_loader), total=len(data_loader), desc=f"Epoch {epoch+1}", leave=False)
for i, (inputs, targets) in progress_bar:
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
# Predict
predictions = model(inputs)
predictions = predictions.view(-1, predictions.size(-1)) # Make sure predictions are the right shape
targets = targets.view(-1) # Flatten targets to match prediction shape if necessary
# Calculate loss
loss = criterion(predictions, targets)
loss.backward()
# Gradient clipping for stabilization
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
# Update total loss and the number of items
total_loss += loss.item() * inputs.size(0) # Multiply loss by batch size
total_items += inputs.size(0)
loss_history.append(loss.item())
progress_bar.set_postfix({"Loss": loss.item()})
average_loss = total_loss / total_items # Correctly compute average loss
print(f"Epoch {epoch+1}, Average Loss: {average_loss:.6f}")
return loss_history
class WarmupLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, warmup_steps, scheduler_step_lr):
self.warmup_steps = warmup_steps
self.scheduler_step_lr = scheduler_step_lr # The subsequent scheduler
super(WarmupLR, self).__init__(optimizer)
def get_lr(self):
if self._step_count <= self.warmup_steps:
warmup_factor = float(self._step_count) / float(max(1, self.warmup_steps))
for base_lr in self.base_lrs:
yield base_lr * warmup_factor
else:
self.scheduler_step_lr.step() # Update the subsequent scheduler
for param_group in self.optimizer.param_groups:
yield param_group['lr']
class GERU(nn.Module):
def __init__(self, in_features):
super(GERU, self).__init__()
self.alpha = nn.Parameter(torch.rand(in_features))
def forward(self, x):
return torch.max(x, torch.zeros_like(x)) + self.alpha * torch.min(x, torch.zeros_like(x))
def generate_text(model, tokenizer, seed_text, num_generate, temperature=1.0):
model.eval()
generated_tokens = []
# Encode the seed text using the tokenizer
encoded_input = tokenizer.encode(seed_text)
input_ids = torch.tensor(encoded_input.ids, dtype=torch.long).unsqueeze(0).to(device)
# Generate num_generate tokens
with torch.no_grad():
for _ in range(num_generate):
output = model(input_ids)
# Get the last logits and apply temperature
logits = output[:, -1, :] / temperature
probabilities = F.softmax(logits, dim=-1)
next_token_id = torch.multinomial(probabilities, num_samples=1).item()
# Append generated token ID and prepare the new input_ids
generated_tokens.append(next_token_id)
input_ids = torch.cat([input_ids, torch.tensor([[next_token_id]], dtype=torch.long).to(device)], dim=1)
# Decode the generated token IDs back to text
generated_text = tokenizer.decode(generated_tokens)
return generated_text
def count_tokens_in_dataset(dataset):
return sum([len(pair[0]) + len(pair[1]) for pair in dataset.pairs])
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
# ---------- Hyperparameters and Model Instantiation ----------
# Transformer :
d_model = 128
nhead = 4
dim_feedforward = 512
num_encoder_layers = 8
num_experts = 1
hidden_dims = []
dropout_rate = 0.3
# Gating Network :
gating_network = GatingNetwork(
input_feature_dim=d_model,
num_experts=num_experts,
hidden_dims=hidden_dims,
dropout_rate=dropout_rate,
)
# Training Parameters
batch_size = 32 # Adjustable batch size
warmup_steps = 6000 # Warmup steps for learning rate
optimizer_type = "AdamW" # Could be “SGD”, “RMSprop”, etc.
learning_rate = 9e-2
weight_decay = 1e-5 # For L2 regularization
num_epochs = 4
# Dataset :
path_to_dataset = "C:/Users/L14/Documents/Projets/Easy-MoE/Easy-MoE/data/Training-math-4K.jsonl"
tokenizer_path = "BPE_tokenizer(Training-math-4K).json"
seq_len = 32
dataset = QAJsonlDataset(path_to_dataset, seq_len, tokenizer_path)
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn, pin_memory=True)
num_tokens = count_tokens_in_dataset(dataset)
print(f"Total number of tokens in the dataset: {num_tokens}")
# Load the tokenizer
tokenizer = Tokenizer.from_file(tokenizer_path)
# Determine the vocabulary size
vocab_size = tokenizer.get_vocab_size()
moe = MixtureOfTransformerExperts(
input_size=d_model,
d_model=d_model,
output_size=vocab_size,
nhead=nhead,
dim_feedforward=dim_feedforward,
num_experts=num_experts,
num_encoder_layers=num_encoder_layers
).to(device)
moe_transformer_model = MoETransformerModel(vocab_size, d_model, moe).to(device)
# Count of total parameters :
total_params = count_parameters(moe_transformer_model)
print(f"Total trainable parameters: {total_params}")
# ---------- Training ----------
# Adjusting optimizer setup to include weight decay and allow switching between types
if optimizer_type == "AdamW":
optimizer = torch.optim.AdamW(moe_transformer_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
elif optimizer_type == "SGD":
optimizer = torch.optim.SGD(moe_transformer_model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=weight_decay)
elif optimizer_type == "Adam":
optimizer = torch.optim.Adam(moe_transformer_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# Instantiate your main scheduler (StepLR)
step_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.925)
# Wrap it with WarmupLR
scheduler = WarmupLR(optimizer, warmup_steps=warmup_steps, scheduler_step_lr=step_lr_scheduler)
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
# Train the model
loss_history = train_model(moe_transformer_model, criterion, optimizer, num_epochs, data_loader)
# Evaluating the model
plot_loss(loss_history)
train_accuracy = evaluate_model(moe_transformer_model, data_loader, device)
# ---------- Inference ----------
def interactive_text_generation(model, dataset, max_length=32, temperature=1.0):
while True:
try:
# Get user input
seed_text = input("Enter seed text (type 'quit' to exit and save the model): ").strip()
# Check if the user wants to quit the interaction
if seed_text.lower() == 'quit':
print("Exiting text generation mode.")
