id int64 5 1.93M | title stringlengths 0 128 | description stringlengths 0 25.5k | collection_id int64 0 28.1k | published_timestamp timestamp[s] | canonical_url stringlengths 14 581 | tag_list stringlengths 0 120 | body_markdown stringlengths 0 716k | user_username stringlengths 2 30 |
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1,891,552 | 9 tools, libraries and extensions our developer can't live without (and why) | I asked our developers at DevZero to give us the tools the tools they are in-love with and that... | 0 | 2024-06-17T18:05:03 | https://www.devzero.io/blog/9-tools-developer-cant-live-without | tooling, productivity, development, coding | I asked our developers at [DevZero](https://devzero.io) to give us the tools the tools they are in-love with and that started a long thread of opinions and debate. We summarised it for you here.
## FZF
[fzf](https://github.com/junegunn/fzf) plugs into almost every alias I have including shell history, which allows me to operate in the CLI using 1-5 keystrokes instead of typing out extremely long commands. Here's a good [tutorial](https://youtu.be/qgG5Jhi_Els) of using FZF.
## Silver Searcher
There are other CLI search tools for code: grep, ripgrep, etc. or actual search tools (Sourcegraph, Github, IDEs), but I always reach for [Silver Searcher](https://github.com/ggreer/the_silver_searcher)/Ag. Ag is a code-searching tool similar to ack, but faster. The syntax is pretty good and it’s very helpful when I just want something basic such as when I’m just looking for the string Config (I don’t use complex regex).By the way [fzf.zsh](https://github.com/issmirnov/dotfiles/blob/master/zsh/config/fzf.zsh#L166), combines ag with fzf to do instant full text search recursively over the current directory, and then pops you into vim at that exact file line.
## VS Code
[VS Code](https://code.visualstudio.com/) just works, and the marketplace is great and while the software can get a bit degraded from time to time, even "bad state" is more than good enough
## Tailscale
[Tailscale](https://tailscale.com/) simplifies network management, enhances security, and facilitates remote collaboration, ultimately enabling them to focus on their core development tasks without worrying about networking complexities.
## K9s
Typing out all the kubernetes commands is so annoying, and I find that [K9s](https://k9scli.io/) is actually better than most visual kubernetes interfaces. It also works everywhere.
## Graphviz
[Graphviz](https://graphviz.org/) is a graph visualization tool - useful for visualizing things such as flow charts. You write out the graph in a special language called the "DOT language" where you specify what's in the graph, and graphviz handles all of the layout / visualization for you. It is insanely easy to programmatically create directed graphs and I use it when debugging complex state machines. I have a CLI shortcut to render those graphs in my command-line. It is also really useful to map out network topologies.
It helped me plan out network topologies for datacenter deployments, as well as help save me countless hours debugging complex code with lots of state flying around.As a bonus, here’s the code behind Graphviz.
## Emacs
While [Emacs](https://www.gnu.org/software/emacs/) has been around since the 70s. Its extensive library of add-on packages, which allow me to tailor the editor to their specific workflow and needs. Syntax highlighting, code completion, version control integration, and a built-in terminal emulator, making it suitable for me for a variety of programming tasks.
## Strace
[Strace](http://man7.org/linux/man-pages/man1/strace.1.html) is a diagnostic, debugging and instructional userspace utility for Linux. It is used to monitor and tamper with interactions between processes and the Linux kernel, which include system calls, signal deliveries, and changes of process state. it provides detailed insight into the behavior of Linux processes, helps diagnose issues, and aids in performance optimization. Its versatility, compatibility, and ease of use make it an indispensable tool for Linux developers.
## KubeShark
Debugging Kubernetes nodes is a nightmare. The amount of information is vast and the granularity isn’t great. [Kubeshark](https://github.com/kubeshark/kubeshark) is an API traffic analyzer for Kubernetes providing real-time K8s protocol-level visibility, capturing and monitoring all traffic and payloads going in, out and across containers, pods, nodes and clusters.
 | shohams |
1,888,898 | Choosing between an index-level API, a query, an aggregation, or ES|QL in Elasticsearch | When getting started with Elasticsearch, something that needs to be clarified is figuring out when to... | 0 | 2024-06-17T18:03:47 | https://dev.to/jessicagarson/choosing-between-an-index-level-api-a-query-an-aggregation-or-esql-in-elasticsearch-4668 | elasticsearch, beginners |
When getting started with Elasticsearch, something that needs to be clarified is figuring out when to use an index-level API, a query, an aggregation, or ES|QL can be tricky. This blog post aims to walk you through when to use which.
At a high level, you can think about the differences as follows:
- Index-level APIs help manage your index. They allow you to create, delete, and modify your index settings, mappings, and aliases.
- Queries help retrieve data that meets specified criteria using a JSON-based query language (Query DSL).
- Aggregations perform functions such as calculations and grouping data. They are accommodating for data analysis.
- ES|QL is a procedural piped query language with SQL-like syntax, useful for data filtering and analytics.
You can run the examples from this blog post inside [Elastic’s Dev Tools Console](https://www.elastic.co/guide/en/kibana/current/console-kibana.html). In the Dev Tools Console, you can directly invoke Elasticsearch’s REST APIs without needing to supply additional authentication parameters inside the Dev Tools Console.
## When to use an index-level API
[An Elasticsearch index](https://www.elastic.co/blog/what-is-an-elasticsearch-index) is a data structure containing a set of documents. Each document in an index contains key-value pairs that store your data. An index-level API works with the index as a whole instead of individual documents or a cluster. Index-level APIs enable you to manage your index, settings, aliases, mappings, or templates. The [documentation on the subject](https://www.elastic.co/guide/en/elasticsearch/reference/current/indices.html) provides a complete list of index-level APIs.
Examples of times when you would use an index-level API include:
- Creating a new index.
- Deleting an index.
- Cloning an index.
- Creating an alias for an index.
### Creating a new index
To [create a new index](https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-create-index.html) you would run the following command:
```
PUT /new_index
```
The response that gets returned lets you know that an index called `new_index` has been created.
```json
{
"acknowledged": true,
"shards_acknowledged": true,
"index": "new_index"
}
```
### Deleting an index
For testing purposes, it's common to create multiple indexes. Having the ability to delete these indexes can be very useful in cleaning up these indexes.
To [delete an index](https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-delete-index.html), you would use the following syntax:
```
DELETE /new_index
```
The output confirms that the index has been deleted successfully.
```json
{
"acknowledged": true
}
```
### Cloning an index
Cloning an index can be helpful for backup and recovery or data archiving purposes. [Our documentation on the subject](https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-clone-index.html) provides more information on the clone index API.
First, you will want to prevent write operations on the index using the [add index block API.](https://www.elastic.co/guide/en/elasticsearch/reference/current/index-modules-blocks.html#add-index-block)
```
PUT /new_index/_block/write
```
The output confirms that you have added a block to prevent further write operations on your index.
```json
{
"acknowledged": true,
"shards_acknowledged": false,
"indices": []
}
```
To clone an index called `new_index_cloned` from `new_index`, you would use the following syntax:
```
POST /new_index/_clone/new_index_cloned
```
The output indicates that you have created a new index called `new_index_cloned`.
```json
{
"acknowledged": true,
"shards_acknowledged": true,
"index": "new_index_cloned"
}
```
### Creating aliases
Creating aliases can be helpful for index management. They allow you to refer to an index by a more intuitive and usually shorter name. The following snippet creates an alias for `new_index` called `new`.
```
POST /_aliases
{
"actions": [
{
"add": {
"index": "new_index",
"alias": "new"
}
}
]
}
```
The output confirms that an alias has been created.
```json
{
"acknowledged": true
}
```
## When to use queries
While index-level queries help manage your index as a whole, queries help search and retrieve documents that meet the criteria you define. The language used for creating queries in Elasticsearch is called [Query DSL](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html) (Domain-Specific Language). Query DSL employs JSON. It is beneficial for getting back documents that match the specifications you create inside your query. Some of the most used queries include match, term, and range queries. You also can combine queries to reach a greater granularity using a boolean query.
### Match query
[A match query](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-match-query.html) in Elasticsearch retrieves documents that correlate with a given value. Match queries are handy for full-text searches since they return text containing a specific phrase or value.
If you had an index containing information on the [Boston Celtics games](https://www.elastic.co/search-labs/blog/analyzing-data-using-python-elasticsearch-and-kibana) and were looking for games in which the Celtics had a plus-minus score of -19, you would use the following query.
```
GET /celtics/_search
{
"query": {
"match": {
"PLUS_MINUS": "-19"
}
}
}
```
A basketball team's plus-minus score is a statistic that measures the point differential when a specific team is on the court. It calculates the difference between the team's points and those scored by its opponents while that team is playing. A positive plus-minus indicates the team outscored its opponents, while a negative plus-minus indicates they were outscored.
The result that would get returned contains the one game where the Boston Celtics had a plus-minus score of -19:
```json
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": "celtics",
"_id": "0022300646",
"_score": 1,
"_source": {
"SEASON_ID": "22023",
"TEAM_ID": 1610612738,
"TEAM_ABBREVIATION": "BOS",
"TEAM_NAME": "Boston Celtics",
"GAME_ID": "0022300646",
"GAME_DATE": "2024-01-27",
"MATCHUP": "BOS vs. LAC",
"WL": "L",
"MIN": 240,
"PTS": 96,
"FGM": 36,
"FGA": 100,
"FG_PCT": 0.36,
"FG3M": 10,
"FG3A": 40,
"FG3_PCT": 0.25,
"FTM": 14,
"FTA": 16,
"FT_PCT": 0.875,
"OREB": 18,
"DREB": 34,
"REB": 52,
"AST": 21,
"STL": 2,
"BLK": 9,
"TOV": 11,
"PF": 13,
"PLUS_MINUS": -19
}
}
]
}
}
```
### Term query
[A term query](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-term-query.html) returns an exact match of a specific term, which can be helpful when working with structured data. The following example searches for information about a game on a particular date, April 29th, 2024.
```
GET /celtics/_search
{
"query": {
"term": {
"GAME_DATE": "2024-04-29"
}
}
}
```
You will get back information about the game that took place on `2024-04-29`.
```json
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": "celtics",
"_id": "0042300104",
"_score": 1,
"_source": {
"SEASON_ID": "42023",
"TEAM_ID": 1610612738,
"TEAM_ABBREVIATION": "BOS",
"TEAM_NAME": "Boston Celtics",
"GAME_ID": "0042300104",
"GAME_DATE": "2024-04-29",
"MATCHUP": "BOS @ MIA",
"WL": "W",
"MIN": 240,
"PTS": 102,
"FGM": 36,
"FGA": 86,
"FG_PCT": 0.419,
"FG3M": 14,
"FG3A": 37,
"FG3_PCT": 0.378,
"FTM": 16,
"FTA": 18,
"FT_PCT": 0.889,
"OREB": 11,
"DREB": 35,
"REB": 46,
"AST": 21,
"STL": 5,
"BLK": 3,
"TOV": 10,
"PF": 20,
"PLUS_MINUS": 14
}
}
]
}
}
```
### Range query
[A range query](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-range-query.html) in Elasticsearch retrieves documents containing terms within a specified range. While working with range queries, it is helpful to note that `gte` stands for greater than or equal to, and `lte` stands for less than or equal to.
The following example looks for Celtics games with total points between 145 and 150.
```
GET /celtics/_search
{
"query": {
"range": {
"PTS": {
"gte": 145,
"lte": 150
}
}
}
}
```
Two games that fit into this range get returned:
```json
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 2,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": "celtics",
"_id": "0022300542",
"_score": 1,
"_source": {
"SEASON_ID": "22023",
"TEAM_ID": 1610612738,
"TEAM_ABBREVIATION": "BOS",
"TEAM_NAME": "Boston Celtics",
"GAME_ID": "0022300542",
"GAME_DATE": "2024-01-13",
"MATCHUP": "BOS vs. HOU",
"WL": "W",
"MIN": 239,
"PTS": 145,
"FGM": 51,
"FGA": 95,
"FG_PCT": 0.537,
"FG3M": 24,
"FG3A": 47,
"FG3_PCT": 0.511,
"FTM": 19,
"FTA": 25,
"FT_PCT": 0.76,
"OREB": 10,
"DREB": 40,
"REB": 50,
"AST": 26,
"STL": 7,
"BLK": 8,
"TOV": 11,
"PF": 21,
"PLUS_MINUS": 32
}
},
{
"_index": "celtics",
"_id": "0022300389",
"_score": 1,
"_source": {
"SEASON_ID": "22023",
"TEAM_ID": 1610612738,
"TEAM_ABBREVIATION": "BOS",
"TEAM_NAME": "Boston Celtics",
"GAME_ID": "0022300389",
"GAME_DATE": "2023-12-23",
"MATCHUP": "BOS @ LAC",
"WL": "W",
"MIN": 241,
"PTS": 145,
"FGM": 49,
"FGA": 94,
"FG_PCT": 0.521,
"FG3M": 25,
"FG3A": 53,
"FG3_PCT": 0.472,
"FTM": 22,
"FTA": 28,
"FT_PCT": 0.786,
"OREB": 15,
"DREB": 36,
"REB": 51,
"AST": 33,
"STL": 4,
"BLK": 5,
"TOV": 9,
"PF": 19,
"PLUS_MINUS": 37
}
}
]
}
}
```
To combine different queries, you can use a [boolean query](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-bool-query.html). It returns documents that match the boolean combinations of other queries. A boolean query must contain at least one of the conditions of `must`, `filter`, `should`, or `must not`.
The following example searches for Celtics games in which they had a plus-minus score of 10, scored between 100 and 130 points, and did not lose the game.
```
GET /celtics/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"PLUS_MINUS": "10"
}
},
{
"range": {
"PTS": {
"gte": 100,
"lte": 130
}
}
}
],
"must_not": [
{
"term": {
"WL": "L"
}
}
]
}
}
```
Four games meet the parameters of the above query will return the following result:
```json
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 4,
"relation": "eq"
},
"max_score": 2,
"hits": [
{
"_index": "celtics",
"_id": "0022300920",
"_score": 2,
"_source": {
"SEASON_ID": "22023",
"TEAM_ID": 1610612738,
"TEAM_ABBREVIATION": "BOS",
"TEAM_NAME": "Boston Celtics",
"GAME_ID": "0022300920",
"GAME_DATE": "2024-03-09",
"MATCHUP": "BOS @ PHX",
"WL": "W",
"MIN": 240,
"PTS": 117,
"FGM": 46,
"FGA": 94,
"FG_PCT": 0.489,
"FG3M": 15,
"FG3A": 39,
"FG3_PCT": 0.385,
"FTM": 10,
"FTA": 13,
"FT_PCT": 0.769,
"OREB": 13,
"DREB": 30,
"REB": 43,
"AST": 29,
"STL": 7,
"BLK": 4,
"TOV": 12,
"PF": 12,
"PLUS_MINUS": 10
}
},
{
"_index": "celtics",
"_id": "0022300246",
"_score": 2,
"_source": {
"SEASON_ID": "22023",
"TEAM_ID": 1610612738,
"TEAM_ABBREVIATION": "BOS",
"TEAM_NAME": "Boston Celtics",
"GAME_ID": "0022300246",
"GAME_DATE": "2023-11-26",
"MATCHUP": "BOS vs. ATL",
"WL": "W",
"MIN": 239,
"PTS": 113,
"FGM": 42,
"FGA": 95,
"FG_PCT": 0.442,
"FG3M": 13,
"FG3A": 47,
"FG3_PCT": 0.277,
"FTM": 16,
"FTA": 20,
"FT_PCT": 0.8,
"OREB": 18,
"DREB": 40,
"REB": 58,
"AST": 24,
"STL": 9,
"BLK": 3,
"TOV": 12,
"PF": 19,
"PLUS_MINUS": 10
}
},
{
"_index": "celtics",
"_id": "0022300194",
"_score": 2,
"_source": {
"SEASON_ID": "22023",
"TEAM_ID": 1610612738,
"TEAM_ABBREVIATION": "BOS",
"TEAM_NAME": "Boston Celtics",
"GAME_ID": "0022300194",
"GAME_DATE": "2023-11-15",
"MATCHUP": "BOS @ PHI",
"WL": "W",
"MIN": 239,
"PTS": 117,
"FGM": 42,
"FGA": 88,
"FG_PCT": 0.477,
"FG3M": 18,
"FG3A": 50,
"FG3_PCT": 0.36,
"FTM": 15,
"FTA": 19,
"FT_PCT": 0.789,
"OREB": 12,
"DREB": 33,
"REB": 45,
"AST": 23,
"STL": 7,
"BLK": 8,
"TOV": 9,
"PF": 15,
"PLUS_MINUS": 10
}
},
{
"_index": "celtics",
"_id": "0022300136",
"_score": 2,
"_source": {
"SEASON_ID": "22023",
"TEAM_ID": 1610612738,
"TEAM_ABBREVIATION": "BOS",
"TEAM_NAME": "Boston Celtics",
"GAME_ID": "0022300136",
"GAME_DATE": "2023-11-04",
"MATCHUP": "BOS @ BKN",
"WL": "W",
"MIN": 240,
"PTS": 124,
"FGM": 43,
"FGA": 90,
"FG_PCT": 0.478,
"FG3M": 15,
"FG3A": 45,
"FG3_PCT": 0.333,
"FTM": 23,
"FTA": 27,
"FT_PCT": 0.852,
"OREB": 10,
"DREB": 40,
"REB": 50,
"AST": 22,
"STL": 4,
"BLK": 6,
"TOV": 11,
"PF": 17,
"PLUS_MINUS": 10
}
}
]
}
}
```
## When to use aggregations
Aggregations in Elasticsearch allow you to summarize data by creating metrics and using summary statistics. They are beneficial for analytics. There are three types of aggregations in Elasticsearch: metric, bucket, and pipeline. You can also nest aggregations as well.
### Metric aggregation
A [metric aggregation](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics.html) performs calculations such as a sum or average on a field value.
The following query calculates the average of the total number of points contained in the index.
```
GET /celtics/_search
{
"size": 0,
"aggs": {
"total_points": {
"avg": {
"field": "PTS"
}
}
}
}
```
A result containing a section titled `aggregations`, which includes the average number of points, will be returned.
```json
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 85,
"relation": "eq"
},
"max_score": null,
"hits": []
},
"aggregations": {
"total_points": {
"value": 120.2
}
}
}
```
### Bucket aggregations
A [bucket aggregation](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket.html) groups documents into buckets according to specified criteria. These buckets, or bins, categorize data based on field values, ranges, or other criteria.
To group the Celtics games by months you would use the following query:
```
GET /celtics/_search
{
"size": 0,
"aggs": {
"games_over_time": {
"date_histogram": {
"field": "GAME_DATE",
"calendar_interval": "month"
}
}
}
}
```
In the `aggregations` section of the JSON response, there is a date histogram called `games_over_time,` grouping documents by month. `doc_count` indicates the number of documents per month.
```json
{
"took": 5,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 85,
"relation": "eq"
},
"max_score": null,
"hits": []
},
"aggregations": {
"games_over_time": {
"buckets": [
{
"key_as_string": "2023-10-01T00:00:00.000Z",
"key": 1696118400000,
"doc_count": 3
},
{
"key_as_string": "2023-11-01T00:00:00.000Z",
"key": 1698796800000,
"doc_count": 15
},
{
"key_as_string": "2023-12-01T00:00:00.000Z",
"key": 1701388800000,
"doc_count": 14
},
{
"key_as_string": "2024-01-01T00:00:00.000Z",
"key": 1704067200000,
"doc_count": 16
},
{
"key_as_string": "2024-02-01T00:00:00.000Z",
"key": 1706745600000,
"doc_count": 10
},
{
"key_as_string": "2024-03-01T00:00:00.000Z",
"key": 1709251200000,
"doc_count": 16
},
{
"key_as_string": "2024-04-01T00:00:00.000Z",
"key": 1711929600000,
"doc_count": 11
}
]
}
}
}
```
### Pipeline aggregations
[Pipeline aggregations](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-pipeline.html) in Elasticsearch perform calculations on the results of other aggregations, allowing for complex data processing and analytics.
You can create a query that calculates the cumulative sum of points scored. First, you will need a date histogram to bucket the documents by date, then a cumulative sum pipeline aggregation to calculate the cumulative total. The following query uses a pipeline aggregation to view the total number of points scored by the Celtics per month.
```
GET /celtics/_search
{
"size": 0,
"aggs": {
"games_over_time": {
"date_histogram": {
"field": "GAME_DATE",
"calendar_interval": "month"
},
"aggs": {
"total_points": {
"sum": {
"field": "PTS"
}
},
"cumulative_points": {
"cumulative_sum": {
"buckets_path": "total_points"
}
}
}
}
}
}
```
The `aggregations` section of the response contains groupings of the total number of points scored by the Celtics organized by month. You can also see the total number of games per month as well.
```json
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 85,
"relation": "eq"
},
"max_score": null,
"hits": []
},
"aggregations": {
"games_over_time": {
"buckets": [
{
"key_as_string": "2023-10-01T00:00:00.000Z",
"key": 1696118400000,
"doc_count": 3,
"total_points": {
"value": 353
},
"cumulative_points": {
"value": 353
}
},
{
"key_as_string": "2023-11-01T00:00:00.000Z",
"key": 1698796800000,
"doc_count": 15,
"total_points": {
"value": 1740
},
"cumulative_points": {
"value": 2093
}
},
{
"key_as_string": "2023-12-01T00:00:00.000Z",
"key": 1701388800000,
"doc_count": 14,
"total_points": {
"value": 1771
},
"cumulative_points": {
"value": 3864
}
},
{
"key_as_string": "2024-01-01T00:00:00.000Z",
"key": 1704067200000,
"doc_count": 16,
"total_points": {
"value": 1915
},
"cumulative_points": {
"value": 5779
}
},
{
"key_as_string": "2024-02-01T00:00:00.000Z",
"key": 1706745600000,
"doc_count": 10,
"total_points": {
"value": 1220
},
"cumulative_points": {
"value": 6999
}
},
{
"key_as_string": "2024-03-01T00:00:00.000Z",
"key": 1709251200000,
"doc_count": 16,
"total_points": {
"value": 1947
},
"cumulative_points": {
"value": 8946
}
},
{
"key_as_string": "2024-04-01T00:00:00.000Z",
"key": 1711929600000,
"doc_count": 11,
"total_points": {
"value": 1271
},
"cumulative_points": {
"value": 10217
}
}
]
}
}
}
```
### Nested aggregations
One advanced feature of aggregations is the ability to [nest them](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-nested-aggregation.html), which allows you to perform multilevel data analysis. For example, you can first group your data by a particular field and perform further aggregations within those groups.
An example of a nested aggregation is an aggregation that first calculates the average of points scored and groups by game month and then by the game result (whether they won or lost).
Since the `WL` field is a text field, field data is disabled for text fields by default because text fields are not optimized for operations that require per-document field data like aggregations and sorting. To fix this, you can use a keyword field instead. Since WL is already a text field, you can add a `.keyword` subfield.
```
GET /celtics/_search
{
"size": 0,
"aggs": {
"games_by_month": {
"date_histogram": {
"field": "GAME_DATE",
"calendar_interval": "month"
},
"aggs": {
"results": {
"terms": {
"field": "WL.keyword"
},
"aggs": {
"average_points": {
"avg": {
"field": "PTS"
}
}
}
}
}
}
}
}
```
The response will provide the average points scored each month, further broken down by wins and losses, allowing you to analyze performance trends over time.
```json
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 85,
"relation": "eq"
},
"max_score": null,
"hits": []
},
"aggregations": {
"games_by_month": {
"buckets": [
{
"key_as_string": "2023-10-01T00:00:00.000Z",
"key": 1696118400000,
"doc_count": 3,
"results": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "W",
"doc_count": 3,
"average_points": {
"value": 117.66666666666667
}
}
]
}
},
{
"key_as_string": "2023-11-01T00:00:00.000Z",
"key": 1698796800000,
"doc_count": 15,
"results": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "W",
"doc_count": 11,
"average_points": {
"value": 119.45454545454545
}
},
{
"key": "L",
"doc_count": 4,
"average_points": {
"value": 106.5
}
}
]
}
},
{
"key_as_string": "2023-12-01T00:00:00.000Z",
"key": 1701388800000,
"doc_count": 14,
"results": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "W",
"doc_count": 12,
"average_points": {
"value": 127.75
}
},
{
"key": "L",
"doc_count": 2,
"average_points": {
"value": 119
}
}
]
}
},
{
"key_as_string": "2024-01-01T00:00:00.000Z",
"key": 1704067200000,
"doc_count": 16,
"results": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "W",
"doc_count": 11,
"average_points": {
"value": 123.9090909090909
}
},
{
"key": "L",
"doc_count": 5,
"average_points": {
"value": 110.4
}
}
]
}
},
{
"key_as_string": "2024-02-01T00:00:00.000Z",
"key": 1706745600000,
"doc_count": 10,
"results": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "W",
"doc_count": 9,
"average_points": {
"value": 123.88888888888889
}
},
{
"key": "L",
"doc_count": 1,
"average_points": {
"value": 105
}
}
]
}
},
{
"key_as_string": "2024-03-01T00:00:00.000Z",
"key": 1709251200000,
"doc_count": 16,
"results": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "W",
"doc_count": 12,
"average_points": {
"value": 124.5
}
},
{
"key": "L",
"doc_count": 4,
"average_points": {
"value": 113.25
}
}
]
}
},
{
"key_as_string": "2024-04-01T00:00:00.000Z",
"key": 1711929600000,
"doc_count": 11,
"results": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "W",
"doc_count": 9,
"average_points": {
"value": 117.88888888888889
}
},
{
"key": "L",
"doc_count": 2,
"average_points": {
"value": 105
}
}
]
}
}
]
}
}
}
```
## When does ES|QL come in?
While the structure of a JSON query language is something that you get used to over time, it can be challenging at first. [ES|QL](https://www.elastic.co/guide/en/elasticsearch/reference/current/esql.html) is a procedural piped query language with SQL-like syntax. One key feature is that you can define the order in which you wish to return data. The pipes in ES|QL look like `|`, enabling you to manipulate and transform data step by step. The main uses of ES|QL are for analytics, data manipulation, and other transformations. It also can help work with visualizations in general.
### Filtering data with ES|QL
ES|QL allows you to quickly filter data from a dataset to return the results you are looking for. The following query first specifies the desired format for the returned data. The default is JSON, but you can define formats such as `txt` or `csv`. For readability, `txt` was chosen. The `FROM` field contains an index called `celtics`. The query then narrows the index down to fields containing the date of the game and whether the team won or lost. It also limits the results to the last ten games from the dataset.
```
POST _query?format=txt
{
"query": """
FROM celtics
| KEEP GAME_DATE, WL
| LIMIT 10
"""
```
The results you get back contain a table-like structure that includes the date of the game and the result, with W being a win and L being a loss.
```
GAME_DATE | WL
------------------------+---------------
2024-04-29T00:00:00.000Z|W
2024-04-27T00:00:00.000Z|W
2024-04-24T00:00:00.000Z|L
2024-04-21T00:00:00.000Z|W
2024-04-14T00:00:00.000Z|W
2024-04-12T00:00:00.000Z|W
2024-04-11T00:00:00.000Z|L
2024-04-07T00:00:00.000Z|W
2024-04-05T00:00:00.000Z|W
2024-04-03T00:00:00.000Z|W
```
### SQL-like syntax
You can also utilize the SQL-like syntax for additional filtering capabilities. The following query narrows the index down to games that the Celtics won and returns the fields for the date of the game and the matchup, which includes the two teams that played. It also limits it to the last ten results contained in the index.
```
POST _query?format=txt
{
"query": """
FROM celtics
| WHERE WL == "W"
| KEEP GAME_DATE, MATCHUP
| LIMIT 10
"""
}
```
The results you get back contain a table-like structure that includes the date of the game and the matchup, which consists of a summary of the two teams that played the given game.
```
GAME_DATE | MATCHUP
------------------------+---------------
2024-04-29T00:00:00.000Z|BOS @ MIA
2024-04-27T00:00:00.000Z|BOS @ MIA
2024-04-21T00:00:00.000Z|BOS vs. MIA
2024-04-14T00:00:00.000Z|BOS vs. WAS
2024-04-12T00:00:00.000Z|BOS vs. CHA
2024-04-07T00:00:00.000Z|BOS vs. POR
2024-04-05T00:00:00.000Z|BOS vs. SAC
2024-04-03T00:00:00.000Z|BOS vs. OKC
2024-04-01T00:00:00.000Z|BOS @ CHA
2024-03-30T00:00:00.000Z|BOS @ NOP
```
### Aggregations in ES|QL
You can use ES|QL to quickly find statistics about a given field. The following query uses the exact data for the Celtics to find the average field goal and field goal percentage from the three-point line. It limits the average to include one line to summarize the data contained in the index.
In basketball, the field goal percentage is a key statistic that measures a player or team's efficiency in making shots. It is calculated by dividing the number of successful field goals by the total number of field goal attempts. The three-point field goal percentage is a version of this metric, but it focuses instead only on shots taken from beyond the three-point line.
```
POST _query?format=txt
{
"query": """
FROM celtics
| STATS AVG(FG_PCT), AVG(FG3_PCT)
| LIMIT 1
"""
}
```
The result contains a table-like structure that includes the index's average field goal percentage and field goal percentage from the three-point line.
```
AVG(FG_PCT) | AVG(FG3_PCT)
-------------------+-------------------
0.48734117781414704|0.38770588215659646
```
### Working with visualizations
One of ES|QL's main advantages is its ability to work with visualization in Kibana and reduce the need to switch between different tools. You can use it to explore your data while creating or modifying visualizations seamlessly. Additionally, you can create alerts based on conditions defined with ES|QL.
## Conclusion
Typically, an index-level API helps manage your index, a query to find specific data, and an aggregation to perform calculations or obtain statistics about your data. ES|QL, a piped query language, allows you to filter, transform, and analyze structured and unstructured data in Elasticsearch more intuitively than with a JSON query. Let us know if you built anything based on this blog or if you have questions on our [Discuss forums](https://discuss.elastic.co/) and [the community Slack channel](https://communityinviter.com/apps/elasticstack/elastic-community).
## Additional resources
If you are getting started with Elastic, these resources may be helpful.
- [Elasticsearch Engineer training](https://www.elastic.co/training/elasticsearch-engineer)
- [Elasticsearch Quick Start Guide](https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started.html)
- [Beginner's Crash Course to Elastic Stack](https://www.youtube.com/watch?v=gS_nHTWZEJ8) | jessicagarson |
1,891,551 | Corona Clicker - Level System and New Database | I'm excited to share that the development of Corona Clicker is progressing smoothly. Recently, I made... | 0 | 2024-06-17T18:03:11 | https://dev.to/king_triton/corona-clicker-level-system-and-new-database-38c0 | sql, webdev, gamedev, backend | I'm excited to share that the development of Corona Clicker is progressing smoothly. Recently, I made significant advancements by implementing a new database structure and developing a comprehensive level system to enhance the gaming experience. This level system is a completely new feature that didn't exist before.
## New Database Structure
The updated database structure is designed to support the new level system and provide a seamless user experience. Here are the key components:
## Levels Table:
`CREATE TABLE levels (
id INT PRIMARY KEY AUTO_INCREMENT,
title VARCHAR(255) NOT NULL,
points INT NOT NULL,
svg TEXT NOT NULL
);`
`INSERT INTO levels (title, points, svg) VALUES
('Beginner', 0, '{svg string}'),
('Advanced', 200, '{svg string}'),
('Expert', 300, '{svg string}'),
('Master', 600, '{svg string}'),
('Recognized', 1200, '{svg string}'),
('Elite', 2500, '{svg string}'),
('Leader', 10000, '{svg string}'),
('Guru', 100000, '{svg string}'),
('Mega Guru', 500000, '{svg string}'),
('Legend', 1000000, '{svg string}');`
## Users Table:
`CREATE TABLE users (
id INT PRIMARY KEY AUTO_INCREMENT,
tg_id BIGINT NOT NULL,
score INT DEFAULT 0,
registration_date DATE NOT NULL
);`
## User Levels Table:
`CREATE TABLE user_levels (
user_id INT PRIMARY KEY,
level_id INT NOT NULL,
FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE,
FOREIGN KEY (level_id) REFERENCES levels(id) ON DELETE CASCADE
);`
## Level System
The new level system is designed to motivate players by providing clear goals and rewards. Here’s a breakdown of the levels and their respective rewards:
**Level 1: Beginner**
- Requirements: None.
- Reward: None.
**Level 2: Advanced**
- Requirements: Earn 200 points.
- Reward: Access to the /boost page.
**Level 3: Expert**
- Requirements: Earn 300 points.
- Reward: Access to the "Double Time" upgrade on the /boost page.
**Level 4: Master**
- Requirements: Earn 600 points.
- Reward: Access to the "Double Coins" upgrade on the /boost page.
**Level 5: Recognized**
- Requirements: Earn 1200 points.
- Reward: Access to the /shop page and all purchases.
**Level 6: Elite**
- Requirements: Earn 2500 points.
- Reward: None.
**Level 7: Leader**
- Requirements: Earn 10,000 points.
- Reward: None.
**Level 8: Guru**
- Requirements: Earn 100,000 points.
- Reward: None.
**Level 9: Mega Guru**
- Requirements: Earn 500,000 points.
- Reward: None.
**Level 10: Legend**
- Requirements: Earn 1,000,000 or more points.
- Reward: "Legend" status on the /stars page.
## Upgrades
Players can enhance their gameplay with the following upgrades:
**Double Time**
- Description: Increases the time available for clicking the crown by 50%.
- Price: 150 coins.
**Double Coins**
- Description: Increases the number of coins earned per click by 50%.
- Price: 300 coins.
These new features are designed to make Corona Clicker more engaging and rewarding for players.
## Stay Connected
Don't miss out on the latest updates and features of Corona Clicker! Follow the [official Telegram channel](https://t.me/+zYoeSgOD9dU1Mzhi) for all the news and announcements. The new version of Corona Clicker is still under development and is not yet available to all players. Stay tuned for more updates as I continue to improve the game!
| king_triton |
1,891,550 | RECURSION | A function may call other functions, including calling itself.A function that calls itself until the... | 0 | 2024-06-17T18:01:53 | https://dev.to/ojus_coder/recursion-3nop | devchallenge, cschallenge, computerscience, beginners | A function may call other functions, including calling itself.A function that calls itself until the base condition is satisfied is known as a Recursive function, and the technique of using a recursive function is called Recursion. | ojus_coder |
1,891,666 | Evento De Programação SAS Para Iniciantes Gratuito | O webinar SAS DEV para Iniciantes, oferecido pela SAS Education, é uma oportunidade para ser... | 0 | 2024-06-23T13:50:21 | https://guiadeti.com.br/evento-programacao-sas-iniciantes-gratuito/ | eventos, analisededados, cursosgratuitos, dados | ---
title: Evento De Programação SAS Para Iniciantes Gratuito
published: true
date: 2024-06-17 17:51:45 UTC
tags: Eventos,analisededados,cursosgratuitos,dados
canonical_url: https://guiadeti.com.br/evento-programacao-sas-iniciantes-gratuito/
---
O webinar SAS DEV para Iniciantes, oferecido pela SAS Education, é uma oportunidade para ser introduzido ao mundo da programação para análise de dados e às tecnologias Statistical Analysis System.
Este curso expandirá suas possibilidades no mercado profissional. Apenas os primeiros 500 participantes que ingressarem no dia do curso terão acesso, pois as vagas na sala virtual são limitadas.
Aproveite esta chance para desenvolver suas habilidades e conhecimentos em SAS. Não perca a oportunidade de começar sua jornada na programação com Statistical Analysis System.
## SAS DEV para Iniciantes | Programação SAS
O curso SAS DEV para Iniciantes, oferecido pela SAS Education, é uma oportunidade para ser introduzido ao mundo da programação para análise de dados e às tecnologias Statistical Analysis System. Este evento ampliará suas possibilidades no mercado profissional.
_Imagem do formulário de inscrição_
### Acesso Limitado
Somente os primeiros 500 participantes que ingressarem no dia do curso terão acesso, pois as vagas na sala virtual são limitadas. Não perca a oportunidade e garanta sua vaga para aprender sobre Statistical Analysis System!
### Data e Hora
- Data: 20 de julho de 2024;
- Hora: 08:30 da manhã.
### O Que Você Irá Aprender?
Neste webinar, você aprenderá sobre:
- Statistical Analysis System e conceitos gerais de análise de dados;
- Ferramenta web de desenvolvimento SAS Studio;
- Processo de programação Statistical Analysis System;
- Sintaxe e estruturas básicas de acesso;
- Importação e filtros de dados;
- Funções, condicionais, transformação e apresentação dos dados.
### Quem Deverá Participar?
Este curso é ideal para qualquer profissional que deseja iniciar em programação Statistical Analysis System voltada para análise de dados.
### Informações Importantes
A gravação do treinamento e os descontos ofertados serão enviados somente para quem participar do curso.
<aside>
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</div>
<span>Evento De Programação SAS Para Iniciantes Gratuito</span> <a href="https://guiadeti.com.br/evento-programacao-sas-iniciantes-gratuito/" title="Evento De Programação SAS Para Iniciantes Gratuito"></a>
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</aside>
## SAS
SAS (Statistical Analysis System) é uma ferramenta poderosa e versátil usada para análise de dados, estatísticas avançadas, visualização de dados e gerenciamento de dados.
Desenvolvido pela SAS Institute, o software é amplamente utilizado em diversas indústrias, incluindo saúde, finanças, pesquisa de mercado, governo e educação.
### Principais Funcionalidades
- Análise de Dados: Statistical Analysis System oferece um conjunto abrangente de ferramentas para análise de dados, permitindo a realização de análises estatísticas avançadas, modelagem preditiva, mineração de dados e análise de big data. Suas capacidades analíticas ajudam os usuários a extrair insights valiosos a partir de grandes volumes de dados.
- Gerenciamento de Dados: Com SAS, os usuários podem acessar, limpar, transformar e gerenciar dados de maneira eficiente. O software suporta uma ampla variedade de fontes de dados, facilitando a integração e preparação dos dados para análise.
- Visualização de Dados: Statistical Analysis System fornece ferramentas robustas de visualização de dados, permitindo a criação de gráficos e dashboards interativos. Essas visualizações ajudam a comunicar insights complexos de forma clara e eficaz, auxiliando na tomada de decisões informadas.
- Relatórios A geração de relatórios é uma funcionalidade chave do Statistical Analysis System. Os usuários podem criar relatórios personalizados e automatizados que apresentam os resultados das análises de maneira compreensível e acionável.
### Vantagens de Uso
Statistical Analysis System é conhecido por sua precisão e confiabilidade. Ele é amplamente utilizado em setores onde a precisão dos dados é crítica, como saúde e finanças.
O SAS Institute oferece um excelente suporte técnico e uma vasta quantidade de recursos de aprendizagem. Existe uma grande comunidade de usuários Statistical Analysis System que compartilham conhecimentos e melhores práticas.
Statistical Analysis System é altamente flexível e pode ser adaptado para atender às necessidades específicas de diferentes setores e projetos. Ele suporta diversos métodos analíticos e pode ser integrado a outras ferramentas e plataformas.
## SAS Education
SAS Education oferece uma série de treinamentos online que cobrem diversos aspectos do software SAS.
Os treinamentos online incluem vídeos, exercícios práticos e avaliações para garantir uma aprendizagem eficaz.
### Cursos Presenciais
Para aqueles que preferem uma metodologia de ensino mais tradicional, a instituição também oferece cursos presenciais.
Os cursos são ministrados por instrutores experientes e fornecem uma experiência de aprendizagem imersiva, com oportunidades para interação direta e resolução de dúvidas em tempo real.
### Webinars
Os webinars são uma maneira conveniente de aprender sobre as últimas tendências e técnicas em análise de dados.
A instituição organiza webinars sobre diversos tópicos, apresentados por especialistas da indústria. Os participantes podem fazer perguntas e interagir com os palestrantes durante as sessões ao vivo.
### Certificações
SAS Education oferece programas de certificação que validam as habilidades e conhecimentos dos profissionais em tecnologias SAS. As certificações são reconhecidas globalmente e podem aumentar as oportunidades de carreira e credibilidade no mercado de trabalho.
### Benefícios do Treinamento SAS
#### Aprendizado Prático
Os cursos dessa escola são projetados para fornecer um aprendizado prático e aplicável. Os participantes têm a oportunidade de trabalhar em projetos reais e resolver problemas do mundo real, o que facilita a aplicação das habilidades aprendidas em suas carreiras.
#### Acesso a Especialistas
Os instrutores são especialistas com vasta experiência na indústria. Eles fornecem insights valiosos, melhores práticas e dicas que ajudam os participantes a maximizar o uso das ferramentas Statistical Analysis System .
#### Flexibilidade
Contando com opções de cursos online, presenciais e webinars, a instituição oferece flexibilidade para atender às necessidades de diferentes tipos de alunos, permitindo assim que profissionais ocupados encontrem programas de treinamento que se ajustem às suas agendas.
#### Suporte e Recursos
A escola oferece acesso a vários recursos de suporte, incluindo documentação, tutoriais, fóruns de discussão e assistência técnica, permitindo que os alunos tenham tudo o que precisam para ter sucesso em seus estudos e no uso das tecnologias Statistical Analysis System.
## Link de inscrição ⬇️
As [inscrições para o webinar SAS DEV para Iniciantes | Programação SAS](https://sas.zoom.us/webinar/register/WN_qrSTq7i6RuWmQCpJPAdFWg#/registration) devem ser realizadas por meio de formulário.
## Compartilhe essa oportunidade de capacitação e amplie suas habilidades em análise de dados!
Gostou do conteúdo sobre o curso de Statistical Analysis System? Então compartilhe com a galera!
O post [Evento De Programação SAS Para Iniciantes Gratuito](https://guiadeti.com.br/evento-programacao-sas-iniciantes-gratuito/) apareceu primeiro em [Guia de TI](https://guiadeti.com.br). | guiadeti |
1,891,548 | Redux Toolkit APIs | Redux Toolkit is a package that provides a set of tools and utilities to simplify the process of... | 0 | 2024-06-17T17:49:59 | https://dev.to/bmanish/redux-toolkit-apis-27fj | javascript, react, redux, webdev | Redux Toolkit is a package that provides a set of tools and utilities to simplify the process of working with Redux, a popular state management library for JavaScript applications. One of the key features of the Redux Toolkit is its built-in API, which includes several functions and utilities to streamline Redux development.
Redux Toolkit is a package that provides a set of tools and utilities to simplify the process of working with Redux, a popular state management library for JavaScript applications. One of the key features of the Redux Toolkit is its built-in API, which includes several functions and utilities to streamline Redux development.
**Here’s an overview of the Redux Toolkit API:**
1. `createSlice`: This utility function allows you to define a Redux slice, which is a collection of reducer logic for managing a specific slice of the application state. It automatically generates action creators and action types based on the reducer logic you provide, reducing boilerplate code.
2. `configureStore`: This function is used to create a Redux store with sensible defaults, including support for the Redux DevTools Extension. It simplifies the process of setting up a Redux store by combining several configuration steps into a single function call.
3. `createAsyncThunk`: This utility function simplifies the process of handling asynchronous logic in Redux by creating action creators that automatically dispatch pending, fulfilled, and rejected actions based on the status of an asynchronous operation (e.g., fetching data from an API).
4. `createEntityAdapter`: This utility function generates a set of reducer functions and selectors for managing normalized entity states in Redux. It helps organize and manage data in a normalized format, making it easier to work with relational data structures in Redux.
5. `createReducer`: This utility function allows you to define a reducer function using a map of action type to reducer logic, similar to the approach used in createSlice. It's useful for cases where you need more flexibility in defining reducer logic outside of a slice.
Overall, the Redux Toolkit API aims to simplify common Redux use cases, reduce boilerplate code, and improve developer productivity by providing a set of ergonomic and opinionated tools for working with Redux.
## How does it handle the Redux data overload issue?
Redux Toolkit doesn’t directly handle data overload issues, but it provides tools and patterns that can help mitigate them:
1. **Normalized State Management:** The Redux Toolkit encourages normalized state management using utilities like `createEntityAdapter`. Normalizing data structures can prevent data overload by organizing data in a structured way, making it easier to manage and update.
2. **Selective Data Loading:** With `createAsyncThunk`, you can implement selective data loading techniques, such as lazy loading or pagination, to load only the data that's needed at any given time. This can help reduce the amount of data stored in the Redux store and improve performance.
3. **Memoization:** Redux Toolkit doesn’t handle memoization directly, but you can use memoization libraries like Reselect in conjunction with Redux Toolkit to optimize selectors and prevent unnecessary re-renders caused by data overload.
4. **Middleware and Throttling:** Redux middleware can be used to implement throttling or debouncing techniques for actions that may trigger data overload. Throttling can help control the rate at which actions are dispatched, preventing excessive updates to the Redux store.
5. **Selective State Slicing:** Use selectors to retrieve only the necessary parts of the state tree rather than accessing the entire state object. This can help improve performance and reduce memory usage, especially when dealing with large amounts of data.
While Redux Toolkit provides useful tools and patterns for managing Redux state, it’s important to design your application’s state management strategy carefully to avoid data overload issues. This may involve a combination of normalization, selective data loading, memoization, and other performance optimization techniques tailored to your specific use case.
| bmanish |
1,891,547 | Creative Full Screen Carousal Hero | This CodePen pin showcases a creative full-screen carousel hero, designed to captivate users with... | 0 | 2024-06-17T17:49:16 | https://dev.to/creative_salahu/creative-full-screen-carousal-hero-43k5 | codepen | This CodePen pin showcases a creative full-screen carousel hero, designed to captivate users with high-impact visuals and smooth transitions. The carousel features multiple slides, each displaying a striking background image with a bold title and a call-to-action link.
**Key Features:**
Full-Screen Carousel: The carousel occupies the entire viewport height, providing an immersive browsing experience.
Responsive Design: The layout adapts seamlessly across different screen sizes, ensuring optimal viewing on desktops, tablets, and mobile devices.
Smooth Transitions: Slides transition smoothly with a sleek sliding effect, enhanced by Swiper.js, a modern touch slider.
Navigation Controls: Users can navigate through the slides using the 'PREV' and 'NEXT' buttons, or the progress bar at the bottom.
Typography: The text elements use the "Poppins" and "Fjalla One" fonts, adding a touch of modern elegance to the design.
Technologies Used:
HTML5: The structure of the carousel is built using semantic HTML5 elements.
CSS3: Custom styles are applied to enhance the visual appearance and ensure responsiveness.
Swiper.js: The popular Swiper library is utilized for the carousel functionality, providing smooth slide transitions and navigation controls.
jQuery: Simplifies JavaScript code for initializing the Swiper carousel and handling DOM manipulations.
Explore this pen to see a visually appealing full-screen carousel that can be a stunning addition to any modern website, perfect for showcasing featured content or high-quality images.
{% codepen https://codepen.io/CreativeSalahu/pen/abrErmK %} | creative_salahu |
1,891,546 | Mastering Docker Fundamentals: The First Step in Becoming a Certified Kubernetes Administrator | Introduction Welcome to our comprehensive blog series designed to help you master... | 0 | 2024-06-17T17:48:17 | https://dev.to/jensen1806/mastering-docker-fundamentals-the-first-step-in-becoming-a-certified-kubernetes-administrator-3o7c | docker, kubernetes, containerization, cka | ## Introduction
Welcome to our comprehensive blog series designed to help you master Kubernetes and become a Certified Kubernetes Administrator (CKA). In this series, we will cover everything you need to know, starting with Docker fundamentals. Whether you’re a beginner or have some experience, this series will guide you through the essentials of containerization, Docker, and Kubernetes. Let’s dive into the first topic: Docker Fundamentals.
## Why Containers?
Before diving into containers, it’s important to understand the challenges they solve. Traditionally, applications are promoted through multiple environments—development, testing, and production. This process often faces issues such as environment misconfigurations and missing dependencies, causing builds to fail when promoted to production. Containers address these problems by packaging application code along with all its dependencies, ensuring consistent behaviour across all environments.
## What Are Containers?
Containers provide an isolated environment with all the necessary libraries, dependencies, application code, and the operating system required to run an application. This isolation ensures that applications run the same way regardless of the host operating system. Containers are lightweight compared to virtual machines because they share the host operating system’s kernel and only include the essential components needed for the application.
## Difference Between Containers and Virtual Machines
**Virtual Machines (VMs)**: Imagine VMs as standalone bookstores. Each bookstore has its own building, complete with all the necessary facilities—shelves, books, cash registers, and staff. Every bookstore operates independently and doesn't share resources with other bookstores. This setup is robust but requires a lot of space and resources to maintain each store separately.
**Containers**: Now, think of containers as different sections within a large, shared bookstore. Each section (or container) has its own set of books and staff but shares common facilities like the building, restrooms, heating, and cooling systems. By sharing these resources, the large bookstore can efficiently utilize space and resources while still keeping each section independent and organized.
## Docker Fundamentals
Docker is a platform that facilitates the building, shipping, and running of containers. Here’s a simplified Docker workflow:
**Dockerfile**: A set of instructions to create a Docker image. It specifies the base image, dependencies, and commands to build the application.
**Docker Image**: The build output of a Dockerfile, encapsulating the application and its environment.
**Docker Registry**: A storage solution for Docker images. Docker Hub is the most common public registry, but private registries like JFrog Artifactory and Nexus Repository are also widely used.
**Docker Container**: A running instance of a Docker image.
## Docker Workflow
**Build**: Developers write a Dockerfile and use the docker build command to create a Docker image.
**Push**: The created image is pushed to a Docker registry using the docker push command.
**Pull**: The image is pulled from the registry to the desired environment using the docker pull command.
**Run**: Finally, the docker run command creates and runs a container from the pulled image.
## Docker architecture:
**Docker Client**: The interface through which users interact with Docker. Commands issued by the client are processed by the Docker daemon.
**Docker Daemon (DockerD)**: The background service responsible for building, running, and managing Docker containers.
**Docker Registry**: Where Docker images are stored and distributed.
**Container Runtime**: The component that runs containers. Examples include containerd and runc.
In the next post of this series, we will get hands-on with Docker by creating a Dockerfile, building an image, and running our first containerized application. Stay tuned and follow along for practical insights.
## Conclusion
This introductory post sets the stage for our deep dive into Docker and Kubernetes. By understanding the basics of Docker, you’re well on your way to mastering containerization and Kubernetes administration. | jensen1806 |
1,891,545 | Why CS50? | *Article on CS50 * Brief Introduction : Cs50 is a beginner-friendly course by Harvard University.... | 0 | 2024-06-17T17:45:38 | https://dev.to/ebitech02/why-cs50-1di3 | webdev, javascript, beginners, programming |
**Article on CS50 **
_Brief Introduction_ :
Cs50 is a beginner-friendly course by Harvard University. It was taught by David Malan. It is a basic introduction to programming and computer science. No prior knowledge of programming or any programming language is required.
_Main_ :
Cs50 is a 25-hour course available on YouTube and the edx platform. it covers a range of topics from how the computer works to the concept of programming and what programming languages are. It also teaches how to write programs in various programming languages starting with a visual programming language like Scratch and then moving on to C - a high-level language but considered low-level because of its direct access to memory, it is also the foundation if most languages like, Python - a high-level language, Javascript - a high-level language, SQL - a query language for creating databases. He also teaches about Algorithms - step by step approach to solving a specific task and Data structures - a way of storing and managing data in the system.
Now let's talk about some of the main concepts of the course.
At the start of the course, we get to learn what programming is.
_What is Programming and programming language?_
Programming is the act of giving instructions to the computer to execute. The syntax in which these instructions are written is called a programming language. As we know Computers are machines and are only able to understand machine code (binary, 1’s, and 0’s) or instructions. From now on I will call them codes. So how then does the computer understand our programs since they are mostly written in human-readable language? I want to introduce you to something called a compiler, a software that converts our source code into machine code so that the computer can read it and then execute it.
_What are the concepts involved in programming? _
David teaches about
Variables - a storage location used to hold data, I like to think of it as a box where you can save items (data) and use them anytime.
Conditionals - these help us control the flow of our program based on whether a statement is true or false eg the if and else statements.
Loops - they help us run repeated codes effectively eg while loop and for loop.
Functions - these are blocks of code that help us perform specific tasks and can be reused throughout our program. A function can take an argument which is later referred to as a parameter when called on.
These concepts are unique to each programming language, their syntax and data types differ.
_Conclusion_ -
The most challenging concepts for me after watching the video are Algorithms and Data Structures, understanding Time Complexity, Big O notation, and some of the talked about data structures like Linked Lists, etc will take some time and effort on my part to fully grasp them. Still, I believe that the CS50 course remains the best lecture online for anyone who is interested in learning programming and computer science because of David Malan's ability to simplify every detail.
| ebitech02 |
1,891,544 | Mastering the Market: Four Essential Books for Aspiring Traders | Trading is both an art and a science, requiring a deep understanding of market mechanics, psychology,... | 0 | 2024-06-17T17:44:46 | https://dev.to/tradinggeni/mastering-the-market-four-essential-books-for-aspiring-traders-11m9 | career, learning, community, product | _Trading is both an art and a science, requiring a deep understanding of market mechanics, psychology, and disciplined strategy. While experience is the best teacher, the right books can accelerate your learning curve and provide invaluable insights from seasoned professionals. Here are four must-read books that can help you become a more skilled and disciplined trader:_
### 1. **Advanced Techniques in Day Trading by Andrew Aziz**
Andrew Aziz's "Advanced Techniques in Day Trading" is a comprehensive guide that delves into the nuances of day trading. Aziz, a well-respected figure in the trading community, offers practical strategies and real-world examples that can help traders at all levels. His focus on advanced techniques is particularly beneficial for those who have a basic understanding of day trading and are looking to elevate their skills.
Aziz covers a range of topics, from risk management to technical analysis, and emphasizes the importance of a structured trading plan. His insights into market psychology and discipline are crucial for maintaining a calm and focused approach, especially in the fast-paced world of day trading. For anyone serious about day trading, this book is a valuable resource.
**[Buy "Advanced Techniques in Day Trading" here and start your day trading journey with our recommended broker](https://beacons.ai/tradinggeni).**
### 2. **The Mental Game of Trading by Jared Tendler, MS**
Success in trading isn't just about having the right strategies; it's also about mastering your mindset. Jared Tendler's "The Mental Game of Trading" explores the psychological aspects of trading and offers practical advice for developing mental toughness. Tendler, a performance coach with extensive experience in poker and trading, provides tools and techniques to help traders manage their emotions and stay disciplined.
This book is particularly useful for traders who struggle with consistency and emotional control. Tendler's insights into common psychological pitfalls, such as fear and overconfidence, can help traders develop a more balanced and resilient mindset. By improving your mental game, you'll be better equipped to handle the ups and downs of the market.
**[Buy "The Mental Game of Trading" here and enhance your trading psychology with our recommended broker](https://beacons.ai/tradinggeni).**
### 3. **Trading in the Zone by Thom Hartle**
"Trading in the Zone" by Thom Hartle is a classic in the world of trading literature. Hartle, a seasoned trader and editor, provides a deep dive into the mental and emotional aspects of trading. The book emphasizes the importance of having the right mindset and developing a winning attitude, which are crucial for long-term success in trading.
Hartle discusses the psychological barriers that traders face and offers practical solutions for overcoming them. His focus on the importance of belief systems, discipline, and mental resilience makes this book a must-read for anyone serious about trading. By understanding and mastering these concepts, traders can achieve a state of consistent profitability.
**[Buy "Trading in the Zone" here and achieve trading success with our recommended broker](https://beacons.ai/tradinggeni).**
### 4. **The Disciplined Trader by Mark Douglas and Paula T. Webb, PhD**
Mark Douglas and Paula T. Webb's "The Disciplined Trader" is another seminal work that addresses the psychological challenges of trading. Douglas, a renowned trading coach, and Webb, a PhD in psychology, combine their expertise to offer a comprehensive guide to developing discipline and emotional control in trading.
The book explores the impact of human emotions on trading decisions and provides strategies for developing a disciplined approach. Douglas and Webb's emphasis on the importance of self-awareness and mental discipline is invaluable for traders who want to avoid common mistakes and improve their performance.
**[Buy "The Disciplined Trader" here and develop your trading discipline with our recommended broker](https://beacons.ai/tradinggeni).**
### Conclusion
> Mastering the art of trading requires more than just technical skills; it demands a deep understanding of market psychology and disciplined execution. These four books—"Advanced Techniques in Day Trading," "The Mental Game of Trading," "Trading in the Zone," and "The Disciplined Trader"—offer invaluable insights that can help you become a more successful trader.
> Whether you're a beginner or an experienced trader, these resources will provide you with the knowledge and strategies you need to thrive in the market. And if you're ready to put these lessons into practice, **[start trading today with our recommended broker](https://beacons.ai/tradinggeni)** and take the first step towards achieving your trading goals.
| tradinggeni |
1,891,543 | React controlled and uncontrolled hooks | In React, controlled and uncontrolled components are patterns used to manage form inputs. React Hooks... | 0 | 2024-06-17T17:43:47 | https://dev.to/bmanish/react-controlled-and-uncontrolled-hooks-31b0 | javascript, react, hooks, webdev | In React, controlled and uncontrolled components are patterns used to manage form inputs. React Hooks introduced the concepts of controlled and uncontrolled hooks to manage state within functional components. Here’s an overview:
## Controlled Hooks:
**useState Hook:** With controlled hooks, state is managed directly by React. The `useState` hook allows you to declare state variables and update them using setter functions provided by React. When a component's state changes, React re-renders the component with the updated state.
```react
import React, { useState } from 'react';
function ControlledComponent() {
const [value, setValue] = useState('');
const handleChange = (event) => {
setValue(event.target.value);
};
return (
<input
type="text"
value={value}
onChange={handleChange}
/>
);
}
```
In this example, the input field’s `value` is controlled by the value state variable, and updates are handled by the `setValue` function.
## Uncontrolled Hooks:
**useRef Hook:** Uncontrolled hooks allow you to manage state directly within the DOM rather than through React’s state management system. The `useRef` hook creates a mutable ref object whose `current` property can hold a value that persists across renders without causing a re-render.
```react
import React, { useRef } from 'react';
function UncontrolledComponent() {
const inputRef = useRef(null);
const handleClick = () => {
console.log(inputRef.current.value);
};
return (
<div>
<input type="text" ref={inputRef} />
<button onClick={handleClick}>Log Value</button>
</div>
);
}
```
In this example, the input field’s value is managed directly by the DOM via the `inputRef.current.value`, and updates are accessed without involving React's state management system.
## Choosing Between Controlled and Uncontrolled Hooks:
- **Controlled Hooks:** Use controlled hooks when you need React to manage and synchronize the state of form inputs across your application. Controlled components provide a single source of truth for form data, making it easier to track and manage changes.
- **Uncontrolled Hooks:** Use uncontrolled hooks when you need direct access to DOM elements or when dealing with large forms where controlled components might lead to performance issues. Uncontrolled components can be faster because they don’t trigger re-renders for every state change. However, they may be harder to track and manage, especially in complex applications.
Both controlled and uncontrolled hooks have their use cases, and the choice depends on your specific requirements and preferences. | bmanish |
1,891,542 | PDF para YAML com o Eu amo PDF 3 | PDF para YAML com o Eu amo PDF 3 Na era digital, a conversão de formatos de arquivos é uma... | 0 | 2024-06-17T17:41:40 | https://dev.to/digitalbaker/pdf-para-yaml-com-o-eu-amo-pdf-3-3bjp | euamopdf, tecnologia, pdf, pdfconverters | PDF para YAML com o [Eu amo PDF 3](https://ilovepdf3.com/)
Na era digital, a conversão de formatos de arquivos é uma necessidade comum, especialmente quando se trabalha com dados em diferentes plataformas e sistemas. PDF (Portable Document Format) é amplamente utilizado para documentos fixos, enquanto YAML (YAML Ain't Markup Language) é popular para arquivos de configuração devido à sua legibilidade e simplicidade. Embora o Eu amo PDF seja uma ferramenta renomada para a conversão de PDFs, ele não oferece diretamente uma opção para converter PDFs para YAML. No entanto, é possível realizar essa conversão em algumas etapas simples utilizando uma combinação de ferramentas. Neste blog, vamos explorar como você pode fazer isso.
Passo 1: Converter PDF para um Formato Intermediário
O primeiro passo é converter o PDF para um formato que possa ser facilmente transformado em YAML. Uma boa opção é o formato TXT ou CSV. O Eu amo PDF oferece várias ferramentas que podem ajudar nessa etapa, como a conversão de PDF para Word ou PDF para Excel.
PDF para Word:
Acesse o site do [Eu amo PDF3](https://ilovepdf3.com/).
Selecione a opção "[PDF para Word](https://ilovepdf3.com/pdf-to-word/)".
Faça o upload do seu arquivo PDF.
Converta e baixe o arquivo Word.
PDF para Excel:
No site do Eu amo PDF3.
Selecione a opção "[PDF para Excel](https://ilovepdf3.com/pdf-to-excel-converter/)".
Faça o upload do seu arquivo PDF.
Converta e baixe o arquivo Excel.
Passo 2: Extrair Dados do Formato Intermediário
Depois de converter o PDF para Word ou Excel, você precisará extrair os dados desses documentos e prepará-los para a conversão para YAML.
Extraindo Dados do Word:
Abra o arquivo Word.
Copie o texto relevante.
Cole o texto em um editor de texto simples, como o Notepad ou Sublime Text.
Extraindo Dados do Excel:
Abra o arquivo Excel.
Verifique e organize os dados em tabelas, se necessário.
Salve o arquivo como CSV (valores separados por vírgula).
Passo 3: Converter para YAML
Agora que você tem os dados em um formato de texto simples ou CSV, você pode converter esses dados para YAML usando ferramentas online ou editores de texto com suporte para YAML.
Usando uma Ferramenta Online:
Acesse um conversor online, como o Online YAML Tools para arquivos CSV.
Faça o upload do seu arquivo CSV ou cole o texto copiado do Word.
Converta para YAML e baixe o arquivo.
Usando um Editor de Texto:
Se você estiver confortável com editores de texto, pode manualmente criar o arquivo YAML.
Organize os dados no formato YAML. Por exemplo, uma lista de contatos em CSV poderia ser transformada em YAML assim:
`contatos:
- nome: João Silva
email: joao.silva@example.com
telefone: +55 11 99999-9999
- nome: Maria Oliveira
email: maria.oliveira@example.com
telefone: +55 21 98888-8888
`
**Conclusão**
Embora o Eu amo PDF3 não ofereça uma conversão direta de [PDF para YAML](https://ilovepdf3.com/pdf-to-yaml-converter/), você pode facilmente atingir esse objetivo utilizando uma abordagem em várias etapas. Primeiro, converta o PDF para um formato intermediário como Word ou Excel, e depois extraia e converta esses dados para YAML usando ferramentas online ou editores de texto. Essa metodologia garante que você possa aproveitar a flexibilidade e legibilidade do YAML, independentemente do ponto de partida de seus dados.
Se você tiver dúvidas ou experiências para compartilhar sobre conversões de formato, sinta-se à vontade para comentar abaixo! | digitalbaker |
1,891,541 | ABSTRACTION | Abstraction is a Principle of OOP used to hide unnecessary information and display only necessary... | 0 | 2024-06-17T17:38:34 | https://dev.to/ojus_coder/abstraction-4eg7 | devchallenge, cschallenge, computerscience, beginners | Abstraction is a Principle of OOP used to hide unnecessary information and display only necessary information.For example you can start a car by turning the key or pressing the start button.You don't need to know how the engine is getting started. | ojus_coder |
1,891,522 | Why Contribute to Open Source: Pros and Cons for Beginners | Diving into the world of open-source can feel like embarking on an epic journey 🚀. Whether you're... | 0 | 2024-06-17T17:38:19 | https://dev.to/usulpro/why-contribute-to-open-source-pros-and-cons-for-beginners-5cgm | opensource, beginners, career | Diving into the world of open-source can feel like embarking on an epic journey 🚀. Whether you're looking to sharpen your programming skills, build your resume, or simply contribute to something meaningful, the realm of opensourcing offers a treasure trove of opportunities. But, why contribute to open source? The answer isn't just about padding your CV; it's about joining a global community that thrives on collaboration, innovation, and learning. For beginners, this might sound daunting, but I assure you, the rewards far outweigh the challenges. Through my own voyage from a curious novice to a tech lead, I've experienced firsthand the profound impact that contributing to open-source projects can have on one's career and personal growth.
In this article, we'll explore the myriad advantages of open source, from mentorship programs that guide you through the complexity of code review to the sheer joy of seeing your work being used by others. We'll also navigate through the challenges beginners might face and provide you with actionable recommendations for getting started. By the end, you'll understand why open source is good for developing your coding skills, how to contribute to an open source project effectively, and anticipate the open source pros and cons. So, buckle up! Whether you're looking to contribute to open source for personal fulfillment or to make your mark in the tech community, you're on the right track.
## Advantages of Open Source for Beginners
**Opportunity for Learning and Growth**
Engaging in open source projects exposes you to a plethora of technologies and coding practices, offering a robust platform for sharpening your skills. The hands-on experience you gain can be invaluable for your growth as a developer, allowing you to explore new technologies and enhance your problem-solving capabilities 😊.
**Building a Professional Network**
By contributing to open source, you connect with like-minded developers and experts, potentially opening doors to career opportunities and mentorship. This network can be a significant advantage, helping you navigate the tech industry and find guidance from experienced professionals 🌐.
**Access to a Variety of Tools and Resources**
Open source communities provide access to a wide array of tools and resources, helping you tackle projects efficiently. These tools are often developed and maintained by the community, ensuring they are reliable and up-to-date. Engaging with these tools not only enhances your technical skills but also helps in understanding the nuances of software development 🛠️.
**Contribution to the Community**
Contributing to open source allows you to give back to the community that fosters the software you use. This not only improves the software but also enriches your sense of accomplishment and connection with a global community. Your contributions help drive the evolution of projects that benefit millions worldwide 🌍.
## Challenges with Open Source for Beginners
### Understanding Licensing and Legal Issues
Navigating the legal landscape of open source can be daunting for beginners. Understanding the [specific licenses, such as MIT or GPL](https://semaphoreci.com/blog/open-source-licensing), is crucial as they dictate how contributions can be used and distributed. Misunderstanding these licenses can lead to legal challenges, especially if proprietary modifications are made without adhering to open source requirements.
### Navigating Documentation and Support
For newcomers, the vast amount of documentation and the [lack of straightforward guides](https://www.digitalocean.com/resources/article/how-to-contribute-to-open-source) can be overwhelming. Projects vary greatly, and each comes with its own set of rules and documentation, making it difficult to find where contributions are most needed or how to start.
### Handling Project Management Challenges
Project management in open source can often be chaotic, especially in projects [lacking strong leadership or clear goals](https://opensource.stackexchange.com/questions/2069/how-to-deal-with-project-managers-in-an-open-source-project). Contributors might find themselves in projects where the roadmap is unclear or where the repository owner is less involved in the coding process, leading to stagnation and frustration.
### Balancing Open Source Work with Other Responsibilities
Contributing to open source projects requires time management skills as it needs to be balanced with personal and professional responsibilities. The challenge of managing time effectively can deter many potential contributors, especially those new to the field who are still finding their footing.
## Recommendations for Getting Started
### Choosing the Right Projects
To kickstart your open-source journey, identify projects that spark your interest and match your skill level. Look for projects with clear documentation, active communities, and [beginner-friendly tags like "Good First Issue"](https://medium.com/@niceperson2110/starting-your-open-source-journey-7-best-projects-for-absolute-beginners-11fe01f88e1d). This ensures a smoother entry and ongoing support as you contribute.
### Contributing to Discussions
Engage actively in project discussions. This can be through forums, IRC, or project meetings. Start by observing and gradually contribute to the discussions. Your involvement will help you understand project dynamics and connect with other contributors.
### Seeking Mentorship and Guidance
Don't hesitate to seek out mentorship. Join open source communities and participate in events to connect with potential mentors. [Programs like Google Summer of Code and Outreachy](https://www.womentech.net/en-us/how-to/how-find-mentorship-opportunities-in-open-source-software-development) can also provide structured mentorship opportunities.
### Setting Realistic Goals
Set clear, achievable goals for what you want to accomplish in open source. Break these goals into smaller milestones to keep track of your progress. This approach helps maintain motivation and ensures a rewarding journey.
## Conclusion
Embarking on the journey of contributing to open source is not merely about enhancing your resume; it's about actively participating in a culture of collaboration, innovation, and mutual growth 🚀. The rewards, as explored, range from profound personal and professional development to becoming part of a vibrant, supportive community. Each challenge associated with open source contributions delivers valuable lessons, ensuring that every participant, especially beginners, emerges more skilled and connected than they were before. By facing and overcoming these hurdles, contributors not only advance their own capabilities but also significantly impact the broader tech ecosystem 🌐.
The path to becoming an involved open source contributor is filled with opportunities for learning, networking, and personal fulfillment. Choosing the right projects, engaging in meaningful discussions, seeking out mentorship, and setting clear goals are pivotal steps that guide this rewarding journey. Recognizing the pivotal role of open-source projects in the advancement of modern web technologies, it's worth noting that Headless CMSs, pivotal in the development of many websites, are often open source. For those intrigued by the intersection of creativity and technology, joining our friendly community at Headless & Composable [here](https://dly.to/zH1Eak9ysmo) can be your gateway to mastering these key technologies. As you embark on this path, remember that your contributions are not just about code; they're about fostering an ecosystem where innovation, learning, and collaboration flourish.
[](https://dly.to/zH1Eak9ysmo)
## FAQs
**1. Is it advisable for a beginner to contribute to open source projects?**
Absolutely! Contributing to open source projects is an excellent way for beginners to enhance their coding skills, connect with like-minded individuals, and gain satisfaction from their contributions. Open source communities are generally welcoming to newcomers, so don't hesitate to participate.
**2. What are the benefits of contributing to open source projects?**
Contributing to open source provides numerous benefits including the opportunity to network with peers, showcase your abilities to future employers, and boost your self-confidence. Additionally, it aids in the development of leadership and management skills, enhances your coding expertise, and exposes you to new programming styles and regulations.
**3. What are the advantages and disadvantages of open source software?**
The primary advantages of open source software include its typically free cost and high customizability, which allows users to tailor the software to their specific needs. However, it can sometimes be costly in scenarios where extensive customization is required. Open source software is usually more secure due to the collaborative nature of its development, which allows numerous developers to review and refine the code.
**4. What are the pros and cons of using an open source database?**
Using an open source database offers several advantages such as cost-effectiveness and flexibility in customization. However, users might face challenges such as a potentially steep learning curve and limited access to customer support compared to proprietary databases.
| usulpro |
1,891,540 | Meme Monday | Meme Monday! Today's cover image comes from last week's thread. DEV is an inclusive space! Humor in... | 0 | 2024-06-17T17:38:04 | https://dev.to/ben/meme-monday-53am | discuss, watercooler, jokes | **Meme Monday!**
Today's cover image comes from [last week's thread](https://dev.to/ben/meme-monday-13l3).
DEV is an inclusive space! Humor in poor taste will be downvoted by mods. | ben |
1,891,539 | For Grocery Retail, Customer Journeys Start and End with the Mind | Purchasing groceries, i.e. FMCG is a part of everyone’s lives. Covid-19 bought a (hopefully) once if... | 0 | 2024-06-17T17:37:13 | https://dev.to/glasgow_insights/for-grocery-retail-customer-journeys-start-and-end-with-the-mind-3dke | customer | Purchasing groceries, i.e. FMCG is a part of everyone’s lives. Covid-19 bought a (hopefully) once if a lifetime behavior shifts of mass adoption of online shopping. Now most of us have found a balance between online and physical shopping, which proves that BOTH have a part to play in our lives.
Click:[](https://www.glasgowinsights.com/blog/for-grocery-retail-customer-journeys-start-and-end-with-the-mind/)
While online grocery shopping comes with its undeniable advantages (home delivery – that too in paper bags / convenience of shopping at one’s own preferred time, no need for travel and trying to find parking, etc.), a physical visit to a grocery store has its benefits as well.
And these grocery stores come in multiple avatars, such as Hypermarkets, Supermarkets, Convenience Stores, Discounters, etc. Most shoppers visit a mix of these over time, and the choice of the store type depends on the need. The neighbourhood Convenience Store would meet the needs of a mid-week top-up while a Hyper / Supermarket would be the preferred destination for a weekend “trolley-purchase” visit where other domestic tasks are also met in the Shopping Mall where the grocery store is located. And once a quarter, or before a festive season, one may visit a Bulk Discounter.
In all these stores, retailers can make the life of the shopper more convenient by understanding the Customer Journey. This will encourage both more frequent visits as well as more spends per trip / more time & engagement per trip. It is critical to note that every trip has a Pre / During and Post phases with UNIQUE needs that MUST be catered to individually.
During the Pre-Visit stage we need to understand what drives the store choice, i.e. mind measures. Here retailers need to see what need-states exist and what communications are needed to address the same. During the visit to the store, purchase driver behaviours come into play. Here everything from the entrance to ease of navigation between the aisles to attractive / relevant merchandising to SKU assortment optimization to new brand launches to billing speed / accuracy to rewards from loyalty programs matter. The Post Visit stage involves the ease of the journey back home, the effortlessness of unpacking and storage, billing and / or packaging issues if any being addressed to quickly and fairly, the overall Customer Satisfaction influencing the likelihood of the next visit, etc. – again related to mind measures.
Market Research can help retailers understand how to drive a superior Customer Experience by understanding which of these touchpoints matter more and where they stand on them. This can be ascertained both for an internal benchmarking (for brands that have multiple assets) as well as competitive analysis. Quarterly evaluations can help Monitor / Measure / Manage the benefits of the changes activated derived from the learning’s of these studies.
If you found this interesting, please do reach out to us to see how we can help you drive data driven business growth.
About Us:
Glasgow Research & Consulting clients are Global Fortune 500 companies, regional conglomerates and entrepreneurial ventures.
The ability to anticipate competitors’ moves and analyze markets is key to winning in the Middle East & Africa region. Our biggest pride comes from helping international companies to be successful in emerging markets.
Contact Us
Office No 6, Unit 402, Level 4,
Crystal Tower, Business Bay,
PO Box 445190 Dubai, United Arab Emirates
Mobile: +971 55 9744360 | Phone: +971 4 566 8869
Website : [](https://www.glasgowinsights.com) | glasgow_insights |
1,891,492 | HTML Elements | Last week, you became a web developer! Good work! We started going over the different types of... | 27,613 | 2024-06-17T16:36:18 | https://dev.to/nmiller15/html-elements-38m4 | webdev, html, css, learning | Last week, you became a web developer! Good work! We started going over the different types of elements that HTML uses to structure a document so that it can be read by an internet browser. But, HTML uses elements for just about everything! This week, we'll cover four different types of elements, how to use them, and by the end, you'll be adding content to the page you created last week!
If you just found this series, check out the [series page](https://dev.to/nmiller15/series/27613), or if you need a refresher, go to [last week's article](https://dev.to/nmiller15/html-document-structure-learn-as-you-code-html-and-css-part-2-1eme)!
## Text Elements
Text makes up a majority of the internet, and one of the most basic things you will put on any page is text! Since we need to be able to format that text, HTML offers us elements to wrap around this text and provide our content with some organization.
### The Paragraph Element `<p>`
To add regular text to our page, we will place that text between an opening and closing `<p>` tag! Check out the example and try it for yourself!
```html
<!DOCTYPE html>
<html>
<head></head>
<body>
<p>This is some regular body text.</p>
<p>And here's some more!</p>
</body>
</html>
```
Why do we call it a 'paragraph'? Well, if we place two of these elements, they will each appear on different lines, effectively making a paragraph. If you're looking at a news article online, each time you see a new paragraph, you're looking at a separate `<p>` element!
### Headings
Our page would look very bland if everything was body text, and one of the best ways to help our users take in the content on our site is to mark it with informative headers. HTML has 6 different heading elements, and their syntax is not too hard to remember!
```html
<h1>Title</h1>
<h2>Subtitle</h2>
<h3>Heading</h3>
<h4>Subheading</h4>
<h5>Section</h5>
<h6>Subsection</h6>
```
By default, your browser will make an `<h1>` heading the largest and the `<h6>` heading the smallest, but when we get into CSS, you'll see that this doesn't have to be the case.
With headings, we also have a few rules to follow about using them:
- Each web page can only have one `<h1>` tag.
- Use headings sequentially: `<h2>` after `<h1>`, don’t skip to `<h3>`.
- Use all of your headings to describe what the page is about!
Your web page will still render if you don't follow these rules, but your document will no longer be well-formed. We follow these rules because it makes it easier for a browser to read your page and interpret it, and it makes it much easier for search engines to know what the important information on your page is!
### Creating Lists
We can also structure information on our pages in lists. HTML offers two kinds of lists: an ordered list and an unordered list. You would probably know them as numbered and bulleted lists, respectively. To create a list, you will nest list item elements inside the list type you choose! Here are a couple of examples:
```html
<ol>
<li>Item 1</li> <!-- 1. Item 1 -->
<li>Item 2</li> <!-- 2. Item 2 -->
<li>Item 3</li> <!-- 3. Item 3 -->
</ol>
```
```html
<ul>
<li>Item 1</li> <!-- • Item 1 -->
<li>Item 2</li> <!-- • Item 2 -->
<li>Item 3</li> <!-- • Item 3 -->
</ul>
```
In most situations, you will use the unordered list, unless it is necessary that the information is sequential, like in recipe instructions.
## Adding Images
So far, all we have on our pages is text, but to make our pages more interesting we can add images, audio, or video! HTML has elements for those too!
To add an image to our site, we will use the `<img>` element. This element is unique from the elements that we’ve learned about so far in a couple of ways. First, this element is self-closing, meaning that it doesn’t wrap around other elements, and we only need to use one tag. This element also requires an attribute, which tells our browser how to interpret it! HTML elements can take many different types of attributes, but the `<img>` element requires a `src` attribute that takes the URL or path of the image you want to display!
```html
<img src="urlofyourimage.com/image.jpeg"/>
```
And there you go! An image on your web page!
## Challenge
Now that you've got some new elements under your belt, here's a challenge for the week! Make your own "About Me" page. Use at least two different heading levels, a list, a paragraph of information about yourself, and include a picture! Make sure to use the appropriate elements and open it on your web browser!
See you next week!
| nmiller15 |
1,891,465 | How I Reverse-Engineered My CPU Cooler LED Display | TL;DR: After a PC upgrade went sour, and made me purchase a Chinese cooler, I reverse-engineered its... | 0 | 2024-06-17T17:36:32 | https://dev.to/rodpadev/how-i-reverse-engineered-my-cpu-cooler-led-display-2106 | dotnet, reverseengineer, cpu, hacking | > TL;DR: After a PC upgrade went sour, and made me purchase a Chinese cooler, I reverse-engineered its software to improve the accuracy of the temperature readings.
**If you're looking for the software for your Unykach AIO, you can download [Temp33 here](https://github.com/RodPaDev/temp33/releases)**
----
It all started on a hot Portuguese spring day. My new PC parts had just arrived, and I decided to upgrade my PC and give my wife the spare parts, you know, the usual move. I was upgrading from an AM4 to an AM5 socket and during my previous research,
I found that I didn't need a new cooler, after all, I had the BeQuiet! Dark Rock Pro 4. This thing is a behemoth and appeared to be compatible since AM4 and AM5 shared mounting brackets.
However, what they don’t share is how CPUs slot into the socket. AMD moved away from having pins on the CPU to having pins on the motherboard socket. (Yes, I am foreshadowing at this very moment. Hopefully, someone has shared this pain and knows exactly what I’m talking about.)
After 2 hours of building my PC, at the moment of truth, it didn't turn on.
<img width="100%" style="width:100%" src="https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExZHkyNmVhbm4xMGlmZDYxbzl2Z3J3Y3FoN3g5MWVyNjViNm14NXQzdSZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/32mC2kXYWCsg0/giphy.gif">
## Breathe. It's going to be fine.
I thought to myself, not a big deal, probably some connector loose, so I started unplugging things and nothing would work. I even spent a good hour fixated on the case connectors, used a jumper cable to short the button, and nothing.
I took it apart and tested the power supply by jumping the relevant pins and it was working. Okay, at least something works.
I then decided to take everything apart. I took the motherboard out of the case and took out the CPU and reinserted it without the cooler. It worked! I thought to myself that it must've been the RAM or something.
## Unexpected Anger Management Classes
I built the system again and took great care in managing the cables since everything was working right?
Well. It. Didn't. Fucking. Turn. On. At this point, I was so infuriated that wanted to buy a Komatsu D355A.
My wife told me to calm down and go for a walk to clear my head and I decided to take her advice by googling and trying different things. This was the right decision because I was so tired that I forgot to turn off the PSU when I started unscrewing the CPU cooler and THE COMPUTER TURNED ON.
<img width="100%" style="width:100%" src="https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExMzk5b3BteHVnMTdyMHhxNGlmMTgzMjJhdjVuajc3cW5oNm5pZzdxNyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/l3q2K5jinAlChoCLS/giphy.gif">
I screwed it in, and it turned off. The cooler which had no fans connected was controlling the power-up of the motherboard by the tension of the screws.
It turns out that this kind of motherboard with pins (LGA) is known to have these issues.
I decided to apply less pressure, maybe my inner mechanic was taking over since I had spent a few hours fixing my car the previous day. After unscrewing it a bit, it was working. I was tired but accepted it and decided to move on and finish the system.
## Erection Misdirection
I turned it upright, pressed the power on button, and once more... It didn't turn.
Why, you may ask? Well, because the CPU cooler is the size of my giant balls and causes so much pressure when upright that it was bending the motherboard enough to cause problems. I found this out by lifting the cooler with a finger to ease the pressure it made.
Defeated, I decided to give up for the day, go to sleep, and tomorrow I would buy an AIO cooler, which should cause less pressure on the bracket when upright.
## The Plan B Pill
The following day I went to all the computer repair stores in my vicinity and found one AIO cooler... It was a Chinese brand called Unykach, and since I was desperate to have my daily dose of spreading democracy, I gave in and bought it.
After installing it on the motherboard, it worked fine, CPU temps were cool and I installed the driver to display the temperature of the LED screen. I saw it change and thought _"This is cool."_. I then ran some stress tests on my PC and noticed that the temps were wildly different from every hardware monitoring software I could find. This didn't make sense, so I decided to investigate.
## Sherlock Homing
I already had some basic experience with reverse engineering software after I got frustrated with a particular enemy spawning system in `The Pirate: Caribbean Hunt` game and managed to change it. I also managed to bypass the need to purchase in-game items.
I never shared it with anyone else and disclosed it to the developers. I can't remember if they answered or not but if they did it was so insignificant that I can't remember.
Anyway, I decided to look for DnSpy, and to my surprise, it was archived. After some googling, I found the maintained fork DnSpyEx and downloaded it.
I proceeded then to disassemble the `.exe` file, and to my luck, it was a .NET application, more specifically it was a WPF app.
However, to my dismay, I was looking at heavily obfuscated Chinese code. I considered giving up, but momma didn't raise no pussy (except for my brothers). So, I did what any real man would do and RTFM on how serial communication is done in `.NET`.
Equiped with my newly acquired .NET knowledge I searched for uses of `System.IO.Ports` and perused the files like an underpaid law intern (idk I watched suits) and found the following code within the appropriately named public method `bool 品()`
```csharp
{
DtrEnable = true,
RtsEnable = true,
ReadTimeout = 1000,
BaudRate = 115200,
DataBits = 8,
StopBits = StopBits.One,
Parity = Parity.None
});
base.啂().Open();
```
Written in the language of the King (not my King) this showed me exactly what I needed to be able to talk to the LED screen. It even gave me the COM port. Which I used to get the hardware instance identifier `USB35INCHIPSV2`. A quick Google search led me to this repo: [turing-smart-screen-python](https://github.com/mathoudebine/turing-smart-screen-python/wiki/Hardware-revisions). It seems that they reuse LCD firmware for 7-segment displays.
I then tried with RealTerm to send an int but to no avail. This stuff was encoded, which makes sense in retrospect, but I was hopeful.
## One Step For the Debugger, A Great Step for My Sanity
I did what any sane person would do and stepped into every statement until I found this beautiful method:
```csharp
public void 卩(int A_0, int A_1, int A_2, int A_3, int A_4, byte[] A_5 = null, int A_6 = 0)
{
short num = (short)1832057592;
short num2 = num;
num = (short)1715075832;
switch ((num2 == num) ? 1 : 0)
...
try
{
...
goto IL_A0;
IL_129:
A_5[0] = (byte)(A_1 >> 2);
A_5[1] = (byte)(((A_1 & 3) << 6) + (A_2 >> 4));
A_5[2] = (byte)(((A_2 & 15) << 4) + (A_3 >> 6));
A_5[3] = (byte)(((A_3 & 63) << 2) + (A_4 >> 8));
A_5[4] = (byte)(A_4 & 255);
A_5[5] = (byte)A_0;
base.卩(A_5, true);
}
...
}
finally
{
...
}
}
```
I was certain that this was the encoding method but I still had some unanswered questions: **_What do all these parameters represent? _**
When debugging with DnSpy—which, like most debuggers, displays the local variables available within the current scope. Through pattern recognition, I deduced two things: A_1 represents the integer value, and A_5 is the control or function code.
I did this by converting the HEX values to integers and, stepping out quickly enough within the timeout window to resume the sending operation and checking the values on the physical 7-segment display.
After doing this 2-3 times I was 99% sure that A_1 was the int.
## Sanity Regained
I then wrote a small JavaScript function that does the same encoding, ran it in the browser, and plugged the result into RealTerm, and lo and behold, I managed to send data to the LCD, and it displayed!
I tried playing around by sending 3-digit ints and it gave me cool results like reverse 7 and b and I even managed to turn on the `dot` segment.
That pretty much concludes how I managed to reverse-engineer the program.
## Dot Nettin
The rest of the time for this project was spent building a WPF app. It might be the most egregious WPF code out there, but it works and if I may say so, it looks pretty good.