break
# Generate text based on the seed text
if seed_text:
generated_text = generate_text(model, dataset, seed_text, max_length, temperature) # Modify max_length/temperature as needed
print("Generated Text:", generated_text)
else:
print("Seed text cannot be empty. Please enter some text.")
except Exception as e:
# Handle potential errors gracefully
print(f"An error occurred: {e}. Try again.")
interactive_text_generation(moe_transformer_model, tokenizer)
# ---------- Save Trained Model ----------
torch.save(moe_transformer_model.state_dict(), "Transformer-Alpha-v04.pth")
|
9b8e67b75add3281cf073aa724743126
|
{
"intermediate": 0.4508342742919922,
"beginner": 0.34573766589164734,
"expert": 0.2034279853105545
}
|
42,354
|
how do I create something that determines if it is an AI voice or human voice
|
bb3a8a03464c023f659ee9679a20c0a7
|
{
"intermediate": 0.20255768299102783,
"beginner": 0.128061905503273,
"expert": 0.6693803668022156
}
|
42,355
|
ИСПРАВЬ ПЖ ПРОБЕЛЫ import json
with open('data.json', 'rb') as infile:
data = json.load(infile) # открываем json
data_list = data['events_data']
report_clients = []
for item in data_list:
if item['category'] == 'report' and item["id"] not in report_clients:
report_clients.append(item["id"])
print(len(report_clients), "уникальных клиентов совершили действия с категорией report")
|
aa746d4ba45f0bcb00b0577ce71fa56b
|
{
"intermediate": 0.4314744174480438,
"beginner": 0.35537290573120117,
"expert": 0.2131527215242386
}
|
42,356
|
(java)Create a question following this format for this content:
VAR-2.A.1
The use of array objects allows multiple related
items to be represented using a single variable.
VAR-2.A.2
The size of an array is established at the time of
creation and cannot be changed.
VAR-2.A.3
Arrays can store either primitive data or object
reference data.
VAR-2.A.4
When an array is created using the keyword
new, all of its elements are initialized with a
specific value based on the type of elements:
§ Elements of type int are initialized to 0
§ Elements of type double are initialized to 0.0
§ Elements of type boolean are initialized
to false
§ Elements of a reference type are initialized
to the reference value null. No objects are
automatically created
VAR-2.A.5
Initializer lists can be used to create and
initialize arrays.
VAR-2.A.6
Square brackets ([ ]) are used to access and
modify an element in a 1D array using an index.
VAR-2.A.7
The valid index values for an array are
0 through one less than the number of
elements in the array, inclusive. Using an index
value outside of this range will result in an
ArrayIndexOutOfBoundsException
being thrown.
Question body
Responses (or correct response if it is a complete the code or find the error)
Explanation of the correct answer
Tip
Difficulty (easy, medium, hard)
Your questions must test one or 2 of these skills:
1.A Determine an
appropriate program
design to solve a
problem or accomplish
a task (not assessed).
1.B Determine code
that would be used
to complete code
segments.
1.C Determine code
that would be used to
interact with completed
program code.
2.A Apply the
meaning of specific
operators.
2.B Determine the
result or output
based on statement
execution order in a
code segment without
method calls (other
than output).
2.C Determine the
result or output
based on the
statement execution
order in a code
segment containing
method calls.
2.D Determine the
number of times
a code segment
will execute.
4.A Use test-cases
to find errors or
validate results.
4.B Identify errors in
program code.
4.C Determine if
two or more code
segments yield
equivalent results.
5.A Describe the
behavior of a
given segment of
program code.
5.B Explain why a
code segment will
not compile or work
as intended.
5.C Explain how
the result of program
code changes, given
a change to the
initial code.
5.D Describe the
initial conditions that
must be met for a
program segment
to work as intended
or described.
|
30c94eeb6ef979aaa3b0b65e0498fa74
|
{
"intermediate": 0.3909771144390106,
"beginner": 0.31924888491630554,
"expert": 0.28977394104003906
}
|
42,357
|
(java)Create a question following this format for this content:
VAR-2.A.1
The use of array objects allows multiple related
items to be represented using a single variable.
VAR-2.A.2
The size of an array is established at the time of
creation and cannot be changed.
VAR-2.A.3
Arrays can store either primitive data or object
reference data.