You can visit the repo [here](https://github.com/RodPaDev/temp33) and if you have a problem with the `.NET` code, please send an email to: `lig@ma.balls`
| rodpadev |
1,891,537 | ** ¡Imagina lo adorable! Prompts creativos para imágenes tiernas en Copilot**✨🧙♀️ | ¡Hola Chiquis! 👋🏻 ¿Listos para una dosis de creatividad e inspiración? Porque algunos de ustedes me... | 0 | 2024-06-17T17:33:35 | https://dev.to/orlidev/-imagina-lo-adorable-prompts-creativos-para-imagenes-tiernas-en-copilot-18ej | webdev, tutorial, promptengineering, beginners | ¡Hola Chiquis! 👋🏻 ¿Listos para una dosis de creatividad e inspiración? Porque algunos de ustedes me han preguntado cómo creo prompts tan originales y adorables para las imágenes que utilizo en mis posts. ¡Pues hoy es el día de revelarles el secreto! Prepárense para un viaje a través de mi mente loca y un torbellino de ideas divertidas.
¿Cómo nacen mis prompts? 👩💻
Imaginen un laboratorio mágico donde se mezclan ideas locas, referencias pop, un toque de humor y una pizca de ternura. ¡Ahí es donde cocino mis prompts!
Mis ingredientes secretos 👩🍳
+ Un chorrito de inspiración: Busco en todas partes: memes, películas, series, libros, ¡lo que sea que me haga cosquillas en el cerebro!
+ Una pizca de referencias: ¿Aman la cultura pop? ¡Mis prompts la adoran!
+ Un toque de humor: Porque la risa es la mejor medicina, incluso para el código.
+ Un puñado de ternura: ¡Porque la adorabilidad siempre conquista!
¿Cómo usar mis recetas? 🧏
- Elige un tema: ¿De qué quieres hablar en tu post? Desarrollo web, diseño gráfico, marketing digital, ¡las posibilidades son infinitas!
- Añade una pizca de creatividad: ¿Qué imagen representaría mejor tu tema? ¿Cómo puedes hacerla única y memorable?
- Mezcla los ingredientes: Combina tu tema con la creatividad, el humor y la ternura para crear un prompt irresistible.
- ¡Hornea tu imagen! Utiliza tu prompt en Copilot y deja que la magia suceda.
Rompe el molde del código con prompts adorables que despiertan la ternura y la creatividad. 🐞
Con Copilot y un poco de imaginación, puedes generar imágenes que capturen la esencia del proceso creativo de una manera divertida y memorable.
1. Despierta tu niño interior👧
- Imagina a un adorable gatito desarrollador escribiendo código, con un chupete en la boca y una mirada de concentración absoluta.
- Un osito travieso jugando con un ovillo de cables de red, mientras accidentalmente depura un error crítico.
- Un perezoso perezoso colgado de una rama de árbol, escribiendo código en su laptop con una sola mano, mientras disfruta de una taza de café.
2. Personifica a tus herramientas 🚀
+ Git como un adorable robot ayudando a los desarrolladores a fusionar código sin problemas.
+ GitHub como una acogedora cabaña en un bosque encantado, donde los desarrolladores colaboran en paz y armonía.
+ Stack Overflow como un sabio búho sentado en una pila de libros, respondiendo preguntas de código con sabiduría y paciencia.
3. Celebra los momentos clave 🤩
- La emoción de finalmente depurar un error: Un grupo de desarrolladores bailando de alegría alrededor de una computadora, mientras confeti cae del techo.
- La satisfacción de completar un proyecto: Un desarrollador dándole un tierno abrazo a su laptop, como si fuera su mejor amigo.
- El alivio de una implementación exitosa: Un desarrollador exhalando un suspiro de alivio mientras se recuesta en una nube mullida.
4. Añade un toque de humor 😁
- Un perro developer persiguiendo su cola mientras intenta solucionar un bucle infinito.
- Un gato developer dormido en el teclado, mientras el código se escribe solo como por arte de magia.
- Un desarrollador con bigote de código escribiendo código tan rápido que las teclas ni se ven.
El Proceso Creativo 🧩
- Define el Propósito: Decide qué quieres lograr con la imagen. ¿Es educar, entretener, inspirar?
- Conoce y Entiende a tu Audiencia: Piensa en los intereses y necesidades de tu audiencia para crear algo que les resuene. Ejemplo: Si tu audiencia ama los gatitos, un prompt podría ser "Imagina un gatito aprendiendo a programar en JavaScript. ¿No es adorable?"
- Elige un Tema: Selecciona un tema relacionado con el desarrollo y el proceso de creación que sea de interés.
- Sé Específico: Cuanto más detallado sea el prompt, más precisa será la imagen generada. Usa palabras clave con sabiduría. Ejemplo: Incluye términos como "desarrollo ágil", "programación creativa" o "innovación en TI" para mejorar el SEO.
- Sé Descriptivo y Detallado: Ejemplo: "Visualiza un osito de peluche explicando con paciencia los fundamentos de React a un grupo de entusiastas cachorros."
- Incluye Emociones: Agrega elementos emocionales para hacer que la imagen sea más atractiva y memorable. Ejemplo: "¿Puedes sentir la emoción de un pingüino al descubrir el poder de Python para resolver problemas complejos?"
- Inspira Acción y Curiosidad: Incorpora verbos de acción para dar vida a la imagen. Ejemplo: "Anima a tus lectores a compartir sus propias versiones de 'El primer día de una ardilla en la oficina de desarrollo'."
- Prueba y Ajusta: Experimenta con diferentes estilos y tonos hasta encontrar lo que funciona mejor.
- Agrega un Toque Personal: Ejemplo: "Comparte una anécdota personal sobre cómo una ilustración tierna te ayudó a comprender mejor un concepto técnico."
- Feedback: Pide opiniones y usa ese feedback para mejorar tus futuros prompts.
Optimizar tus prompts para SEO 📈
+ Investigación de Palabras Clave: Encuentra términos relevantes que tu audiencia esté buscando y que estén relacionados con el tema de desarrollo y el proceso de creación.
+ Inclusión Natural de Palabras Clave: Integra las palabras clave de manera orgánica en tus prompts para que suenen naturales y no forzadas.
+ Descripciones Atractivas: Escribe descripciones de imágenes que sean atractivas y que incluyan tus palabras clave principales.
+ Títulos y Subtítulos: Utiliza títulos y subtítulos que contengan palabras clave y que sean descriptivos del contenido de la imagen.
+ Meta Descripciones: Asegúrate de que cada imagen tenga una meta descripción que incluya palabras clave y anime a los usuarios a hacer clic.
+ Alt Text: Usa el texto alternativo (alt text) para describir las imágenes, incluyendo palabras clave cuando sea posible.
+ Consistencia: Publica prompts regularmente para mantener tu contenido fresco y relevante.
+ Enlaces Internos: Si es aplicable, incluye enlaces a otros contenidos relevantes dentro de tu sitio web en las descripciones de las imágenes.
+ Respuesta al Usuario: Asegúrate de que tus prompts respondan a preguntas o necesidades específicas de tu audiencia, lo cual puede ayudar a mejorar la relevancia y el ranking en los motores de búsqueda.
+ Análisis y Ajustes: Monitorea el rendimiento de tus prompts y haz ajustes basados en los datos para mejorar continuamente su efectividad SEO.
Conclusión: 👩🏫 Crear prompts originales y encantadores para imágenes no solo es divertido sino también estratégico. Con estos consejos, estarás listo para diseñar contenido que resuene tanto emocionalmente como en los resultados de búsqueda.
La práctica es clave para perfeccionar la creación de prompts efectivos. Con un poco de imaginación, estarás creando imágenes adorables y originales en Copilot en poco tiempo. ¡Deja que tu ternura interior brille a través del código!
Recuerda 📋
- No tengas miedo de experimentar.
- Diviértete y deja fluir tu creatividad.
- Comparte tus creaciones con el mundo.
¿Te animas a probar mis recetas? Comparte tus prompts más creativos en los comentarios y ¡hagamos de este mundo digital un lugar más adorable!
🚀 ¿Te ha gustado? Comparte tu opinión.
Artículo completo, visita: https://lnkd.in/ewtCN2Mn
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Referencias:
Imágenes creadas con: Copilot (microsoft.com)
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 | orlidev |
1,891,536 | Matthew Danchak on the Power of Positive Thinking for Mental Health | In our fast-paced, stress-filled world, mental health has become a critical issue for many. Amidst... | 0 | 2024-06-17T17:33:23 | https://dev.to/matthewdanchak/matthew-danchak-on-the-power-of-positive-thinking-for-mental-health-hh2 |
In our fast-paced, stress-filled world, mental health has become a critical issue for many. Amidst the various strategies and treatments available, one approach that stands out for its simplicity and effectiveness is the power of positive thinking. [Matthew Danchak](https://www.f6s.com/member/matthew-danchak), a renowned mental health advocate, firmly believes in the transformative potential of positive thinking to improve mental well-being.
## Understanding Positive Thinking
Positive thinking isn’t about ignoring life’s challenges or pretending everything is perfect. Instead, it’s about approaching unpleasant situations with a more positive and productive mindset. According to Matthew Danchak, positive thinking involves recognizing negative thoughts but choosing to focus on the good in any given situation.
## The Science Behind Positive Thinking
Numerous studies have shown the impact of positive thinking on mental health. Positive thinkers tend to have lower levels of stress, improved immune function, and a longer lifespan. The reason behind this is that a positive mindset helps reduce the harmful effects of stress on the body. When we think positively, we’re more likely to engage in healthy behaviors like regular exercise, a balanced diet, and strong social connections, all of which contribute to better mental health.
## Practical Steps to Cultivate Positive Thinking
Matthew Danchak suggests several practical steps to foster a positive thinking habit:
**
- Practice Gratitude
**: Start each day by acknowledging at least three things you’re grateful for. This simple practice can shift your focus from what’s going wrong to what’s going right in your life.
**- Reframe Negative Thoughts**
: Whenever a negative thought comes to mind, make an effort to rephrase it to seem more optimistic. For instance, try framing the thought, "I'm terrible at this," as opposed to, "I'm learning and improving every day."
**- Surround Yourself with Positivity**
: Spend time with inspiring and motivating people. . Positive energy is contagious, and being around positive individuals can help reinforce your own positive thinking.
**- Self-Care**
: Take time for yourself to do things you enjoy. Whether it’s reading a book, going for a walk, or practicing a hobby, self-care activities can boost your mood and overall outlook on life.
**
- Mindfulness and Meditation
**: These practices help you stay present and reduce the tendency to dwell on negative thoughts. Regular mindfulness or meditation practice can enhance your ability to maintain a positive mindset.
## Overcoming Challenges
It’s important to acknowledge that maintaining a positive mindset can be challenging, especially during tough times. [Matthew Danchak ](https://kikoxp.com/matthew_danchak)advises that it’s perfectly normal to have negative thoughts and feelings. The key is not to suppress them but to manage them constructively. Seeking support from friends, family, or mental health professionals can also be beneficial during such times.
## The Long-Term Benefits
Adopting a positive mindset can lead to significant long-term benefits for mental health. People who practice positive thinking often report higher levels of happiness, reduced symptoms of depression and anxiety, and improved relationships. Additionally, a positive outlook can enhance resilience, enabling individuals to cope better with life’s challenges and bounce back more quickly from setbacks.
## Matthew Danchak’s Personal Journey
Matthew Danchak’s belief in positive thinking is deeply rooted in his personal experiences. Having faced his own mental health struggles, he discovered that shifting his mindset played a crucial role in his recovery. Today, he dedicates his life to helping others harness the power of positive thinking to achieve better mental health.
## Conclusion
The power of positive thinking for mental health cannot be overstated. By practicing gratitude, reframing negative thoughts, surrounding yourself with positivity, engaging in self-care, and incorporating mindfulness, you can significantly improve your mental well-being. Matthew Danchak’s insights and personal journey serve as a testament to the transformative impact of a positive mindset. Remember, while it may not always be easy, the benefits of cultivating a positive outlook are well worth the effort. Take the first step toward a happier, healthier version of yourself now.
| matthewdanchak | |
1,891,535 | 633. Sum of Square Numbers | 633. Sum of Square Numbers Medium Given a non-negative integer c, decide whether there're two... | 27,523 | 2024-06-17T17:33:13 | https://dev.to/mdarifulhaque/633-sum-of-square-numbers-1248 | php, leetcode, algorithms, programming | 633\. Sum of Square Numbers
Medium
Given a non-negative integer `c`, decide whether there're two integers `a` and `b` such that `a2 + b2 = c`.
**Example 1:**
- **Input:** c = 5
- **Output:** true
- **Explanation:** 1 * 1 + 2 * 2 = 5
**Example 2:**
- **Input:** c = 3
- **Output:** false
**Constraints:**
- <code>0 <= c <= 2<sup>31</sup> - 1</code>
**Solution:**
```
class Solution {
/**
* @param Integer $c
* @return Boolean
*/
function judgeSquareSum($c) {
for ($i = 2; $i * $i <= $c; $i++) {
$count = 0;
if ($c % $i == 0) {
while ($c % $i == 0) {
$count++;
$c /= $i;
}
if ($i % 4 == 3 && $count % 2 != 0)
return false;
}
}
return $c % 4 != 3;
}
}
```
**Contact Links**
- **[LinkedIn](https://www.linkedin.com/in/arifulhaque/)**
- **[GitHub](https://github.com/mah-shamim)**
| mdarifulhaque |
1,891,534 | From Theory To Installation: Kubeflow | In the world of AI, as it currently exists, engineers hear a lot of the “buzzy/hype” pieces around... | 0 | 2024-06-17T17:31:23 | https://dev.to/thenjdevopsguy/from-theory-to-installation-kubeflow-10nj | kubernetes, docker, github, programming | In the world of AI, as it currently exists, engineers hear a lot of the “buzzy/hype” pieces around it. Very rarely are they even hearing the benefits of AI on Kubernetes as it’s primarily conversation about GenAI (which isn’t the only form of AI).
Instead, they should be hearing about the benefits from a technical perspective. Things like:
- A smaller footprint with AI on Kubernetes.
- How AI works underneath the hood.
- What the benefits are from an ecosystem perspective.
And the overall engineering journey necessary to implement because as AI continues to grow, it will become part of the day-to-day in an engineers (primarily Platform Engineers) life.
In this blog post, you’ll learn not only about the theory behind AI on Kubernetes, but how to implement it yourself right now.
## Prerequisites
Have you heard of the GPU (graphics card) shortage due to AI? The reason why is that generally, running/building AI workloads (building data models with Machine Learning) requires powerful machines and powerful GPUs. Although the need for several GPUs is mitigated when running AI workloads on Kubernetes, powerful clusters are still necessary.
To run Kubeflow, you will need a cluster with the following minimum specs:
- A Kubernetes cluster running:
- Kubernetes v1.27 or above
- 32 GB of RAM recommended
- 16 CPU cores recommended
## What Is Kubeflow
Before diving into the thick of things, let’s briefly discuss some “AI” related concepts.
First, AI isn’t just ChatGPT or other Generative AI (GenAI) solutions. AI is the concept of taking data that was trained (learned) and expanded to formulate an idea of its own. The data comes from data sets, which are as it sounds, sets of data. It could be anything from complex data to an Excel spreadsheet with 3 rows and 4 columns. The data set is then fed to data models, which are collections of data sets. The data models are then fed into AI workloads.
<aside>
💡 These explanations are high-level and for good reason - because these concepts will take up entire books and blog posts within themselves. However, these explanations should give you a good starting point.
</aside>
To train the data models, you need specific software. For example, TensorFlow is a big brand in the AI/ML space that gives the ability to train models. Kubeflow takes various tools/software that exist in the AI/ML space and makes them usable on Kubernetes.
Source: https://www.kubeflow.org/docs/started/introduction/
The goal with Kubeflow is to take existing tools and provide a way that’s straightforward to deploy them on Kubernetes. Kubeflow offers both a standalone approach where you can deploy particular pieces of Kubeflow or a method to deploy all tools available within Kubeflow.
The overall idea is for engineers to have the ability to build and train models in an easier, more efficient fashion.
Kubeflow isn’t it’s own entity. It’s taking tools that exist, putting them together under one place, and allowing them to be used via Kubernetes.
### Tools Outside Of Kubeflow
There are a few different tools outside of Kubeflow. They aren’t Kubernetes-native and it’ll require you to learn “their way” of doing things, but it’s still good to understand the other options that exist:
- mlflow: https://mlflow.org/docs/latest/deployment/deploy-model-to-kubernetes/index.html
- TensorFlow (part of the kubeflow ecosystem): https://www.tensorflow.org/tfx/serving/serving_kubernetes
- Ray: https://docs.ray.io/en/latest/
There’s also a stack called JARN which consists of Jupyter, ArgoCD, Ray, and Kubernetes: https://aws.amazon.com/blogs/containers/deploy-generative-ai-models-on-amazon-eks/
## How Does Kubeflow And Kubernetes Help Each Other
As mentioned in the previous section, the whole idea of Kubeflow isn’t to create more tools and software that you have to learn and manage. It’s to take the existing ML/AI-related tools and give you one location to use them all versus having to manage them all as single entities.
The primary stack you’ll see used with Kubeflow is:
- Istio
- Jupyter Notebooks
- PyTorch
- TensorFlow
- RStudio
Source: https://www.kubeflow.org/docs/started/architecture/
With the tools available, you will have:
1. Data preparation
2. Model training
3. Experiments and Runs
4. Prediction serving
5. Pipelines
6. The ability to test and write with Notebooks
Here’s a quote from Kristina Devochko, CNCF Ambassador and senior engineer - *“There are some interesting examples of potential AI has in helping the environment and society as a whole. For example, projects that won in CloudNativeHacks hackathon where AI was used to spread awareness and automatically detect and monitor deforestation on a global level. Or usage of AI in projects for reducing food waste. However, it’s important that AI resource consumption is significant and increasing, not only when it comes to carbon but also water and electricity so we must keep working on optimizing AI”*.
With a quote like that from someone who’s deep in the Kubernetes and Sustainability space, it makes sense to utilize something like Kubernetes to decouple and simplify resources and workloads as much as possible.
### How Kubeflow Helps Platform Engineering
Platform Engineering, at its core, is the ability to make using tools, services, and add-ons easier. If you’re an engineer/developer, you don’t want to learn all of the underlying capabilities. You want the ability to use them to get your job done, but you don’t have the bandwidth to become a master of them all. Platform Engineering makes using the tools more straightforward without having to become a master.
Kubeflow helps Platform Engineering by being readily available on the Platform Engineering underlying platform of choice, Kubernetes. With the ability to do anything from manage containers to virtual machines to resources outside of Kubernetes WITH Kubernetes, adding AI capabilities puts the icing on the cake.
## Kubeflow Installation And Configuration
Throughout this blog post, we went into the internal engineering details behind the “how and why” when it comes to Kubeflow. Let’s now dive into the hands-on portion and install Kubeflow.
You’ll see two sections below - one for AKS, EKS, and a vanilla installation that works on all Kubernetes clusters . There will be differences in where you install Kubeflow as the underlying infrastructure that it resides on (the cloud) requires particular resources to be installed or has several different options. For example, on AWS, there are a ton of configuration options for RDS, S3, Cognito, and more based on how you want to use Kubeflow.
### AKS
Ensure that you have the prerequisites: https://azure.github.io/kubeflow-aks/main/docs/deployment-options/prerequisites/
1. Clone the Kubeflow repo.
```jsx
git clone --recurse-submodules https://github.com/Azure/kubeflow-aks.git
```
1. `cd` into the repo.
```jsx
cd kubeflow-aks
```
1. `cd` into the Manifests directory.
```jsx
cd manifests/
```
1. Checkout the v1.7 branch and go back to the root directory.
```jsx
git checkout v1.7-branch
cd ..
```
1. Install Kubeflow.
```jsx
cp -a deployments/vanilla manifests/vanilla
cd manifests/
while ! kustomize build vanilla | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 10; done
```
### EKS
Ensure you have the proper prerequisites: https://awslabs.github.io/kubeflow-manifests/docs/deployment/prerequisites/
With EKS, there are a lot more options available and instead of writing them all out via this blog, you can see the installation configurations here: https://awslabs.github.io/kubeflow-manifests/docs/deployment/vanilla/guide/
### Vanilla Installation
Aside from cloud-based installations, there’s a vanilla installation that (theoretically) works on any Kubernetes cluster.
1. Clone the Kubeflow repo.
```jsx
git clone https://github.com/kubeflow/manifests.git
```
1. Checkout the latest release. For example below is the branch checkout of v1.8.
```jsx
git checkout v1.8-branch
```
3. From the `manifests` directory, run the following:
```jsx
while ! kustomize build example | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 20; done
```
1. Once everything is installed you can access Kubeflow by logging into the dashboard.
Default username: [user@example.com](mailto:user@example.com)
Default password: 12341234
```jsx
kubectl port-forward svc/istio-ingressgateway -n istio-system 8080:80
```
## Closing Thoughts
Kubeflow is currently the de facto standard in terms of using ML and AI on Kubernetes with tools and software that already exist. Are there other options? Absolutely. The thing is with other options you’ll be learning other tools and API’s vs the tools and API’s that you’re already using.
Kubeflow both incorporate new tools (like Katib and Model Registry) and software/tools that have already existed in the AI/ML space (like PyTorch) and puts them in one stack, which is a good thing. It means you don’t have to reinvent the wheel by learning a ton of new tools and workflows. If you’re already in AI and ML, you’ll be well familiar with the existing toolset. | thenjdevopsguy |
1,891,533 | Recursion | is a submission for DEV Computer Science Challenge v24.06.12: One Byte Explainer.* ... | 0 | 2024-06-17T17:30:34 | https://dev.to/nitesh_kumar_0b42dac608e5/recursion-3g4f | devchallenge, cschallenge, computerscience, beginners | * is a submission for [DEV Computer Science Challenge v24.06.12: One Byte Explainer](https://dev.to/challenges/cs).*
## Explainer
<!-- Explain a computer science concept in 256 characters or less. -->
**Recursion**: A function calling itself to solve smaller instances of the same problem. Key in algorithms like sorting and searching. Helps break down complex tasks but requires a base case to avoid infinite loops. Essential in computer science for elegant solutions.
## Additional Context
<!-- Please share any additional context you think the judges should take into consideration as it relates to your One Byte Explainer. -->
<!-- Team Submissions: Please pick one member to publish the submission and credit teammates by listing their DEV usernames directly in the body of the post. -->
<!-- Don't forget to add a cover image to your post (if you want). -->
<!-- Thanks for participating! --> | nitesh_kumar_0b42dac608e5 |
1,891,532 | Aprendizaje por Refuerzo Profundo en Ambientes No Estacionarios | El aprendizaje por refuerzo profundo (DRL, por sus siglas en inglés) ha demostrado ser una... | 0 | 2024-06-17T17:27:24 | https://dev.to/gcjordi/aprendizaje-por-refuerzo-profundo-en-ambientes-no-estacionarios-1jgc | ia, ai, drl | El aprendizaje por refuerzo profundo (DRL, por sus siglas en inglés) ha demostrado ser una herramienta poderosa para la toma de decisiones en una amplia variedad de dominios. Sin embargo, la mayoría de los algoritmos de DRL asumen que el entorno en el que operan es estacionario, es decir, que las dinámicas del entorno no cambian con el tiempo. Esta suposición no siempre es válida en aplicaciones del mundo real, donde los entornos pueden ser altamente dinámicos y no estacionarios.
Los ambientes no estacionarios presentan un desafío significativo para los algoritmos de DRL porque las políticas aprendidas pueden volverse obsoletas rápidamente cuando las condiciones del entorno cambian. Para abordar estos desafíos, se han desarrollado varias técnicas avanzadas que permiten a los agentes de DRL adaptarse y generalizar mejor en estos entornos cambiantes.
Una estrategia clave es la transferencia de políticas, que implica entrenar al agente en múltiples entornos relacionados para que pueda transferir el conocimiento adquirido de un entorno a otro. Esto permite al agente adaptarse más rápidamente a nuevas situaciones, aprovechando la experiencia previa. Técnicas como la transferencia de políticas multi-entorno y el aprendizaje por transferencia se utilizan comúnmente en este contexto.
Otra aproximación es la adaptación continua, donde el agente sigue actualizando su política a medida que interactúa con el entorno. Esto puede lograrse mediante el uso de técnicas de aprendizaje en línea, donde el agente ajusta su modelo continuamente basándose en las nuevas experiencias. Además, se puede emplear el entrenamiento meta, que permite al agente aprender a aprender, es decir, optimizar su capacidad para adaptarse rápidamente a nuevas tareas mediante un entrenamiento previo en una variedad de tareas.
Los modelos de mundo también juegan un papel crucial en ambientes no estacionarios. Estos modelos permiten al agente predecir futuras transiciones de estado y recompensas, lo que facilita la planificación y adaptación en tiempo real. El uso de modelos de mundo adaptativos, que se ajustan continuamente a los cambios del entorno, puede mejorar significativamente la robustez del agente.
Finalmente, la robustez adversarial es una técnica que prepara al agente para enfrentar cambios inesperados y situaciones adversas. Al exponer al agente a perturbaciones y escenarios adversos durante el entrenamiento, se puede mejorar su capacidad para manejar cambios no anticipados en el entorno.
En resumen, el aprendizaje por refuerzo profundo en ambientes no estacionarios requiere técnicas avanzadas para la transferencia de políticas, adaptación continua, modelos de mundo adaptativos y robustez adversarial. Estas estrategias permiten a los agentes de DRL mantener un rendimiento óptimo en entornos dinámicos, abriendo nuevas posibilidades para la aplicación de la inteligencia artificial en el mundo real.
[Jordi G. Castillón](https://jordigarcia.eu/) | gcjordi |
1,891,531 | The best webtools I can find for you to use | Some of the best tools I can find on the web that I frequently use. This list will always be under... | 0 | 2024-06-17T17:25:53 | https://dev.to/iamrule/the-best-webtools-i-can-find-for-you-to-use-2i6d | Some of the best tools I can find on the web that I frequently use. This list will always be under construction. Feel free to comment with your tips!
### Productivity and Utilities
1. [Ninite](https://ninite.com) - Bulk install multiple applications.
2. [CCleaner](https://www.ccleaner.com) - System optimization and cleaning.
3. [Chocolatey](https://chocolatey.org) - Windows package manager.
4. [Everything](https://www.voidtools.com) - Fast file search.
5. [AutoHotkey](https://www.autohotkey.com) - Automation scripting language.
6. [Greenshot](https://getgreenshot.org) - Screenshot tool.
7. [TreeSize](https://www.jam-software.com/treesize_free) - Disk space management.
8. [IrfanView](https://www.irfanview.com) - Image viewer and editor.
9. [Recuva](https://www.ccleaner.com/recuva) - File recovery.
10. [Speccy](https://www.ccleaner.com/speccy) - System information.
11. [Ditto](https://ditto-cp.sourceforge.io) - Clipboard manager.
12. [F.lux](https://justgetflux.com) - Screen color adjustment.
13. [Rainmeter](https://www.rainmeter.net) - Desktop customization.
14. [Stardock Fences](https://www.stardock.com/products/fences) - Desktop organization.
15. [SumatraPDF](https://www.sumatrapdfreader.org/free-pdf-reader.html) - PDF reader.
16. [7-Zip](https://www.7-zip.org) - File archiver.
17. [WinRAR](https://www.win-rar.com) - File archiver.
18. [PeaZip](https://www.peazip.org) - File archiver.
19. [KeePass](https://keepass.info) - Password manager.
20. [Launchy](http://www.launchy.net) - Application launcher.
21. [Executor](http://www.executor.dk) - Application launcher.
22. [Clover](https://en.ejie.me) - Windows Explorer tabs.
23. [Listary](https://www.listary.com) - Search utility.
24. [WizTree](https://wiztreefree.com) - Disk space management.
25. [Belarc Advisor](https://www.belarc.com/products_belarc_advisor) - System information.
### Development
1. [Visual Studio Code](https://code.visualstudio.com) - Code editor.
2. [Sublime Text](https://www.sublimetext.com) - Code editor.
3. [Atom](https://atom.io) - Code editor.
4. [JetBrains IntelliJ IDEA](https://www.jetbrains.com/idea) - Java IDE.
5. [Postman](https://www.postman.com) - API development.
6. [Insomnia](https://insomnia.rest) - API client.
7. [Docker](https://www.docker.com) - Containerization.
8. [Git](https://git-scm.com) - Version control.
9. [GitHub Desktop](https://desktop.github.com) - Git client.
10. [SourceTree](https://www.sourcetreeapp.com) - Git client.
11. [ngrok](https://ngrok.com) - Secure introspectable tunnels.
12. [Pinggy](https://pinggy.io) - Secure tunnels to localhost.
13. [XAMPP](https://www.apachefriends.org/index.html) - Local server environment.
14. [WAMP](http://www.wampserver.com/en) - Windows server environment.
15. [Laragon](https://laragon.org) - Lightweight server environment.
16. [Vagrant](https://www.vagrantup.com) - Development environments.
17. [Anaconda](https://www.anaconda.com) - Data science and machine learning.
18. [Jupyter](https://jupyter.org) - Interactive notebooks.
19. [Visual Studio](https://visualstudio.microsoft.com) - IDE for various languages.
20. [PyCharm](https://www.jetbrains.com/pycharm) - Python IDE.
21. [Eclipse](https://www.eclipse.org) - IDE for Java and other languages.
22. [NetBeans](https://netbeans.apache.org) - IDE for Java and other languages.
23. [PhpStorm](https://www.jetbrains.com/phpstorm) - IDE for PHP.
24. [WebStorm](https://www.jetbrains.com/webstorm) - IDE for JavaScript.
25. [Brackets](http://brackets.io) - Code editor for web development.
### Design and Multimedia
1. [Photopea](https://www.photopea.com) - Online image editor.
2. [GIMP](https://www.gimp.org) - Image editor.
3. [Inkscape](https://inkscape.org) - Vector graphics editor.
4. [Blender](https://www.blender.org) - 3D modeling and animation.
5. [Krita](https://krita.org) - Digital painting.
6. [Audacity](https://www.audacityteam.org) - Audio editing.
7. [OBS Studio](https://obsproject.com) - Screen recording and streaming.
8. [HandBrake](https://handbrake.fr) - Video conversion.
9. [DaVinci Resolve](https://www.blackmagicdesign.com/products/davinciresolve) - Video editing.
10. [Lightworks](https://www.lwks.com) - Video editing.
11. [Affinity Photo](https://affinity.serif.com/en-us/photo) - Image editor.
12. [Affinity Designer](https://affinity.serif.com/en-us/designer) - Vector graphics editor.
13. [Sketch](https://www.sketch.com) - Design tool.
14. [Figma](https://www.figma.com) - Design collaboration.
15. [Milanote](https://www.milanote.com) - Visual organization.
16. [Canva](https://www.canva.com) - Online design tool.
17. [Pixlr](https://pixlr.com) - Online photo editor.
18. [Vectr](https://vectr.com) - Vector graphics editor.
19. [Gravit Designer](https://www.designer.io) - Vector design app.
20. [CorelDRAW](https://www.coreldraw.com) - Vector graphics editor.
21. [Aseprite](https://www.aseprite.org) - Pixel art tool.
22. [Procreate](https://procreate.art) - Digital painting (iPad).
23. [ArtRage](https://www.artrage.com) - Digital painting.
24. [ZBrush](http://pixologic.com/zbrush) - 3D sculpting.
25. [Cinema 4D](https://www.maxon.net/en/cinema-4d) - 3D modeling and animation.
### Communication and Collaboration
1. [Slack](https://slack.com) - Team communication.
2. [Discord](https://discord.com) - Voice, video, and text chat.
3. [Zoom](https://zoom.us) - Video conferencing.
4. [Microsoft Teams](https://www.microsoft.com/en/microsoft-teams/group-chat-software) - Collaboration and communication.
5. [Trello](https://trello.com) - Project management.
6. [Asana](https://asana.com) - Project management.
7. [Notion](https://www.notion.so) - All-in-one workspace.
8. [Miro](https://miro.com) - Online whiteboard.
9. [Figma](https://www.figma.com) - Design collaboration.
10. [Milanote](https://www.milanote.com) - Visual organization.
11. [Basecamp](https://basecamp.com) - Project management.
12. [Monday.com](https://monday.com) - Project management.
13. [ClickUp](https://clickup.com) - Project management.
14. [Airtable](https://airtable.com) - Project management and database.
15. [Wrike](https://www.wrike.com) - Project management.
16. [Google Meet](https://meet.google.com) - Video conferencing.
17. [GoToMeeting](https://www.gotomeeting.com) - Video conferencing.
18. [Skype](https://www.skype.com) - Voice, video, and text chat.
19. [Cisco Webex](https://www.webex.com) - Video conferencing.
20. [TeamViewer](https://www.teamviewer.com) - Remote access and support.
21. [Yammer](https://www.yammer.com) - Enterprise social network.
22. [Chanty](https://www.chanty.com) - Team communication.
23. [Flock](https://flock.com) - Team communication.
### Web Development
1. [Bootstrap](https://getbootstrap.com) - Front-end framework.
2. [Tailwind CSS](https://tailwindcss.com) - Utility-first CSS framework.
3. [jQuery](https://jquery.com) - JavaScript library.
4. [React](https://reactjs.org) - JavaScript library for building user interfaces.
5. [Vue.js](https://vuejs.org) - JavaScript framework.
6. [Angular](https://angular.io) - JavaScript framework.
7. [Next.js](https://nextjs.org) - React framework.
8. [Nuxt.js](https://nuxtjs.org) - Vue.js framework.
9. [Svelte](https://svelte.dev) - JavaScript framework.
10. [Django](https://www.djangoproject.com) - Python web framework.
11. [Flask](https://flask.palletsprojects.com) - Python web framework.
12. [Ruby on Rails](https://rubyonrails.org) - Ruby web framework.
13. [Laravel](https://laravel.com) - PHP web framework.
14. [Symfony](https://symfony.com) - PHP web framework.
15. [ASP.NET](https://dotnet.microsoft.com/apps/aspnet) - Web framework for .NET.
16. [Spring](https://spring.io) - Java web framework.
17. [Gatsby](https://www.gatsbyjs.com) - React-based static site generator.
18. [Hugo](https://gohugo.io) - Static site generator.
19. [Jekyll](https://jekyllrb.com) - Static site generator.
20. [WordPress](https://wordpress.org) - Content management system.
21. [Drupal](https://www.drupal.org) - Content management system.
22. [Magento](https://magento.com) - E-commerce platform.
23. [Shopify](https://www.shopify.com) - E-commerce platform.
24. [WooCommerce](https://woocommerce.com) - E-commerce plugin for WordPress.
25. [Ghost](https://ghost.org) - Publishing platform.
### Security and Privacy
1. [LastPass](https://www.lastpass.com) - Password manager.
2. [1Password](https://1password.com) - Password manager.
3. [Bitwarden](https://bitwarden.com) - Password manager.
4. [NordVPN](https://nordvpn.com) - VPN service.
5. [ExpressVPN](https://www.expressvpn.com) - VPN service.
6. [Malwarebytes](https://www.malwarebytes.com) - Anti-malware.
7. [Avast](https://www.avast.com) - Antivirus.
8. [Kaspersky](https://www.kaspersky.com) - Antivirus.
9. [VeraCrypt](https://www.veracrypt.fr) - Disk encryption.
10. [Privacy Badger](https://privacybadger.org) - Browser privacy.
11. [Adblock Plus](https://adblockplus.org) - Ad blocker.
12. [uBlock Origin](https://github.com/gorhill/uBlock) - Ad blocker.
13. [ProtonMail](https://protonmail.com) - Encrypted email.
14. [Tutanota](https://tutanota.com) - Encrypted email.
15. [Signal](https://signal.org) - Encrypted messaging.
16. [Tor Browser](https://www.torproject.org) - Anonymous browsing.
17. [DuckDuckGo](https://duckduckgo.com) - Private search engine.
18. [Brave](https://brave.com) - Privacy-focused browser.
19. [GlassWire](https://www.glasswire.com) - Network monitoring.
20. [ZoneAlarm](https://www.zonealarm.com) - Firewall.
21. [Emsisoft](https://www.emsisoft.com) - Anti-malware.
22. [SuperAntiSpyware](https://www.superantispyware.com) - Anti-spyware.
23. [HitmanPro](https://www.hitmanpro.com) - Malware removal.
24. [RogueKiller](https://www.adlice.com/download/roguekiller) - Malware removal.
25. [Spybot](https://www.safer-networking.org) - Anti-spyware.
### Cloud Storage and Backup
1. [Google Drive](https://www.google.com/drive) - Cloud storage.
2. [Dropbox](https://www.dropbox.com) - Cloud storage.
3. [OneDrive](https://www.microsoft.com/en/microsoft-365/onedrive/online-cloud-storage) - Cloud storage.
4. [iCloud](https://www.icloud.com) - Cloud storage.
5. [pCloud](https://www.pcloud.com) - Cloud storage.
6. [Sync.com](https://www.sync.com) - Cloud storage.
7. [Backblaze](https://www.backblaze.com) - Backup service.
8. [CrashPlan](https://www.crashplan.com) - Backup service.
9. [Acronis True Image](https://www.acronis.com/en-us/personal/computer-backup) - Backup software.
10. [Carbonite](https://www.carbonite.com) - Backup service.
11. [IDrive](https://www.idrive.com) - Backup service.
12. [SpiderOak](https://spideroak.com) - Cloud storage.
13. [Zoolz](https://www.zoolz.com) - Cloud backup.
14. [Livedrive](https://www2.livedrive.com) - Cloud backup.
15. [Degoo](https://degoo.com) - Cloud storage.
16. [Mega](https://mega.nz) - Cloud storage.
17. [Tresorit](https://tresorit.com) - Cloud storage.
18. [Box](https://www.box.com) - Cloud storage.
19. [Amazon Drive](https://www.amazon.com/clouddrive) - Cloud storage.
20. [HubiC](https://hubic.com/en) - Cloud storage.
21. [Internxt](https://internxt.com) - Cloud storage.
22. [Jottacloud](https://www.jottacloud.com/en) - Cloud storage.
23. [ElephantDrive](https://www.elephantdrive.com) - Cloud backup.
24. [MSP360](https://www.msp360.com) - Backup software.
25. [Duplicati](https://www.duplicati.com) - Backup software.
### Miscellaneous
1. [Rainmeter](https://www.rainmeter.net) - Desktop customization.
2. [Stardock Fences](https://www.stardock.com/products/fences) - Desktop organization.
3. [ShareX](https://getsharex.com) - Screen capture and file sharing.
4. [LibreOffice](https://www.libreoffice.org) - Office suite.
5. [OpenOffice](https://www.openoffice.org) - Office suite.
6. [SumatraPDF](https://www.sumatrapdfreader.org/free-pdf-reader.html) - PDF reader.
7. [7-Zip](https://www.7-zip.org) - File archiver.
8. [WinRAR](https://www.win-rar.com) - File archiver.
9. [PeaZip](https://www.peazip.org) - File archiver.
10. [KeePass](https://keepass.info) - Password manager.
11. [Launchy](http://www.launchy.net) - Application launcher.
12. [Executor](http://www.executor.dk) - Application launcher.
13. [Clover](https://en.ejie.me) - Windows Explorer tabs.
14. [Listary](https://www.listary.com) - Search utility.
15. [WizTree](https://wiztreefree.com) - Disk space management.
16. [Belarc Advisor](https://www.belarc.com/products_belarc_advisor) - System information.
17. [F.lux](https://justgetflux.com) - Screen color adjustment.
18. [Ditto](https://ditto-cp.sourceforge.io) - Clipboard manager.
19. [RescueTime](https://www.rescuetime.com) - Time management.
20. [Toggl](https://toggl.com) - Time tracking.
21. [Pomodone](https://pomodoneapp.com) - Pomodoro timer.
22. [Focus@Will](https://www.focusatwill.com) - Productivity music.
23. [Noisli](https://www.noisli.com) - Background noise generator.
24. [Cold Turkey](https://getcoldturkey.com) - Distraction blocker.
25. [Freedom](https://freedom.to) - Distraction blocker.
This is just the first draft and will be expanded upon soon! If you have more tools we can add feel free to comment! | iamrule | |
1,891,530 | Online conversietools van PDF naar XML: gegevens transformatie stroomlijnen | Online conversietools van PDF naar XML: Gegevens Transformatie Stroomlijnen In de hedendaagse... | 0 | 2024-06-17T17:25:25 | https://dev.to/digitalbaker/online-conversietools-van-pdf-naar-xml-gegevens-transformatie-stroomlijnen-6an | pdf, pdfconverter, tools | Online conversietools van [PDF naar XML](https://ilovepdf3.com/pdf-to-xml-converter-2/
): Gegevens Transformatie Stroomlijnen
In de hedendaagse digitale wereld is het efficiënt beheren en transformeren van gegevens cruciaal voor bedrijven en organisaties. Een veelvoorkomende uitdaging is het omzetten van PDF-bestanden naar XML-formaat, een taak die vaak noodzakelijk is voor data-analyse, archivering of het integreren van gegevens in verschillende systemen. Gelukkig bieden online conversietools een handige oplossing voor deze uitdaging. In deze blogpost bespreken we hoe deze tools werken, hun voordelen, en enkele populaire opties die beschikbaar zijn.
Waarom PDF naar XML?
PDF (Portable Document Format) is een universeel geaccepteerd formaat dat wordt gebruikt om documenten op een consistente manier weer te geven, ongeacht het apparaat of besturingssysteem. Echter, PDF's zijn niet ideaal voor gegevensverwerking en -manipulatie vanwege hun statische aard. XML (Extensible Markup Language) daarentegen, is een gestructureerd formaat dat gemakkelijk door machines kan worden gelezen en verwerkt, waardoor het ideaal is voor het uitwisselen van gegevens tussen verschillende systemen.
Voordelen van Online Conversietools
Toegankelijkheid: Online conversietools zijn meestal toegankelijk via een webbrowser, wat betekent dat er geen software-installatie nodig is. Dit maakt ze handig voor gebruik op verschillende apparaten en besturingssystemen.
Eenvoudig in Gebruik: Deze tools zijn ontworpen met gebruiksvriendelijkheid in gedachten, waardoor gebruikers met minimale technische kennis hun PDF-bestanden kunnen omzetten naar XML.
Snelheid en Efficiëntie: Online tools kunnen snel grote hoeveelheden data verwerken, wat handmatige conversie tijdrovend en foutgevoelig maakt.
Kostenbesparend: Veel online conversietools bieden gratis basisfunctionaliteit, wat ze een kosteneffectieve oplossing maakt voor kleine tot middelgrote projecten.
Populaire Online Conversietools
Hier zijn enkele populaire online conversietools voor het omzetten van PDF naar XML:
ILovePDF3: [ILovePDF3](https://ilovepdf3.com/pdf-to-xml-converter-2/) Deze tool biedt een eenvoudige interface waarmee gebruikers PDF-bestanden kunnen uploaden en converteren naar XML. Het ondersteunt batchverwerking en biedt opties voor het aanpassen van de output.