VAR-2.A.4
When an array is created using the keyword
new, all of its elements are initialized with a
specific value based on the type of elements:
§ Elements of type int are initialized to 0
§ Elements of type double are initialized to 0.0
§ Elements of type boolean are initialized
to false
§ Elements of a reference type are initialized
to the reference value null. No objects are
automatically created
VAR-2.A.5
Initializer lists can be used to create and
initialize arrays.
VAR-2.A.6
Square brackets ([ ]) are used to access and
modify an element in a 1D array using an index.
VAR-2.A.7
The valid index values for an array are
0 through one less than the number of
elements in the array, inclusive. Using an index
value outside of this range will result in an
ArrayIndexOutOfBoundsException
being thrown.
Question body
Responses (or correct response if it is a complete the code or find the error)
Explanation of the correct answer
Tip
Difficulty (easy, medium, hard)
Your questions must test these skills:
1.A Determine an
appropriate program
design to solve a
problem or accomplish
a task (not assessed).
1.B Determine code
that would be used
to complete code
segments.
1.C Determine code
that would be used to
interact with completed
program code.
2.A Apply the
meaning of specific
operators.
2.B Determine the
result or output
based on statement
execution order in a
code segment without
method calls (other
than output).
2.C Determine the
result or output
based on the
statement execution
order in a code
segment containing
method calls.
2.D Determine the
number of times
a code segment
will execute.
4.A Use test-cases
to find errors or
validate results.
4.B Identify errors in
program code.
4.C Determine if
two or more code
segments yield
equivalent results.
5.A Describe the
behavior of a
given segment of
program code.
5.B Explain why a
code segment will
not compile or work
as intended.
5.C Explain how
the result of program
code changes, given
a change to the
initial code.
5.D Describe the
initial conditions that
must be met for a
program segment
to work as intended
or described.
|
029fd5abceef2f3f0c3eef12ac1de925
|
{
"intermediate": 0.44609540700912476,
"beginner": 0.2954820990562439,
"expert": 0.25842249393463135
}
|
42,358
|
(java)Create a question following this format for this content(Question type- multiple choice (you get 4 choices), complete the code, find the error):
VAR-2.A.1
The use of array objects allows multiple related
items to be represented using a single variable.
VAR-2.A.2
The size of an array is established at the time of
creation and cannot be changed.
VAR-2.A.3
Arrays can store either primitive data or object
reference data.
VAR-2.A.4
When an array is created using the keyword
new, all of its elements are initialized with a
specific value based on the type of elements:
§ Elements of type int are initialized to 0
§ Elements of type double are initialized to 0.0
§ Elements of type boolean are initialized
to false
§ Elements of a reference type are initialized
to the reference value null. No objects are
automatically created
VAR-2.A.5
Initializer lists can be used to create and
initialize arrays.
VAR-2.A.6
Square brackets ([ ]) are used to access and
modify an element in a 1D array using an index.
VAR-2.A.7
The valid index values for an array are
0 through one less than the number of
elements in the array, inclusive. Using an index
value outside of this range will result in an
ArrayIndexOutOfBoundsException
being thrown.
Question body
Responses (or correct response if it is a complete the code or find the error)
Explanation of the correct answer
Tip
Difficulty (easy, medium, hard)
Your questions must test these skills:
1.A Determine an
appropriate program
design to solve a
problem or accomplish
a task (not assessed).
1.B Determine code
that would be used
to complete code
segments.
1.C Determine code
that would be used to
interact with completed
program code.
2.A Apply the
meaning of specific
operators.
2.B Determine the
result or output
based on statement
execution order in a
code segment without
method calls (other
than output).
2.C Determine the
result or output
based on the
statement execution
order in a code
segment containing
method calls.
2.D Determine the
number of times
a code segment
will execute.
4.A Use test-cases
to find errors or
validate results.
4.B Identify errors in
program code.
4.C Determine if
two or more code
segments yield
equivalent results.
5.A Describe the
behavior of a
given segment of
program code.
5.B Explain why a
code segment will
not compile or work
as intended.
5.C Explain how
the result of program
code changes, given
a change to the
initial code.
5.D Describe the
initial conditions that
must be met for a
program segment
to work as intended
or described.
|
7c5a19f1d8f9fda7ae10e6e905d31d89
|
{
"intermediate": 0.3814617991447449,
"beginner": 0.3060853183269501,
"expert": 0.31245288252830505
}
|
42,359
|
If tcp_mtu_probing is 1, will tcp mtu be adjusted only if ICMP packet will be received? Does it mean that the mtu in this case may only be decreased, not increased?
|
e538138fde73c2de993f6bb2706a3e0b
|
{
"intermediate": 0.4482899606227875,
"beginner": 0.17161916196346283,
"expert": 0.3800908327102661
}
|
42,360
|
create flutter reservation restaurant
|
90ccd8de0fa4d2deead7d0651cc607d4
|
{
"intermediate": 0.3323868215084076,
"beginner": 0.32307177782058716,
"expert": 0.34454143047332764
}
|
42,361
|
How to create your own GPT
|
66d987290997df84870b1bca1b5702ea
|
{
"intermediate": 0.2737436294555664,
"beginner": 0.2376028150320053,
"expert": 0.4886535704135895
}
|
42,362
|
(java)Create a question following this format for this content:
VAR-2.A.1
The use of array objects allows multiple related
items to be represented using a single variable.
VAR-2.A.2
The size of an array is established at the time of
creation and cannot be changed.
VAR-2.A.3
Arrays can store either primitive data or object
reference data.
VAR-2.A.4
When an array is created using the keyword
new, all of its elements are initialized with a
specific value based on the type of elements:
§ Elements of type int are initialized to 0
§ Elements of type double are initialized to 0.0
§ Elements of type boolean are initialized
to false
§ Elements of a reference type are initialized
to the reference value null. No objects are
automatically created
VAR-2.A.5
Initializer lists can be used to create and
initialize arrays.
VAR-2.A.6
Square brackets ([ ]) are used to access and
modify an element in a 1D array using an index.
VAR-2.A.7
The valid index values for an array are
0 through one less than the number of
elements in the array, inclusive. Using an index
value outside of this range will result in an
ArrayIndexOutOfBoundsException
being thrown.
Question body
Responses (or correct response if it is a complete the code or find the error)
Explanation of the correct answer
Tip
Difficulty (easy, medium, hard)
Your questions must test these skills:
1.A Determine an
appropriate program
design to solve a
problem or accomplish
a task (not assessed).
1.B Determine code
that would be used
to complete code
segments.
1.C Determine code
that would be used to
interact with completed
program code.
2.A Apply the
meaning of specific
operators.
2.B Determine the
result or output
based on statement
execution order in a
code segment without
method calls (other
than output).
2.C Determine the
result or output
based on the
statement execution
order in a code
segment containing
method calls.
2.D Determine the
number of times
a code segment
will execute.
4.A Use test-cases
to find errors or
validate results.
4.B Identify errors in
program code.
4.C Determine if
two or more code
segments yield
equivalent results.
5.A Describe the
behavior of a
given segment of
program code.
5.B Explain why a
code segment will
not compile or work
as intended.
5.C Explain how
the result of program
code changes, given
a change to the
initial code.
5.D Describe the
initial conditions that
must be met for a
program segment
to work as intended
or described.
|
1a1cebf49685034bfbcc4c69eb3f7842
|
{
"intermediate": 0.44609540700912476,
"beginner": 0.2954820990562439,
"expert": 0.25842249393463135
}
|
42,363
|
(java)Create a question following this format for this content:
VAR-2.A.1
The use of array objects allows multiple related
items to be represented using a single variable.
VAR-2.A.2
The size of an array is established at the time of
creation and cannot be changed.
VAR-2.A.3
Arrays can store either primitive data or object
reference data.
VAR-2.A.4
When an array is created using the keyword
new, all of its elements are initialized with a
specific value based on the type of elements:
§ Elements of type int are initialized to 0
§ Elements of type double are initialized to 0.0
§ Elements of type boolean are initialized
to false
§ Elements of a reference type are initialized
to the reference value null. No objects are
automatically created
VAR-2.A.5
Initializer lists can be used to create and
initialize arrays.
VAR-2.A.6
Square brackets ([ ]) are used to access and
modify an element in a 1D array using an index.
VAR-2.A.7
The valid index values for an array are
0 through one less than the number of
elements in the array, inclusive. Using an index
value outside of this range will result in an
ArrayIndexOutOfBoundsException
being thrown.
Your questions must test these skills:
1.A Determine an
appropriate program
design to solve a
problem or accomplish
a task (not assessed).
1.B Determine code
that would be used
to complete code
segments.
1.C Determine code
that would be used to
interact with completed
program code.
2.A Apply the
meaning of specific
operators.
2.B Determine the
result or output
based on statement
execution order in a
code segment without
method calls (other
than output).
2.C Determine the
result or output
based on the
statement execution
order in a code
segment containing
method calls.
2.D Determine the
number of times
a code segment
will execute.
4.A Use test-cases
to find errors or
validate results.
4.B Identify errors in
program code.
4.C Determine if
two or more code
segments yield
equivalent results.
5.A Describe the
behavior of a
given segment of
program code.
5.B Explain why a
code segment will
not compile or work
as intended.
5.C Explain how
the result of program
code changes, given
a change to the
initial code.
5.D Describe the
initial conditions that
must be met for a
program segment
to work as intended
or described.
|
960893ca0d0311ba8c05868f9bfab840
|
{
"intermediate": 0.3665544092655182,
"beginner": 0.2983943521976471,
"expert": 0.3350512385368347
}
|
42,364
|
what is wrong with my code : import torch
import torch.nn as nn
import torch.nn.functional as F
import json
import math
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset
from collections import Counter
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
from tokenizers import Tokenizer
# ---------- Device Configuration ----------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# ---------- Utility Functions ----------
def positional_encoding(seq_len, d_model, device):
pos = torch.arange(seq_len, dtype=torch.float, device=device).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)).to(device)
pe = torch.zeros(seq_len, d_model, device=device)
pe[:, 0::2] = torch.sin(pos * div_term)
pe[:, 1::2] = torch.cos(pos * div_term)
return pe.unsqueeze(0)
# -------- Performance ----------
def evaluate_model(model, data_loader, device):
model.eval()
all_preds, all_targets = [], []
with torch.no_grad():
for inputs, targets in data_loader:
inputs, targets = inputs.to(device), targets.to(device)
outputs = model(inputs)
predictions = torch.argmax(outputs, dim=-1).view(-1) # Flatten predicted indices
all_preds.extend(predictions.cpu().numpy())
all_targets.extend(targets.view(-1).cpu().numpy()) # Ensure targets are also flattened