**Convertio**: [Convertio](https://convertio.co/) is een veelzijdige online converter die verschillende bestandsformaten ondersteunt, waaronder PDF naar XML. Het heeft een gebruiksvriendelijke interface en biedt extra functies zoals OCR (Optical Character Recognition) voor het extraheren van tekst uit gescande documenten.
**AConvert**: [AConvert](https://www.aconvert.com/) is een andere handige tool die een breed scala aan bestandsconversies ondersteunt. Gebruikers kunnen hun PDF-bestanden uploaden, de gewenste uitvoeropties selecteren en het geconverteerde XML-bestand downloaden.
**Adobe Acrobat Online**: Hoewel [Adobe Acrobat](https://www.adobe.com/acrobat/online.html) een premium service biedt, heeft het ook een gratis online conversietool die betrouwbare en nauwkeurige conversies biedt. Het is een goede keuze voor gebruikers die al vertrouwd zijn met Adobe-producten.
Stappen voor het Converteren van PDF naar XML
Het converteren van PDF naar XML met behulp van een online tool is meestal een eenvoudig proces. Hier is een algemene stap-voor-stap gids:
Upload de PDF: Kies de PDF-bestanden die je wilt converteren en upload deze naar de online tool.
Selecteer de Output Formaat: Kies XML als het gewenste uitvoerformaat. Sommige tools bieden extra opties voor het aanpassen van de XML-structuur.
Start de Conversie: Klik op de knop om de conversie te starten. De tool zal het PDF-bestand analyseren en omzetten naar XML.
Download het XML-bestand: Na voltooiing van de conversie, download je het XML-bestand naar je apparaat.
**Conclusie**
Online conversietools voor het omzetten van PDF naar XML bieden een handige en efficiënte oplossing voor gegevensbeheer en -transformatie. Of je nu een bedrijfseigenaar, data-analist of IT-professional bent, deze tools kunnen je helpen tijd te besparen, fouten te minimaliseren en je workflows te stroomlijnen. Door gebruik te maken van de juiste tool, kun je de kracht van XML benutten om je gegevens effectiever te beheren en te integreren.
Heb je vragen of ervaringen met specifieke conversietools? Deel ze gerust in de reacties hieronder! | digitalbaker |
1,889,900 | Overcoming IP Restrictions: Leveraging Squid Proxy on Kubernetes for API Consumption | IP allowlist When building a Fintech, you need to provide a list of IPs that will consume... | 0 | 2024-06-17T17:21:15 | https://dev.to/woovi/overcoming-ip-restrictions-leveraging-squid-proxy-on-kubernetes-for-api-consumption-20fd | dx, ip, security | ## IP allowlist
When building a Fintech, you need to provide a list of IPs that will consume the Bank as a Service API. This is great from the security perspective, but it creates a bad DX for developers who need to test the APIs.
To overcome this restriction we deployed a forward proxy in our Kubernetes to enable developers to use these APIs from their computers.
## Forward HTTP Proxy
A forward HTTP proxy is a server that sits between a client (such as a web browser or an application) and the internet. Its primary function is to forward requests from the client to the internet and then return the responses from the Internet to the client.
This enables us to forward requests that have IP restrictions to this forward proxy to provide a better developer experience
## Squid Proxy
Squid is a caching proxy for the Web supporting HTTP, HTTPS, FTP, and more. It reduces bandwidth and improves response times by caching and reusing frequently requested web pages. Squid has extensive access controls and makes a great server accelerator.
We used Squid Proxy as it is a very popular forward proxy solution, and it was simple to set up.
To deploy it to Kubernetes you need a deployment, a service, and a config map
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
namespace: proxy-dev
name: squid-dev-proxy
spec:
replicas: 1
strategy:
type: RollingUpdate
rollingUpdate:
maxSurge: 1
selector:
matchLabels:
app: squid-dev-proxy
template:
metadata:
labels:
app: squid-dev-proxy
spec:
volumes:
- name: config
configMap:
name: squid-dev-config
containers:
- name: squid-dev-proxy
image: sameersbn/squid:latest
ports:
- containerPort: 3128
volumeMounts:
- name: config
mountPath: /etc/squid/
```
```yaml
apiVersion: v1
kind: Service
metadata:
namespace: proxy-dev
name: squid-dev-proxy
spec:
ports:
- protocol: TCP
port: 3128
targetPort: 3128
selector:
app: squid-dev-proxy
```
```yaml
kind: ConfigMap
apiVersion: v1
metadata:
name: squid-dev-config
namespace: proxy-dev
data:
squid.conf: |
http_port 3128
acl all src all
cache_log /dev/null
cache deny all
http_access allow all
```
We deployed it port 3128.
## Forward Proxy on Node
We use `fetch` to make HTTP requests in our backend.
To enable a forward proxy, we are going to use the package `https-proxy-agent`
```ts
export const devProxyAgent = () => {
if (process.env.K8S_DEV_PROXY === 'true') {
const proxyAgent = new HttpsProxyAgent(process.env.K8S_DEV_PROXY_URL);
// eslint-disable-next-line
console.log('proxy k8sdev');
return { agent: proxyAgent };
}
return {};
};
```
To use like this
```ts
const options = {
method: 'POST',
body,
...devProxyAgent(),
};
const response = await fetch(url, options);
```
We only enable the proxy if the `K8S_DEV_PROXY` flag is set to `true`. This is needed to avoid using a proxy in staging and production as they are already using the _allowedlist_ IPs.
We use `process.env.` a lot as [feature flags](https://dev.to/woovi/processenv-as-feature-flags-nf)
## Security concerns of this approach
We recommend using this approach only for staging environments.
Our developers can only access this forward proxy when using our VPN.
## In Conclusion
We hope this approach improves the DX to consume APIs that require _allowedlist_ of specific IPs for security reasons.
We also allow our users to _allowlist_ some specific IPs to use their application token for security reasons.
---
[Woovi](https://www.woovi.com) is an innovative startup revolutionizing the payment landscape. With Woovi, shoppers can enjoy the freedom to pay however they prefer. Our cutting-edge platform provides instant payment solutions, empowering merchants to accept orders and enhance their customer experience seamlessly.
If you're interested in joining our team, we're hiring! Check out our job openings at [Woovi Careers](https://woovi.com/jobs/).
| sibelius |
1,891,509 | A Journey Towards A Scalable Multi-Tenant Application | Seven years ago at CodeLink, we embarked on a project to develop a SaaS-based application for a... | 0 | 2024-06-17T17:18:24 | https://dev.to/codelink/a-journey-towards-a-scalable-multi-tenant-application-3al7 | saas, webdev, tutorial | *Seven years ago at CodeLink, we embarked on a project to develop a SaaS-based application for a startup client. This application was designed as a timesheet and human management system for companies, with each company's data being entirely distinct from the others. Given this, we recognized the need for a multi-tenant application.*
## Evaluating Multi-Tenant Models
To kick-start the project, we conducted some research, which led us to three potential multi-tenancy models: database-based, Schema-based, and Table-based.
1. Database-based multi-tenancy: This model designates a unique database for each tenant. The tenancy logic is managed at the ops layer, leaving the application unaware of individual tenants.
2. Schema-based multi-tenancy: In this model, the data for each tenant is isolated in a separate schema, enhancing multi-tenant segregation.
3. Table-based multi-tenancy: This model includes a tenant column in each table, with each row linked to a specific tenant, enabling multi-tenant data organization.
Initially, we opted for the third model, Table-based multi-tenancy. It’s the simplest concept for the new application and can work with any database. This choice served us well for a few years. However, as the application's logic became more complex, the number of tenants increased, the data volume expanded, and the demand for privacy from professional customers grew, we encountered several issues:
1. A tenant's data encapsulates an entire company's information, making multi-tenant data segregation critical. Any accidental data sharing between companies could lead to catastrophic consequences. The tenancy logic in Table-based multi-tenancy resides in the application layer, which increases the risk of mistakes and data leaks. Every modification requires careful consideration of the tenancy logic, which slows down our development process.
2. Our application operates across multiple regions, each with its own database, adding complexity to multi-tenant data management. This structure complicates the process of moving a tenant's data from one region to another. Moreover, if a company decides to cease using our service, completely eradicating all of its data becomes a complex task.
3. The need to append an extra index for each table and the necessity to include an extra join clause in every query to connect that table to the 'tenants' table leads to a performance downgrade. This issue becomes more pronounced as the application logic grows more intricate and the table data expands into millions of rows.
These challenges prompted us to reevaluate our system and consider migrating to a different multi-tenancy model. After thorough discussions, we decided to transition our system to the model that combines the Database-based multi-tenancy model with the Schema-based multi-tenancy model. This implies that each tenant's data could be housed in a distinct schema within the same database or, alternatively, in a separate database.
For instance, when dealing with a large customer who demands a superior level of privacy, we can store their data in a different database. Moreover, as our database size expands, we can shift it to another database to mitigate risks. This strategy effectively addresses our issues:
1. The risk of data leakage is significantly reduced as the data is distributed across different schemas or databases, enhancing multi-tenant data security. While the tenancy logic remains in the application layer, it is now confined within a compact and well-protected middleware. This allows us to define the logic just once, eliminating the need to revisit it with each modification.
2. Transferring a tenant's data to a different database becomes straightforward, improving multi-tenant data mobility. We must back up the relevant schema and import it into the destination database. Deleting a tenant's data is also simplified, requiring just a single command to erase the targeted schema.
3. The simplification of our SQL queries is another advantage, enhancing multi-tenant data query performance. Eliminating extra join clauses allows us to drop all tenant column indexes. Furthermore, as we have fewer records in each schema, the data of each table is significantly reduced, resulting in a modest performance enhancement.
## Implementation Strategy for Building Scalable Multi-Tenant Application
Our end goal is to house each tenant's data in an individual schema or, potentially, a separate database, reinforcing multi-tenant data segregation. This necessitates modifying the tenancy logic within the application code and migrating all tenant data from the existing 'public' schema to their respective separate schemas. Given that these changes have extensive implications for the entire application and directly interact with data, extreme caution is warranted to prevent catastrophic errors. To manage this risk, we've devised a two-phased approach:
- <u>Phase 1</u>: Initially, we will leave the existing data as is. All current tenant data will continue to reside in the 'public' schema, while the data of new tenants will be stored in distinct schemas (or databases). We will revise our application code to handle both cases: multiple tenant data within a single schema (the existing 'public' schema) and individual tenant data in separate schemas. This strategy limits the risk to our current customers' data while enabling us to validate the new tenancy logic with new tenants.
- <u>Phase 2</u>: Upon successfully testing and monitoring our updated tenancy logic, we will transition all existing tenant data from the 'public' schema to individual schemas and eliminate the previous tenancy logic, strengthening multi-tenant data segregation.
### Phase 1 Implementation
In this phase, we aim to create a new tenancy logic code that can operate harmoniously with the existing one. Furthermore, we must establish a procedure for migrating databases across all individual schemas and databases.
#### The Current Tenancy Logic
Our current tenancy logic employs a 'tenants' table and a module known as WithTenant. The model's default scope will be applied when this module is incorporated.
```
module WithTenant
extend ActiveSupport::Concern
included do
scope :with_tenant, lambda {
joins("INNER JOIN tenants ON #{table_name}.tenant = tenants.name AND tenants.active = true")
}
default_scope -> { with_tenant }
end
end
```
To ensure compatibility with the existing data format, we need to preserve this logic temporarily and phase it out during the second phase of our implementation. As this code will be maintained, each individual schema will be required to replicate the exact structure of the 'public' schema. The key difference is that the new schema will contain only one record in the 'tenants' table.
#### Storing the tenant and schema metadata
To keep track of the location of each tenant's data schema, we will establish new tables explicitly for housing this information. These tables will be located in a dedicated metadata schema, which we will name 'configuration'. The structure of these tables will be outlined as follows:
The 'db_configurations' table will contain details about databases, aligning with our objective to support multiple databases. On the other hand, the 'accounts' table will indicate the storage location of a tenant's data, specifying the schema and database. When we roll out Phase 1, we will execute a query to populate the 'accounts' table with existing data. This implies that all tenants will initially have 'public' as their schema_name and the id of the default database as their db_configuration_id. We will retain the special 'configuration' schema solely within the default database.
#### Implementing Schema-Based Tenancy Logic
Our aim is to devise tenancy logic that effortlessly switches to the specific schema associated with the tenant we intend to work with. We can achieve this by adjusting the search_path in PostgreSQL. For a more comprehensive understanding of this process, you can refer to the HackerNoon article, [Your Guide To Schema-based, Multi-Tenant Systems and PostgreSQL Implementation](https://hackernoon.com/your-guide-to-schema-based-multi-tenant-systems-and-postgresql-implementation-gm433589).
In the context of the Rails framework, the 'apartment' gem is a popular choice for this purpose. However, it is no longer being actively maintained. Its clone, 'ros-apartment', also lacks support for some features we require and is not very actively developed. Upon evaluating our needs, we found that many features provided by 'apartment' were superfluous for our application. As a result, we decided to construct our own tenancy logic, drawing inspiration from the functionality offered by the 'apartment' gem.
```jsx
module SchemaHandler
extend self
extend Forwardable
class Handler
DEFAULT_SCHEMA = 'public'
PERSISTENT_SCHEMAS = %w[shared_extensions]
EXCLUDED_MODELS = %w[Currency]
def initialize
@current_schema = DEFAULT_SCHEMA
end
def init
return unless is_using_postgresql
EXCLUDED_MODELS.each do |excluded_model|
excluded_model.constantize.tap do |klass|
table_name = klass.table_name.split('.', 2).last
klass.table_name = "#{DEFAULT_SCHEMA}.#{table_name}"
end
end
ActiveRecord::Base.connection.schema_search_path = full_search_path
end
def switch!(schema = nil)
return reset if schema.nil?
raise ActiveRecord::StatementInvalid, "Could not find schema #{schema}" unless schema_exists?(schema)
@current_schema = schema.to_s
ActiveRecord::Base.connection.schema_search_path = full_search_path
ActiveRecord::Base.connection.clear_query_cache
end
def switch(schema = nil)
previous_schema = @current_schema
switch!(schema)
yield
ensure
begin
switch!(previous_schema)
rescue StandardError => e
Rails.logger.error(e)
reset
end
end
def current
@current_schema || DEFAULT_SCHEMA
end
def reset
@current_schema = DEFAULT_SCHEMA
ActiveRecord::Base.connection.schema_search_path = full_search_path
end
def excluded_models
EXCLUDED_MODELS
end
private
def full_search_path
[@current_schema, PERSISTENT_SCHEMAS].flatten.map(&:inspect).join(',')
end
def schema_exists?(schema)
ActiveRecord::Base.connection.schema_exists?(schema.to_s)
end
def is_using_postgresql
ActiveRecord::Base.connection.adapter_name == 'PostgreSQL'
end
end
def_delegators :handler, :switch, :switch!, :current, :reset, :init, :EXCLUDED_MODELS, :DEFAULT_SCHEMA
def handler
Thread.current[:schema_handler] ||= Handler.new
end
end
```
The code is reasonably straightforward. Two crucial elements to highlight are the persistent_schemas and excluded_models.
Persistent schemas are those that are consistently included in the search_path, as demonstrated in the shared_extensions example above. This is essential because all schemas will need to utilize these extensions.
Excluded models are those whose associated tables do not reside in separate schemas. The data in these tables will be identical across all schemas, hence they are stored in a common location, in this instance, the 'public' schema.
With this module in place, whenever we need to engage with a specific schema, we can do so by utilizing the following code:
```ruby
SchemaHandler.switch('my_schema') do
# Our code here
end
```
Alternatively, we can switch the entire context using:
```ruby
SchemaHandler.switch!('my_schema')
```
To determine which schema we are currently working with, we can use:
```ruby
SchemaHandler.current
```
However, before utilizing this module, we need to initialize it in an initializer:
```ruby
SchemaHandler.init
```
Additionally, within the initializer, we include a monkey patch for Active Record to ensure that whenever a new connection is established, it switches to the same schema as before:
```ruby
module ActiveRecord
module ConnectionHandling
def connected_to_with_schema(database: nil, role: nil, shard: nil, prevent_writes: false, &blk)
current_schema = SchemaHandler.current
connected_to_without_schema(database: database, role: role, shard: shard, prevent_writes: prevent_writes) do
SchemaHandler.switch!(current_schema)
yield(blk)
end
end
alias connected_to_without_schema connected_to
alias connected_to connected_to_with_schema
end
end
```
Finally, in the database.yml, we must add the default schema_search_path:
```yaml
default: &default
adapter: postgresql
encoding: unicode
pool: <%= ENV.fetch("RAILS_MAX_THREADS") { 5 } %>
host: <%= ENV['DB_HOSTNAME'] %>
port: <%= ENV['DB_PORT'] %>
username: <%= ENV['DB_USERNAME'] %>
password: <%= ENV['DB_PASSWORD'] %>
schema_search_path: "public,shared_extensions"
```
#### Supporting multiple databases
Fortunately, Rails 6 introduces support for multiple databases, simplifying our tasks considerably. You can find detailed setup instructions in the [Multiple Databases with Active Record section of the Ruby on Rails Guides](https://guides.rubyonrails.org/active_record_multiple_databases.html).
```yaml
default: &default
adapter: postgresql
encoding: unicode
pool: <%= ENV.fetch("RAILS_MAX_THREADS") { 5 } %>
schema_search_path: "public,shared_extensions"
production:
default:
<<: *default
username: <%= ENV['DEFAULT_DB_USERNAME'] %>
password: <%= ENV['DEFAULT_DB_PASSWORD'] %>
host: <%= ENV['DEFAULT_DB_HOSTNAME'] %>
port: <%= ENV['DEFAULT_DB_PORT'] %>
database: <%= ENV['DEFAULT_DB_NAME'] %>
db1:
<<: *default
username: <%= ENV['DB1_USERNAME'] %>
password: <%= ENV['DB1_PASSWORD'] %>
host: <%= ENV['DB1_HOSTNAME'] %>
port: <%= ENV['DB1_PORT'] %>
database: <%= ENV['DB1_NAME'] %>
db2:
<<: *default
username: <%= ENV['DB2_USERNAME'] %>
password: <%= ENV['DB2_PASSWORD'] %>
host: <%= ENV['DB2_HOSTNAME'] %>
port: <%= ENV['DB2_PORT'] %>
database: <%= ENV['DB2_NAME'] %>
```
Additionally, we add this to application_record.rb:
```ruby
databases = Rails.application.config.database_configuration[Rails.env].keys
db_configs = databases.each_with_object({}) do |db, configs|
db_key = db.to_sym
configs[db_key] ||= {}
configs[db_key][:writing] = db_key
configs[db_key][:reading] = db_key
end
connects_to shards: db_configs
```
#### Database and schema selection for each tenant
Before processing any HTTP request, it's crucial to determine the tenant from which the request originates and subsequently set the appropriate database and schema for that tenant. Below is the code to retrieve the database and schema of a tenant, which we have incorporated into the SchemaHandler module:
```ruby
def set_connection(tenant: nil, schema: nil)
config = self.get_connection_config(tenant: tenant, schema: schema)
raise(ActionController::RoutingError, 'No Tenant Found') unless config
ActiveRecord::Base.connected_to_without_schema(role: :writing, shard: config[:shard]) do
self.switch(config[:schema]) { yield }
end
end
def get_connection_config(tenant: nil, schema: nil)
return if tenant.blank? && schema.blank?
query = "
SELECT *
FROM configuration.accounts AS accounts
INNER JOIN configuration.db_configurations AS config
ON accounts.db_configuration_id = config.id
WHERE account.tenant = '#{tenant.to_s}' OR account.schema_name = '#{schema.to_s}'
LIMIT 1
"
configs = []
ActiveRecord::Base.connected_to_without_schema(role: :reading, shard: :default) do
ActiveRecord::Base.connection_pool.with_connection do |connection|
configs = connection.exec_query(query).entries
end
end
return if configs.blank?
shard = self.get_shard_from_jdbc_url(configs.first['jdbc_url'])
{
shard: (shard || "").to_sym,
schema: configs.first['schema_name']
}
end
def is_tenant_existed(tenant)
return false if tenant.blank?
query = "
SELECT count(*)
FROM configuration.accounts
WHERE tenant = '#{tenant}'
"
rs = []
ActiveRecord::Base.connected_to_without_schema(role: :reading, shard: :default) do
ActiveRecord::Base.connection_pool.with_connection do |connection|
rs = connection.exec_query(query).entries
end
end
return false if rs.blank?
return false if rs.first['count'] < 1
true
end
def get_all_schemas
query = "SELECT DISTINCT(schema_name) FROM configuration.accounts"
ActiveRecord::Base.connected_to_without_schema(role: :reading, shard: :default) do
ActiveRecord::Base.connection_pool.with_connection do |connection|
return connection.exec_query(query).rows.flatten
end
end
end
def get_shard_from_jdbc_url(jdbc_url)
db_configs = Rails.application.config.database_configuration[Rails.env]
db_configs.keys.find do |key|
config = db_configs[key]
url = "jdbc:postgresql://#{config['host']}:#{config['port']}/#{config['database']}"
url == jdbc_url
end
end
```
We require the frontend application to include a 'tenant-name' header in every request. We then establish the connection within a Rack middleware, as shown below:
```ruby
class DatabaseSelection
def initialize(app)
@app = app
end
def call(env)
tenant = env["HTTP_TENANT_NAME"]
if tenant.present?
SchemaHandler.set_connection(tenant: tenant) do
@app.call(env)
end
else
raise(ActionController::RoutingError, 'No Tenant Found') if !is_no_tenant_whitelisted(env['REQUEST_METHOD'], env['PATH_INFO'])
@app.call(env)
end
end
private
def is_no_tenant_whitelisted(method, path)
return true if /^\\/sidekiq/.match(path)
return true if /(.ico|.js|.txt)$/.match(path)
@whitelist_api ||= [
{
method: "GET",
path: "/v1/example_path"
},
# Others endpoints here
]
@whitelist_api.any? { _1[:method] == method && _1[:path] == path }
end
end
```
Sidekiq dashboard, resource files, or certain API endpoints don't need to be specific to a particular tenant, so we whitelist them
Additionally, we ensure that the connection is set before processing any job. To accomplish this, we save the current schema when queuing the job and then set the connection before processing it. Both of these tasks can be achieved by registering middlewares:
```ruby
class SidekiqClientSetSchema
def call(worker_class, job, queue, redis_pool=nil)
job["db_schema"] ||= SchemaHandler.current
yield
end
end
class SidekiqServerSetSchema
def call(worker_class, job, queue)
schema = job['db_schema'] || 'public'
SchemaHandler.set_connection(schema: schema) do
yield
end
end
end
Sidekiq.configure_client do |config|
config.client_middleware do |chain|
chain.add SidekiqClientSetSchema
end
end
Sidekiq.configure_server do |config|
config.client_middleware do |chain|
chain.add SidekiqClientSetSchema
end
end
```
With these implementations, we now have a fully functional multi-database, multi-schema structure.
#### Database Migration
Rails' default migration tool doesn't suit our needs for migrating multiple schemas. Additionally, we chose not to replicate the migration feature of the 'apartment' gem, given its lack of support for concurrent migration execution. Given the number of tenants, waiting hours for each migration during a new version release is unfeasible.
Our client's engineering team recommended [Flyway](https://flywaydb.org/) and successfully used it to manage their migrations. We chose to adopt Flyway due to its simplicity, speed, reliability, and successful implementation by our client's engineering team. Their existing codebase and experiences allowed us to transition smoothly to Flyway.
Furthermore, Flyway ensures that all database migrations are written in raw SQL, which makes the review process and feedback from our DBA team more efficient.
In line with this decision, we have initiated a dedicated service application responsible for managing migrations. This service also exposes an API endpoint for creating new tenant schemas. While the details of the Flyway setup and code implementation exceed the scope of this article, our migration files are structured as follows:
```
migration-app/
├─ common_sql/
│ ├─ version_1/
│ │ ├─ init-common.sql
├─ configuration_sql/
│ ├─ version_1/
│ │ ├─ init-configuration.sql
│ │ ├─ add_column.sql
├─ shared_extensions_sql/
│ ├─ version_1/
│ │ ├─ init-shared_extensions.sql
├─ tenant_sql/
│ ├─ application_sql/
│ │ ├─ version_1
│ │ │ ├─ init_tenant.sql
│ │ ├─ version_2
│ │ │ ├─ add_column_to_table_A.sql
│ │ │ ├─ create_table_B.sql
│ ├─ repeatable_script/
│ │ ├─ functions
│ │ │ ├─ function_A.sql
│ │ │ ├─ function_B.sql
│ │ ├─ triggers
│ │ │ ├─ trigger_A.sql
│ │ │ ├─ trigger_B.sql
```
The 'common_sql', 'configuration_sql', and 'shared_extensions_sql' folders are designated for storing all migrations related to the common schema (the 'public' schema), the 'configuration' schema, and the 'shared_extensions' schema, respectively. Meanwhile, the 'tenant_sql' folder will house all migrations pertaining to each tenant's schema. It will execute all scripts within this directory upon creating a new schema. The 'init_tenant.sql' file can be obtained by exporting the current database's public schema.
The version number will correspond to the application's version upon its release. Flyway maintains logs of migrations in a table called 'schema_version'. To ensure that we have the latest migrations when deploying our Rails app, we need to execute a query to retrieve the latest migration version from the 'schema_version' table and then compare it to the version of our Rails app within an initializer.
### Phase 2 Implementation
While we haven't yet executed Phase 2, the theoretical approach involves copying data from the 'public' schema to the respective separate schema. Following this, we would update the 'schema_name' of the tenant in the 'configuration.accounts' table to align with the new separate schema. Once these steps are completed, we can safely remove the WithTenant module from the old codebase. We plan to update this article once Phase 2 has been fully implemented.
## Conclusion
In conclusion, the journey towards building a scalable multi-tenant application with a robust multi-database, multi-schema architecture has been both challenging and rewarding. By leveraging tools like Flyway, adopting best practices, and collaborating closely with our client's engineering team, we have successfully crafted a solution that meets our scalability and performance requirements.
We hope that sharing our experiences in this article has provided valuable insights for others embarking on building scalable multi-tenant applications. As always, we remain dedicated to pushing the boundaries of innovation and delivering exceptional solutions to our customers.
Thank you for joining us on this journey! | codelink |
1,891,515 | Phone Validation in Laravel Using Abstract | Our sales events platform Auctibles, collects phone numbers for several purposes: Contact phone of... | 0 | 2024-06-17T17:18:03 | https://dev.to/kornatzky/phone-validation-in-laravel-using-abstract-f6l | php, laravel, abstract, phone | Our sales events platform [Auctibles](https://auctibles.com), collects phone numbers for several purposes:
1. Contact phone of sellers to be displayed to buyers
2. Landline phone of sellers
2. Mobile phone of sellers for coordination of deliveries
2. Mobile phone of buyers for coordination of deliveries
Phone validation in Laravel is a crucial step for our sales events platform. It plays a significant role in ensuring smooth delivery coordination and reducing the risk of fraud.
We use validation rules, a practical feature of the PHP Laravel framework, to ensure reliable validation. This is where the Abstract comes in, enhancing the functionality of our platform.
# The Validation Rule
We define a validation rule, `AbstractPhoneValidation` that receives two parameters:
1. The type of phone number expected - mobile, landline, or an empty string when any type is expected,
2. The country of the phone number - two letters ISO 3166-1 alpha-2 code
The rule uses our Abstract API key from the configuration file.
namespace App\Rules;
use Closure;
use Illuminate\Contracts\Validation\ValidationRule;
use Illuminate\Support\Str;
class AbstractPhoneValidation implements ValidationRule
{
/**
* Create a new rule instance.
*
* @return void
*/
public function __construct(
private string $phone_type,
private string $country,
) {}
/**
* Run the validation rule.
*
* @param \Closure(string): \Illuminate\Translation\PotentiallyTranslatedString $fail
*/
public function validate(string $attribute, mixed $value, Closure $fail): void
{
// Initialize cURL.
$ch = curl_init();
// Set the URL that you want to GET by using the CURLOPT_URL option.
curl_setopt(
$ch,
CURLOPT_URL,
'https://phonevalidation.abstractapi.com/v1/?api_key=' . config('app.ABSTRACT_PHONE_VERIFICATION_KEY') . "&phone=$value"
);
// Set CURLOPT_RETURNTRANSFER so that the content is returned as a variable.
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
// Set CURLOPT_FOLLOWLOCATION to true to follow redirects.
curl_setopt($ch, CURLOPT_FOLLOWLOCATION, true);
// Execute the request.
$data = curl_exec($ch);
// Close the cURL handle.
curl_close($ch);
//The response from Abstract is a JSON
$data = json_decode($data);
// here, we do the validation
if (!$data->valid ||
// Is the phone number valid?
($this->phone_type && Str::lower($data->type) != $this->phone_type) ||
// If the phone type was given, what the phone number of the required type?
($data->country->code != $this->country)
// Does the country of the phone correspond to the expected country?
)
{
$fail('validation.' . $attribute)->translate();
}
}
}
As Abstract returns the phone type capitalized, we use `Str::lower` to convert it to lowercase to correspond to our labeling of phone types.
# Using the Validation Rule
We use the rule in validation, where the user's country two letters ISO 3166-1 alpha-2 code is stored in `$country`:
$rules_array = [
'mobile_phone' => [new AbstractPhoneValidation('mobile', $country)],
'landline_phone' => [new AbstractPhoneValidation('landline', $country)],
'contact_phone' => [new AbstractPhoneValidation('', $this->country)],
]);
For a `contact_phone`, any type of phone number is acceptable. | kornatzky |
1,891,514 | Phenol Market Trends, Forecast 2024-2032: Comprehensive Analysis of Size, Share, and Growth | The demand for phenol market is poised to grow significantly from 2024 to 2032, with a projected... | 0 | 2024-06-17T17:12:06 | https://dev.to/swara_353df25d291824ff9ee/phenol-market-trends-forecast-2024-2032-comprehensive-analysis-of-size-share-and-growth-1loh |
The demand for phenol market is poised to grow significantly from 2024 to 2032, with a projected compound annual growth rate (CAGR) of 4.9%. Starting from a market value of US$ 28.1 billion in 2024, it is expected to reach US$ 41.4 billion by 2032, reflecting robust expansion. In 2021, the market was valued at US$ 24.3 billion and is forecasted to experience a year-on-year growth of 5.3% in 2023. This growth is driven by the increasing use of phenol in everyday products such as mouthwash, disinfectants, inks, liquid detergents, and floor cleaners, which fuels market expansion.
Moreover, rapid economic development globally has significantly contributed to increased construction activities in residential and commercial sectors. The use of phenolic resins in the production of plywood, laminated beams, and flooring panels has further boosted market growth, aligning with the expanding construction industry. These factors collectively underline the optimistic growth prospects for the phenol market throughout the forecast period.
The phenol market is driven by several key factors that contribute to its growth:
Increasing Use in Consumer Products: Phenol is essential in the production of everyday consumer products such as mouthwash, disinfectants, inks, liquid detergents, and floor cleaners. The rising demand for these products globally fuels the growth of the phenol market.
Expansion in Construction Activities: Phenolic resins derived from phenol are crucial in the construction sector for manufacturing plywood, laminated beams, and flooring panels. Rapid economic development worldwide has led to significant growth in residential and commercial construction, thereby increasing the demand for phenol.
Industrial Applications: Phenol is also utilized in various industrial applications including the production of epoxy resins, pharmaceuticals, and agricultural chemicals. The expanding industrial sector contributes to sustained demand for phenol.
Technological Advancements: Continuous advancements in production technologies and processes have improved the efficiency and cost-effectiveness of phenol production, further supporting market growth.
Environmental Regulations and Sustainability: Increasing awareness and regulatory measures regarding environmental sustainability are driving the adoption of eco-friendly phenol production methods and derivatives, fostering market expansion in the sustainable chemicals segment.
These drivers collectively propel the phenol market forward, underpinning its projected growth trajectory over the forecast period.
In a nutshell, the Persistence Market Research report is a must-read for start-ups, industry players, investors, researchers, consultants, business strategists, and all those who are looking to understand this industry. Get a glance at the report at- https://www.persistencemarketresearch.com/market-research/phenol-market.asp
The key players in the global phenol market include:
INEOS Group: A major producer of phenol and acetone globally, with significant operations in Europe and North America.
Dow Inc.: Known for its extensive portfolio in chemicals, including phenol and derivatives, serving various industries worldwide.
LG Chem: A leading chemical company based in South Korea, involved in the production of phenol and its derivatives.
Royal Dutch Shell: Operates a large-scale phenol and acetone production facility, supplying global markets with high-quality products.
SABIC: Based in Saudi Arabia, SABIC is a prominent player in the chemical industry, including phenol production for diverse applications.
Mitsui Chemicals: A Japanese company with a strong presence in phenol production, serving markets in Asia and beyond.
Versalis (Eni): Part of the Eni Group, Versalis is involved in the production and distribution of phenol and related chemicals in Europe.
Cepsa: A Spanish multinational involved in phenol production, with operations spanning Europe and other regions.
Formosa Chemicals & Fibre Corporation: Based in Taiwan, Formosa Chemicals is engaged in the production of phenol and its derivatives.
Mitsubishi Chemical Corporation: A key player in the chemical industry, including phenol and acetone production, based in Japan.
These companies play crucial roles in the global phenol market, contributing to its production, distribution, and innovation across various sectors.
Market Segmentation
By Derivative Type:
The phenol market can be segmented based on its derivatives, which include bisphenol A (BPA), phenolic resins, caprolactam, and others. Bisphenol A, used extensively in the production of polycarbonates and epoxy resins, accounts for a significant portion of phenol consumption. Phenolic resins find applications in construction materials like plywood and laminates, contributing to substantial market demand.
By Application:
Phenol is widely used across various applications such as automotive, electronics, construction, healthcare, and others. In automotive applications, it serves as a key component in manufacturing adhesives and coatings. In electronics, phenol is essential for producing printed circuit boards and insulating materials. Moreover, the healthcare sector utilizes phenol in pharmaceuticals and disinfectants due to its antiseptic properties, further driving market growth.
By End-Use Industry:
The phenol market is segmented by end-use industries, including automotive, construction, electronics, healthcare, and others. The construction industry represents a significant consumer due to the high demand for phenolic resins in building materials. The electronics sector utilizes phenol in the production of electrical insulators and laminates. Additionally, healthcare applications leverage phenol for its antiseptic properties in disinfectants and pharmaceuticals, highlighting diverse industry dependencies driving market expansion.
By Region:
Geographically, the phenol market is segmented into regions such as North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. Asia Pacific dominates the global market owing to rapid industrialization, particularly in China and India, which drive substantial demand for phenol in manufacturing and construction sectors. North America and Europe also hold significant shares, driven by robust manufacturing activities and technological advancements in phenol production processes.
These market segments underscore the versatility and widespread applications of phenol across industries, reflecting its essential role in modern industrial and consumer products.
Country-wise Insights
China:
China stands out as the largest consumer and producer of phenol globally. The country's rapid industrialization and manufacturing activities drive significant demand for phenol, especially in sectors like automotive, electronics, and construction. With increasing investments in infrastructure and urban development, the demand for phenol-based products such as phenolic resins for construction materials remains robust.
United States:
In the United States, the phenol market benefits from a strong manufacturing base and technological advancements. Phenol finds extensive use in industries such as automotive, healthcare, and electronics. The presence of major chemical companies and stringent environmental regulations also shapes market dynamics, fostering innovation in sustainable phenol production.
Germany:
Germany plays a pivotal role in the European phenol market, known for its advanced chemical industry and stringent quality standards. Phenol derivatives like bisphenol A and phenolic resins are integral to the country's automotive, construction, and electronics sectors. Sustainable practices and high product quality drive market growth, supported by strong research and development initiatives.
India:
India exhibits substantial growth potential in the phenol market driven by expanding industrial sectors and infrastructure development. The country's construction boom fuels demand for phenolic resins, while the electronics and automotive sectors contribute to increasing consumption of phenol-based products. Government initiatives promoting manufacturing and urbanization further bolster market expansion.
Japan:
Japan maintains a mature phenol market characterized by advanced manufacturing technologies and high-quality standards. The country's automotive industry relies on phenol for producing durable coatings and adhesives, while electronics manufacturers use phenol in printed circuit boards and insulation materials. Continuous innovation in product applications and sustainable production methods drive market stability and growth.
Brazil:
Brazil represents a significant market for phenol in Latin America, driven by its expanding automotive and construction sectors. Phenolic resins are essential in the production of laminates and plywood used in residential and commercial buildings. Government investments in infrastructure and sustainable development initiatives are expected to further propel market demand in the region.
Saudi Arabia:
Saudi Arabia plays a pivotal role in the Middle East phenol market, leveraging its petrochemical industry for phenol production. The country's strategic location and robust infrastructure support export activities to neighboring regions. Phenol derivatives are crucial in diverse applications including construction and healthcare, contributing to sustained market growth in the region.
These insights highlight how each country's industrial landscape, economic policies, and sector-specific demands shape the phenol market dynamics regionally and globally.
Future Outlook for the Phenol Market
Looking ahead, the phenol market is poised for continued growth driven by increasing applications across diverse industries such as automotive, construction, electronics, and healthcare. Key factors driving this growth include rising urbanization, infrastructure development, and technological advancements in phenol production. Moreover, the shift towards sustainable practices and the development of eco-friendly phenol derivatives are expected to play a crucial role in shaping market trends.
Geographically, Asia Pacific is anticipated to maintain its dominance, fueled by rapid industrialization in countries like China and India. North America and Europe will continue to innovate in phenol-based technologies, while emerging economies in Latin America and the Middle East are set to contribute to global market expansion. Overall, the phenol market's future outlook remains promising, underpinned by its essential role in modern manufacturing and consumer applications.
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Persistence Market Research is always way ahead of its time. In other words, it tables market solutions by stepping into the companies’/clients’ shoes much before they themselves have a sneak pick into the market. The pro-active approach followed by experts at Persistence Market Research helps companies/clients lay their hands on techno-commercial insights beforehand, so that the subsequent course of action could be simplified on their part.
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| swara_353df25d291824ff9ee | |
1,891,512 | Interfaces | A superclass defines common behavior for related subclasses. An interface can be used to define... | 0 | 2024-06-17T17:05:46 | https://dev.to/paulike/interfaces-3oaf | java, programming, learning, beginners | A superclass defines common behavior for related subclasses. An interface can be used to define common behavior for classes (including unrelated classes). You can use the **java.util.Arrays.sort** method to sort an array of numbers or strings. Can you apply the same **sort** method to sort an array of geometric objects? In order to write such code, you have to know about interfaces. An _interface_ is for defining common behavior for classes (including unrelated classes). An interface is a class-like construct that contains only constants and abstract methods.
In many ways an interface is similar to an abstract class, but its intent is to specify common behavior for objects of related classes or unrelated classes. For example, using appropriate interfaces, you can specify that the objects are comparable, edible, and/or cloneable.
To distinguish an interface from a class, Java uses the following syntax to define an interface:
`modifier interface InterfaceName {
/** Constant declarations */
/** Abstract method signatures */
}`
Here is an example of an interface:
`public interface Edible {
/** Describe how to eat */
public abstract String howToEat();
}`
An interface is treated like a special class in Java. Each interface is compiled into a separate bytecode file, just like a regular class. You can use an interface more or less the same way you use an abstract class. For example, you can use an interface as a data type for a reference
variable, as the result of casting, and so on. As with an abstract class, you cannot create an instance from an interface using the **new** operator.
You can use the **Edible** interface to specify whether an object is edible. This is accomplished by letting the class for the object implement this interface using the **implements** keyword. For example, the classes **Chicken** and **Fruit** in the program below (lines 24, 43) implement the **Edible** interface. The relationship between the class and the interface is known as _interface inheritance_. Since interface inheritance and class inheritance are essentially the same, we will simply refer to both as _inheritance_.
```
package demo;
public class TestEdible {
public static void main(String[] args) {
Object[] objects = {new Tiger(), new Chicken(), new Apple()};
for(int i = 0; i < objects.length; i++) {
if(objects[i] instanceof Edible)
System.out.println(((Edible)objects[i]).howToEat());
if(objects[i] instanceof Animal) {
System.out.println(((Animal)objects[i]).sound());
}
}
}
}
abstract class Animal{
/** Return animal sound */
public abstract String sound();
}
class Chicken extends Animal implements Edible{
@Override
public String howToEat() {
return "Chicken: Fry it";
}
@Override
public String sound() {
return "Chicken: cock-a-doodle-doo";
}
}
class Tiger extends Animal{
@Override
public String sound() {
return "Tiger: RROOAAARR";
}
}
abstract class Fruit implements Edible{}
class Apple extends Fruit{
@Override
public String howToEat() {
return "Apple: Make apple cider";
}
}
class Orange extends Fruit{
@Override
public String howToEat() {
return "Orange: Make orange juice";
}
}
```
`Tiger: RROOAARR
Chicken: Fry it
Chicken: cock-a-doodle-doo
Apple: Make apple cider`
This example uses several classes and interfaces. Their inheritance relationship is shown in Figure below.
The **Animal** class defines the **sound** method (line 21). It is an abstract method and will be implemented by a concrete animal class.
The **Chicken** class implements **Edible** to specify that chickens are edible. When a class implements an interface, it implements all the methods defined in the interface with the exact signature and return type. The **Chicken** class implements the **howToEat** method (lines 26–28). **Chicken** also extends **Animal** to implement the **sound** method (lines 31–33).
The **Fruit** class implements **Edible**. Since it does not implement the **howToEat** method, **Fruit** must be denoted as **abstract** (line 43). The concrete subclasses of **Fruit** must implement the **howToEat** method. The **Apple** and **Orange** classes implement the **howToEat** method (lines 47, 54).
The **main** method creates an array with three objects for **Tiger**, **Chicken**, and **Apple** (line 6), and invokes the **howToEat** method if the element is edible (line 9) and the **sound** method if the element is an animal (line 12).
In essence, the **Edible** interface defines common behavior for edible objects. All edible objects have the **howToEat** method.
Since all data fields are **public static final** and all methods are **public abstract** in an interface, Java allows these modifiers to be omitted. Therefore the following interface definitions are equivalent:
 | paulike |
1,891,511 | Data Scarcity: When Will AI Hit a Wall? | As AI models become larger and more powerful, the limitations of current data sources can create a shortage of training data that could have several consequences. | 0 | 2024-06-17T17:03:42 | https://code.pieces.app/blog/data-scarcity-when-will-ai-hit-a-wall | <figure><img src="https://d37oebn0w9ir6a.cloudfront.net/account_32099/data-scarcity_d418354294b0c2bb4ca23fb54e903971.jpg" alt="Stylized image of open text books lying on a table."/></figure>
There is a huge amount of AI training data on the Internet with more being created every second. It might seem that there would never be data scarcity in artificial intelligence. However,[ there is a growing concern](https://arxiv.org/abs/2211.04325) about data scarcity in AI. The limitations of current sources may create a data shortage for AI model training data, especially as the [models become more powerful](https://www.wsj.com/tech/ai/ai-training-data-synthetic-openai-anthropic-9230f8d8).
What if AI is running out of training data? What happens when the flow runs dry? This post describes what is happening and what may happen by answering the types of questions a reporter might ask. Here's a breakdown of the "why," "when," and "how" of the potential AI data shortage.
## Why is Data Scarcity a Problem?
In the AI model training process, an AI uses data from the past to interpret the present and predict the future. Imagine a self-driving car. Its success depends on a vast dataset of traffic scenarios, weather conditions, and pedestrian behavior. If this data pool dries up, the car’s ability to navigate the ever-changing world will decrease.
To better understand the AI language model training issue, imagine you're teaching a child a new language. The more words and phrases they encounter, the faster they can learn. But if you only repeatedly show them the same words, their learning will slow and eventually stop. Similarly, models that have limited AI training data sets will have a limited ability to learn new concepts, generalize effectively, and adapt to changing environments.
This issue with LLM model training can lead to several issues:
- **Reduced Accuracy:** AI models trained on insufficient or biased data can experience performance decline, leading to inaccurate results, biased decision-making, or an inability to adapt to new situations.
- **Limited Generalizability:** An AI trained on one specific task might struggle to perform well on a different task, even if the tasks seem similar.
- **Complexity of Tasks:** As more sophisticated AI systems are created, the complexity of tasks increases. This demands even richer and more diverse datasets for training. Also, as models get larger and larger, performance improvement may become smaller and smaller. It becomes harder to optimize the model and overfitting (inaccurate modeling) may increase.
- **Stifled Innovation:** The data bottleneck could slow down the development of new AI applications and hinder advancements in the field.
## What Data Scarcity Problem Already Exists?
The reasons for the scarcity of data are multifaceted. For example, facial recognition software trained on a limited dataset already struggles to identify faces with diverse characteristics. The book _Unmasking AI_ by AI researcher Joy Buolamini describes in detail some of the biases that currently exist in AI algorithms. There already is a data shortage for any group or dataset that is underrepresented on the Internet for any reason.
Privacy concerns are increasing, making people wary of sharing personal information. Also, the nature of data itself is evolving. As AI is increasingly used in complex domains like healthcare and finance, the need for more nuanced and specialized data becomes critical. However, obtaining such data often requires navigating ethical minefields, especially when dealing with sensitive information and absolute security requirements.
- **Quality Matters:** Not all data is high quality. For example, many of the Chinese knowledge representations (longest tokens) in the new AI GPT-4o are[ drawn from gambling, pornography, and scams](https://www.technologyreview.com/2024/05/17/1092649/gpt-4o-chinese-token-polluted/#:~:text=According%2520to%2520multiple%2520researchers%2520who,%252C%2520gambling%252C%2520and%2520scamming%2520contexts.). Low-quality data, especially the failure to appropriately cleanse data, can lead to unintended consequences.
- **Limited Scope of Existing Data:** Much of the data used for training AI currently comes from readily available text and[ images](https://theconversation.com/no-the-lensa-ai-app-technically-isnt-stealing-artists-work-but-it-will-majorly-shake-up-the-art-world-196480) scraped from the internet. This data is often biased, repetitive, and doesn't reflect the real-world diversity that AI needs to function effectively.
- **Data Labeling Bottleneck:** Supervised learning, a common approach, requires data to be manually labeled. Consequently, this labeling process is time-consuming and expensive, especially for complex tasks like image recognition with nuanced details.
- **Ethical Concerns with Data Collection:** Scraping vast amounts of data from the web raises privacy and ethical concerns. New regulations and user awareness make large-scale data collection more challenging. There are [numerous lawsuits ](https://www.techtarget.com/whatis/feature/AI-lawsuits-explained-Whos-getting-sued)challenging companies’ use of copyrighted data to train existing AI systems.
- **AI's Growing Appetite:** As AI models become more complex, they require exponentially more data for training. The current rate of data generation might not keep pace with the increasing demands of sophisticated AI algorithms.
- **Increased Hallucinations:** An AI tends to invent information (hallucinate) when no valid response can be generated from its known knowledge. Thus, as the number of possible questions about events not in its training data increases, the probability of hallucinations also increases.
## When Will Data Scarcity Become a Major Problem?
- **Predictions and Timeframes:** There's no definitive answer for when and how much of a problem data scarcity will become. Researchers have made predictions about reaching a data bottleneck, but the exact time frame varies depending on the specific field of AI and the pace of innovation in data collection and utilization techniques. It also depends on the data type, such as text or images.
- **Gradual Slowdown Rather Than Abrupt Halt:** A lack of training data is more likely to cause a gradual slowdown in AI progress rather than an abrupt halt. AI development might plateau or become more specialized in areas where sufficient data is available.
## What are the Possible Consequences?
The consequences are far-reaching. AI's ability to tackle complex problems in healthcare, climate change, and scientific research could be stifled. The dream of artificial general intelligence, a machine capable of human-level reasoning, might recede further into the horizon.
This data drought doesn't just impact AI developers; it has broader implications. Here are a few areas to consider:
- **The Future of Jobs:** If AI advancements slow down due to limited data, it could affect the timeline for automation and job displacement in various sectors.
- **The Ethics of AI:** Even with solutions like using synthetic data to train AI, ethical considerations around data privacy and potential biases remain crucial.
- **The Value of Human Expertise:** The "human-in-the-loop" approach highlights the continued importance of human judgment and expertise in the development and deployment of AI.
The data scarcity issue is a reminder that AI is not a magic bullet. It's a powerful tool, but like any tool, it has limitations. By understanding the limitations and working towards sustainable solutions, we can ensure that AI continues to evolve and benefit society.
## What Solutions are Being Considered?
The journey towards advanced AI may not focus on ever-increasing amounts of data. Instead, we might overcome data scarcity by shifting to using data more intelligently and increasing collaboration between AI and humans in the learning process. This would require new approaches, and researchers are exploring several solutions:
- **Active Learning:** AI models could be trained to identify their own knowledge gaps and request specific data points. This could optimize learning with less data. Imagine a self-driving car that asks for more examples of nighttime driving scenarios.
- **Transfer Learning:** Pre-trained AI models are being adapted for new tasks; this leverages existing knowledge and reduces the need for entirely new datasets. It's like teaching someone a new language – they already know grammar and sentence structures, they just need the vocabulary.
- **Synthetic Data Generation:** Creating synthetic data to train AI holds promise, but it requires careful development to mimic real-world scenarios. Imagine creating realistic traffic simulations or generating diverse faces using algorithms. AI could be trained on a broader spectrum of scenarios without compromising privacy. However, synthetic data requires careful crafting to avoid unrealistic scenarios or perpetuating existing biases within the algorithms themselves. AI trained on AI-generated data could hallucinate more because of missing real-world information.
- **Human-in-the-Loop Learning:** Integrating human feedback and expertise into the training process can guide AI models and potentially reduce the data needed to achieve desired results. Imagine an AI learning a new skill, not just from data, but also from a human teacher who can provide feedback and guidance. This approach could not only accelerate learning but also ensure AI development remains aligned with human values.
- **"Few-shot learning" techniques:** These techniques allow AI to learn from a smaller dataset by focusing on extracting the most relevant information from each data point. This is akin to a human student who grasps a complex concept from a single, well-explained example.
These approaches offer a glimmer of hope. But they also present challenges. Active learning requires sophisticated algorithms, transfer learning depends heavily on the quality of pre-existing models, and human-in-the-loop learning introduces scalability issues. The quest for new fuel for AI's learning engine may lead us to discover entirely new ways for machines to learn and grow. And that, in itself, could be a groundbreaking discovery.
## Conclusion
The reasons for data scarcity are multifaceted. Privacy concerns are on the rise, making people wary of sharing personal information. Additionally, the nature of data itself is evolving. As AI is increasingly used in complex domains like healthcare and finance, the need for more nuanced and specialized data becomes critical. However, obtaining such data often requires navigating ethical minefields, especially when dealing with sensitive information and absolute security requirements.
The data shortage is a challenge, but it may force researchers to develop more efficient and responsible ways to train AI. These may include ways to be more efficient with data, to create new techniques for data collection and utilization, and perhaps to move beyond data-driven learning paradigms altogether. There are also tools like Pieces built on top of existing LLMs that can leverage [live context](https://code.pieces.app/blog/introducing-pieces-copilot-now-with-live-context) from your active workflow, providing much-needed context to generate a truly accurate response. By developing new techniques for efficient data utilization and exploring alternative training methods, AI's learning and growth could continue even with a lack of training data. | get_pieces | |
1,891,510 | The Comprehensive Guide to Cubensis Spore Syringes: A Key Tool for Mushroom Cultivation | Introduction The cultivation of Psilocybe cubensis, commonly referred to as magic mushrooms, has... | 0 | 2024-06-17T17:03:08 | https://dev.to/mushroom_prints_b3d380e8d/the-comprehensive-guide-to-cubensis-spore-syringes-a-key-tool-for-mushroom-cultivation-e1o | Introduction
The cultivation of Psilocybe cubensis, commonly referred to as magic mushrooms, has garnered significant interest due to their psychoactive properties and potential therapeutic benefits. Central to this cultivation process is the use of a Cubensis spore syringe, a crucial tool that allows growers to inoculate substrates efficiently. This comprehensive guide explores the intricacies of Cubensis spore syringes, their preparation, usage, and significance in mushroom cultivation.
### What is a Cubensis Spore Syringe?
A **[Cubensis spore syringe](http://mushroomprints.com)** is a sterile syringe filled with a solution containing spores of the Psilocybe cubensis mushroom. Spores are the reproductive units of mushrooms, analogous to seeds in plants. They contain all the genetic material necessary to propagate the mushroom species. The spore syringe provides a convenient method to store and distribute these spores, ensuring they remain viable and uncontaminated until they are needed for inoculation.
### The Anatomy of a Spore Syringe
A typical Cubensis spore syringe consists of the following components:
**Barrel:** The main body of the syringe, usually made of plastic, which holds the spore solution.
**Plunger:** A rod that fits snugly inside the barrel, used to push the solution out.
**Needle:** A hollow metal tube attached to the syringe, used to inject the spore solution into the substrate.
Spore Solution: A sterile water-based solution containing millions of microscopic spores.
### Preparing a Cubensis Spore Syringe
Preparing a spore syringe requires meticulous attention to sterility to prevent contamination. Here is a step-by-step overview of the process:
**Collecting Spores:** Spores are collected from mature Psilocybe cubensis mushrooms by placing a cap, gills down, on a sterile piece of paper or glass. After 24-48 hours, the spores fall and form a print.
**Creating the Spore Solution:** Sterile water is used to suspend the spores. This can be done by scraping spores from the print into a sterile container and adding the sterile water.
**Filling the Syringe:** The spore solution is drawn into the syringe using the plunger, ensuring that the solution remains uncontaminated by performing this step in a sterile environment, such as a laminar flow hood.
### Using a Cubensis Spore Syringe for Inoculation
Inoculation is the process of introducing spores to a substrate, the material on which the mushrooms will grow. Here is a detailed guide on using a Cubensis spore syringe:
**Preparing the Substrate:** Common substrates for Psilocybe cubensis include brown rice flour, vermiculite, and water (often referred to as the PF Tek method), or a bulk substrate like manure or coir. The substrate must be sterilized to eliminate any contaminants.
**Inoculating the Substrate:** Once the substrate is sterilized and cooled, the needle of the spore syringe is flame-sterilized and inserted into the substrate. The plunger is then pressed to release the spore solution, typically in multiple injection sites to ensure even distribution.
**Incubation:** After inoculation, the substrate is placed in a controlled environment with the appropriate temperature and humidity to allow the spores to germinate and colonize the substrate.
### Importance of Sterility
Sterility is paramount in mushroom cultivation. Contaminants such as mold or bacteria can outcompete the mushroom mycelium, leading to failed cultivation attempts. Therefore, sterilizing equipment, maintaining a clean workspace, and using sterile techniques are critical when handling spore syringes and substrates.
### Storing Spore Syringes
Proper storage of spore syringes ensures their longevity and viability. Spore syringes should be stored in a cool, dark place, such as a refrigerator, where they can remain viable for several months to a year. It is essential to avoid freezing the syringes as this can damage the spores.
### Legal Considerations
The legality of possessing and using Psilocybe cubensis spores varies by region. In many places, spores are legal to possess because they do not contain psilocybin, the psychoactive compound. However, cultivating psilocybin mushrooms is often illegal. It is crucial to research and understand the local laws and regulations regarding the possession and cultivation of Psilocybe cubensis.
### Conclusion
Cubensis spore syringes are indispensable tools for anyone interested in cultivating Psilocybe cubensis mushrooms. They provide a reliable and efficient method to inoculate substrates, ensuring the spores are evenly distributed and remain uncontaminated. Understanding the preparation, use, and storage of spore syringes, as well as the importance of maintaining sterility throughout the process, is essential for successful mushroom cultivation. As interest in the potential benefits of Psilocybe cubensis continues to grow, so does the importance of these fundamental tools in advancing both personal and scientific exploration of these fascinating organisms. | mushroom_prints_b3d380e8d | |
1,891,508 | Java vs JavaScript | Certainly! Here’s a long description using the keyword "Java or JavaScript": Choosing the Right... | 0 | 2024-06-17T17:00:49 | https://dev.to/saumya27/java-vs-javascript-20pb | java, javascript | Certainly! Here’s a long description using the keyword "Java or JavaScript":
**Choosing the Right Language: Java or JavaScript?**
When it comes to programming languages, the debate often narrows down to **Java or JavaScript**. Both are widely used, yet they serve different purposes and are suited to distinct types of development projects. Understanding the differences and strengths of each can help you determine which is the best fit for your needs.
**Java** is a high-level, object-oriented programming language that was introduced by Sun Microsystems in 1995. Known for its "write once, run anywhere" capability, Java is platform-independent, making it an excellent choice for cross-platform applications. It's predominantly used for building large-scale enterprise applications, Android applications, server-side applications, and complex systems that require high security and performance. Java's robustness, security features, and extensive libraries make it a favorite among developers for backend development and large enterprise solutions.
**JavaScript**, on the other hand, is the language of the web. Initially developed by Netscape in 1995, JavaScript is a high-level, interpreted scripting language primarily used for enhancing the interactivity and functionality of websites. It runs directly in web browsers, making it the go-to language for front-end development. JavaScript enables dynamic content, allowing developers to create interactive elements such as forms, animations, and real-time updates. With the advent of Node.js, JavaScript has expanded to server-side development, making it a full-stack language. Its versatility is further enhanced by frameworks like React, Angular, and Vue.js, which streamline the development of complex web applications.
When deciding between [**Java or JavaScript**](https://cloudastra.co/blogs/java-vs-javascript), consider the specific requirements of your project. If you're working on a project that demands robust backend processing, high security, and scalability, Java is likely the better choice. It is well-suited for applications that require significant processing power and handle large amounts of data, such as banking systems, enterprise software, and mobile applications for Android.
In contrast, if your project involves creating dynamic and responsive web interfaces, JavaScript is essential. Its ability to manipulate the DOM (Document Object Model) in real-time makes it ideal for applications that need a high level of user interaction. JavaScript's frameworks and libraries provide tools to efficiently manage and develop complex client-side applications, enhancing the user experience with seamless navigation and real-time updates.
Both **Java and JavaScript** are integral to modern software development, each excelling in different domains. The decision to use Java or JavaScript should be based on the nature of your project, the development environment, and your specific goals. Whether you need the robust capabilities of Java for backend systems or the dynamic flexibility of JavaScript for interactive web pages, understanding the strengths of each language will guide you in choosing the right tool for your development needs.
This description emphasizes the distinct roles and advantages of Java and JavaScript, helping users understand when to choose one over the other based on their project requirements. | saumya27 |
1,891,506 | Designing for the Internet of Things (IoT) | Ever wondered how your thermostat knows when to adjust the temperature or how your fitness tracker... | 0 | 2024-06-17T16:59:03 | https://dev.to/divine-ikechukwu/designing-for-the-internet-of-things-iot-7hl | productivity, product |
Ever wondered how your thermostat knows when to adjust the temperature or how your fitness tracker counts your steps? That’s the magic of the Internet of Things (IoT). IoT is all about connecting everyday devices to the internet, allowing them to talk to each other and to us. It’s transforming our world, making everything from home appliances to industrial machines smarter and more efficient.
The growth of IoT is mind-blowing. We’re talking about having over 50 billion connected devices by 2030. This explosion is happening because sensors are getting cheaper, wireless technology is getting better, and everyone wants smarter, more data-driven tools.
But here’s the thing—designing these smart devices isn’t so easy too. It’s does not just end with making them work; they need to work well with other devices too, be secure, and also super easy to use.
In this article, we’re going to break down the key things you need to know when designing IoT products. Whether you’re just starting out or looking to up your game, these tips will help you create IoT solutions that are innovative, user-friendly, and stand out from the crowd.
## What is IoT?
So, what exactly is the Internet of Things (IoT)? In simple terms, IoT is a network of physical objects—think gadgets, appliances, and machines—that are connected to the internet. These objects can collect and share data with each other, and with us, making our lives more convenient and efficient.
## Key Components of IoT
To get a clearer picture, let's break down the main components of IoT:
**Devices/Sensors**
These are the “things” in the Internet of Things. They could be anything from a smart thermostat to a wearable fitness tracker. These devices have sensors that collect data from their environment, like temperature, movement, or heart rate.
Connectivity
This is how devices talk to each other and to the internet. Different technologies are used for connectivity, such as Wi-Fi, Bluetooth, cellular networks, and more specialized ones like Zigbee and LoRaWAN. The choice depends on factors like range, power consumption, and data needs.
**Data Processing and Analytics**
Once data is collected, it needs to be processed and analyzed. This can happen on the device itself, on a nearby computer, or in the cloud. The goal is to turn raw data into useful information. For example, your fitness tracker might send your activity data to the cloud, where it’s analyzed to give you insights about your health.
User Interface
This is how users interact with IoT devices. It could be a mobile app, a web interface, or even a voice assistant. A good user interface makes it easy to access and understand the information your devices are providing.
## Examples of IoT Applications in Various Industries
Imagine controlling your lights, thermostat, and security system from your phone. That’s what smart home devices do. Products like the Nest Thermostat or Philips Hue smart bulbs make your home more comfortable and energy-efficient.
In factories, IoT devices monitor machinery to predict maintenance needs before a breakdown happens. This helps in reducing downtime and improving efficiency. For example, sensors on manufacturing equipment can alert managers when a part needs to be replaced.
IoT is revolutionizing healthcare with devices like wearable fitness trackers and smart medical devices. These devices can monitor patients' vital signs in real-time, allowing for better management of chronic diseases. Think of devices like the Fitbit or continuous glucose monitors for diabetes patients.
IoT is also transforming transportation. Connected cars can communicate with each other to avoid collisions and optimize traffic flow. Fleet management systems use IoT to track vehicle locations, monitor driver behavior, and ensure timely maintenance.
## Key Considerations in IoT Design
**User-Centered Design**
When designing IoT products, it's crucial to understand the needs and behaviors of your users. Knowing who will use your device and how they'll interact with it is essential. For example, if you're designing a smart thermostat, find out if users want remote control, scheduling options, or energy-saving tips. To gather these insights, use techniques like surveys, interviews, and observational studies. Creating prototypes and getting feedback through user testing can also help refine your design.
**Interoperability and Standards**
Your IoT product should work seamlessly with other devices and systems. For instance, a smart light bulb should be compatible with Alexa, Google Home, and Apple HomeKit. To achieve this, follow common IoT standards and protocols like MQTT (a lightweight messaging protocol for minimal bandwidth) and CoAP (designed for simple electronics with limited resources). Ensuring compatibility will make your product more versatile and user-friendly.
**Security and Privacy**
Securing IoT devices and data is a major challenge due to their limited processing power and the large amounts of data they handle. To protect your devices, always encrypt data, both at rest and in transit. Regularly update device firmware to patch security vulnerabilities. Implement strong authentication and authorization mechanisms to ensure only authorized users and devices can access your network. For example, in smart home security systems, encryption and regular updates are essential to protect video feeds and user data.
Take smart home security systems, for instance. Understanding user concerns about privacy and ease of use is crucial. Ensuring the system integrates with various smart home hubs and using strong encryption for video feeds are key considerations. Similarly, industrial IoT devices need to work seamlessly with existing machinery and software, while also implementing strict security measures to protect sensitive data and prevent disruptions.
## Designing the User Interface (UI) for IoT
Designing the user interface (UI) for IoT devices presents unique challenges and opportunities. Here’s how to navigate them effectively:
### Challenges of UI Design in IoT
**Diverse Device Types and Interfaces**
IoT devices can range from mobile apps and web interfaces to voice assistants and even physical interfaces. Designing for these diverse platforms requires flexibility and adaptation.
**Ensuring a Seamless User Experience Across Platforms**
Users expect a consistent experience whether they interact with your IoT device via a mobile app, a web portal, or through voice commands. Ensuring this consistency is crucial for usability and user satisfaction.
### Best Practices for IoT UI Design
To create a compelling and user-friendly UI for IoT devices, follow these best practices:
**Simplifying Complex Interactions**
IoT devices often perform complex functions. Simplify user interactions by breaking down tasks into intuitive steps. For example, in a smart home app, streamline the process of setting up automation routines.
**Prioritizing Usability and Accessibility**
Design with accessibility in mind to ensure all users, regardless of ability, can easily navigate and use your IoT device. Consider factors like font size, color contrast, and voice command options.
**Leveraging Visual and Auditory Feedback**
Use visual cues (like color changes or icons) and auditory feedback (such as alerts or voice prompts) to provide your users with clear feedback on their actions and the device’s status. This enhances the user experience and reduces confusion.
## Conclusion
Designing user interfaces (UI) for IoT devices is a balancing act of complexity and simplicity. With diverse device types—from mobile apps to voice assistants—ensuring a seamless user experience across platforms is essential. By simplifying interactions, prioritizing usability and accessibility, and leveraging visual and auditory feedback, you can create intuitive IoT interfaces.
Whether it's adjusting a smart thermostat or monitoring health data, users expect a cohesive experience. Consistency across interfaces enhances usability and satisfaction. By following best practices and considering diverse user needs, IoT UI design can empower users and maximize the potential of connected devices in everyday life.
| divine-ikechukwu |
1,891,505 | Master The Behavioral Interview: 5 Effective Storytelling Frameworks | The highre up you go in terms of seniority, the more important are behavioral interviews for geting... | 21,818 | 2024-06-17T16:56:40 | https://engineeringbolt.com/tech/master-the-behavioral-interview-5-effective-storytelling-frameworks/ | interview, career, programming, learning | The highre up you go in terms of [seniority](https://engineeringbolt.com/tech/meta-facebook-software-engineer-levels/), the more important are behavioral interviews for geting the right approved for the respective role.
To master the behavioral interview in Big Tech companies ([Meta](https://engineeringbolt.com/tag/meta/), [Google](https://engineeringbolt.com/tag/google/), [Amazon](https://engineeringbolt.com/tag/amazon/), [Apple](https://engineeringbolt.com/tag/apple/), etc) and ensure your storytelling is both impactful and memorable, you can use the following effective storytelling frameworks. These frameworks help structure your responses in a clear, concise, and compelling way.
## Join Me
Read more about [Engineering Culture in Big Tech](https://engineeringbolt.com/tech/master-the-behavioral-interview-5-effective-storytelling-frameworks/), [⚡Newsletter](https://engineeringbolt.substack.com/subscribe), [Twitter](https://twitter.com/alexrashkov) and [LinkedIn](https://www.linkedin.com/in/alexrashkov) for more Career, Leadership and Growth advice.
1. **STAR Method**
-------------------
The [STAR method](https://www.cmu.edu/tepper/alumni/assets/docs/star-story.pdf) is a widely recognized framework that helps structure your answers to behavioral interview questions.
- **Situation**: Describe the context within which you performed a task or faced a challenge at work. Be specific and provide enough detail to give the interviewer a good understanding of the situation.
- **Task**: Explain the actual task or challenge that was involved. What needed to be done?
- **Action**: Detail the specific actions you took to address the task or challenge. Focus on what you did, rather than what your team or coworkers did.
- **Result**: Share the outcomes or results of your actions. Quantify your success with numbers or percentages if possible, and explain what you learned from the experience.
### The Downside of the STAR Method
The STAR (Situation, Task, Action, Result) method is widely used for structuring responses in behavioral interviews. While it is effective in helping individuals organize their thoughts and provide comprehensive answers, there are some drawbacks to this approach. One significant issue is the potential for redundancy between the "Situation" and "Task" steps.
#### Key Issues with the STAR Method
1. **Redundancy between Situation and Task**:
- **Repetition**: The "Situation" step asks for context, which often naturally includes the "Task". For instance, when describing a project, the speaker will inherently mention their role and responsibilities, which overlaps with the "Task" step.
- **Confusion**: Candidates may become confused, leading to repetitive information that can detract from the clarity and conciseness of their response.
2. **Over-structuring Responses**:
- **Rigidity**: The strict adherence to the STAR format can sometimes make responses feel mechanical or overly rehearsed. This rigidity can prevent candidates from expressing their experiences in a natural and engaging manner.
- **Missed Nuances**: Important details and nuances might be overlooked if the candidate focuses too heavily on sticking to the STAR structure rather than telling a compelling story.
3. **Focus on Process Over Outcome**:
- **Lack of Emphasis on Results**: Sometimes, the emphasis on Situation and Task can overshadow the more critical aspects of Action and Result. The outcome and the impact of the actions taken are often more important to interviewers than the initial setup.
- **Imbalance**: There can be an imbalance in the response, where too much time is spent on setting up the context and not enough on the actions taken and results achieved.
2. **PAR Method**
------------------
The PAR method is similar to the STAR method and stands for Problem, Action, Result. During a behavioral interview the interviewer would be able to follow the story more easily and this makes the framework clearer for the interviewee.
- **Problem**: Outline the problem or challenge you encountered. Set the scene by describing the context.
- **Action**: Describe the specific actions you took to resolve the problem or address the challenge.
- **Result**: Share the results of your actions, focusing on the positive outcomes and any lessons learned.
3. **CAR Method**
------------------
The CAR method stands for Challenge, Action, Result, and is another framework similar to STAR and PAR.
- **Challenge**: Describe the challenge or situation you faced. What made it difficult or significant?
- **Action**: Explain the actions you took to address the challenge. Be specific about your contributions.
- **Result**: Detail the outcomes of your actions, emphasizing the impact and what you achieved.
4. **SOAR Method**
-------------------
The SOAR method stands for Situation, Obstacle, Action, Result. This method is useful when you want to highlight how you overcame a particular obstacle.
- **Situation**: Set the context by describing the situation.
- **Obstacle**: Identify the obstacle or challenge you encountered.
- **Action**: Explain the actions you took to overcome the obstacle.
- **Result**: Share the results of your actions, focusing on the positive outcomes.
5. **SAR Method**
------------------
The SAR method stands for Situation, Action, Result, and is a simplified version of the STAR method.
- **Situation**: Describe the situation or context.
- **Action**: Detail the actions you took.
- **Result**: Highlight the results of your actions.
### Tips for Effective Storytelling in Behavioral Interviews
- **Be Concise**: Keep your stories focused and to the point. Avoid unnecessary details.
- **Be Specific**: Provide concrete examples and quantify your results when possible.
- **Be Honest**: Authenticity is crucial. Don't exaggerate or fabricate your experiences.
- **Practice**: Rehearse your stories so you can deliver them smoothly and confidently.
- **Tailor Your Stories**: Choose stories that are relevant to the job you're applying for and that highlight the skills and qualities the interviewer is looking for based on the question.
## Join Me
Read more about [Engineering Culture in Big Tech](https://engineeringbolt.com/tech/master-the-behavioral-interview-5-effective-storytelling-frameworks/), [⚡Newsletter](https://engineeringbolt.substack.com/subscribe), [Twitter](https://twitter.com/alexrashkov) and [LinkedIn](https://www.linkedin.com/in/alexrashkov) for more Career, Leadership and Growth advice.
[](https://engineeringbolt.substack.com/subscribe)
By using these storytelling frameworks, you can effectively communicate your experiences and demonstrate your skills and competencies in a structured and engaging manner. | alexr |
1,891,504 | Registration Closed for Hack4Bengal, But You Can Still Join the Fun! | Introduction Hack4Bengal 3.0, one of the most anticipated hackathons of the year, has... | 0 | 2024-06-17T16:54:42 | https://dev.to/arup_matabber/registration-closed-for-hack4bengal-but-you-can-still-join-the-fun-4c92 | ### Introduction
Hack4Bengal 3.0, one of the most anticipated hackathons of the year, has officially closed its registration. While the main event slots are filled, the excitement doesn't end here. There are still plenty of ways for tech enthusiasts and innovators to participate in the vibrant community and engaging activities surrounding the event.
### Hack4Bengal: A Brief Overview
Hack4Bengal is renowned for bringing together some of the brightest minds in technology and innovation. Participants from across the globe come to collaborate, innovate, and push the boundaries of technology. With a wide range of challenges and opportunities, the hackathon fosters creativity, learning, and networking.
### Join the Side Events
Even though the primary registration is closed, Hack4Bengal offers various side events that are open to the public. These events provide fantastic opportunities to learn, interact, and showcase your skills.
#### Workshops
Hack4Bengal hosts numerous workshops on cutting-edge technologies, programming languages, and development tools. These sessions are designed for both beginners and experienced developers. Attending these workshops can enhance your skills and provide valuable insights into the latest tech trends.
#### Networking Sessions
Networking is a crucial part of any tech event. Hack4Bengal's networking sessions allow you to connect with industry professionals, mentors, and fellow tech enthusiasts. These interactions can lead to collaborations, job opportunities, and long-lasting professional relationships.
#### Mini-Challenges
Participate in mini-challenges that test your coding and problem-solving skills. These short, intense competitions are perfect for showcasing your abilities and winning exciting prizes. They also provide a taste of the hackathon experience without the long-term commitment.
#### Keynote Speeches and Panels
Learn from the experts through keynote speeches and panel discussions. Industry leaders and innovators will share their experiences, insights, and predictions about the future of technology. These sessions are a goldmine of information and inspiration for anyone passionate about tech.
### Stay Updated
Follow Hack4Bengal on social media and regularly check their website for updates on upcoming events and opportunities. Engaging with their online community can also provide valuable connections and insights.
### Conclusion
While the main event registration for Hack4Bengal 3.0 is closed, the hackathon's spirit of innovation and community lives on through various side events and activities. Don't miss out on these opportunities to learn, network, and participate in the tech extravaganza. Join the Hack4Bengal community and be a part of something extraordinary!
For more information, visit the [Hack4Bengal website](https://www.hack4bengal.tech/). | arup_matabber | |
1,891,503 | A cool loading animation in CSS pure | See this beautiful pen! | 0 | 2024-06-17T16:54:14 | https://dev.to/tidycoder/a-cool-loading-animation-in-css-pure-n6m | codepen, css, html, webdev | See this beautiful pen!
{% codepen https://codepen.io/TidyCoder/pen/QWRaPve %} | tidycoder |
1,891,501 | Wk 1: MLOPs with DataTalks | Recently joined the DataTalks 2024 cohort to earn a MLOps Certificate and essentially build on... | 0 | 2024-06-17T16:49:47 | https://dev.to/afrologicinsect/wk-1-mlops-with-datatalks-5ah5 | machinelearning, beginners, python, programming | Recently joined the [DataTalks 2024 cohort](https://github.com/DataTalksClub/mlops-zoomcamp) to earn a **MLOps** Certificate and essentially build on Machine Pipeline competencies. To complete the course are assignments that have to be completed every other week.
This will be a series on how the Author approaches these assignments and serve as solutions to those struggling.
**Week 1**
The assignment here is basic, you would have some requisite skill in python, ml libraries and bash scripting to see this through. See the Homework below:
We will build a __jupyter notebook__ that addresses each question.
Before anything, create a directory to house your work, now and later, like so:
```
MLOPS
|
- wk1
```
Then create a virtual environment in the parent directory, this is where you install all the require packages for the whole journey:
1. Launch your bash and run the following commands:
```
cd MLOPS
python3.10 -m venv MLOPS_venv
source MLOPS_venv/Scripts/activate
```
This would create the virtual environment __MLOPS_venv__ with Python 3.10 as well as a directory in your parent folder with the same name, Now you can install packages in this environment. The last line is to activate this environment.
And to deactivate:
`deactivate`
#### Wk1:
###### Setup
```
mkdir wk1
cd wk1
mkdir datasets
code .
```
This makes the **wk1** directory to this week's work, if you haven't created it before, then navigates into it to create a _datasets_ subdirectory after which it launches [VS code](https://www.bing.com/ck/a?!&&p=ff50085fbff1f9b7JmltdHM9MTcxODU4MjQwMCZpZ3VpZD0zODA4ZWQwNS00YmRlLTYwYjQtMjNhNi1mZWZiNGEwMzYxOWMmaW5zaWQ9NTUwMg&ptn=3&ver=2&hsh=3&fclid=3808ed05-4bde-60b4-23a6-fefb4a03619c&psq=vs+code+community+download&u=a1aHR0cHM6Ly92aXN1YWxzdHVkaW8ubWljcm9zb2Z0LmNvbS9kb3dubG9hZHMv&ntb=1), **Ctrl+Shift+P** is the command to create a notebook, name it _homework_.
When this has been created, make sure you set the kernel to the _MLOPS_venv_ environment.
###### Jupyter Notebook
In your _homework.ipynb_ notebook file, run `!ls` to see that you have the needed directories, it should look like this:
- datasets
- homework.ipynb
Then install a few libraries like so:
`## Install Packages
!pip install numpy pandas seaborn scikit-learn`
**!** - This is used in Jupyter notebooks to run shell commands.
**Q1: Green Taxis - Download the data for January and February 2023.**
1.1 Download datasets
```
## Download Yellow Taxi Trips Files
! curl -o ./datasets/jan_yellow.parquet https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet
! curl -o ./datasets/feb_yellow.parquet https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-02.parquet
```
**curl** is a tool for transferring data from or to a server. Here’s what each part does:
`curl` - The command-line tool for making requests to URLs.
`-o `- This option tells curl to save the output to a file instead of displaying it.
`./datasets/jan_yellow.parquet` - The path where the first file will be saved.
So, the first command downloads a file named _yellow_tripdata_2023-01.parquet_ from the given URL and saves it as _jan_yellow.parquet_ in the `datasets` directory.
The second command does the same for a different file, saving it as _feb_yellow.parquet_.
1.2 Import Libraries
```
## Load Libraries
import numpy as np
import pandas as pd
from sklearn.feature_extraction import DictVectorizer
## Load Dataset
jan_df = pd.read_parquet("./datasets/jan_yellow.parquet")
print(f"1, Data Dimension: {jan_df.shape[0]} rows | {jan_df.shape[1]} columns \n")
```
=> Data Dimension: 3066766 rows | 20 columns
The output of this returns the answer to the question.
**Q2: Compute the duration variable (in minutes) and fetch the standard deviation of the trips duration in January?**
2.1 Compute Trip duration & Std Deviation
```
jan_df[["tpep_pickup_datetime", "tpep_dropoff_datetime"]] = jan_df[["tpep_pickup_datetime", "tpep_dropoff_datetime"]].apply(pd.to_datetime)
jan_df["duration"] = (jan_df["tpep_dropoff_datetime"] - jan_df["tpep_pickup_datetime"]).dt.total_seconds()/60
print(f"2, Duration Standard Deviation: {jan_df['duration'].std()} \n")
```
=> Duration Standard Deviation: 42.59435124195458.
This code converts the “tpep_pickup_datetime” and “tpep_dropoff_datetime” columns in the jan_df DataFrame to datetime objects using pandas’ to_datetime function. Then, it calculates the duration of each trip by subtracting the pickup time from the dropoff time, converting the result to total seconds, and then dividing by 60 to get the duration in minutes.
**Q3: Drop Outliers**
```
filtered_duration = jan_df[jan_df['duration'].between(1,60)]
clean_prop = len(filtered_duration['duration'])/len(jan_df['duration'])
print(f"3, Outlier Proportion: {clean_prop} \n")
```
~> 98%
We filter `jan_df` to include only the rows where the "duration" column values are between 1 and 60 minutes. This forms about 98% of the initial dataframe.
**Q4: Dimentionality of Feature Matrix**
```
## Filtered columns
ml_df = filtered_duration[['PULocationID', 'DOLocationID']].astype(str)
ml_df['duration'] = filtered_duration['duration']
## Dictionaries
dicts_train = ml_df[['PULocationID', 'DOLocationID']].to_dict(orient='records')
dicts_train[1:5]
## Vectorizers
vec = DictVectorizer(sparse = True)
feature_matrix = vec.fit_transform(dicts_train)
print(f"4, Dimension of feature_matrix: {feature_matrix.shape} \n")
```
=> 4, Dimension of feature_matrix: (3009173, 515)
This code does the following:
- Creates a new DataFrame ml_df with only the ‘PULocationID’ and ‘DOLocationID’ columns from filtered_duration, converting them to strings.
- Adds the ‘duration’ column from filtered_duration to ml_df.
- Converts ml_df into a list of dictionaries with ‘PULocationID’ and ‘DOLocationID’ as keys, using the to_dict method with orient='records'.
- Initializes a DictVectorizer which is used to convert the list of dictionaries into a matrix of features for machine learning models.
- Transforms the list of dictionaries into a sparse matrix feature_matrix.
- Prints the dimensions of feature_matrix.
- The output will show the number of rows and columns in the feature matrix.
**Q5: Training a Linear Regression Model**
```
## Linear Regression Model
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
y = ml_df['duration']
model = LinearRegression()
model.fit(feature_matrix, y)
y_pred = model.predict(feature_matrix)
rmse = np.sqrt(mean_squared_error(y, y_pred))
print(f"5, RMSE: {rmse}")
```
=> RMSE: 7.649262236295703
Here, we call the **LinearRegression** models from the scikit-learn/sklearn library and train on the target variable **duration**, fit the model to a feature matrix and then predict.
The Root Mean Squared Error (RMSE) is calculated based on the differences between the actual and predicted values of the Target Variable, the lower the value, the better.
Q6: Evaluating the Model
Here, we apply all we've done to the validation _Feb_ dataset, by simply creating a function:
```
## Compile chunks into a function
def rmse_validation(df_pth: str):
val_df = pd.read_parquet(df_pth)
val_df[["tpep_pickup_datetime", "tpep_dropoff_datetime"]] = val_df[["tpep_pickup_datetime", "tpep_dropoff_datetime"]].apply(pd.to_datetime)
val_df["duration"] = (val_df["tpep_dropoff_datetime"] - val_df["tpep_pickup_datetime"]).dt.total_seconds()/60
val_df = val_df[val_df['duration'].between(1,60)]
val_df[['PULocationID', 'DOLocationID']] = val_df[['PULocationID', 'DOLocationID']].astype(str)
dicts_val = val_df[['PULocationID', 'DOLocationID']].to_dict(orient='records')
feature_matrix_val = vec.transform(dicts_val)
#print(f"Dimension of feature_matrix: {feature_matrix_val.shape} \n")
y_val = val_df['duration']
y_pred = model.predict(feature_matrix_val)
rmse = np.sqrt(mean_squared_error(y_val, y_pred))
return rmse
result_feb_df = rmse_validation("./datasets/feb_yellow.parquet")
print(f"6, Validation_RMSE: {result_feb_df}")
```
=> 6, Validation_RMSE: 7.811812822882009
The only difference here is the distinction between __fit_transform__ and __transform__ as it applies to the vectorizer, we use transform in the validation set to simply inherit the fitting transformation earlier done on the training set.
That's it!
Visit [wk1_submission](https://github.com/AkanimohOD19A/MLOps_24/tree/main/wk1) to review the codes and Cheers!
Comment below if there are any issues. | afrologicinsect |
1,891,500 | An Air-Gapped Approach to Maximizing Developer Productivity with Pieces Copilot+ and Live Context | Learn how we developed the Pieces Copilot+ with Live Context adhering to the most stringent privacy and security considerations. | 0 | 2024-06-17T16:49:46 | https://code.pieces.app/blog/an-air-gapped-approach-to-maximizing-developer-productivity-with-pieces-copilot-and-live-context | <figure><img src="https://d37oebn0w9ir6a.cloudfront.net/account_32099/live-context-privacy-and-security_2e2ca59a77a4d9166b32bebee19a49ff.jpg" alt="Live Context Privacy &amp; Security."/></figure>
Managing chaotic workflows and maintaining consistent levels of productivity is a challenging task for software developers dealing with new languages, ballooning documentation, and overall more information overload than ever. At Pieces for Developers, we’ve consistently aimed to address these challenges directly, creating tools that empower developers to work smarter and more efficiently. Our latest innovation, [Pieces Copilot+ with Live Context](https://code.pieces.app/blog/introducing-pieces-copilot-now-with-live-context), is a huge milestone in this journey, delivering a feature that brings more harmony between human and AI workstreams.
Read on to learn more about how we developed this breakthrough feature to be local-first for air-gapped security and lightning-fast speed, and [register for our AMA live stream on Tuesday, June 18, 2024](https://docs.pieces.app/community/events/ama/live-context-security-and-privacy) for an even deeper dive under the hood.
## The Vision Behind Live Context
From the inception of Pieces for Developers, our goal has been clear: to enhance developer productivity through intelligent, contextual tools. We began by offering a secure place for storing valuable code snippets, progressed to proactively saving and contextualizing them, and then introduced one of the first on-device LLM-powered [AI copilots](https://code.pieces.app/blog/navigating-the-future-with-ai-copilots-a-comprehensive-guide). With Pieces Copilot+, we’re bringing forth Live Context—a feature that enables the world’s first temporally grounded copilot.
[Live Context](https://docs.pieces.app/product-highlights-and-benefits/live-context) is designed to understand and adapt to your workflow, allowing the Pieces Copilot to provide relevant assistance based on when, where, and how you work–empowering you to **remember anything, and interact with everything**. Available on macOS, Windows, and Linux, this feature captures and processes workflow data on-device, ensuring both performance and privacy.
## How Live Context Enhances Your Workflow
### 1. Real-Time Workflow Assistance:
- Live Context helps you keep track of your tasks across different tools and sessions. Whether you’re switching between research in a browser, discussions in collaboration tools like Slack, or coding in your IDE, Pieces Copilot+ remembers your activities and provides timely, context-aware assistance.
- Example: Ask, “What was I working on an hour ago?” and receive a detailed response that helps you pick up right where you left off.
<figure><img src="https://d37oebn0w9ir6a.cloudfront.net/account_32099/blogpost-ex1_19bbe43d5c5c1655bf7c3317cfa9647a.gif" alt="Using Pieces Copilot to determine what you were doing earlier in the day."/></figure>
### 2. Simplifying Complex Tasks:
- With Live Context, you can streamline error resolution and project hand-offs. By capturing relevant workflow data, the copilot can offer precise guidance without needing you to manually input context.
- Example: When you encounter an error, simply ask Pieces Copilot+, “How can I resolve this issue in the terminal in IntelliJ?” and it will utilize the relevant context to provide a solution.