# Calculate precision, recall, and F1 score after ensuring all_preds and all_targets are correctly aligned.
accuracy = accuracy_score(all_targets, all_preds)
precision = precision_score(all_targets, all_preds, average='macro', zero_division=0)
recall = recall_score(all_targets, all_preds, average='macro', zero_division=0)
f1 = f1_score(all_targets, all_preds, average='macro', zero_division=0)
print(f"Accuracy: {accuracy:.4f}")
print(f"Precision: {precision:.4f}")
print(f"Recall: {recall:.4f}")
print(f"F1 Score: {f1:.4f}")
return accuracy ,precision, recall, f1
# Function to plot loss over time
def plot_loss(loss_history):
plt.figure(figsize=(10, 5))
plt.plot(loss_history, label='Training Loss')
plt.xlabel('Batches')
plt.ylabel('Loss')
plt.title('Training Loss Over Time')
plt.legend()
plt.show()
# ---------- Model Definitions ----------
class TransformerExpert(nn.Module):
def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers=1):
super(TransformerExpert, self).__init__()
self.d_model = d_model
self.input_fc = nn.Linear(input_size, d_model)
self.pos_encoder = nn.Parameter(positional_encoding(1, d_model, device), requires_grad=True)
encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True,
norm_first=True)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_encoder_layers)
self.output_fc = nn.Linear(d_model, output_size)
self.norm = nn.LayerNorm(d_model)
def forward(self, x):
x = self.norm(self.input_fc(x)) + self.pos_encoder
transformer_output = self.transformer_encoder(x)
output = self.output_fc(transformer_output)
return output
class GatingNetwork(nn.Module):
def __init__(self, input_feature_dim, num_experts, hidden_dims=[6144, 3072, 1536, 768], dropout_rate=0.2):
super(GatingNetwork, self).__init__()
layers = []
last_dim = input_feature_dim
for hidden_dim in hidden_dims:
layers.extend([
nn.Linear(last_dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout_rate),
])
last_dim = hidden_dim
layers.append(nn.Linear(last_dim, num_experts))
self.fc_layers = nn.Sequential(*layers)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = x.mean(dim=1) # To ensure gating is based on overall features across the sequence
x = self.fc_layers(x)
return self.softmax(x)
class MixtureOfTransformerExperts(nn.Module):
def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_experts, num_encoder_layers=1):
super(MixtureOfTransformerExperts, self).__init__()
self.num_experts = num_experts
self.output_size = output_size
self.experts = nn.ModuleList([TransformerExpert(input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers) for _ in range(num_experts)])
self.gating_network = GatingNetwork(d_model, num_experts)
def forward(self, x):
gating_scores = self.gating_network(x)
expert_outputs = [expert(x) for expert in self.experts]
stacked_expert_outputs = torch.stack(expert_outputs)
expanded_gating_scores = gating_scores.unsqueeze(2).unsqueeze(3)
expanded_gating_scores = expanded_gating_scores.expand(-1, -1, x.size(1), self.output_size)
expanded_gating_scores = expanded_gating_scores.transpose(0, 1)
mixed_output = torch.sum(stacked_expert_outputs * expanded_gating_scores, dim=0)
return mixed_output
class MoETransformerModel(nn.Module):
def __init__(self, vocab_size, d_model, moe):
super(MoETransformerModel, self).__init__()
self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=d_model)
self.moe = moe
self.dropout = nn.Dropout(p=0.1)
def forward(self, x):
embedded = self.dropout(self.embedding(x))
return self.moe(embedded)
# ---------- Dataset Definitions ----------
class QAJsonlDataset(Dataset):
def __init__(self, path, seq_len, tokenizer_path):
# Load the trained tokenizer
self.tokenizer = Tokenizer.from_file(tokenizer_path)
self.seq_len = seq_len
self.pairs = self.load_data(path)
# Using BPE, so no need for manual vocab or idx2token.
# Tokenization will now happen using self.tokenizer.
self.tokenized_pairs = [(self.tokenize(q), self.tokenize(a)) for q, a in self.pairs]
def load_data(self, path):
pairs = []
with open(path, "r", encoding="utf-8") as f:
for line in f:
data = json.loads(line.strip())
question, answer = data.get("user", ""), data.get("content", "")
pairs.append((question, answer)) # Store questions and answers as raw strings
return pairs
def tokenize(self, text):
# Tokenizing using the BPE tokenizer
encoded = self.tokenizer.encode(text)
tokens = encoded.ids
# Padding/truncation
if len(tokens) < self.seq_len:
# Padding
tokens += [self.tokenizer.token_to_id("<pad>")] * (self.seq_len - len(tokens))
else:
# Truncation
tokens = tokens[:self.seq_len - 1] + [self.tokenizer.token_to_id("<eos>")]
return tokens
def __len__(self):
return len(self.tokenized_pairs)
def __getitem__(self, idx):
tokenized_question, tokenized_answer = self.tokenized_pairs[idx]
return torch.tensor(tokenized_question, dtype=torch.long), torch.tensor(tokenized_answer, dtype=torch.long)
def collate_fn(batch):
questions, answers = zip(*batch)
questions = pad_sequence(questions, batch_first=True, padding_value=0)
answers = pad_sequence(answers, batch_first=True, padding_value=0)
return questions, answers
# ---------- Training and Inference Functions ----------
def train_model(model, criterion, optimizer, num_epochs, data_loader):
model.train()
loss_history = [] # Initialize a list to keep track of losses
for epoch in range(num_epochs):
total_loss = 0
total_items = 0 # Keep track of total items processed
progress_bar = tqdm(enumerate(data_loader), total=len(data_loader), desc=f"Epoch {epoch+1}", leave=False)
for i, (inputs, targets) in progress_bar:
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
# Predict
predictions = model(inputs)
predictions = predictions.view(-1, predictions.size(-1)) # Make sure predictions are the right shape
targets = targets.view(-1) # Flatten targets to match prediction shape if necessary
# Calculate loss
loss = criterion(predictions, targets)
loss.backward()
# Gradient clipping for stabilization
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
# Update total loss and the number of items
total_loss += loss.item() * inputs.size(0) # Multiply loss by batch size
total_items += inputs.size(0)
loss_history.append(loss.item())
progress_bar.set_postfix({"Loss": loss.item()})
average_loss = total_loss / total_items # Correctly compute average loss
print(f"Epoch {epoch+1}, Average Loss: {average_loss:.6f}")
return loss_history
class WarmupLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, warmup_steps, scheduler_step_lr):
self.warmup_steps = warmup_steps
self.scheduler_step_lr = scheduler_step_lr # The subsequent scheduler
super(WarmupLR, self).__init__(optimizer)
def get_lr(self):
if self._step_count <= self.warmup_steps:
warmup_factor = float(self._step_count) / float(max(1, self.warmup_steps))
for base_lr in self.base_lrs:
yield base_lr * warmup_factor
else:
self.scheduler_step_lr.step() # Update the subsequent scheduler
for param_group in self.optimizer.param_groups:
yield param_group['lr']
class GERU(nn.Module):
def __init__(self, in_features):
super(GERU, self).__init__()
self.alpha = nn.Parameter(torch.rand(in_features))
def forward(self, x):
return torch.max(x, torch.zeros_like(x)) + self.alpha * torch.min(x, torch.zeros_like(x))
def generate_text(model, tokenizer, seed_text, num_generate, temperature=1.0):
model.eval()
generated_tokens = []
# Encode the seed text using the tokenizer
encoded_input = tokenizer.encode(seed_text)
input_ids = torch.tensor(encoded_input.ids, dtype=torch.long).unsqueeze(0).to(device)
# Generate num_generate tokens
with torch.no_grad():
for _ in range(num_generate):
output = model(input_ids)
# Get the last logits and apply temperature
logits = output[:, -1, :] / temperature
probabilities = F.softmax(logits, dim=-1)
next_token_id = torch.multinomial(probabilities, num_samples=1).item()
# Append generated token ID and prepare the new input_ids
generated_tokens.append(next_token_id)
input_ids = torch.cat([input_ids, torch.tensor([[next_token_id]], dtype=torch.long).to(device)], dim=1)
# Decode the generated token IDs back to text
generated_text = tokenizer.decode(generated_tokens)
return generated_text
def count_tokens_in_dataset(dataset):
return sum([len(pair[0]) + len(pair[1]) for pair in dataset.pairs])
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
# ---------- Hyperparameters and Model Instantiation ----------
# Transformer :
d_model = 384
nhead = 12
dim_feedforward = 1536
num_encoder_layers = 12
num_experts = 2
# Training Parameters
batch_size = 384 # Adjustable batch size
warmup_steps = 2000 # Warmup steps for learning rate
optimizer_type = "AdamW" # Could be “SGD”, “RMSprop”, etc.