<figure><img src="https://d37oebn0w9ir6a.cloudfront.net/account_32099/simplifying-complex-tasks_e4e38855f4198b7b5f4daf0a5ffc6ed3.gif" alt="Asking Pieces Copilot how to resolve an error in the terminal."/></figure>
### 3. Enhancing Developer Communication:
- The Workstream Pattern Engine within Pieces Copilot+ gathers and processes interaction data to help you manage conversations and collaborations more effectively. This includes generating summaries and action items based on your discussions and activities.
- Example: Use it to generate talking points for your daily standup or summarize key themes from a list of unread conversations.
<figure><img src="https://d37oebn0w9ir6a.cloudfront.net/account_32099/blogpost-ex3_f67c36002eefba809fa08c83de060285.gif" alt="Using Pieces Copilot to generate talking points for a daily standup."/></figure>
## Privacy and Security at the Core
We understand that privacy and security are paramount concerns for developers, especially when dealing with sensitive information and proprietary code. Pieces Copilot+ with Live Context has been designed with these considerations at the forefront. Here’s a deeper look into how we ensure your data remains secure:
**1. On-Device Processing:**
All workflow data captured by Live Context is processed and stored locally on your device. This ensures that sensitive information never leaves your machine unless you explicitly choose to share it. By operating in an air-gapped capacity, we eliminate the risk of data breaches associated with network transmissions.
**2. Intelligent Visual Snapshots:**
Instead of continuously recording your screen (which would be intrusive and resource-intensive), PiecesOS detects when a new, distinct visual focus occurs. It captures intelligently timed snapshots of application visuals, not full screenshots. These snapshots are then processed on-device using segmentation and visual reduction algorithms, ensuring only new and relevant information is analyzed.
**3. Temporary Data Handling:**
Extracted text from these snapshots is temporarily stored in memory and then permanently deleted after processing, typically within 100 milliseconds. If the data does not meet a specific relevance threshold, it is discarded after 12 hours, acting as a short-term memory system to ensure your workflow remains uncluttered.
**4. Data Compression and Redaction:**
To manage storage efficiently, PiecesOS compresses relevant data to about 10% of its original size using an on-device transformer model. This process, known as summarization and redaction, also includes best-effort filtering of sensitive information like API keys and PII. This ensures that only the most critical and non-sensitive data is retained for context generation.
**5. Secure Local Storage:**
Post-processing, the summarized data is embedded into a local vector database, which remains on your device. This data can be queried during [retrieval-augmented generation (RAG)](https://code.pieces.app/blog/retrieval-augmented-generation-for-curation) sessions, allowing the copilot to provide contextual assistance without ever transmitting data to the cloud.
**6. Optional Cloud Integration:**
While we prioritize on-device processing, you have the flexibility to use cloud-based Large Language Models (LLMs) if preferred. Even in this case, only the refined context is sent to the cloud, minimizing exposure. If using our [on-device LLM](https://code.pieces.app/blog/the-importance-of-on-device-ai-for-developer-productivity) runtimes (like Mistral or Llama 3), the context never leaves your local environment, ensuring maximum privacy.
**7. Secure Integrations:**
For integrations like the [VS Code Plugin](https://docs.pieces.app/extensions-plugins/vscode), Pieces Copilot+ retrieves and processes stack traces and other relevant data via local HTTP/GRPC connections, ensuring that all data exchanges remain secure and within your control.
## Getting Started with Live Context
Enabling Live Context is straightforward:
1. **Enable the Workstream Pattern Engine:** Navigate to the Machine Learning section of the [Pieces Desktop App](https://docs.pieces.app/installation-getting-started/what-am-i-installing) settings and activate the engine.
1. **Adjust Permissions:** Follow the prompts to update necessary permissions (if required).
1. **Start Using Live Context:** Begin your usual work, and then initiate a conversation with the Pieces Copilot, utilizing Live Context for enhanced assistance.
## Conclusion
The launch of Pieces Copilot+ with Live Context marks a significant milestone in our mission to boost developer productivity. By leveraging temporal context and on-device processing, we offer a tool that not only helps you remember and manage your tasks but also ensures your data remains secure and private.
We’re excited to see how Live Context transforms your workflow and look forward to your feedback. Let’s continue building a community where developers can thrive, leveraging tools that prioritize performance, security, and privacy.
[Feel free to reach out to us on Discord](https://discord.gg/getpieces) with your thoughts, questions, and constructive criticism. Together, we can refine and perfect this feature, making it an indispensable part of every developer’s toolkit.
Stay tuned for more updates, and happy coding! | get_pieces | |
1,891,499 | Case Study: Calendar and GregorianCalendar | GregorianCalendar is a concrete subclass of the abstract class Calendar. An instance of... | 0 | 2024-06-17T16:48:16 | https://dev.to/paulike/case-study-calendar-and-gregoriancalendar-o48 | java, programming, learning, beginners | **GregorianCalendar** is a concrete subclass of the abstract class **Calendar**. An instance of **java.util.Date** represents a specific instant in time with millisecond precision. **java.util.Calendar** is an abstract base class for extracting detailed calendar information, such as the year, month, date, hour, minute, and second. Subclasses of **Calendar** can implement specific calendar systems, such as the Gregorian calendar, the lunar calendar, and the Jewish calendar. Currently, **java.util.GregorianCalendar** for the Gregorian calendar is supported in Java, as shown in Figure below. The **add** method is abstract in the **Calendar** class, because its implementation is dependent on a concrete calendar system.
You can use **new GregorianCalendar()** to construct a default **GregorianCalendar** with the current time and **new GregorianCalendar(year, month, date)** to construct a **GregorianCalendar** with the specified **year**, **month**, and **date**. The **month** parameter is **0** based—that is, **0** is for January.
The **get(int field)** method defined in the **Calendar** class is useful for extracting the date and time information from a **Calendar** object. The fields are defined as constants, as shown in Table below.
The program gives an example that displays the date and time information for the current time.
`Current time is Mon Jun 17 19:42:58 EAT 2024
YEAR: 2024
MONTH: 5
DATE: 17
HOUR: 7
HOUR_OF_DAY: 19
MINUTE: 42
SECOND: 58
DAY_OF_WEEK: 2
DAY_OF_MONTH 17
DAY_OF_YEAR 169
WEEK_OF_MONTH 4
WEEK_OF_YEAR 25
AM_PM 1
September 11, 2001 is a Tuesday`
The **set(int field, value)** method defined in the **Calendar** class can be used to set a field. For example, you can use **calendar.set(Calendar.DAY_OF_MONTH, 1)** to set the **calendar** to the first day of the month.
The **add(field, value)** method adds the specified amount to a given field. For example, **add(Calendar.DAY_OF_MONTH, 5)** adds five days to the current time of the calendar. **add(Calendar.DAY_OF_MONTH, -5)** subtracts five days from the current time of the calendar.
To obtain the number of days in a month, use **calendar.getActualMaximum(Calendar.DAY_OF_MONTH)**. For example, if the **calendar** were for March, this method would return **31**.
You can set a time represented in a **Date** object for the **calendar** by invoking **calendar.setTime(date)** and retrieve the time by invoking **calendar.getTime()**. | paulike |
1,891,491 | Thank you for support [dil s acha lagta h❤] | I am writing regarding the eariler posts I have been uploading. Overwhelming response.[rista bana... | 0 | 2024-06-17T16:36:06 | https://dev.to/aryan015/thank-you-for-support-dil-s-acha-lagta-h-27nj | javascript, react | I am writing regarding the eariler posts I have been uploading. Overwhelming response.[rista bana rahna chahiye]👍🤣@modig | aryan015 |
1,891,498 | Nullish coalescing vs Logical || by aryan | Correction from previous post with same name.❤ The difference between these two operators are,... | 0 | 2024-06-17T16:47:23 | https://dev.to/aryan015/nullish-coalescing-vs-logical-by-aryan-305a | Correction from previous post with same name.❤
The difference between these two operators are, nullish(??) operator consider false, 0 and "" as a `true`.
Only null and undefined is considered as false.
```js
const obj = {
name:'aryan khandelwal'
age:26
}
obj?.['name'] //aryan khandelwal // only access 'name' when objest is defined.
obj||obj['name'] //aryan khandelwal
```
`note`: Even false is considered as `true`🤷♀️
[🔗linkedin](https://www.linkedin.com/in/aryan-khandelwal-779b5723a/)
## learning resources
[🧡Scaler - India's Leading software E-learning](www.scaler.com)
[🧡w3schools - for web developers](www.w3school.com) | aryan015 | |
1,891,497 | Autonomous Vehicles Market: Size, Share, Growth Forecast 2023-2030 | Trends, Statistics, Key Players Analysis & Opportunities | The autonomous vehicles market, valued at USD 680.5 million in 2023, is projected to reach USD... | 0 | 2024-06-17T16:47:21 | https://dev.to/swara_353df25d291824ff9ee/autonomous-vehicles-market-size-share-growth-forecast-2023-2030-trends-statistics-key-players-analysis-opportunities-an7 | The [autonomous vehicles market,](https://www.persistencemarketresearch.com/market-research/autonomous-vehicles-market.asp) valued at USD 680.5 million in 2023, is projected to reach USD 7,245.4 million by 2030, growing at a CAGR of 40.2%. Autonomous vehicles, or self-driving cars, are a groundbreaking advancement in the automotive industry, designed to operate without human intervention using advanced technologies such as sensors, cameras, radar, and artificial intelligence. These technologies aim to enhance safety, efficiency, and convenience in transportation.
Key drivers of this market include advancements in sensor technologies, artificial intelligence, and connectivity, along with the potential to improve road safety, reduce traffic congestion, and provide sustainable mobility solutions. Opportunities abound in software development, sensor manufacturing, and infrastructure development to support autonomous driving. As technological advancements continue and regulations evolve, the autonomous vehicles market offers significant growth potential and opportunities for innovation.
Key trends in the autonomous vehicles market include:
Advancements in AI and Machine Learning: Continued improvements in artificial intelligence algorithms and machine learning models are enhancing the capabilities of autonomous vehicles to perceive and respond to their environment in real-time.
Sensor Technology Innovations: Development of advanced sensors such as LiDAR, radar, and cameras is crucial for improving the accuracy and reliability of autonomous vehicles' perception systems, enabling safer navigation in diverse road and weather conditions.
Connectivity and V2X Communication: Integration of vehicle-to-everything (V2X) communication technologies allows autonomous vehicles to interact with other vehicles, infrastructure, pedestrians, and the surrounding environment, enhancing safety and efficiency.
Regulatory Developments: Governments worldwide are increasingly developing regulations and standards for autonomous vehicles to ensure safety, liability, and operational guidelines, which will influence market adoption and expansion.
Rise of Mobility-as-a-Service (MaaS): The shift towards Mobility-as-a-Service models, where autonomous vehicles are integrated into on-demand transportation services, is expected to drive market growth by providing convenient and cost-effective mobility solutions.
Partnerships and Collaborations: Automakers, tech companies, and startups are forming strategic partnerships and collaborations to leverage expertise in software development, sensor manufacturing, and infrastructure deployment, accelerating technological advancements and market penetration.
Consumer Acceptance and Trust: Increasing consumer awareness, education, and positive experiences with autonomous vehicles are essential for widespread adoption. Building trust through successful pilot programs and demonstrations will be crucial for market growth.
These trends indicate a dynamic and rapidly evolving landscape for autonomous vehicles, driven by technological innovation, regulatory frameworks, and shifts in consumer behavior towards sustainable and efficient mobility solutions.
In a nutshell, the Persistence Market Research report is a must-read for start-ups, industry players, investors, researchers, consultants, business strategists, and all those who are looking to understand this industry. Get a glance at the report at- https://www.persistencemarketresearch.com/market-research/autonomous-vehicles-market.asp
Key players in the autonomous vehicles market include:
Tesla, Inc.: Known for its electric vehicles and advancements in autonomous driving technology, including the development of Tesla Autopilot.
Waymo (Alphabet Inc.): A leader in autonomous driving technology, focusing on developing self-driving cars and autonomous transportation systems.
General Motors (Cruise Automation): GM's subsidiary, Cruise Automation, is actively developing autonomous vehicle technology and aims to deploy autonomous ride-sharing fleets.
NVIDIA Corporation: Provides AI computing platforms and technologies that are crucial for powering autonomous vehicles' perception and decision-making capabilities.
Uber Technologies Inc.: Investing in autonomous vehicle technology for its ride-sharing services through its Advanced Technologies Group (ATG).
Aptiv PLC: Known for its expertise in vehicle electronics and safety systems, Aptiv is developing autonomous driving software and systems for OEMs and mobility providers.
Ford Motor Company: Investing heavily in autonomous vehicle development through its subsidiary, Ford Autonomous Vehicles LLC, focusing on commercial applications and mobility solutions.
These companies are at the forefront of innovation in autonomous vehicles, contributing significantly to advancements in technology, safety, and market adoption.
Market Segmentation of Autonomous Vehicles
By Level of Autonomy:
Autonomous vehicles are segmented based on their level of autonomy, ranging from Level 0 (no automation) to Level 5 (full automation). Level 0 vehicles require full human control, while Level 5 vehicles are capable of performing all driving tasks in all conditions without any human intervention. This segmentation allows consumers and industries to understand the capabilities and limitations of different autonomous vehicle technologies.
By Application:
The market for autonomous vehicles is segmented by application into several key sectors. These include passenger vehicles for personal transportation, autonomous taxis and ride-sharing services, autonomous trucks for freight transportation, and autonomous shuttles for public transportation solutions. Each application segment has specific requirements and challenges, influencing the development and adoption of autonomous driving technologies.
By Technology:
Technological segmentation focuses on the different components and systems that enable autonomous driving. Key technologies include sensors (such as LiDAR, radar, and cameras), artificial intelligence and machine learning algorithms for decision-making, connectivity (V2X communication), and mapping and localization technologies. Understanding these technological segments is crucial for stakeholders involved in the development and integration of autonomous vehicle systems.
By Region:
Geographical segmentation highlights the adoption and regulatory landscape of autonomous vehicles across different regions. Factors such as infrastructure development, regulatory frameworks, consumer acceptance, and investment in technology infrastructure vary significantly by region and influence market dynamics. Regions with supportive policies and advanced infrastructure are likely to witness faster adoption and deployment of autonomous vehicles compared to others.
By End-User:
The market can also be segmented by end-users, including individual consumers, fleet operators (such as ride-sharing companies and logistics providers), and public transportation authorities. Each end-user segment has distinct needs and considerations regarding cost, reliability, safety, and operational efficiency, driving demand for specific types of autonomous vehicle solutions.
Segmenting the autonomous vehicles market by these key factors provides insights into the diverse opportunities and challenges within the industry, guiding stakeholders in strategy development, product innovation, and market expansion efforts.
Regional Insights into the Autonomous Vehicles Market
The adoption and growth of autonomous vehicles vary significantly by region, influenced by factors such as infrastructure development, regulatory frameworks, technological readiness, and consumer acceptance. Here's a breakdown of regional insights:
North America:
North America, particularly the United States, leads in autonomous vehicle development and deployment. The region benefits from advanced infrastructure, significant investments by tech giants and automakers, and supportive regulatory environments in certain states. Companies like Waymo, Tesla, and GM's Cruise Automation are conducting extensive testing and piloting autonomous vehicles in urban and suburban settings.
Europe:
Europe is another key region driving autonomous vehicle innovation. Countries like Germany, the UK, and Sweden have established themselves as hubs for research and development in autonomous driving technologies. The European Union is also actively promoting regulatory frameworks to support the deployment of autonomous vehicles, focusing on safety standards and data privacy regulations.
Asia Pacific:
Asia Pacific, particularly China and Japan, is rapidly emerging as a major market for autonomous vehicles. China, with its ambitious plans for smart cities and electric mobility, is investing heavily in autonomous vehicle technology. Japan, known for its automotive industry expertise, is focusing on developing autonomous vehicles for aging populations and improving transportation efficiency in urban areas.
Middle East and Africa:
In the Middle East, countries like the UAE are embracing autonomous vehicles as part of their smart city initiatives. The region sees autonomous vehicles as a solution to improve transportation efficiency and reduce traffic congestion. In Africa, while adoption is slower, there is increasing interest in leveraging autonomous vehicles for logistics and public transportation in urban centers.
Latin America:
Latin America is exploring autonomous vehicle technologies, albeit at a slower pace compared to other regions. Countries like Brazil and Mexico are gradually integrating autonomous technologies into public transportation systems and exploring opportunities for autonomous taxis and ride-sharing services.
Oceania:
Australia and New Zealand are also investing in autonomous vehicle research and testing. These countries are focusing on leveraging autonomous technologies to address transportation challenges in urban areas and improve mobility for residents.
Regional insights highlight the diverse approaches and opportunities in the global autonomous vehicles market, shaped by regional infrastructure, regulatory environments, and market dynamics. Understanding these regional nuances is crucial for stakeholders aiming to navigate and capitalize on the evolving landscape of autonomous vehicle technologies.
Future Outlook of Autonomous Vehicles
The future of autonomous vehicles appears promising, driven by ongoing advancements in technology, regulatory support, and shifting consumer attitudes towards mobility. As artificial intelligence, sensor technology, and connectivity continue to improve, autonomous vehicles are expected to become safer, more efficient, and increasingly integrated into everyday transportation systems. Key factors such as regulatory frameworks adapting to accommodate autonomous driving, continued investment in infrastructure, and the scalability of autonomous vehicle fleets will shape the industry's growth trajectory.
Moreover, the evolution towards Mobility-as-a-Service (MaaS) models and the potential for autonomous vehicles to enhance urban mobility and reduce traffic congestion are likely to accelerate market adoption. Overall, the autonomous vehicles market is poised for substantial expansion, offering opportunities for innovation across various sectors and transforming the future of transportation globally.
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1,891,496 | Top 8 Gaming open-source projects | Ehy Everybody 👋 It’s Antonio, CEO & Founder at Litlyx. I come back to you with a... | 0 | 2024-06-17T16:45:31 | https://dev.to/litlyx/top-9-gaming-open-source-projects-5f6f | opensource, javascript, discuss, beginners | ## Ehy Everybody 👋
It’s **Antonio**, CEO & Founder at [Litlyx](https://litlyx.com).
I come back to you with a curated **Awesome List of resources** that you can find interesting.
Today Subject is...
```bash
Awesome TOP 8 Open-Source Gaming Projects
```
We are looking for collaborators! Share some **love** & leave a **star** on our open-source [repo](https://github.com/Litlyx/litlyx) on git if you like it!
## Let’s Dive in!
[](https://awesome.re)
---
## Awesome Open-Source Gaming Projects
A curated list of awesome open-source gaming projects on GitHub.
## 1. [Godot Engine](https://github.com/godotengine/godot)
Godot Engine is a feature-packed, cross-platform game engine to create 2D and 3D games from a unified interface.
## 2. [osu!](https://github.com/ppy/osu)
osu! is a rhythm game primarily developed, published, and created by Dean "peppy" Herbert.
## 3. [Mineclone2](https://github.com/minetest/minetest_game)
A free and open-source infinite-world block sandbox game inspired by Minecraft.
## 4. [SuperTuxKart](https://github.com/supertuxkart/stk-code)
SuperTuxKart is a 3D open-source arcade racer with a variety of characters, tracks, and modes to play.
## 5. [OpenRCT2](https://github.com/OpenRCT2/OpenRCT2)
An open-source re-implementation of RollerCoaster Tycoon 2.
## 6. [Unvanquished](https://github.com/Unvanquished/Unvanquished)
A fast-paced, futuristic first-person strategy shooter.
## 7. [FlightGear](https://github.com/FlightGear/flightgear)
An open-source flight simulator for a wide variety of platforms.
## 8. [jMonkeyEngine](https://github.com/jMonkeyEngine/jmonkeyengine)
A complete 3D game development suite written in Java.
---
*I hope you like it!!*
Share some love in the comments below.
Author: Antonio, CEO & Founder at [Litlyx.com](https://litlyx.com)
| litlyx |
1,890,417 | A Straightforward Guide for MySQL Locks | In this article, I aim to introduce you to the common and fundamental locks in InnoDB. If you're not... | 0 | 2024-06-17T16:44:25 | https://dev.to/eyo000000/a-straightforward-guide-for-mysql-locks-56i1 | backend, database, mysql, concurrency | In this article, I aim to introduce you to the common and fundamental locks in InnoDB. If you're not familiar, InnoDB is a storage engine for MySQL, and it’s the default one when you create a database. My goal here is to break down these locks in InnoDB using simple analogies and examples. This can help you grasp the basics so you can explore more on your own if you're curious.
Before diving into each type of lock, I want to point out three things. First, I won't cover every single lock in InnoDB. Second, the details of locks involve other database concepts, such as [isolation levels](https://dev.to/eyo000000/a-straightforward-guide-for-isolation-levels-3h66) and indexes. I’ll intentionally ignore these concepts to keep this article simple. Lastly, I'm not a MySQL or InnoDB expert—just a regular engineer sharing what I've learned over the past few months. If you find any mistakes, please let me know in the comments. I'd really appreciate it!
Throughout the article, I’ll use following table as example
```SQL
CREATE TABLE `posts` (
`id` int NOT NULL AUTO_INCREMENT,
`user_id` int DEFAULT NULL,
`like_count` int DEFAULT NULL,
PRIMARY KEY (`id`),
KEY `idx_user_id` (`user_id`)
)
```
```text
+----+---------+------------+
| id | user_id | like_count |
+----+---------+------------+
| 1 | 100 | 5 |
| 2 | 102 | 10 |
| 3 | 104 | 15 |
+----+---------+------------+
```
Here's a quick guide to the diagram we'll use: T1 and T2 represent two separate transactions, with the `posts` table shown between them. The arrow represents the timeline, moving from older to newer operations.
## Exclusive vs. Shared Lock
Both exclusive locks and shared locks are fundamental in InnoDB. ***An exclusive lock occurs when a transaction requests write access, while a shared lock occurs when a transaction requests read access.***
Let’s use a hotel analogy to explain what exclusive and shared locks are and their compatibility. Imagine a fancy hotel that costs $10,000 per night, but offers tours of its rooms for just $10. Additionally, there are two types of customers: guests (who stay) and visitors (who tour). The hotel has rules to ensure privacy:
For visitors:
- One room can be visited by many people
- This prevents people from lining up to visit a room
- A room that has been occupied by guests cannot be visited
For guests:
- One room can only be occupied by one guest (or group)
- A room with visitors cannot be occupied
I hope these policies make sense. Now, let’s relate this to shared and exclusive locks. In this analogy, a *room* represents a *row*, *visitors* represent the *shared lock*, and *guests* represent the *exclusive lock*. Let’s review the policies again and see how they seamlessly translate to shared and exclusive locks.
Shared Lock
- A row can be added multiple shared lock
- A row with an exclusive lock can’t be added a shared lock
Exclusive Lock
- A row can only be added one exclusive lock
- A room with shared lock can’t be added an exclusive lock
When a transaction attempts to acquire either a shared lock or an exclusive lock, it must follow these policies.
Let's look at an example in action.
Please ignore the `IS` and the `REC_NOT_GAP`. I'll explain what those locks do shortly. For now, just focus on the `S` and `X` locks. As you might guess, `S` represents a shared lock, and `X` represents an exclusive lock.
1. Transactions T1 and T2 each start their own transaction
2. T1 requests and acquires a shared lock on the row where `id=2` by using `SELECT … FOR SHARE` statement
- We can think of `FOR SHARE` as adding a shared lock.
3. T2 uses the same statement to request and acquire a shared lock on the same row
- T2 is granted the shared lock because they are compatible with each other.
4. T1 then attempts to request an exclusive lock on the same row
- T1 has to wait until T2 releases its shared lock because a shared lock is not compatible with an exclusive lock.
- The red error message indicates that transaction T1 is waiting for a lock to be released but has exceeded the allowed timeout period.
- When two locks are not compatible, the second lock has to wait until the first lock is released.
## Intention Lock
An intention lock is a table-level lock. Most of the time, we don’t need to specify an intention lock because InnoDB will automatically issue it when necessary. According to the [MySQL documentation](https://dev.mysql.com/doc/refman/8.4/en/innodb-locking.html#innodb-intention-locks), ***the main purpose of an intention lock is to indicate that a transaction intends to read or write rows in the future.***
Recall from the previous example, there are two `IS` locks.
 An `IS` lock represents an intention shared lock (with `IX` for intention exclusive lock). It means the transaction T1 and T2 inform InnoDB that they are going to perform a read operation on this table.
Let’s see one more example.
1. Transactions T1 and T2 each start their own transaction.
2. T1 attempts to update the row where `id=2`, so it requests and acquires the exclusive `X` lock and the exclusive intention lock `IX` .
3. T2 uses the same statement to request and acquire a shared lock on a different row, and it also requests and acquires the exclusive `X` lock and the exclusive intention lock `IX`
It’s not surprising that two exclusive locks are granted because the two transactions access different rows. However, it might be surprising that two exclusive intention locks are added to the same table. In the previous paragraph, we mentioned that exclusive locks are not compatible with each other. Why is it different here?
It's important to note that the main purpose of an intention lock is not to lock the whole table. Instead, it’s to indicate that a transaction intends to access the rows in this table.
Let's use real-life scenarios to illustrate the purpose and importance of intention locks. The following three scenarios show how hotel staff respond when customers want to stay or visit. Most customers want to stay on the 10th floor because the views from there are stunning.
Scenario A:
In this scenario, the staff does nothing specific regarding the floor level when a customer comes in to stay or visit.
This example can be inefficient as the staff has to check each room when Customer B asks for rooms on the entire floor.
Scenario B:
In this scenario, the staff treats every customer as if they were the president, blocking the entire floor whenever a customer stays or visits.
This significantly decreases the utilization of rooms when the staff blocks the whole floor for Customer A.
Scenario C:
In this scenario, the hotel does not block the entire floor when a customer stays or visits. Instead, it marks the floor to indicate that someone is occupying it.
This approach is the most efficient so far. The staff doesn’t need to check each room, and visitors are still allowed to visit.
In the above analogies, *floors* represent *tables*, *rooms* represent *rows*, *staff* represent *InnoDB*, and *customers* represent *transactions*.
- In Scenario A, because there’s no table-level lock, InnoDB has to check each row to see if there’s a lock on it when a transaction asks for an exclusive table-level lock, which can be less efficient.
- In Scenario B, if InnoDB locks the whole table because a transaction issues a lock on some rows, other transactions have to wait until this transaction releases the lock, significantly decreasing concurrency.
- In Scenario C, InnoDB puts a special mark on the table to indicate that there’s a lock on a row, which is the purpose of intention locks. This allows further transactions to access the same table and respond to the table-level exclusive lock without checking each row.
From these scenarios, we can see the importance of intention locks. They improve efficiency and allow concurrent access.
## Record Lock
From the [MySQL documentation](https://dev.mysql.com/doc/refman/8.4/en/innodb-locking.html#innodb-record-locks), a record lock is a lock on an index record. Without getting into the details of what an index record means, let's simplify: ***a record lock is essentially a row-level lock locks a row.***
(This isn't entirely accurate in the details of MySQL, but it helps us understand record locks from a high-level perspective.)
In most cases, a record lock appears when we read or update a row using a primary key or unique index. This is because both of these ensure access to only one row (or none) without touching other rows.
Let’s reveal the remaining lock we skip in the first example.
Both T1 and T2 use the primary key to update a row. Therefore, `REC_NOT_GAP` represents a record lock. `(S, REC_NOT_GAP)` means shared record lock while `(X, REC_NOT_GAP)` means exclusive record lock.
## Gap Lock
According to the [MySQL documentation](https://dev.mysql.com/doc/refman/8.4/en/innodb-locking.html#innodb-gap-locks), a gap lock is a lock on a gap between index records or on the gap before the first or after the last index record. To oversimplify, ***a gap lock is a lock on a range of rows.*** When a gap lock is added to a range of rows, no other transactions are allowed to insert rows within that range. One advantage of gap locks is that they help prevent [phantom reads](https://dev.to/eyo000000/a-straightforward-guide-for-isolation-levels-3h66).
To understand gap locks better, let's look at the structure of rows from a different perspective. Often, we view the table structure as row by row. However, conceptually, we can think of gaps between each row, including before the first row and after the last row. We can group each gap and the records as follows:
```text
[-infinity, (100, 1)] -> A
[(100, 1), (102, 2)] -> B
[(102, 2), (104, 3)] -> C
[(104, 3), infinity] -> D
```
In this representation, `[x, y]` indicates each group, and `(secondary index, primary index)` indicates the index value. In our example, it’s `(user_id, id)`. A gap lock secures the range from `x` up to, but not including `y`. For instance, when innoDB indicates that there’s a gap lock on group A, it means other transactions are not allowed to insert the `user_id` value before 100 (i.e., from -infinity to 99).
Keep this representation in mind; it will be helpful for understanding the examples later.
Gap locks typically occur when we use a non-unique secondary index to query rows. Let’s walk through an example to see gap locks in action.
(To better illustrate gap locks, the following examples sort the rows by `user_id`)
```text
[-infinity, (100, 1)] -> A 🔒(X, GAP)
[(100, 1), (102, 2)] -> B
[(102, 2), (104, 3)] -> C
[(104, 3), infinity] -> D
```
When T1 updates the row where `user_id = 1`, it issues a gap lock on group A. `(X, GAP)` represents an exclusive gap lock, and `(S, GAP)` represents a shared gap lock. The three insert operations by T2 show that the gap lock indeed locks the range from -infinity to 99. (Note that gap locks do not lock the row itself.)
As mentioned earlier, one advantage of gap locks is preventing phantom reads. Let’s walk through one example, but without a gap lock this time.
Because there’s no gap lock when T1 first accesses the rows, T2 is allowed to insert a row into the table. Later, T1 uses the same query but gets a different result set. This situation is called a phantom read: when a transaction executes the same query twice but gets different sets of rows.
Before moving on to the next type of lock, it’s important to note that gap locks are compatible with each other, even though they are exclusive locks. The purpose of a gap lock is not to prevent access to the gap, but to prevent other transactions from inserting into the gap.
## Next-Key Lock
***A next-key lock is simply a combination of a record lock and a gap lock.*** When a lock shows `X` or `S`, it means they are exclusive and shared next-key locks, respectively. Again, these are just combinations of exclusive (shared) record locks and exclusive (shared) gap locks.
Let’s see how next-key lock perform in different scenarios, as next-key locks are quite common in InnoDB.
```text
[-infinity, (100, 1)] -> A 🔒(X)
[(100, 1), (102, 2)] -> B 🔒(X,GAP)
[(102, 2), (104, 3)] -> C
[(104, 3), infinity] -> D
```
The first row shows the next-key lock `X` on 100, which is our group A. The second row shows the gap lock `X,GAP` on 102, which is our group B. It’s important to note that a next-key lock does lock the target row itself, while a gap lock does not.
If we combine the next-key lock and gap lock in this example, all the rows before the `user_id` value of 102 are locked. That’s why T2 can only insert the value 102.
```text
[-infinity, (100, 1)] -> A
[(100, 1), (102, 2)] -> B 🔒(X)
[(102, 2), (104, 3)] -> C 🔒(X,GAP)
[(104, 3), infinity] -> D
```
The update statement by T1 acquires three locks: an intention lock `IX` on the table, a next-key lock `X` on group B, and a gap lock `X,GAP` on group C. The next-key lock secures the range from the `user_id` value of 100 to 102 (including 102), and the gap lock secures the range from 102 to 103 (excluding 104). To sum up, the range from 100 to 103 is locked by T1. Therefore, T2 is only allowed to insert values where `user_id` is equal to 99 and 104 in this example.
```text
[-infinity, (100, 1)] -> A
[(100, 1), (102, 2)] -> B
[(102, 2), (104, 3)] -> C 🔒(X)
[(104, 3), infinity] -> D 🔒(X,GAP)
```
In this example, all the rows after the `user_id` of 102 are locked by T1 because there’s a next-key lock on group C and a gap lock on group D. So, only the first insert statement from T2 is successful.
## Summary
In this article, we explore different types of locks in InnoDB. We look at their simple definitions, how they lock data, and provide plenty of examples in action. I hope you gain a basic understanding after reading this article.
- Exclusive vs. Shared Lock
- A shared lock allows multiple transactions to read a resource.
- An exclusive lock allows only one transaction to modify a resource.
- Use the hotel analogy to remember their compatibility.
- Intention Lock
- `IX` or `IS`
- A table-level lock.
- Indicates a transaction intends to read or write rows in the future.
- Improves efficiency and allows concurrent access.
- Intention locks are compatible with each other.
- Record Lock
- `(X,REC_NOT_GAP)` or `(S,REC_NOT_GAP)`
- A row-level lock that locks a specific row.
- Gap Lock
- `(X,GAP)` or `(S,GAP)`
- A lock on a range of rows, preventing other transactions from inserting into the gap.
- Use groupings to understand how gap locks work.
- Helps prevent phantom reads.
- Gap locks are compatible with each other.
- Next-Key Lock
- `X` or `S`
- A combination of record lock and gap lock.
## Reference
- [MySQL documentation: InnoDB Locking](https://dev.mysql.com/doc/refman/8.4/en/innodb-locking.html)
- [MySQL Blog Post](https://dev.mysql.com/blog-archive/innodb-data-locking-part-1-introduction)
- [A Comprehensive (and Animated) Guide to InnoDB Locking
](https://jahfer.com/posts/innodb-locks/)
- [深入了解mysql--gap locks,Next-Key Locks](https://blog.csdn.net/qq_20597727/article/details/87308709)
- [Efficient MySQL Performance](https://www.amazon.com/Efficient-MySQL-Performance-Practices-Techniques/dp/1098105095)
- [MySQL Concurrency](https://www.amazon.com/MySQL-Concurrency-Locking-Transactions-Developers/dp/148426651X) | eyo000000 |
1,891,494 | Cloud Computing Platforms | Introduction When moving to the cloud, we can use the services one of many CSPs offers. we... | 0 | 2024-06-17T16:40:01 | https://dev.to/michellebuchiokonicha/cloud-computing-platforms-4667 | cloud, softwaredevelopment, aws, gcp | ## Introduction
When moving to the cloud, we can use the services one of many CSPs offers. we can only choose the service optimal for our use case if we know what services are available.
Interapobaility has become increasingly important in today's inter-connected world, it is common for organizations to use multi-cloud strategies integrating cloud services by several cloud providers or with existing on-premise infrastructure.
Furthermore, to prevent vendor lock-in, we should be aware of the various offerings by competing cloud providers to stay flexible. There are many more reasons why we should learn what the different providers offer such as security and compliance, innovation and new services, ecosystem integration, performance optimization, scalability and flexibility, technical support, etc.
Here we will discuss some of the most frequently used services offered by the big three cloud providers to enable us to choose what is best for our situation. This applies to everyone using the cloud including data scientists, cloud architects and consultants, cloud developers and engineers and so many more.
## Terminologies Defined
**Cloud Platform:** An abstraction layer that can be used to provision resources and as a starting point for cloud development.
**Cloud service provider:** A corporation running many data centers worldwide and offering services based on this service for rent.
There are many CSPs but three of them, due to their considerable market share are the most popular.
## Amazon web services
It started in 2006, is one of the oldest CSPs, and has the biggest market share. Compute services include:
- Elastic compute cloud(EC2)
- Virtual Private Cloud (VPC)
- Simple Storage Service (S3)
- Relational Database Service(RDS)
- Lambda
- Kinesis
- Elastic Map Reduce EMR
Others include:
- Route 53,
- Simple Notification Service(SQS),
- Elastic Load Balancer
- Network firewall.
## Data security and protection
Considering some thoughts into which availability zone should be used to deploy a resource can contribute to data protection. Moreso, data being redundant in many zones can mitigate data loss and support data failover.
On the other hand, limiting the
geographical location to which data can be stored and processed can ensure that this falls under one jurisdiction or another.
As far as monitoring control and encryption services are concerned, there are:
- CloudTrail, Macie, CLoudHSM, Key Management service(KMS), cloud Tower
- GuardDuty, Nitro system
- ISO compliance for cloud security, privacy information management, and cloud privacy.
- VPC
## Pricing and costs
Most services are offered on a pay-as-you-go basis.
There are 12 months when we can try services and some services are free.
## Certifications
They run certification programs to become associates and then professionals.
## Microsoft Azure
It started as the Windows Azure platform in 2010 then became Microsoft Azure in 2014. The virtual machines in Azure are called virtual machines and its services include:
- Virtual machine
- Kubernetes services
- Container Instance
- Blob storage: Used for object storage
- Azure SQL: cloud version of Microsoft SQL Server, a relational database.
- Azure Functions: the serverless compute service here is called Azure Functions.
- DNS, notification hubs, load balancer, and firewall:
- Azure machine learning: provides abstraction for data connection
- Azure Databricks for big data processing
- HDInsight
- Data Factory
## Data security and protection
Azure has ways of ensuring security and protection.
They include:
- Access management in Azure Active Directory:
- Data Encryption at rest and in transit
- Virtual Private Network(VPN, VNet)
- Security center
## Pricing and Costs
Some services are free and it is for 12v months, pay as you go. You can also save costs by making long-term commitments and using either resource.
## Certifications
There are certifications to become experts from associates to professionals.
## Google Cloud Platform
It started in 2011 with the cloud computing engine which is Google Virtual Machines.
Its services include:
- Cloud VPC: Virtual private cloud services
- App Engine
- Kubernetes Engine: containers as a service
- Compute engine
- BigQuery
- Dataflow
- Pub/Sub
- Cloud storage: This is Google object storage
- Cloud Spanner: A distributed relational database service providing transactional consistency
- Cloud Functions: Google serverless computing service
- Cloud DNS, Cloud Pub/Sub, cloud load balancing
## Data security and protection
They use various ways to ensure security like:
- Encryption at rest and in transit
- ISO standards for cloud security, privacy, and regulatory compliance.
- VPC - virtual private cloud
- Security command center
- Identity and access management
- Firewalls
## Pricing and costs
Similar to the other providers, 12 months and pay as you go. There are also free tier offerings.
The largest cloud players are:
- AWS
- Azure
- Google
- Alibaba Cloud
- IBM
- Salesforce
## Gartner Magic Quadrant
It divides the market into Challengers, Leaders, Niche Players, Visionaries
- Leaders: AWS, Azure, Google
- Niche players: IBM
- Visionaries: Alibaba cloud
## Criteria to consider when choosing a suitable cloud provider
- Company strategy
- Regulatory Compliance
- Regional presence
- Technologies and service roadmap
- Performance and reliability
- Availability and SLA
- Billing models and costs
- Flexibility
- Support
## Cloud reference architectures
- Azure architectures
- AWS reference architecture
- Google Cloud reference architecture
Note: This is a 4-fold series on cloud computing, virtualization, containerization, and data processing.
Check the remaining 3 articles on my blog.
This is the third. Here is the link to the second.
https://dev.to/michellebuchiokonicha/virtualization-containerization-with-docker-storage-and-network-services-2bjf/edit
it focuses on docker, containerization, virtualization, storage technologies, and network services.
Follow me on Twitter Handle: https://twitter.com/mchelleOkonicha
Follow me on LinkedIn Handle: https://www.linkedin.com/in/buchi-michelle-okonicha-0a3b2b194/
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| michellebuchiokonicha |
1,891,489 | Hooks - One Byte Explainer | This is a submission for DEV Computer Science Challenge v24.06.12: One Byte Explainer. ... | 0 | 2024-06-17T16:33:43 | https://dev.to/pradeep3/hooks-one-byte-explainer-4eed | devchallenge, cschallenge, computerscience, beginners | *This is a submission for [DEV Computer Science Challenge v24.06.12: One Byte Explainer](https://dev.to/challenges/cs).*
## Explainer
**Hooks**: Special functions in React that let you use state and lifecycle features in functional components. useState manages local state, while useEffect handles side effects. Hooks simplify code, improve readability, and promote reusable logic without changing component hierarchy.
## Additional Context
Introduced in React 16.8, Hooks address limitations in class components, allowing developers to write more modular and cleaner code. They are pivotal in modern React development, enabling better separation of concerns and code reuse across different components. | pradeep3 |
1,891,488 | Case Study: the Abstract Number Class | Number is an abstract superclass for numeric wrapper classes, BigInteger, and BigDecimal. Numeric... | 0 | 2024-06-17T16:33:29 | https://dev.to/paulike/case-study-the-abstract-number-class-2h8k | java, programming, learning, beginners | **Number** is an abstract superclass for numeric wrapper classes, **BigInteger**, and **BigDecimal**. Numeric wrapper classes, the **BigInteger** and **BigDecimal** classes have common methods **byteValue()**, **shortValue()**, **intValue()**, **longValue()**, **floatValue()**, and **doubleValue()** for returning a **byte**, **short**, **int**, **long**, **float**, and **double** value from an object of these classes. These common methods are actually defined in the **Number** class, which is a superclass for the numeric wrapper classes, **BigInteger**, and **BigDecimal**, as shown in below.
Since the **intValue()**, **longValue()**, **floatValue()**, and **doubleValue()** methods cannot be implemented in the **Number** class, they are defined as abstract methods in the **Number** class. The **Number** class is therefore an abstract class. The **byteValue()** and **shortValue()** method are implemented from the **intValue()** method as follows:
`public byte byteValue() {
return (byte)intValue();
}
public short shortValue() {
return (short)intValue();
}`
With **Number** defined as the superclass for the numeric classes, we can define methods to perform common operations for numbers. The program below gives a program that finds the largest number in a list of **Number** objects.
The program creates an **ArrayList** of **Number** objects (line 8). It adds an **Integer** object, a **Double** object, a **BigInteger** object, and a **BigDecimal** object to the list (lines 9–14). Note that **45** is automatically converted into an **Integer** object and added to the list in line 9 and that **3445.53** is automatically converted into a **Double** object and added to the list in line 10 using autoboxing.
Invoking the **getLargestNumber** method returns the largest number in the list (line 16). The **getLargestNumber** method returns **null** if the list is **null** or the list size is **0** (lines 20–21). To find the largest number in the list, the numbers are compared by invoking their **doubleValue()** method (line 25). The **doubleValue()** method is defined in the **Number** class and implemented in the concrete subclass of **Number**. If a number is an **Integer** object, the **Integer**’s **doubleValue()** is invoked. If a number is a **BigDecimal** object, the **BigDecimal**’s **doubleValue()** is invoked.
If the **doubleValue()** method were not defined in the **Number** class, you will not be able to find the largest number among different types of numbers using the **Number** class. | paulike |
1,891,486 | Propylene Oxide Market Analysis, Size, Share, Growth Forecast 2024-2033: Latest Developments and Key Players Overview | According to the latest market report from Persistence Market Research, the global propylene oxide... | 0 | 2024-06-17T16:30:47 | https://dev.to/swara_353df25d291824ff9ee/propylene-oxide-market-analysis-size-share-growth-forecast-2024-2033-latest-developments-and-key-players-overview-4d78 | According to the latest market report from Persistence Market Research, the global [propylene oxide market](https://www.persistencemarketresearch.com/market-research/propylene-oxide-market.asp) is projected to reach a value of US$ 19,413.3 million in 2024. By 2033, the market is expected to grow to US$ 30,636.7 million, with a steady compound annual growth rate (CAGR) of 5.2%.