learning_rate = 1e-1
weight_decay = 1e-5 # For L2 regularization
num_epochs = 10
# Dataset :
path_to_dataset = "C:/Users/L14/Documents/Projets/Easy-MoE/Easy-MoE/data/Real_talk.jsonl"
tokenizer_path = "BPE_tokenizer(Real-Talk).json"
seq_len = 256
dataset = QAJsonlDataset(path_to_dataset, seq_len, tokenizer_path)
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn, pin_memory=True)
num_tokens = count_tokens_in_dataset(dataset)
print(f"Total number of tokens in the dataset: {num_tokens}")
# Load the tokenizer
tokenizer = Tokenizer.from_file(tokenizer_path)
# Determine the vocabulary size
vocab_size = tokenizer.get_vocab_size()
moe = MixtureOfTransformerExperts(
input_size=d_model,
d_model=d_model,
output_size=vocab_size,
nhead=nhead,
dim_feedforward=dim_feedforward,
num_experts=num_experts,
num_encoder_layers=num_encoder_layers
).to(device)
moe_transformer_model = MoETransformerModel(vocab_size, d_model, moe).to(device)
# Count of total parameters :
total_params = count_parameters(moe_transformer_model)
print(f"Total trainable parameters: {total_params}")
# ---------- Training ----------
# Adjusting optimizer setup to include weight decay and allow switching between types
if optimizer_type == "AdamW":
optimizer = torch.optim.AdamW(moe_transformer_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
elif optimizer_type == "SGD":
optimizer = torch.optim.SGD(moe_transformer_model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=weight_decay)
elif optimizer_type == "Adam":
optimizer = torch.optim.Adam(moe_transformer_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# Instantiate your main scheduler (StepLR)
step_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.95)
# Wrap it with WarmupLR
scheduler = WarmupLR(optimizer, warmup_steps=warmup_steps, scheduler_step_lr=step_lr_scheduler)
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
# Train the model
loss_history = train_model(moe_transformer_model, criterion, optimizer, num_epochs, data_loader)
# Evaluating the model
plot_loss(loss_history)
train_accuracy = evaluate_model(moe_transformer_model, data_loader, device)
# ---------- Inference ----------
def interactive_text_generation(model, dataset, max_length=32, temperature=1.0):
while True:
try:
# Get user input
seed_text = input("Enter seed text (type 'quit' to exit and save the model): ").strip()
# Check if the user wants to quit the interaction
if seed_text.lower() == 'quit':
print("Exiting text generation mode.")
break
# Generate text based on the seed text
if seed_text:
generated_text = generate_text(model, dataset, seed_text, max_length, temperature) # Modify max_length/temperature as needed
print("Generated Text:", generated_text)
else:
print("Seed text cannot be empty. Please enter some text.")
except Exception as e:
# Handle potential errors gracefully
print(f"An error occurred: {e}. Try again.")
interactive_text_generation(moe_transformer_model, tokenizer)
# ---------- Save Trained Model ----------
torch.save(moe_transformer_model.state_dict(), "Transformer-Alpha-v04.pth")
|
a2b3360f85610ce65623f03e4283715a
|
{
"intermediate": 0.4663510322570801,
"beginner": 0.34427258372306824,
"expert": 0.18937642872333527
}
|
42,365
|
(java)Create a frq with these names: Armaan, Ganesh, Srinath and Daksh for this content:
VAR-2.A.1
The use of array objects allows multiple related
items to be represented using a single variable.
VAR-2.A.2
The size of an array is established at the time of
creation and cannot be changed.
VAR-2.A.3
Arrays can store either primitive data or object
reference data.
VAR-2.A.4
When an array is created using the keyword
new, all of its elements are initialized with a
specific value based on the type of elements:
§ Elements of type int are initialized to 0
§ Elements of type double are initialized to 0.0
§ Elements of type boolean are initialized
to false
§ Elements of a reference type are initialized
to the reference value null. No objects are
automatically created
VAR-2.A.5
Initializer lists can be used to create and
initialize arrays.
VAR-2.A.6
Square brackets ([ ]) are used to access and
modify an element in a 1D array using an index.
VAR-2.A.7
The valid index values for an array are
0 through one less than the number of
elements in the array, inclusive. Using an index
value outside of this range will result in an
ArrayIndexOutOfBoundsException
being thrown.
VAR-2.B.1
Iteration statements can be used to access
all the elements in an array. This is called
traversing the array.
VAR-2.B.2
Traversing an array with an indexed for
loop or while loop requires elements to be
accessed using their indices.
VAR-2.B.3
Since the indices for an array start at
0 and end at the number of elements
− 1, “off by one” errors are easy to make
when traversing an array, resulting in an
ArrayIndexOutOfBoundsException
being thrown.
VAR-2.C.1
An enhanced for loop header includes a
variable, referred to as the enhanced for
loop variable.
VAR-2.C.2
For each iteration of the enhanced for loop,
the enhanced for loop variable is assigned a
copy of an element without using its index.
VAR-2.C.3
Assigning a new value to the enhanced for
loop variable does not change the value stored
in the array.
VAR-2.C.4
Program code written using an enhanced for
loop to traverse and access elements in an
array can be rewritten using an indexed for
loop or a while loop.
CON-2.I.1
There are standard algorithms that utilize array
traversals to:
§ Determine a minimum or maximum value
§ Compute a sum, average, or mode
§ Determine if at least one element has a
particular property
§ Determine if all elements have a particular
property
§ Access all consecutive pairs of elements
§ Determine the presence or absence of
duplicate elements
§ Determine the number of elements meeting
specific criteria
CON-2.I.2
There are standard array algorithms that utilize
traversals to:
§ Shift or rotate elements left or right
§ Reverse the order of the elements
Use these task verbs in your question:
Assume
Complete (program code)
Implement/Write
|
89935d3549e6e65567efa54e9035643a
|
{
"intermediate": 0.37421172857284546,
"beginner": 0.2333906888961792,
"expert": 0.39239758253097534
}
|
42,366
|
(java)Create a frq with these names in an array:Armaan, Ganesh, Srinath and Daksh for this content:
VAR-2.A.1
The use of array objects allows multiple related
items to be represented using a single variable.
VAR-2.A.2
The size of an array is established at the time of
creation and cannot be changed.
VAR-2.A.3
Arrays can store either primitive data or object
reference data.