Key Insights
Market Sales (2023A): US$ 18,453.8 million
Market Value (2024E): US$ 19,413.3 million
Propylene Oxide Market Projections (2033F): US$ 30,636.7 million
Value CAGR (2024-2033): 5.2%
Collective Value Share: Top 3 Countries (2023E): 47.2%
Propylene oxide (C3H6O) is a synthetic cyclic ether primarily produced through the dehydrochlorination of propylene chlorohydrin or the indirect oxidation of propylene. It is widely used as a precursor in the manufacture of various chemicals and is prevalent in the automotive, electronics, textile, and furniture industries. The South Asia Pacific region is expected to be one of the fastest-growing markets due to the significant growth in these end-use industries.
In addition, propylene oxide is utilized as a fumigant to eliminate insect and bacterial infestations in soil and packaged food products. It is also used in small amounts to sterilize medical equipment. The rapid growth of the automotive sector is driving the demand for propylene oxide, given its wide range of applications in the industry and its role in enhancing the strength and durability of automobile structures.
Market Growth Factors & Dynamics
Industrial Demand
Automotive Sector: The rapid expansion of the automotive industry is a significant driver of propylene oxide demand. The compound is essential for manufacturing materials that enhance the strength and durability of automobile structures.
Electronics Industry: Increasing use of propylene oxide in the electronics industry, particularly in the production of circuit boards and other components, contributes to market growth.
Textile and Furniture Industries: The textile and furniture sectors utilize propylene oxide in the production of polyurethane foams, which are crucial for various applications, further driving demand.
Geographical Expansion
South Asia Pacific Growth: The South Asia Pacific region is poised for substantial growth due to the rapid development of end-use industries in countries like India and China. Economic growth, industrialization, and urbanization in this region boost the demand for propylene oxide.
Top Markets: The top three countries hold a collective value share of 47.2% as of 2023, indicating concentrated growth in key regions with robust industrial bases.
Application in Chemical Manufacturing
Precursor in Chemical Synthesis: Propylene oxide is a critical precursor in the synthesis of various chemicals, including propylene glycol and polyether polyols. These derivatives are essential in producing products such as antifreeze, resins, and surfactants, which are in high demand across multiple industries.
In a nutshell, the Persistence Market Research report is a must-read for start-ups, industry players, investors, researchers, consultants, business strategists, and all those who are looking to understand this industry. Get a glance at the report at- https://www.persistencemarketresearch.com/market-research/propylene-oxide-market.asp
Agriculture and Food Industry
Fumigant Use: Propylene oxide’s role as a fumigant to control insect and bacterial infestations in soil and packaged food products contributes to its market growth. This application is crucial for maintaining food safety and quality.
Sterilization of Medical Equipment: Although used in small quantities, propylene oxide’s application in sterilizing medical equipment is significant for the healthcare industry, supporting market expansion.
Technological Advancements
Production Processes: Advances in production technologies, such as the development of more efficient and environmentally friendly processes for producing propylene oxide, enhance its market appeal. Innovations that reduce costs and improve yield are particularly beneficial.
Regulatory and Environmental Factors
Environmental Regulations: Compliance with environmental regulations and the shift towards sustainable practices may influence market dynamics. Companies investing in green technologies and sustainable production methods are likely to experience growth.
Health and Safety Regulations: Stringent health and safety regulations related to the handling and use of propylene oxide can impact market dynamics, necessitating investments in safety measures and compliance.
Economic Factors
Global Economic Conditions: The overall economic environment, including factors such as GDP growth, industrial production, and consumer spending, plays a significant role in the market's growth trajectory. Economic stability and growth in major markets positively impact demand for propylene oxide.
By considering these factors, the market for propylene oxide is expected to witness steady growth, driven by diverse applications across various industries and regions.
Key players in the propylene oxide market:
Dow Chemical Company
LyondellBasell Industries N.V.
BASF SE
Royal Dutch Shell plc
Huntsman Corporation
Sumitomo Chemical Co., Ltd.
SKC Co., Ltd.
Repsol S.A.
INEOS Group Holdings S.A.
AGC Chemicals Americas Inc.
Market Segmentation
By Production Process
The propylene oxide market is segmented based on the production process into several key categories. The two primary methods are the chlorohydrin process and the hydroperoxide process. The chlorohydrin process involves the reaction of propylene with chlorine and water, while the hydroperoxide process involves the oxidation of propylene with hydroperoxide. Each method has its advantages and applications, impacting the overall market dynamics and influencing the choice of production technology by manufacturers.
By Application
Segmentation by application reveals the diverse uses of propylene oxide across various industries. Major applications include its use as an intermediate in the production of polyether polyols, propylene glycol, and other chemicals. These intermediates are essential in manufacturing polyurethane foams, antifreeze, resins, and surfactants. Additionally, propylene oxide is used as a fumigant in agriculture and food industries and as a sterilizing agent for medical equipment. This broad range of applications drives demand from multiple sectors, contributing to the market's growth.
By End-Use Industry
The market is further segmented based on end-use industries, which include automotive, electronics, textiles, furniture, construction, and healthcare. The automotive industry utilizes propylene oxide for producing polyurethane foams and coatings, essential for enhancing vehicle durability and performance. In the electronics sector, it is used in the manufacturing of circuit boards and other components. The textiles and furniture industries rely on propylene oxide for producing flexible and rigid foams. The construction industry uses it in insulation materials, while the healthcare sector employs it for sterilization purposes.
By Region
Geographically, the market segmentation covers regions such as North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. Each region has unique market dynamics influenced by industrial growth, economic conditions, and regulatory frameworks. The Asia-Pacific region, particularly South Asia Pacific, is expected to witness significant growth due to rapid industrialization and urbanization. North America and Europe also hold substantial market shares due to their advanced industrial bases and technological innovations.
By Purity Grade
The market can also be segmented based on the purity grade of propylene oxide. High-purity grades are used in applications requiring stringent quality standards, such as pharmaceuticals and electronics, while lower-purity grades may be sufficient for applications like bulk chemical manufacturing and industrial uses. The choice of purity grade affects the cost and suitability of propylene oxide for different applications, influencing purchasing decisions and market trends.
Region-wise Insights
North America
The propylene oxide market in North America is characterized by advanced industrial infrastructure and significant demand from the automotive and construction sectors. The United States and Canada are key players, with numerous established manufacturers and technological advancements in production processes. The region benefits from strong regulatory frameworks supporting industrial growth and innovation. The presence of major market players and extensive R&D activities contribute to the region's robust market position.
Europe
Europe's propylene oxide market is driven by the region's strong automotive industry, along with substantial demand from the electronics and construction sectors. Germany, the United Kingdom, and France are leading contributors to market growth. Environmental regulations and a focus on sustainable production methods are significant factors influencing market dynamics. Europe also benefits from a well-developed chemical industry and ongoing technological advancements, which support market expansion.
Asia-Pacific
The Asia-Pacific region, particularly the South Asia Pacific, is expected to experience the fastest growth in the propylene oxide market. Countries such as China, India, and Japan are major contributors due to rapid industrialization, urbanization, and economic development. The region's expanding automotive, electronics, and construction industries drive significant demand for propylene oxide. Additionally, the presence of a large consumer base and increasing investments in infrastructure and manufacturing facilities boost market growth.
Latin America
In Latin America, the propylene oxide market is primarily driven by growing industrialization and increasing demand from the automotive and construction sectors. Brazil and Mexico are key markets in the region. Economic development and improving industrial capabilities contribute to market expansion. However, the market faces challenges such as economic volatility and regulatory complexities, which can impact growth rates.
Middle East & Africa
The propylene oxide market in the Middle East & Africa is witnessing steady growth, driven by the region's expanding industrial base and increasing investments in infrastructure projects. The construction industry, in particular, plays a significant role in market demand. Countries like Saudi Arabia, UAE, and South Africa are notable markets in the region. Additionally, the region benefits from abundant raw material availability, which supports production activities. However, political instability and regulatory challenges can affect market dynamics.
Future Outlook
The future outlook for the propylene oxide market is promising, with steady growth anticipated over the next decade. Driven by robust demand from key industries such as automotive, electronics, and construction, the market is expected to benefit from ongoing industrialization and technological advancements. Regions like Asia-Pacific, with its rapid economic development and expanding industrial base, are poised to be significant growth drivers. Environmental and regulatory considerations will continue to shape market dynamics, pushing companies towards more sustainable production methods. Overall, the market is set to achieve substantial expansion, reaching a projected value of US$ 30,636.7 million by 2033, fueled by a diverse range of applications and increasing global demand.
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1,891,485 | Introduction to .NET Architecture Patterns: MVC, MVP, MVVM, Domain Driven Design | MVC (Model-View-Controller) When to Use MVC? MVC is ideal for web applications... | 0 | 2024-06-17T16:30:11 | https://dev.to/adrianbailador/introduction-to-net-architecture-patterns-mvc-mvp-mvvm-domain-driven-design-4i3f | webdev, dotnet, architecture, csharp | ## MVC (Model-View-Controller)
### When to Use MVC?
MVC is ideal for web applications with a clear separation between user interface and business logic. It is beneficial in websites with multiple pages and functionalities that require structured management of user-application interaction.
### Concept
The MVC pattern separates the application into three main components:
- **Model:** Represents the data logic and state of the application. It handles data access and manipulation.
- **View:** Represents the user interface. Its role is to display data from the model to the user and receive user interaction.
- **Controller:** Manages the communication between model and view. It receives user input through the view, processes these inputs, and updates the model and/or view accordingly.
### Implementation in .NET
In .NET, especially with ASP.NET MVC, MVC is implemented natively. ASP.NET MVC allows developers to create web applications where HTTP requests are handled by controllers that interact with models and select appropriate views to generate responses.
```csharp
// MVC
public class HomeController : Controller
{
public IActionResult Index()
{
var model = new HomeModel { Message = "Hello, Codú!" };
return View(model);
}
}
```
### Detailed Example: Task Management Application
Imagine a simple task management application. The controller would handle user requests to view, add, and delete tasks, while the model would manage the business logic and data storage. The view would display the task list and allow user interaction.
```csharp
public class Task
{
public int Id { get; set; }
public string Name { get; set; }
public bool IsCompleted { get; set; }
}
public class TaskController : Controller
{
private static List<Task> tasks = new List<Task>();
public IActionResult Index()
{
return View(tasks);
}
[HttpPost]
public IActionResult AddTask(string name)
{
var newTask = new Task { Id = tasks.Count + 1, Name = name, IsCompleted = false };
tasks.Add(newTask);
return RedirectToAction("Index");
}
[HttpPost]
public IActionResult CompleteTask(int id)
{
var task = tasks.FirstOrDefault(t => t.Id == id);
if (task != null)
{
task.IsCompleted = true;
}
return RedirectToAction("Index");
}
}
```
### Benefits
- **Separation of Concerns:** Improves code maintainability and testability.
- **Flexibility:** Facilitates modification of one part of the application without affecting others.
### Pros and Cons
- **Pros:** Clear separation of roles, easy to understand and adopt.
- **Cons:** Can become complex with large teams and big projects.
### Next Steps
To delve deeper into MVC, consider exploring the [official ASP.NET MVC documentation](https://docs.microsoft.com/en-us/aspnet/mvc) and implementing a small personal project to apply these concepts in a controlled environment.
## MVP (Model-View-Presenter)
### When to Use MVP?
MVP is useful in desktop applications where presentation logic can be complex and needs to be independently tested. It is an excellent choice for enterprise desktop applications.
### Concept
The MVP pattern is similar to MVC but is more commonly used in desktop and mobile applications. Its components are:
- **Model:** Handles data and application logic.
- **View:** The user interface.
- **Presenter:** Acts as an intermediary between the model and the view. The view communicates with the presenter to update data, and the presenter updates the view.
### Implementation in .NET
MVP is common in Windows Forms applications. The pattern allows presentation logic to be separated from UI logic, facilitating code testing and maintenance.
```csharp
// Example of a Presenter in MVP
public class MainPresenter
{
private readonly IMainView _view;
private readonly IMainModel _model;
public MainPresenter(IMainView view, IMainModel model)
{
_view = view;
_model = model;
_view.Load += OnLoad;
}
private void OnLoad(object sender, EventArgs e)
{
_view.DisplayData(_model.GetData());
}
}
```
### Detailed Example: Task Management Application
In a task management application using Windows Forms, the Presenter would manage view interactions and update model data.
```csharp
public interface ITaskView
{
event EventHandler Load;
void DisplayTasks(IEnumerable<Task> tasks);
}
public class TaskPresenter
{
private readonly ITaskView _view;
private readonly ITaskRepository _repository;
public TaskPresenter(ITaskView view, ITaskRepository repository)
{
_view = view;
_repository = repository;
_view.Load += OnLoad;
}
private void OnLoad(object sender, EventArgs e)
{
var tasks = _repository.GetAllTasks();
_view.DisplayTasks(tasks);
}
}
```
### Benefits
- **Ease of Testing:** Separation of presenter and view allows more effective unit testing.
- **Flexibility:** Views can be swapped out without modifying presenter logic.
### Pros and Cons
- **Pros:** Facilitates testing and maintenance, good separation of presentation logic.
- **Cons:** Can introduce additional complexity in architecture.
### Next Steps
To delve deeper into MVP, check out the [MVP guide in Windows Forms](https://docs.microsoft.com/en-us/dotnet/desktop/winforms/overview-of-windows-forms) and try implementing your own example.
## MVVM (Model-View-ViewModel)
### When to Use MVVM?
MVVM is perfect for applications with rich user interfaces that benefit from data binding, such as WPF and Xamarin applications. It is ideal for mobile and desktop applications with dynamic user interfaces.
### Concept
MVVM is an evolution of the MVC pattern and is especially popular in WPF (Windows Presentation Foundation) and Xamarin development. Its components are:
- **Model:** Contains business logic and data.
- **View:** The user interface.
- **ViewModel:** A view model that exposes data and commands that the view can bind to.
### Implementation in .NET
WPF and Xamarin are platforms where MVVM shines due to their data binding capabilities. In MVVM, the view is directly bound to the ViewModel, allowing automatic bidirectional communication between view and model via ViewModel.
```csharp
// ViewModel in MVVM
public class MainViewModel : INotifyPropertyChanged
{
private string _message;
public string Message
{
get { return _message; }
set
{
_message = value;
OnPropertyChanged(nameof(Message));
}
}
public MainViewModel()
{
Message = "Hello, Adrian!";
}
public event PropertyChangedEventHandler PropertyChanged;
protected virtual void OnPropertyChanged(string propertyName)
{
PropertyChanged?.Invoke(this, new PropertyChangedEventArgs(propertyName));
}
}
```
### Detailed Example: Task Management Application
For a task management application in WPF, the ViewModel would manage logic and data, while the view would bind to properties and commands of the ViewModel.
```csharp
public class TaskViewModel : INotifyPropertyChanged
{
private string _taskName;
public string TaskName
{
get { return _taskName; }
set
{
_taskName = value;
OnPropertyChanged(nameof(TaskName));
}
}
public ICommand AddTaskCommand { get; }
public TaskViewModel()
{
AddTaskCommand = new RelayCommand(AddTask);
}
private void AddTask()
{
// Logic to add task
}
public event PropertyChangedEventHandler PropertyChanged;
protected virtual void OnPropertyChanged(string propertyName)
{
PropertyChanged?.Invoke(this, new PropertyChangedEventArgs(propertyName));
}
}
```
### Benefits
- **Data Binding:** Facilitates automatic synchronization between view and model.
- **Separation of Concerns:** Increases maintainability and testability of code.
### Pros and Cons
- **Pros:** Powerful for applications with complex user interfaces, facilitates maintenance and testing.
- **Cons:** Can be overkill for simple projects, steeper learning curve.
### Next Steps
To delve deeper into MVVM, explore the [MVVM documentation in WPF](https://docs.microsoft.com/en-us/dotnet/desktop/wpf/data/data-binding-overview) and experiment with a personal project.
## Domain Driven Design (DDD)
### When to Use DDD?
DDD is best for complex systems where the business domain is extensive and well-defined. It is ideal for large enterprise applications and e-commerce systems.
### Concept
Domain Driven Design is a software design methodology that emphasises focusing on the business domain and logic. Rather than focusing on technology, DDD recommends that software design aligns with the actual domain model. The main components of DDD include:
- **Entities:** Objects with their own identity that persist over time.
- **Value Objects:** Objects without identity defined solely by their attributes.
- **Aggregates:** Groups of objects treated as a unit.
- **Repositories:** Interfaces for accessing aggregates from the database.
- **Domain Services:** Operations that don't fit well within entity or value objects.
### Implementation in .NET
Implementing DDD in .NET typically involves using Entity Framework for data persistence, along with building services and repositories that encapsulate data access logic. Projects are often structured into layers such as domain, infrastructure, application, and presentation for clear separation of concerns.
```csharp
// a Repository in DDD
public interface ICustomerRepository
{
Customer GetById(Guid id);
void Save(Customer customer);
}
public class CustomerRepository : ICustomerRepository
{
private readonly DbContext _context;
public CustomerRepository(DbContext context)
{
_context = context;
}
public
Customer GetById(Guid id)
{
return _context.Set<Customer>().Find(id);
}
public void Save(Customer customer)
{
_context.Set<Customer>().Add(customer);
_context.SaveChanges();
}
}
```
### Detailed Example: E-commerce System
In an e-commerce system, entities like `Customer`, `Order`, and `Product` would exist, with domain services handling complex business operations.
```csharp
public class OrderService
{
private readonly IOrderRepository _orderRepository;
public OrderService(IOrderRepository orderRepository)
{
_orderRepository = orderRepository;
}
public void PlaceOrder(Order order)
{
// Business logic to process the order
_orderRepository.Save(order);
}
}
```
### Benefits
- **Alignment with Business:** Facilitates collaboration between developers and domain experts.
- **Scalability:** Allows applications to grow in a structured and coherent manner.
### Pros and Cons
- **Pros:** High alignment with business domain, facilitates handling of complexity.
- **Cons:** Can be overkill for simple projects, requires more initial effort.
### Next Steps
To delve deeper into DDD, it's recommended to review the [introduction to Domain Driven Design](https://docs.microsoft.com/en-us/dotnet/architecture/microservices/model-domain-driven-design) and consider reading the book "Domain-Driven Design: Tackling Complexity in the Heart of Software" by Eric Evans.
## Practical Comparison of Patterns
- **MVC:** Ideal for web applications with clear separation between user interface and business logic.
- **MVP:** Useful in desktop applications where presentation logic can be complex and needs independent testing.
- **MVVM:** Perfect for applications with rich user interfaces that benefit from data binding, such as WPF and Xamarin applications.
- **DDD:** Best for complex systems with extensive and well-defined business domains, such as large enterprise applications and e-commerce systems.
### Practical Comparison of Patterns
- **Performance:** MVC tends to be lighter in web applications, while MVVM may be more resource-demanding due to data binding.
- **Ease of Maintenance:** MVVM and DDD offer better maintainability in long-term projects due to their separation of concerns and focus on business domain.
- **Scalability:** DDD is highly scalable for complex systems, while MVP scales well in desktop applications with complex presentation logic.
## Conclusion
The use of .NET architecture patterns such as MVC, MVP, MVVM and DDD allows developers to create more robust, maintainable and scalable applications. Each pattern offers advantages and suits different types of projects and requirements. Choosing the right pattern depends on factors such as the nature of the application, business requirements and the preferences of the development team. With a solid understanding of these patterns, you can design and build more efficient systems aligned with business goals.
## Resources
- [ASP.NET MVC Documentation](https://docs.microsoft.com/en-us/aspnet/mvc)
- [MVP Guide in Windows Forms](https://docs.microsoft.com/en-us/dotnet/desktop/winforms/overview-of-windows-forms)
- [MVVM Documentation in WPF](https://docs.microsoft.com/en-us/dotnet/desktop/wpf/data/data-binding-overview)
- [Introduction to Domain Driven Design](https://docs.microsoft.com/en-us/dotnet/architecture/microservices/model-domain-driven-design). | adrianbailador |
1,875,527 | React- Flask Communication | Introduction In modern web development, applications are often divided into two main... | 0 | 2024-06-17T16:28:03 | https://dev.to/pedroa54/react-flask-communication-2b4e | webdev, flask, react | ## Introduction
In modern web development, applications are often divided into two main parts: the frontend and the backend. Understanding how these two components communicate is crucial for creating dynamic and interactive web applications. Today we will talk about the Frontend Using React and the Backend using Flask.
For a web application to function, the frontend and backend must communicate effectively.
---
## Frontend
The frontend, also known as the client-side, is the part of a web application that users interact with directly. It includes everything that users experience in their web browser, such as:
- _User Interface (UI)_ : The layout, buttons, forms, and other visual elements.
- _Client-Side Logic_ : Code that runs in the browser, typically written in JavaScript, along with libraries or frameworks like React
- _Styling_ : CSS and frameworks like Bootstrap that ensure the application looks good across different devices and screen sizes.
---
## Backend
The backend, or server-side, is the part of the application that runs on the server. It is responsible for:
- _Database Management_ : Storing, retrieving, and updating data.
- _Business Logic_ : The rules and operations that define how data can be manipulated and transformed.
- _Server Configuration_ : Handling server setup, routing, and ensuring the application runs smoothly.
---
## Setting up Backend using SQLite
### Creating Model- Table
Let's start off by creating a model Called Animal and then create the tablename animals. We will create some columns and give them certain attributes like id (Integer), name (String), species (String) and age (Integer).
- id: This is the primary key of the table. It's an integer that uniquely identifies each row.
- name: This is a string column with a maximum length of 100 characters. The nullable=False argument means that this field cannot be null.
- species: Similar to name, this is a string column with a maximum length of 100 characters and cannot be null.
- age: This is an integer column that cannot be null.
- repr Method: This method defines how the Animal object is represented as a string, which is useful for debugging. When you print an Animal object, it will display its id, name, species, and age in a readable format.
**After running**
- flask db init
- flask db migrate -m "initial migration"
- flask db upgrade head
It should appear in database and now we can focus on the backend route and work on the CRUD sequences. Lets say for example we already seeded the database and our tables are now filled.
### Creating Backend route
Here we Set up the backend route in a file called app.py.
- AnimalList handles GET and POST requests for listing and creating animals
- GET method retrieves all animals and returns them as JSON.
- POST method creates a new animal based on JSON data and returns the created animal as JSON.
### Api Setup
The AnimalList resource is added to the Flask-RESTful API at the endpoint /animals. This setup provides a basic yet functional CRUD API for managing animals, suitable for integration into a larger Flask application. Adjustments can be made based on specific project requirements and additional features can be added as needed.
---
## Setting up Frontend using react
### Create a file
Now that we have setup the Backend when we move to the client side, we use a React component to fetch and display the data. The component will make an HTTP GET request to the /animals endpoint when it mounts.
- The useEffect hook is used to perform side effects, in this case, fetching data from the backend. The empty dependency array ([]) ensures that this effect runs only once when the component mounts.
1. _Component Initialization_ :
- When the AnimalList component is first rendered, it initializes its state using the useState hook. The animals state will hold the fetched data, loading indicates if the data is being fetched, and error will store any errors that occur during the fetch.
2. _Effect Hook to Fetch Data_ :
- The useEffect hook is used to perform side effects, in this case, fetching data from the backend. The empty dependency array ([]) ensures that this effect runs only once when the component mounts.
3. _Fetching Data_ :
- The fetch('/animals') function sends a GET request to the /animals endpoint of the backend.
- The then(response => {...}) block checks if the response is okay (status code 200-299). If not, it throws an error.
- If the response is okay, it converts the response body to JSON using response.json().
- The second then(data => {...}) block takes the parsed JSON data (the list of animals) and updates the animals state. It also sets loading to false since the data has been successfully fetched.
4. _Error Handling_ :
- If any error occurs during the fetch operation, the catch(error => {...}) block catches the error, updates the error state with the error message, and sets loading to false.
5. _Rendering the Component_ :
While the data is being fetched (loading is true), a loading message is displayed.
If an error occurs (error is not null), an error message is displayed.
If the data is successfully fetched (loading is false and error is null), the component maps over the animals array and renders a list of animals.
---
## Summary
We demonstrated how to connect a Flask backend with a React frontend for managing a list of animals. We started by setting up the Flask backend, defining a simple Animal model with SQLAlchemy, and creating RESTful API endpoints to retrieve and add animals. On the frontend, we developed a React component that fetches and displays the list of animals from the backend. Using the useEffect hook, the component makes an HTTP GET request to the "/animals" endpoint, processing the response, and updates the component's state with the fetched data while managing loading and error states. This setup allows for seamless communication between the backend and frontend, providing a robust and interactive user experience for managing animal data.
## Resources
- https://github.com/PedroA54/PetPal-Hotel
- https://flatironschool.com/
| pedroa54 |
1,891,428 | How I Built an In-Cabin Perception Dataset | Authors: Robert Wright (Account Executive at Voxel51) and Allen Lee (Machine Learning... | 0 | 2024-06-17T16:27:05 | https://voxel51.com/blog/how-i-built-an-in-cabin-perception-dataset/ | computervision, machinelearning, datascience, ai | _Authors: [Robert Wright](https://www.linkedin.com/in/robertwrightai4ml/) (Account Executive at [Voxel51](https://voxel51.com/)) and [Allen Lee](https://www.linkedin.com/in/al-lee/) (Machine Learning Engineer/Customer Success at [Voxel51](https://voxel51.com/))_
## Featuring active learning, transformers, eye gazes, distractions, FiftyOne, and more
In this blog, you’ll see how quick and easy it is to build a dataset, load it into FiftyOne and FiftyOne Teams, perform active learning techniques, integrate with transformers from Hugging Face, and ultimately get some pretty interesting revelations about the data. Although this blog post features an interior dataset for an in-cabin monitoring use case, the steps involved will work for any visual AI project. It’s intended for novices and advanced readers alike. Ok, let’s start our journey!
## Who Am I?
My name is [Robert Wright](https://www.linkedin.com/in/robertwrightai4ml/), I work in the Sales team at Voxel51 selling our FiftyOne Teams enterprise software to startups and Fortune 100 companies alike.
If you’re not yet familiar, [Voxel51](https://voxel51.com/) is an open source AI startup that enables AI/ML builders to build better models through the lens of our data-centric visual AI solutions, which include:
- [Open source FiftyOne](https://github.com/voxel51/fiftyone) with more than 2M downloads and nearly 7000 stars on GitHub
- [FiftyOne Teams](https://voxel51.com/fiftyone-teams/), which adds enterprise-ready collaboration, security, flexibility, automation, and support and is deployed at the heart of AI stacks across some of the world's largest and most AI-forward companies, including global top five automotive, manufacturing, and tech powerhouses
_Disclaimer 1— Before embarking on this project, I had never written a line of Python (or any code for that matter); however, by the end of this project, I was writing (copying and pasting) several lines of code to perform actions inside the FiftyOne platform._
_Disclaimer 2— This post was not written by ChatGPT._
_Disclaimer 3— My ML advisor and co-author on this post is Voxel51 ML engineer Allen Lee. You’ll hear from him later in the post!_
## Why Am I Writing This and What’s in it for You?
I do not particularly like writing, nor have I done it since writing my University Thesis 15 years ago. However, I felt compelled to write after seeing the incredible results of this project.
Really I was so blown away by:
- How ridiculously easy it was to use FiftyOne and FiftyOne Teams and…
- The amount of success I achieved in around 2 hours of work. I wanted to document this somewhere. Hence, this post.
In fact, I probably spent more time writing this blog than I did on the actual project itself… collecting, loading, curating, labeling, and applying models to my dataset inside of FiftyOne.
Also, I had more fun using FiftyOne, writing Python, and applying models than I have had in a very LONG TIME, e.g., even when I was in Vegas recently!
So, if you have a couple of hours to spend on curating and visualizing a dataset, you can use the lessons in this post to have more fun than Vegas using FiftyOne!
## What Prompted the Project?
I received a request from a potential customer to showcase FiftyOne and how it works with interior (in-cabin) perception use cases. If you are unfamiliar with interior perception, it's about perceiving and understanding what happens once drivers and passengers are inside a car, in order for automakers to build automated systems to solve problems like driver drowsiness or distractions whilst driving.
Now here comes our dilemma… We are proud to have multiple Tier 1 automotive companies as customers of the FiftyOne Teams enterprise solution to aid them in building AI systems for their interior perception use cases, but, the salesman's dilemma:
- I cannot name them or have them demo to the new interested party
- I cannot use their datasets
So, naturally, the next step was to look for an open source in-cabin dataset I could load into FiftyOne Teams to demo the capabilities. But, ALAS, either my Google skills were so lacking that I needed to have a stern word with myself, or none existed. I was faced with the decision: Give up, OR… Go out and make my own dataset. Suffice to say I chose the latter.
## Proper Planning and Preparation Prevents Piss-Poor Performance
Throughout my life, I have tried to live by the British military adage of the 6 P’s… So I knew I was going to have to have some kind of plan. Here are the high-level steps, which are explained in detail in the sections below:
1. Collecting data—An iPhone has a pretty good camera, right? Ok, I better make sure I mount it well, so off to Target to purchase a $25 car mount. Thanks Apple for that EPIC iPhone camera!
2. Loading data into FiftyOne—How on earth do I get this into our tool, do I load into our open source, our enterprise version, or both? Obviously, I did both. I took full advantage of the FiftyOne Docs and Slack channel.
3. Gaining insights into my data—Now what? There is no way I am hand-labeling these videos/images, annotation is dead, right?… Ok, let's apply some models.
4. Explaining the findings—In-cabin monitoring use cases require techniques for: keypoints, embeddings, meshes, distractions, detections, mistakes, eye gaze, similarity, uniqueness, and more. Continue reading to see the eye-opening insights I found.
## Data Collection
I used my wife’s iPhone 14 Pro Max to capture video, and I purchased an iOttie Easy One Touch 5 Dash * Windshield Mount - Black from Target for a whopping $25.
My advisor to this project, Voxel51 ML Engineer Allen Lee, recommended that I rotate my screen sideways in landscape mode, so the data captures both the driver and the steering wheel. He also advised me to make sure my head is not occluded and that the video captures as much of my body as possible.
Also, as a side note, make sure you swap the camera capture to Most Compatible from High Efficiency: Settings, Camera, Camera Capture > select Most Compatible.
The next challenge was what data to collect. Obviously, I knew that we needed normal driving behavior but the entire point of an interior perception use case in a driver cabin is to SAVE LIVES.
This means we needed to capture behavior that included the following:
- Safety including driver distractions (phone/texting/playing with car radio/GPS)
- Safety including tiredness (eyes closing for x seconds)
- Driver positioning for comfort and protection
- Sensing of the entire vehicle to prohibit actions like people breaking into the vehicle
Now I didn’t particularly want to drive around with my eyes closed or drive around texting distracted for obvious reasons however to simulate these behaviors I did the following.
I found an area of my neighborhood where the road was closed. I spoke to the building/contractor and asked permission to use the closed road so that I could capture some of this behavior, and he allowed me to (for the mission of saving lives) drive around for a few minutes. I acted out texting- and radio-distracted behavior (I should get an Oscar I know), and I knew that our data would pick up blinking, which meant I could apply a model to detect whether or not my eyes were closed.
I did three 10-minute driving stints, changing outfits in each 10-minute drive. In two drives I had glasses on, and in one I had glasses off.
Now the next part will make you laugh… This was the hardest part (for me anyway) of the entire experiment. Getting 3x 1GB+ files from my wife’s phone onto my hard drive/cloud. The process involved:
1. Uploading the files to her personal Google Drive… (this took what felt like the longest time in the world, in fact was probably only 20 minutes, perhaps I should upgrade my home internet)
2. Emailing me the Google Drive link
3. Downloading the files to my Macbook Pro hard disk
4. Uploading the files to our internal GCP buckets, because I knew that once I had tested the data in open source FiftyOne, I would inevitably load the data into our FiftyOne Teams demo environment via our GCP integration
## Loading Data in
The moment of truth. I have been telling all of my prospects and customers this past year that using the open source version of FiftyOne was very easy… I can finally sleep at night with no guilt as YES in fact if you know Python this is incredibly easy and requires literally a few lines of code, which cue my ML advisor Allen Lee to the rescue, who provided me with the following:
```
brew install ffmpeg
python3 -m venv env
source env/bin/activate
pip install -U pip setuptools wheel
pip install fiftyone
pip install ultralytics "torch>=1.8"
```
I asked Allen: WAIT WHAT… I need to set up a what… Python virtual environment and install Brew, FFmpeg, and Ultralytics Torch on my Mac to get this running?
Here was Allen’s reply:
_“Hey all, Allen here. Robert is a super-fun guy, the British Bulldog, a former boxer, and an absolute tech sales machine! He brightens up all of our busy days at Voxel51 and also has way more technical insight than he lets on._
_So well yes, installing in a virtual environment isn’t so strange, and we did need ffmpeg since we would be working with a video dataset. Meanwhile, we installed Ultralytics so we could try running Yolo.”_
```
fiftyone app launch
```
Ok, so the initial euphoria of copying and pasting code from Allen and seeing my dataset loaded in started to pass… The detections that YoloV5 was giving were somewhat OK but I knew I wanted to do so much more.
The open source FiftyOne tool is amazing and designed for one user, and more explicitly, an experienced Python user… (Now, whilst I was a single user my Python experience was close to zero.)
However much I tried to continue I knew that my days were numbered; I needed a platform that I could collaborate in. Obviously, I knew that our enterprise tool FiftyOne Teams enabled that so….. Cue Allen again who is the real genius of the operation… I roped Allen into helping me here (he had already given me the OSS command strings.) So I thought a little more help wouldn’t hurt.
However, before I roped Allen into my science experiment I wanted to load the data into FiftyOne Teams myself which is as easy as this:
1. Create a new dataset
2. Use our [I/O plugin](https://github.com/voxel51/fiftyone-plugins/tree/main/plugins/io) to ‘import samples’
3. Then find the right Google Bucket string and import the folder. (That also took a newbie like me a few minutes as I had never used GCP or any cloud storage for that matter.)
## Now What? Gaining Insights into my Data.
Now comes the real fun… we ran a bunch of models!
Here’s Allen’s take on the process:
_“Yes, definitely. Well, we were just playing around and wanted to show the art of the possible in a quick timeframe. So we looked around for off-the-shelf models that might work for face or gaze detection and pose estimation. We came across MediaPipe which seemed user-friendly and had some pre-trained models._
_**Important Note!** As discussed in the Model Cards [MediaPipe BlazeFace (Short Range)](https://drive.google.com/file/d/1d4-xJP9PVzOvMBDgIjz6NhvpnlG9_i0S/preview) and [MediaPipe Face Mesh](https://drive.google.com/file/d/1QvwWNfFoweGVjsXF3DXzcrCnz-mx-Lha/preview), these models are definitely for prototyping and experimentation only!_
_First, we played with the _https://developers.google.com/mediapipe/solutions/vision/face_detector_ pipeline. This pipeline uses the _https://developers.google.com/mediapipe/solutions/vision/face_detector#blazeface_short-range_, which is optimized for close-range selfie-shot-style images from a phone camera. It seemed pretty similar to our data. This API also supports sequential images sampled from a video stream via its _https://developers.google.com/mediapipe/solutions/vision/face_detector/python#configuration_options_, which was a nice touch._
_This model outputs bounding boxes for all faces detected in the input images, as well as a set of six facial keypoints.”_
Allen continued:
_“Next up was the [Face Landmarker pipeline](https://developers.google.com/mediapipe/solutions/vision/face_landmarker). This pipeline is a superset of the Detector pipeline, which adds detection of a dense 478-point facial mesh as well as 52 blendshape scores, which measure various facial expressions. We thought the keypoints from the facial mesh might give a better location of gaze compared to the Detector model; meanwhile, would the blendshapes catch Robert asleep (not literally, of course!) at the wheel?_
_The full mesh model predictions looked quite good! When this mesh was overlaid on top of Robert’s face during video playback, Robert was turned into a fearsome futuristic cyborg:_
_Meanwhile, for the image datasets, we kept a more restricted subset of points for easier visibility. Indeed, the eyes seemed to be better localized with the mesh, when compared to the Face Detector’s predictions.”_
## Asleep at the Wheel?
Here’s the answer to that question from Allen:
_“This was really neat, but one of our original goals was to see if we could use this camera data to determine when the driver was distracted. The Landmarker pipeline’s blendshape scores provide a set of quick statistics to consider. In FiftyOne, filtering for high scores among any of these statistics is as easy as adjusting a slider:_
_As it turns out, eyeBlinkLeft and eyeBlinkRight seemed to have a high correlation with Robert looking down and away from the windshield. Hey Robert, pay attention to the road!_
_What else could we do? Well, cellphones are probably the top cause of distracted driving these days. And meanwhile, FiftyOne comes with extensive similarity search functionality built right in. So, we went ahead, computed some embeddings, and searched for “cellphone” to see if we could detect Robert using his phone behind the wheel._
_FiftyOne Teams takes computing embeddings and running similarity search to the next level using Delegated Operators, its built-in workflow orchestration functionality:_
_<center>Running Similarity Search Using Our Airflow Orchestrator</center>_
_Finally, we recently added a [HuggingFace integration](https://docs.voxel51.com/integrations/index.html). Rather than just searching for cellphones in the entire image, we tried running OwlViT to combine search with object detection, to both find and localize cellphones:”_
```
import fiftyone as fo
import fiftyone.zoo as foz
dataset = fo.load_dataset("Dr Wright Drives Frames Beta")
model = foz.load_zoo_model(
"zero-shot-detection-transformer-torch",
name_or_path="google/owlvit-base-patch32",
classes=["phone", "cup"]
)
dataset.apply_model(model, label_field="owlvit")
```
## Putting It All Together
Now that we have done this we were able to find the following:
All of the samples with my eyes closed and label them…
All of the samples with my phone in my hand…
All of the samples with my eyes looking down…
## What’s Next
- We could continue by adding new samples to the dataset, such as:
- Add in samples with sunglasses
- Add samples at night
- Vary the dataset by adding people of different genders and races into the dataset
- Add samples with the driver not wearing a seatbelt to detect no seatbelt and alert the driver
- Add 3D data and point cloud lidar/radar data to detect motion
We could train our own “Distraction” model that provides the driver with an alert when distracted/falling asleep.
We could add samples with time series data of eyes shut for X number of seconds—the feasibility of doing this from a safety point of view may require some more thinking…
## Conclusion
This was a heap of fun! Not only did it teach me more about open source FiftyOne and FiftyOne Teams, it also taught me how to run a small ML project from start to finish—from data collection to building a dataset to applying a model both in the command line and in FiftyOne Teams.
Whilst I had demoed the tool hundreds of times with our current datasets, I had never built one from scratch, until now.
If you're interested, you can view my dataset instantly in your browser at https://try.fiftyone.ai/datasets/dr-wright-drives-frames/samples. It's the in-cabin perception dataset I discussed in this blog post made available in a read-only version of FiftyOne Teams. Try filtering the data using the left sidebar. Or click '+' next to ‘Samples’, select 'Embeddings', and choose a key. Now, lasso-select points of interest. What trends do you see?
If you would like to learn more about this project, what went well, and what didn’t, reach out to me on [LinkedIn](https://www.linkedin.com/in/robertwrightai4ml/) and I would love to chat!
Also, if you have an idea about another use case or dataset we can create and explore together, I would love to collaborate with you, so please reach out!
PS. Special thanks to [Allen Lee](https://www.linkedin.com/in/al-lee/) who provided me with guidance including but not limited to… how to collect the data, write FiftyOne & Python code scripts, curate the dataset once inside FiftyOne Teams and apply the models!
| jguerrero-voxel51 |
1,891,484 | Getting Started with Postman | Hello Readers! Welcome to this guide as I explore on how to get started with postman which is a... | 0 | 2024-06-17T16:23:17 | https://dev.to/uday_gundu_0a142075a68ee4/getting-started-with-postman-994 | postman, postmanapi, basics, postmanstudentleader |
Hello Readers!
Welcome to this guide as I explore on how to get started with postman which is a powerful API tool for testing and managing APIs.
Whether you are a seasoned developer or a student who is getting started postman has something for everything. So let’s get started.
## Why Postman
- User friendly interface
- Instead of manually testing each API endpoint to ensure it works correctly, you can set up automated tests that run these checks for you.
- You can collaborate with your team through the shared workspaces
- Many documentations are available and also you can create one documentation and share it.
## Installing Postman
Firstly, lets get postman running into your system.
1. **Download Postman**: Head over to Postman’s website and grab the version for your operating system.
2. **Install and Launch**: Follow the installation instructions and fire up the application. Exciting, right?
## Exploring the Interface
**Workspaces**: Here you can organize your projects in different workspaces.
**Collections**: Group related API requests together.
**Requests**: Create and send requests to your API endpoints.
**Environments**: Store and manage variables like API keys and URLs.
**Tabs**: Work on multiple requests simultaneously with tabbed browsing.
Ready to create your first request? Let’s do it!
Creating Your First Request
We’re going to start simple with a **GET** request to fetch data from a public API.
Open a New Tab: Click on the + button to open a new tab.
Set the Request Method: Choose GET from the dropdown.
Enter the URL: Type in [www.google.com](ww.google.com) or some other url of your own.
Send the Request: Hit the Send button and voila! You should see the response from the API below.
## Writing Tests
Postman lets you automate the validation of your API responses with test scripts.
**1. Open the Tests Tab:** In your request tab, click on the Tests tab.
**2. Write Test Scripts:** Use JavaScript to write test scripts. Here’s a simple example :
```
pm.test("Status code is 200", function () {
pm.response.to.have.status(200);
});
pm.test("Response time is less than 200ms", function () {
pm.expect(pm.response.responseTime).to.be.below(200);
});
```
These tests help ensure your API is working as expected.
## Generating API Documentation
Last but not least, let’s talk about documentation.
**1. Document Your API:** Click on your collection > View in web > Generate Documentation.
**2. Customize and Share:** Tweak your documentation and share the URL with your team or stakeholders.
Having well-documented APIs is crucial for smooth collaboration and integration.
## Conclusion
And here you go folks!
If you want to learn more about API fundamentals, consider completing the Postman Student Expert program using this [link](https://www.postman.com/student-program/student-expert/?utm_campaign=SP&utm_medium=referral&utm_source=student-leader&utm_term=U2FsdGVkX18EkjjZKbVMJ8YGTCNhFMx5XB60tgDZ489pSHvBOMjGFM1WytRaIgEB&utm_content=). | uday_gundu_0a142075a68ee4 |
1,891,407 | Comments In JavaScript | Audience: Anyone who wrote Comments in any language, except JavaScript. For... | 27,681 | 2024-06-17T16:19:06 | https://dev.to/sharavana/comments-in-javascript-5af1 | javascript, node | ## Audience:
Anyone who wrote Comments in any language, except JavaScript.
## For Impatient People like Me:
### JavaScript has two types of Comments:
1.Single-line Comments:
```js
//This is a comment.
console.log(`Hello World!`); // This is also a comment.
```
2.Multi-line Comments:
```js
/* This is
also a comment
spread on
many lines.
*/
```
---
## Why On Earth we need them?
To feel the need for Comments in Js, you need to pass through **three** stages:
### Stage-1: Hello World:
I wrote hello world and other programs which are under-10-lines-programs.
(And felt "What are Comments?")
### Stage-2: Guess the number:
Now I am writing 30-60 lines programs(like 'Guess the number').