VAR-2.A.4
When an array is created using the keyword
new, all of its elements are initialized with a
specific value based on the type of elements:
§ Elements of type int are initialized to 0
§ Elements of type double are initialized to 0.0
§ Elements of type boolean are initialized
to false
§ Elements of a reference type are initialized
to the reference value null. No objects are
automatically created
VAR-2.A.5
Initializer lists can be used to create and
initialize arrays.
VAR-2.A.6
Square brackets ([ ]) are used to access and
modify an element in a 1D array using an index.
VAR-2.A.7
The valid index values for an array are
0 through one less than the number of
elements in the array, inclusive. Using an index
value outside of this range will result in an
ArrayIndexOutOfBoundsException
being thrown.
VAR-2.B.1
Iteration statements can be used to access
all the elements in an array. This is called
traversing the array.
VAR-2.B.2
Traversing an array with an indexed for
loop or while loop requires elements to be
accessed using their indices.
VAR-2.B.3
Since the indices for an array start at
0 and end at the number of elements
− 1, “off by one” errors are easy to make
when traversing an array, resulting in an
ArrayIndexOutOfBoundsException
being thrown.
VAR-2.C.1
An enhanced for loop header includes a
variable, referred to as the enhanced for
loop variable.
VAR-2.C.2
For each iteration of the enhanced for loop,
the enhanced for loop variable is assigned a
copy of an element without using its index.
VAR-2.C.3
Assigning a new value to the enhanced for
loop variable does not change the value stored
in the array.
VAR-2.C.4
Program code written using an enhanced for
loop to traverse and access elements in an
array can be rewritten using an indexed for
loop or a while loop.
CON-2.I.1
There are standard algorithms that utilize array
traversals to:
§ Determine a minimum or maximum value
§ Compute a sum, average, or mode
§ Determine if at least one element has a
particular property
§ Determine if all elements have a particular
property
§ Access all consecutive pairs of elements
§ Determine the presence or absence of
duplicate elements
§ Determine the number of elements meeting
specific criteria
CON-2.I.2
There are standard array algorithms that utilize
traversals to:
§ Shift or rotate elements left or right
§ Reverse the order of the elements
Use these task verbs in your question:
Assume
Complete (program code)
Implement/Write
|
fd045b1e9cf4472a5a62e1570670ec34
|
{
"intermediate": 0.36124131083488464,
"beginner": 0.26268237829208374,
"expert": 0.3760763108730316
}
|
42,367
|
(java) Create a frq with these names in an array:Armaan, Ganesh, Srinath and Daksh for this content:
VAR-2.A.1
The use of array objects allows multiple related
items to be represented using a single variable.
VAR-2.A.2
The size of an array is established at the time of
creation and cannot be changed.
VAR-2.A.3
Arrays can store either primitive data or object
reference data.
VAR-2.A.4
When an array is created using the keyword
new, all of its elements are initialized with a
specific value based on the type of elements:
§ Elements of type int are initialized to 0
§ Elements of type double are initialized to 0.0
§ Elements of type boolean are initialized
to false
§ Elements of a reference type are initialized
to the reference value null. No objects are
automatically created
VAR-2.A.5
Initializer lists can be used to create and
initialize arrays.
VAR-2.A.6
Square brackets ([ ]) are used to access and
modify an element in a 1D array using an index.
VAR-2.A.7
The valid index values for an array are
0 through one less than the number of
elements in the array, inclusive. Using an index
value outside of this range will result in an
ArrayIndexOutOfBoundsException
being thrown.
VAR-2.B.1
Iteration statements can be used to access
all the elements in an array. This is called
traversing the array.
VAR-2.B.2
Traversing an array with an indexed for
loop or while loop requires elements to be
accessed using their indices.
VAR-2.B.3
Since the indices for an array start at
0 and end at the number of elements
− 1, “off by one” errors are easy to make
when traversing an array, resulting in an
ArrayIndexOutOfBoundsException
being thrown.
VAR-2.C.1
An enhanced for loop header includes a
variable, referred to as the enhanced for
loop variable.
VAR-2.C.2
For each iteration of the enhanced for loop,
the enhanced for loop variable is assigned a
copy of an element without using its index.
VAR-2.C.3
Assigning a new value to the enhanced for
loop variable does not change the value stored
in the array.
VAR-2.C.4
Program code written using an enhanced for
loop to traverse and access elements in an
array can be rewritten using an indexed for
loop or a while loop.
CON-2.I.1
There are standard algorithms that utilize array
traversals to:
§ Determine a minimum or maximum value
§ Compute a sum, average, or mode
§ Determine if at least one element has a
particular property
§ Determine if all elements have a particular
property
§ Access all consecutive pairs of elements
§ Determine the presence or absence of
duplicate elements
§ Determine the number of elements meeting
specific criteria
CON-2.I.2
There are standard array algorithms that utilize
traversals to:
§ Shift or rotate elements left or right
§ Reverse the order of the elements
Use these task verbs in your question:
Assume
Complete (program code)
Implement/Write
|
d4249da9217e996bb6863a37630a58c4
|
{
"intermediate": 0.36133527755737305,
"beginner": 0.26136550307273865,
"expert": 0.37729915976524353
}
|
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