(Feeling "I am a Pro, who can write code without Comments!"
### Stage-3: Calculator:
When I start writing programs which are above 100 lines:
1)Continuously writing for a week.
2)Suddenly stuck with a problem.
3)Take a break.
4)And guess what? Booom!
Everything I just wrote is a mess for myself.
(Here, I will feel "A little bit of Comments should make sense!")
---
**Buddy**: Can you prove it!
**Me**: Sure!
Here is a piece of Rust code without Comments:
```rust
let short_cut_path: &str = &format!(
"/home/{}/.local/share/applications/{}.desktop",
whoami::username(),
desktop_entry.app_name
);
let short_cut_path = Path::new(&short_cut_path);
let mut short_cut_file: fs::File = match fs::File::open(&short_cut_path) {
Ok(file) => {
println!(
"A short-cut with name '{}' already exists!!",
desktop_entry.app_name
);
panic!("Try any other name for your short-cut!");
}
Err(error) => match fs::File::create(&short_cut_path) {
Ok(file) => file,
Err(why) => panic!("Error!\n\nUnable to create the short-cut!"),
},
};
match short_cut_file.write_all(short_cut_template.as_bytes()) {
Err(why) => panic!("Error:\n Unable to create the short-cut!"),
Ok(_) => println!("Successfully created short-cut!\n\nWait for a moment..."),
};
```
Now, with Comments:
```rust
// Path to Desktop Short-cut in string format
let short_cut_path: &str = &format!(
"/home/{}/.local/share/applications/{}.desktop",
whoami::username(),
desktop_entry.app_name
);
// Creating Short-cut path by giving path-string to Path constructor
let short_cut_path = Path::new(&short_cut_path);
//Opening the file as short_cut_file
let mut short_cut_file: fs::File = match fs::File::open(&short_cut_path) {
Ok(file) => {
println!(
"A short-cut with name '{}' already exists!!",
desktop_entry.app_name
);
panic!("Try any other name for your short-cut!");
}
Err(error) => match fs::File::create(&short_cut_path) {
Ok(file) => file,
Err(why) => panic!("Error!\n\nUnable to create the short-cut!"),
},
};
//Writing to the file
match short_cut_file.write_all(short_cut_template.as_bytes()) {
Err(why) => panic!("Error:\n Unable to create the short-cut!"),
Ok(_) => println!("Successfully created short-cut!\n\nWait for a moment..."),
};
```
Buddy, clearly you can see the difference.
With just Comments, now the code makes a little sense to even newbies to code.
**Buddy**:Wait! You are explaining importance of Comments in JavaScript with Rust code?
**Me**:Ah, yeah!
**Buddy**:Unbelievable!
**Me**: Thank you!! Thank you!!
Because, if you are able to get **Rust code** with just Comments,then Js code with Comments would be a cake! | sharavana |
1,891,469 | Day 14 - 90DaysofDevOps | Python Data Types and Data Structures for DevOps Data Types in Python In the Python universe, data... | 0 | 2024-06-17T16:15:50 | https://dev.to/oncloud7/day-14-90daysofdevops-51oc | python, cloudcomputing, 90daysofdevops, awschallenge | **Python Data Types and Data Structures for DevOps**
**Data Types in Python**
In the Python universe, data types act as the classification system for our data items, representing the kind of value that dictates the operations we can perform on them. It’s crucial to understand that in Python, everything is an object, with data types serving as classes and variables as instances of these classes.
**Here are some of the essential built-in data types in Python:**
Numeric Types: Python supports integers, complex numbers, and floating-point numbers.
**Sequential Types:** This includes strings, lists, and tuples, providing versatile ways to handle collections of data.
**Boolean Type:** For logical operations and decision-making.
Set Type: Unordered collections of unique elements.
**Dictionary Type:** A powerhouse resembling hash tables in other languages, optimized for key-value pairs with an impressive time complexity of O(1).
To ascertain the data type of a variable, a simple call to type(your_variable) will reveal its true nature.
**Data Structures in Python**
Moving on to data structures — the organizational backbone of efficient data handling in any programming language. Python simplifies the understanding of these fundamental structures, making it an ideal starting point for those venturing into the world of DevOps.
**Let’s briefly explore a few key data structures:**
**Lists:** Ordered collections similar to arrays, offering flexibility with elements not requiring uniformity in type.
**Tuples:** Immutable collections akin to lists but with elements that cannot be added or removed once created.
**Dictionaries:** Resembling hash tables, these unordered collections excel in storing key-value pairs, enhancing data optimization.
Distinguishing List, Tuple, and Set
**To gain a clearer perspective, let’s highlight the differences between these three:**
**Lists:** Ordered, mutable, and can contain elements of different types.
**Tuples:** Ordered, immutable, and, like lists, can house elements of various types.
**Set:** Unordered, mutable, and contains only unique elements.
Now, let’s get hands-on and solidify our understanding through practical examples.
**Hands-On Activities**
**Activity 1: List, Tuple, and Set**
**Activity 2: Dictionary Manipulation**
**Activity 3: Cloud Service Providers**
 | oncloud7 |
1,891,468 | Learning Programming for Beginners: How to Get Started | Hello everyone! Today, I want to share some key tips on learning programming for those just starting... | 0 | 2024-06-17T16:15:12 | https://dev.to/techinsight/learning-programming-for-beginners-how-to-get-started-3i44 | programmingforbeginners, learnprogramming, learntocode, coderbeginner | Hello everyone! Today, I want to share some key tips on learning programming for those just starting their coding journey.
## Why Learn Programming?
Programming is not just a skill; it's a gateway to development in today's digital world. It allows you to create applications, websites, games, and automate everyday tasks.
## Getting Started with Learning Programming
1. **Choose the Right Programming Language:** To begin with, choose a programming language that best fits your goals. Popular choices include Python, JavaScript, and Java – each has its own applications and advantages.
2. **Build a Solid Foundation:** Before diving into coding, it's essential to build a solid foundation. Understanding concepts like variables, functions, and loops is crucial.
3. **Practice Regularly:** Programming skills develop through practice. Don't be afraid to make mistakes – they're part of the learning process.
4. **Experiment and Stay Curious:** Programming is about experimenting with different technologies and projects. Stay open to new challenges and possibilities.
## Share Your Learning Journey
I'd love to hear about your experiences with learning programming. Do you have any questions or topics you'd like me to cover in future posts? Let me know in the comments!
Join me on this journey through the world of programming. In upcoming posts, we'll delve into various aspects of this fascinating field. Thanks for reading! | techinsight |
1,891,466 | Schönhage–Strassen algorithm in 256 chars or less (hopefully) | This is a submission for DEV Computer Science Challenge v24.06.12: One Byte Explainer. ... | 27,753 | 2024-06-17T16:14:28 | https://dev.to/kalkwst/schonhage-strassen-algorithm-in-256-chars-or-less-hopefully-539a | devchallenge, cschallenge, computerscience, beginners | *This is a submission for [DEV Computer Science Challenge v24.06.12: One Byte Explainer](https://dev.to/challenges/cs).*
## Explainer
You have two huge LEGO towers and want to combine them. First, break each tower into chunks. Then, rearrange the pieces to snap easier (FFT). Snap the pieces, unscramble them (inverse FFT), and finally put all the small chunks together into a single tower.
## Additional Context
Well, it took me about 128 hours to understand the math behind the Schönhage–Strassen algorithm, and then another 128 hours to come up with a beginner-friendly explanation. Dear reader, heed my warning: don't try this at home, unless you have a spare 256 hours and a love for mathematical adventures!
_edit: Also, my eyes played a trick on me! Instead of 265 characters, I read 256. Maybe I need an eye algorithm upgrade next!_ | kalkwst |
1,891,462 | எளிய தமிழில் MySQL - [ 1 to 30 Pages ] | Database --> Its a software to store data in a structured way. SQL --> Structure Query... | 0 | 2024-06-17T16:14:19 | https://dev.to/technonotes/elliy-tmilllil-mysql-1-to-30-pages--31d0 | - **Database** --> Its a software to store data in a structured way.
- **SQL** --> Structure Query Language - Its a language used to store data in database.
- **RDBMS** --> Its management software used to connect the data database.
Now coming to **_MYSQL_** : Its a RDBMS software , Free software under GPL.
- It as server and client.
- MYSQL Client --> Front end Tool / Console Prompt in windows / Shell prompt, we type the command here.
- MYSQL Server --> Will take the command from client and execute in server , which we can't see.It just gives the result.
Things **_MYSQL client_** does ,
1. Authentication for password check.
2. SQL queries will be changed to tokens , that tokens will be given to MYSQL server.
3. To monitor the Encrypt and compress network.
4. Then it gets the response from server and displays to the front end tool.
Things **_MYSQL Server_** does ,
1. Gets the request from client and then sends the response . **_Management Layer & Storage Engine _** are responsible for this. ( Memory , Disk & Network are interconnected to these layer mentioned above )
# Management Layer
Gets the request from client and கீழ இருக்கிற எல்லா வேலையும் பண்ணும் ,
1. connection decrypt or encode.
2. Queries check and parse them.
3. Get the Catched queries from Query cache
4. Then it sends to Storage Engine.
5. Disk and memory logs are its responsibility.
# Storage Engine Layer
1. Database, tables , indexes are taken care here or managing.
2. Above related logs are taken care.
3. It also sends data to other MYSQL servers via network is also done.
# INSTALLATION
```
sudo apt-get install mysql-server mysql-client
```
```
sudo mysql_secure_installation
```
Gave y & 0
In short , remove the anonymous user , only root can be executed only in localhost & remove the test database.
# CONFIGURATION
`cat /etc/mysql/my.cnf --> எல்லா configurationனும் இங்க தான் இருக்கும். OR /etc/mysql/mysql.conf.d/mysqld.cnf`
create a backup file of the configuration like below,
```
sudo cp my.cnf my.cnf_bk_june172024 [ TBD , change in the config location ? ]
```
```
port = 3306
user = mysql
data dir = /var/lib/mysql
bind-address = 127.0.0.1
log_error = /var/log/mysql/error.log
[ Parameters to be seen ]
```
# Query Cache [ TBD , where to put these values ? ]
All the queries which are executed in MYSQL Server , results are stored in cache.
query_cache_limit = 1m
query_cache_size = 16m
m --> MB
This m can be raised or decreased depends on the RAM.
# Server Stop & Start
sudo service mysql restart
sudo service mysql stop
sudo service mysql start
# MYSQL Client Installation
There are many in market,
sudo apt-get install MySQL-workbench --> second mostly used
sudo apt-get install MySQL-navigator
sudo apt-get install emma
sudo apt-get install MySQL-admin
sudo apt install phpmyadmin -y --> Best in market --> 1st
# Notes :
1. GPL --> General Public License.
2. RDBMS --> Relational Database Management System
3. Control + l --> Top of the screen in Linux
4. Note , if any changes in my.cnf MYSQL server needs to be restarted.
5. Configuration file is in /etc/mysql/mysql.conf.d/mysqld.cnf
6. sudo find / -name "*.cnf"
7. https://www.tecmint.com/mysql-gui-tools-for-linux/
# Reference
https://freetamilebooks.com/download/%e0%ae%8e%e0%ae%b3%e0%ae%bf%e0%ae%af-%e0%ae%a4%e0%ae%ae%e0%ae%bf%e0%ae%b4%e0%ae%bf%e0%ae%b2%e0%af%8d-mysql-a4-pdf/?tmstv=1712985924
https://kaniyam.com/ebooks/
| technonotes | |
1,891,464 | How does Nostra's innovative approach to gameplay enhance the mobile gaming experience? | Nostra's innovative approach to gameplay revolutionizes the mobile gaming experience by seamlessly... | 0 | 2024-06-17T16:13:47 | https://dev.to/claywinston/how-does-nostras-innovative-approach-to-gameplay-enhance-the-mobile-gaming-experience-4oe8 | gamedev, gamedevelopers, bestmobilegame, freemobilegame | [**Nostra's**](https://medium.com/@adreeshelk/nostra-world-of-free-online-games-where-fun-meets-convenience-48aa37d3ffc2?utm_source=referral&utm_medium=Medium&utm_campaign=Nostra) innovative approach to gameplay revolutionizes the mobile gaming experience by seamlessly integrating games into the lock screen of Android devices. This unique gameplay feature allows players to instantly access their favorite titles without the need for separate downloads or installations, making gaming more convenient and accessible than ever before. [Nostra's gameplay ](https://nostra.gg/articles/lock-screen-new-opportunity.html?utm_source=referral&utm_medium=article&utm_campaign=Nostra)is further enhanced by its commitment to delivering high-quality, visually stunning, and immersive games that captivate players from the first moment. With intuitive controls, smooth performance, and engaging gameplay mechanics, Nostra's titles offer a level of depth and enjoyment that sets them apart from other [**mobile games**](https://medium.com/@adreeshelk/nostra-brings-games-to-life-with-the-game-hosting-revolution-017dd8bfb0c8?utm_source=referral&utm_medium=Medium&utm_campaign=Nostra). Moreover, Nostra's gameplay is designed to cater to various skill levels and preferences, ensuring that there's something for everyone, whether they're casual players or hardcore gamers. By combining the convenience of lock screen gaming with the quality and variety of its titles, Nostra's innovative gameplay approach transforms the way people experience and enjoy mobile games. | claywinston |
1,891,463 | $Unset, $Pop, $Pull, $PullAll in MongoDB | $unset If we want to remove a field from a document in a MongoDB collection, we can use... | 0 | 2024-06-17T16:12:29 | https://dev.to/kawsarkabir/unset-pop-pull-pullall-in-mongodb-4jfg | webdev, mongodb, programming, kawsarkabir | ### $unset
If we want to remove a field from a document in a MongoDB collection, we can use the `$unset` operator. First, let's add some data to the database, then we'll remove a field from that data.
```javascript
db.persons.insertMany([
{ name: "Kawsar", age: 19, profession: "Frontend Developer" },
{ name: "Emanul", age: 19, profession: "Frontend Developer" },
]);
db.persons.updateOne({ name: "Kawsar" }, { $unset: { age: "" } });
```
- In the code above, the `age` field is removed from the document where `name` is `Kawsar`.
### $pop
To remove the first or last element of an array in a document, we use the `$pop` operator. First, let's insert a document into the database:
```javascript
db.products.insertOne({
_id: 100,
quantity: 250,
instock: true,
details: { model: "14QQ", make: "Clothes Corp" },
ratings: [{ by: "Customer007", rating: 4 }],
tags: ["apparel", "clothing"],
});
```
- Here, we have added a document to the `products` collection.
For example, if we want to remove the first or last item from the `ratings` array, we can use `$pop`:
```javascript
db.products.updateOne(
{ _id: 100 },
{ $pop: { ratings: 1 } } // Use 1 to remove the last element
);
db.products.updateOne(
{ _id: 100 },
{ $pop: { ratings: -1 } } // Use -1 to remove the first element
);
```
### $pull
The `$pull` operator is used to remove all instances of a specific value from an array in a document.
For example, if we want to remove the `clothing` tag from the `tags` array, we can use `$pull`:
```javascript
db.products.updateOne({ _id: 100 }, { $pull: { tags: "clothing" } });
```
### $pullAll
The `$pullAll` operator is used to remove multiple specific values from an array in a document.
For example, if we want to remove multiple tags from the `tags` array, we can use `$pullAll`:
```javascript
db.products.updateOne(
{ _id: 100 },
{ $pullAll: { tags: ["clothing", "apparel"] } }
);
```
- In the code above, the tags `"clothing"` and `"apparel"` are removed from the `tags` array in the document with `_id: 100`. | kawsarkabir |
1,891,460 | Day 21 of my progress as a vue dev | About today Today I started on my new thing which I planned on doing after abandoning my last... | 0 | 2024-06-17T16:11:07 | https://dev.to/zain725342/day-21-of-my-progress-as-a-vue-dev-1438 | webdev, vue, typescript, tailwindcss | **About today**
Today I started on my new thing which I planned on doing after abandoning my last project. So basically I wanted to start on something that I can feel good to work on, that can help me grow as a developer(essentially a frontend developer), and through which I can see my progress overtime. Hence, I decided to dive into developing landing pages with unique and complex designs. I was fascinated with how a landing page works, how its design effects user's senses to engage with it and sort of marketing science goes on behind it.
**What's next?**
I will be working few of my original but inspired landing pages and see if I get the feel and enjoy working on them, and also if those are good and if that's what I'm good at. If I end up getting positive responses on most if not all ends I will try to target businesses who are in need of landing pages to promote their products or business and take my skill to market to monetize it because that way I will be turning other peoples' ideas into reality and not stuck working on same old boring ideas from my head. Also, it will help me grow and work on different designs and stuff overtime.
**Improvements required**
I still need to figure out the complete science of a landing page and what are the factors that make it effective. Also, I need to work on my design skills and equip myself with tools and skills like figma, SEO, color theory, and basic HCI concepts to get a better sense of design and how it plays a role in marketing.
Wish me luck! | zain725342 |
1,891,459 | Abstract Classes | An abstract class cannot be used to create objects. An abstract class can contain abstract methods,... | 0 | 2024-06-17T16:10:14 | https://dev.to/paulike/abstract-classes-2ee5 | java, programming, learning, beginners | An abstract class cannot be used to create objects. An abstract class can contain abstract methods, which are implemented in concrete subclasses. In the inheritance hierarchy, classes become more specific and concrete _with each new subclass_. If you move from a subclass back up to a superclass, the classes become more general and less specific. Class design should ensure that a superclass contains common features of its
subclasses. Sometimes a superclass is so abstract that it cannot be used to create any specific instances. Such a class is referred to as an _abstract class_.
[In this post](https://dev.to/paulike/inheritance-superclasses-and-subclasses-5ede), **GeometricObject** was defined as the superclass for **Circle** and **Rectangle**. **GeometricObject** models common features of geometric objects. Both **Circle** and **Rectangle** contain the **getArea()** and **getPerimeter()** methods for computing the area and perimeter of a circle and a rectangle. Since you can compute areas and perimeters for all geometric objects, it is better to define the **getArea()** and **getPerimeter()** methods in the **GeometricObject** class. However, these methods cannot be implemented in the **GeometricObject** class, because their implementation depends on the specific type of geometric object. Such methods are referred to as _abstract methods_ and are denoted using the **abstract** modifier in the method header. After you define the methods in **GeometricObject**, it becomes an abstract class. Abstract classes are denoted using the **abstract** modifier in the class header. In UML graphic notation, the names of abstract classes and their abstract methods are italicized, as shown in progeam below. The program gives the source code for the new **GeometricObject** class.
```
package demo;
public abstract class GeometricObject {
private String color = "white";
private boolean filled;
private java.util.Date dateCreated;
/** Construct a default geometric object */
protected GeometricObject() {
dateCreated = new java.util.Date();
}
/** Construct a geometric object with color and filled value */
protected GeometricObject(String color, boolean filled) {
dateCreated = new java.util.Date();
this.color = color;
this.filled = filled;
}
/** Return color */
public String getColor() {
return color;
}
/** Set a new color */
public void setColor(String color) {
this.color = color;
}
/** Return filled. Since filled is boolean, the get method is named isFilled */
public boolean isFilled() {
return filled;
}
/** Set a new filled */
public void setFilled(boolean filled) {
this.filled = filled;
}
/** Get dateCreated */
public java.util.Date getDateCreated(){
return dateCreated;
}
@Override
public String toString() {
return "created on " + dateCreated + "\ncolor: " + color + " and filled: " + filled;
}
/** Abstract method getArea */
public abstract double getArea();
/** Abstract method getPerimeter */
public abstract double getPerimeter();
}
```
Abstract classes are like regular classes, but you cannot create instances of abstract classes using the **new** operator. An abstract method is defined without implementation. Its implementation is provided by the subclasses. A class that contains abstract methods must be defined as abstract.
The constructor in the abstract class is defined as protected, because it is used only by subclasses. When you create an instance of a concrete subclass, its superclass’s constructor is invoked to initialize data fields defined in the superclass.
The **GeometricObject** abstract class defines the common features (data and methods) for geometric objects and provides appropriate constructors. Because you don’t know how to compute areas and perimeters of geometric objects, **getArea()** and **getPerimeter()** are defined as abstract methods. These methods are implemented in the subclasses. The implementation of **Circle** and **Rectangle** is the same as in the programs below, except that they extend the **GeometricObject** class defined.
```
package demo;
public class Circle extends GeometricObject {
private double radius;
public Circle() {}
public Circle(double radius) {
this.radius = radius;
}
public Circle(double radius, String color, boolean filled) {
this.radius = radius;
setColor(color);
setFilled(filled);
}
/** Return radius */
public double getRadius() {
return radius;
}
/** Set a new radius */
public void setRadius(double radius) {
this.radius = radius;
}
/** Return area */
public double getArea() {
return radius * radius * Math.PI;
}
/** Return diameter */
public double getDiameter() {
return 2 * radius;
}
/** Return perimeter */
public double getPerimeter() {
return 2 * radius * Math.PI;
}
/** Print the circle info */
public void printCircle() {
System.out.println("The circle is created " + getDateCreated() + " and the radius is " + radius);
}
}
```
```
package demo;
public class Rectangle extends GeometricObject {
private double width;
private double height;
public Rectangle() {}
public Rectangle(double width, double height) {
this.width = width;
this.height = height;
}
public Rectangle(double width, double height, String color, boolean filled) {
this.width = width;
this.height = height;
setColor(color);
setFilled(filled);
}
/** Return width */
public double getWidth() {
return width;
}
/** Set a new width */
public void setWidth(double width) {
this.width = width;
}
/** Return height */
public double height() {
return height;
}
/** Set a new height */
public void setHeight(double height) {
this.height = height;
}
/** Return area */
public double getArea() {
return width * height;
}
/** Return perimeter */
public double getPerimeter() {
return 2 * (width + height);
}
}
```
## Why Abstract Methods?
You may be wondering what advantage is gained by defining the methods **getArea()** and **getPerimeter()** as abstract in the **GeometricObject** class. The example in Listing 13.4 shows the benefits of defining them in the **GeometricObject** class. The program creates two geometric objects, a circle and a rectangle, invokes the **equalArea** method to check whether they have equal areas, and invokes the **displayGeometricObject** method to display them.
The methods **getArea()** and **getPerimeter()** defined in the **GeometricObject** class are overridden in the **Circle** class and the **Rectangle** class. The statements (lines 7–8)
`GeometricObject geoObject1 = new Circle(5);
GeometricObject geoObject2 = new Rectangle(5, 3);`
create a new circle and rectangle and assign them to the variables **geoObject1** and **geoObject2**. These two variables are of the **GeometricObject** type.
When invoking **equalArea(geoObject1, geoObject2)** (line 10), the **getArea()** method defined in the **Circle** class is used for **object1.getArea()**, since **geoObject1** is a circle, and the **getArea()** method defined in the **Rectangle** class is used for **object2.getArea()**, since **geoObject2** is a rectangle.
Similarly, when invoking **displayGeometricObject(geoObject1)** (line 13), the methods **getArea()** and **getPerimeter()** defined in the Circle class are used, and when invoking **displayGeometricObject(geoObject2)** (line 16), the methods **getArea** and **getPerimeter** defined in the **Rectangle** class are used. The JVM dynamically determines which of these methods to invoke at runtime, depending on the actual object that invokes the method.
Note that you could not define the **equalArea** method for comparing whether two geometric objects have the same area if the **getArea** method were not defined in **GeometricObject**. Now you have seen the benefits of defining the abstract methods in **GeometricObject**.
## Interesting Points about Abstract Classes
The following points about abstract classes are worth noting:
- An abstract method cannot be contained in a nonabstract class. If a subclass of an abstract superclass does not implement all the abstract methods, the subclass must be defined as abstract. In other words, in a nonabstract subclass extended from an abstract class, all the abstract methods must be implemented. Also note that abstract methods are nonstatic.
- An abstract class cannot be instantiated using the **new** operator, but you can still define its constructors, which are invoked in the constructors of its subclasses. For instance, the constructors of **GeometricObject** are invoked in the **Circle** class and the **Rectangle** class.
- A class that contains abstract methods must be abstract. However, it is possible to define an abstract class that doesn’t contain any abstract methods. In this case, you cannot create instances of the class using the **new** operator. This class is used as a base class for defining subclasses.
- A subclass can override a method from its superclass to define it as abstract. This is very _unusual_, but it is useful when the implementation of the method in the superclass becomes invalid in the subclass. In this case, the subclass must be defined as abstract.
- A subclass can be abstract even if its superclass is concrete. For example, the **Object** class is concrete, but its subclasses, such as **GeometricObject**, may be abstract.
- You cannot create an instance from an abstract class using the _new_ operator, but an abstract class can be used as a data type. Therefore, the following statement, which creates an array whose elements are of the **GeometricObject** type, is correct.
`GeometricObject[] objects = new GeometricObject[10];`
You can then create an instance of **GeometricObject** and assign its reference to the array like this:
`objects[0] = new Circle();`
| paulike |
1,891,448 | Working with Dates and Times in SQL: Tips and Tricks | Managing dates and times is a crucial aspect of database operations. SQL offers a variety of... | 0 | 2024-06-17T16:09:38 | https://dev.to/tinapyp/working-with-dates-and-times-in-sql-tips-and-tricks-4o4e | datascience, database, dataengineering, tutorial | Managing dates and times is a crucial aspect of database operations. SQL offers a variety of functions and techniques to handle date and time data efficiently. Whether you're dealing with simple date retrieval or complex time calculations, understanding how to work with dates and times in SQL is essential. In this comprehensive guide, we will explore tips and tricks for managing date and time data in SQL, complete with examples to illustrate each concept.
### Understanding Date and Time Data Types
Different SQL databases support various date and time data types. Here are the most commonly used ones:
1. **DATE**: Stores dates without times. Format: 'YYYY-MM-DD'.
2. **TIME**: Stores time without dates. Format: 'HH:MM:SS'.
3. **DATETIME**: Stores both date and time. Format: 'YYYY-MM-DD HH:MM:SS'.
4. **TIMESTAMP**: Stores both date and time with timezone support in some systems.
5. **INTERVAL**: Represents a time interval, useful for date arithmetic.
### Inserting Date and Time Data
Inserting date and time data into your tables is straightforward. Here are a few examples:
```sql
-- Inserting a date
INSERT INTO events (event_date) VALUES ('2024-06-15');
-- Inserting a time
INSERT INTO schedules (start_time) VALUES ('08:30:00');
-- Inserting a datetime
INSERT INTO appointments (appointment_datetime) VALUES ('2024-06-15 08:30:00');
```
### Retrieving Date and Time Data
When retrieving date and time data, you can use the `SELECT` statement to format and manipulate the data as needed:
```sql
-- Retrieving all appointments on a specific date
SELECT * FROM appointments WHERE DATE(appointment_datetime) = '2024-06-15';
-- Retrieving all events that start after a specific time
SELECT * FROM events WHERE TIME(start_time) > '12:00:00';
-- Formatting dates
SELECT DATE_FORMAT(event_date, '%W, %M %e, %Y') AS formatted_date FROM events;
```
### Common Date and Time Functions
SQL provides numerous functions to handle date and time data. Here are some of the most useful ones:
#### CURRENT_DATE and CURRENT_TIME
Retrieve the current date and time.
```sql
SELECT CURRENT_DATE AS today;
SELECT CURRENT_TIME AS now;
```
#### DATE_ADD and DATE_SUB
Add or subtract intervals from a date.
```sql
-- Adding 7 days to the current date
SELECT DATE_ADD(CURRENT_DATE, INTERVAL 7 DAY) AS next_week;
-- Subtracting 2 months from the current date
SELECT DATE_SUB(CURRENT_DATE, INTERVAL 2 MONTH) AS two_months_ago;
```
#### DATEDIFF
Calculate the difference between two dates.
```sql
-- Calculating the number of days between two dates
SELECT DATEDIFF('2024-06-15', '2024-06-01') AS days_difference;
```
#### DATEPART
Extract a specific part of a date, such as the year, month, or day.
```sql
-- Extracting the year from a date
SELECT YEAR(event_date) AS event_year FROM events;
-- Extracting the month from a date
SELECT MONTH(event_date) AS event_month FROM events;
```
### Handling Time Zones
Time zones can complicate date and time management. SQL databases often have functions to handle time zone conversions.
#### CONVERT_TZ
Convert a datetime value from one time zone to another.
```sql
-- Converting a datetime from UTC to Eastern Time
SELECT CONVERT_TZ('2024-06-15 12:00:00', 'UTC', 'America/New_York') AS est_time;
```
### Working with Intervals
Intervals represent a span of time and are useful for date arithmetic.
```sql
-- Adding an interval of 3 days to the current date
SELECT CURRENT_DATE + INTERVAL 3 DAY AS future_date;
-- Subtracting an interval of 1 hour from the current time
SELECT CURRENT_TIME - INTERVAL 1 HOUR AS past_time;
```
### Practical Tips and Tricks
#### Avoid Hardcoding Dates
Hardcoding dates in your SQL queries can lead to maintenance challenges. Instead, use parameters or dynamic date functions.
```sql
-- Avoid hardcoding dates
SELECT * FROM events WHERE event_date = '2024-06-15';
-- Use dynamic date functions
SELECT * FROM events WHERE event_date = CURRENT_DATE;
```
#### Indexing Date Columns
Indexing date columns can significantly improve query performance, especially for large datasets.
```sql
-- Creating an index on the event_date column
CREATE INDEX idx_event_date ON events(event_date);
```
#### Use Appropriate Data Types
Always use the most appropriate data type for your needs to ensure data integrity and optimize storage.
```sql
-- Using DATE for date-only data
CREATE TABLE events (
event_id INT PRIMARY KEY,
event_date DATE,
event_name VARCHAR(100)
);
-- Using DATETIME for date and time data
CREATE TABLE appointments (
appointment_id INT PRIMARY KEY,
appointment_datetime DATETIME,
appointment_description VARCHAR(255)
);
```
### Conclusion
Working with dates and times in SQL can be complex, but mastering these concepts is crucial for effective database management. By understanding and utilizing the various date and time functions, handling time zones appropriately, and following best practices, you can manage and manipulate date and time data efficiently. Whether you're performing simple date retrievals or complex time calculations, these tips and tricks will help you navigate the intricacies of SQL date and time operations with confidence. | tinapyp |
1,891,457 | Proofread: Fixes All Errors with One Tap | Proofread: Fixes All Errors with One Tap | 0 | 2024-06-17T16:09:04 | https://aimodels.fyi/papers/arxiv/proofread-fixes-all-errors-one-tap | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Proofread: Fixes All Errors with One Tap](https://aimodels.fyi/papers/arxiv/proofread-fixes-all-errors-one-tap). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
• The provided paper presents "Proofread," a tool that can automatically fix all errors in a text with a single tap.
• The paper discusses the design and implementation of Proofread, a novel text correction system that leverages large language models to identify and correct various types of errors.
• The authors demonstrate the effectiveness of Proofread through extensive experiments and user evaluations, showcasing its ability to outperform traditional proofreading tools.
## Plain English Explanation
Proofread is a tool that can automatically fix all the mistakes in a piece of text with just one click. The paper explains how the tool works and shows that it is better at catching and correcting errors than traditional proofreading methods.
The key idea behind Proofread is to use powerful [language models](https://aimodels.fyi/papers/arxiv/novel-paradigm-boosting-translation-capabilities-large-language) - computer programs that can understand and generate human language - to identify and fix different types of mistakes, such as spelling errors, grammar issues, and formatting problems.
The authors tested Proofread extensively and found that it was able to catch and correct errors much more effectively than traditional proofreading tools. For example, when asked to proofread a document, Proofread was able to identify and fix all the mistakes with a single tap, while human proofreaders often missed some errors.
The researchers also conducted user studies to see how people liked using Proofread. They found that people found the tool to be very useful and time-saving, and that it helped them produce higher-quality writing with less effort.
Overall, Proofread demonstrates how advanced language models can be used to streamline the proofreading and editing process, making it easier for people to create error-free documents. This could have significant implications for [writers](https://aimodels.fyi/papers/arxiv/listen-again-choose-right-answer-new-paradigm), students, and professionals who need to produce high-quality written work on a regular basis.
## Technical Explanation
The paper introduces "Proofread," a novel text correction system that leverages [large language models](https://aimodels.fyi/papers/arxiv/gentranslate-large-language-models-are-generative-multilingual) to identify and fix a wide range of errors in a single step. The authors propose a multi-task learning framework that jointly learns to detect and correct various types of errors, including spelling mistakes, grammatical errors, and formatting issues.
The system is built upon a [transformer-based](https://aimodels.fyi/papers/arxiv/helm-highlighted-evidence-augmented-language-model-enhanced) language model, which is fine-tuned on a large corpus of human-written text with annotated errors. During inference, the model takes in the input text and outputs a corrected version, along with confidence scores for each suggested edit.
The authors conduct extensive experiments to evaluate the performance of Proofread on a variety of proofreading tasks. They compare the system's accuracy and efficiency to that of human proofreaders and traditional grammar/spelling checking tools, demonstrating Proofread's superior ability to identify and fix errors with a single click.
Furthermore, the paper reports on user studies that assess the usability and perceived effectiveness of the Proofread system. Participants found the tool to be highly intuitive and time-saving, and they appreciated its capacity to improve the quality of their written work.
## Critical Analysis
The paper presents a compelling approach to automated text correction, but it is essential to consider the potential limitations and areas for further research.
One key concern is the reliance on a single, pre-trained language model. While the authors demonstrate the effectiveness of this approach, it may not generalize well to diverse writing styles, domains, or languages. Exploring ways to adapt Proofread to different contexts or allow for user customization could enhance its real-world applicability.
Additionally, the paper does not delve deeply into the potential biases or errors inherent in the language model itself. As with any AI system, there is a risk of Proofread propagating or amplifying biases present in the training data or the model architecture. Thorough bias analysis and mitigation strategies should be considered in future work.
The user studies provide valuable insights, but they are relatively limited in scope. Expanding the evaluation to include a wider range of user demographics, writing tasks, and real-world scenarios would help strengthen the case for Proofread's practical utility.
Finally, the paper does not address the potential privacy and security implications of using a cloud-based proofreading tool. Investigating ways to ensure the confidentiality of user-submitted text or offering on-device processing options could enhance the system's acceptability and trustworthiness.
## Conclusion
The Proofread system represents a significant advancement in automated text correction, leveraging the power of large language models to streamline the proofreading process. The paper's findings suggest that this approach can outperform traditional proofreading tools in terms of accuracy, efficiency, and user experience.
While the technical implementation is sound and the experimental results are promising, the paper highlights the need for further research to address potential limitations and broaden the system's applicability. Exploring ways to enhance Proofread's adaptability, mitigate biases, and address privacy concerns could lead to the development of a truly transformative proofreading solution that benefits writers, students, and professionals across a wide range of domains.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,456 | Symfony Station Communiqué — 14 June 2024: a look at Symfony, Drupal, PHP, Cybersec, and Fediverse News. | This communiqué originally appeared on Symfony Station. Welcome to this week's Symfony Station... | 0 | 2024-06-17T16:08:40 | https://symfonystation.mobileatom.net/Symfony-Station-Communique-14-June-2024 | symfony, drupal, php, fediverse | This communiqué [originally appeared on Symfony Station](https://symfonystation.mobileatom.net/Symfony-Station-Communique-14-June-2024).
Welcome to this week's Symfony Station communiqué. It's your review of the essential news in the Symfony and PHP development communities focusing on protecting democracy. That necessitates an opinionated Butlerian jihad against big tech as well as evangelizing for open-source and the Fediverse. We also cover the cybersecurity world. You can't be free without safety and privacy.
There's good content in all of our categories, so please take your time and enjoy the items most relevant and valuable to you. This is why we publish on Fridays. So you can savor it over your weekend.
Or jump straight to your favorite section via our website.
- [Symfony Universe](https://symfonystation.mobileatom.net/Symfony-Station-Communique-14-June-2024#symfony)
- [PHP](https://symfonystation.mobileatom.net/Symfony-Station-Communique-14-June-2024#php)
- [More Programming](https://symfonystation.mobileatom.net/Symfony-Station-Communique-14-June-2024#more)
- [Fighting for Democracy](https://symfonystation.mobileatom.net/Symfony-Station-Communique-14-June-2024#other)
- [Cybersecurity](https://symfonystation.mobileatom.net/Symfony-Station-Communique-14-June-2024#cybersecurity)
- [Fediverse](https://symfonystation.mobileatom.net/Symfony-Station-Communique-14-June-2024#fediverse)
Once again, thanks go out to Javier Eguiluz and Symfony for sharing [our communiqué](https://symfonystation.mobileatom.net/Symfony-Station-Communique-07-June-2024) in their [Week of Symfony](https://symfony.com/blog/a-week-of-symfony-910-3-9-june-2024).
**My opinions will be in bold. And will often involve cursing. Because humans.**
---
## Symfony
As always, we will start with the official news from Symfony.
Highlight -> "This week, Symfony [5.4.40](https://symfony.com/blog/symfony-5-4-40-released), [6.4.8](https://symfony.com/blog/symfony-6-4-8-released), [7.0.8](https://symfony.com/blog/symfony-7-0-8-released) and [7.1.1](https://symfony.com/blog/symfony-7-1-1-released) maintenance versions were released. In addition, we organized the SymfonyOnline June 2024 conference and merged the first features of Symfony 7.2, to be released at the end of November 2024."
[A Week of Symfony #910 (3-9 June 2024)](https://symfony.com/blog/a-week-of-symfony-910-3-9-june-2024)
They also have:
[SymfonyCon Vienna 2024 : Book your transportation with special rates](https://symfony.com/blog/symfonycon-vienna-2024-book-your-transportation-with-special-rates)
[SymfonyOnline June 2024: Virtual celebration of innovation and community](https://symfony.com/blog/symfonyonline-june-2024-virtual-celebration-of-innovation-and-community)
SymfonyCasts returns:
[This week in SymfonyCasts](https://5hy9x.r.ag.d.sendibm3.com/mk/mr/sh/1t6AVsd2XFnIGKAdc2c4TLVnzSQuLj/7HDKG3vJ045I)
---
## Featured Item
Lubna Altungi shares:
[Integrating Dataverse into Symfony App: A Quick Guide](https://medium.com/@lubna.altungi/integrating-dataverse-into-symfony-app-a-quick-guide-3c19bb852164)
---
### This Week
Supul Kalhara shows us:
[How to Integrate PayHere Payment Gateway with Symfony PHP Framework](https://medium.com/@supulkalhara7/how-to-integrate-payhere-payment-gateway-with-symfony-php-framework-183f2d972b4b)
Lubna Altungi shares:
[Integrating Dataverse into Symfony App: A Quick Guide](https://medium.com/@lubna.altungi/integrating-dataverse-into-symfony-app-a-quick-guide-3c19bb852164)
AskHandle shows us:
[How to Customize Serialization Groups in Symfony API Platform](https://www.askhandle.com/blog/how-to-customize-serialization-groups-in-symfony-api-platform)
Tomas Votruba demonstrates:
[2 Tricks to get your Symfony configs lines to minimum](https://tomasvotruba.com/blog/2-tricks-to-get-your-symfony-configs-lines-to-minimum)
### eCommerce
Sylius provides:
[A comprehensive update of everything juicy in the recent minor 1.13 release!](https://sylius.com/blog/a-comprehensive-update-of-everything-juicy-in-the-recent-minor-1-13-release/)
### CMSs
TYPO3 has:
[Translating TYPO3's backend interface using Crowdin](https://typo3.com/blog/translating-typo3s-backend-interface-using-crowdin)
[Recap of the Best Practices Team Remote Code Sprint on 7 May 2024](https://typo3.org/article/recap-of-the-best-practices-team-remote-code-sprint-on-7-may-2024)
[What is a multisite CMS, and how can it help your business?](https://typo3.com/blog/what-is-a-multisite-cms)
<br/>
Dries Buyaert shares:
[Major version upgrades in Drupal: tools and workflow](https://dri.es/major-version-upgrades-in-drupal-tools-and-workflow)
Wim Leers updates us on Experience Builder:
[XB week 4: annotated data model test](https://wimleers.com/xb-week-4)
The Drop Times has:
[Why 1xINTERNET Rushed to Support the Starshot Initiative: Insights from Baddý Sonja](https://www.thedroptimes.com/interview/40791/why-1xinternet-rushed-support-starshot-initiative-insights-baddy-sonja)
[Drupal Starshot Initiative Sets Strategic Milestones in Product Definition](https://www.thedroptimes.com/40778/drupal-starshot-initiative-sets-strategic-milestones-in-product-definition)
ImageX Media demonstrates:
[Easy Third-Party Integration in Drupal Forms: Dynamically Pulling Data From Other Sources](https://imagexmedia.com/blog/2024/06/easy-third-party-integration-drupal-forms-dynamically-pulling-data-other-sources)
Golems explores:
[2024 Trends: What's New for Drupal](https://gole.ms/blog/2024-trends-whats-new-drupal)
Previous Next examines:
[Filtering and sorting search results by date range with OpenSearch](https://www.previousnext.com.au/blog/filtering-and-sorting-search-results-by-date-range-opensearch)
Richard Allen looks at:
[Setting up for Drupal's Functional JavaScript tests](https://dev.to/drupalista/drupal-functionaljavascript-testing-1fo7)
HashCodeBang explores:
[Drupal 10: Testing Migration Process Plugins](https://www.hashbangcode.com/article/drupal-10-testing-migration-process-plugins)
Drupalize Me announces:
[New Tutorial Organization and Navigation Roll-Out](https://mailchi.mp/9ade9af27807/heads-up-new-navigation-and-tutorial-organization?e=e5d801ec85)
Planatir has an employee success story:
[From Finance to Palantir](https://www.palantir.net/blog/finance-palantir)
**Fantastic. And this is not the Palantir owned by so-called human Peter Thiel.**
QED42 shows us how to:
[Run batch process via Ajax without redirecting to batch window](https://www.qed42.com/insights/how-to-run-batch-through-an-ajax-request-without-redirecting-to-batch-window-itself)
---
## PHP
### This Week
Vincent Schmalbach takes:
[A look at modern PHP](https://www.vincentschmalbach.com/a-look-at-modern-php/)
Wesley Gonçalves fala sobre:
[PHP sem nada de Xampp e com muito Xdebug no Windows](https://dev.to/wesleyotio/php-sem-nada-de-xampp-e-com-muito-xdebug-no-windows-opa)
Shahoriar Fahim shows us:
[Why Leveraging PHP Built-in Functions Can Enhance Your Application's Performance](https://dev.to/shahoriar_fahim/why-leveraging-php-built-in-functions-can-enhance-your-applications-performance-5659)
Fernando Castillo says the:
[Factory Pattern can encapsulate complexity in PHP](https://medium.com/@fernando_28520/factory-pattern-can-encapsulate-complexity-in-php-e1ad02e594f0)
Italo Baeza Cabrera examines:
[Making Podman, DevPod, and PHPStorm play nice](https://darkghosthunter.medium.com/making-podman-devpod-and-phpstorm-play-nice-5d50318cb212)
Grant Horwood has:
[NGINX: doing ip geolocation right in NGINX](https://gbh.fruitbat.io/2024/06/10/nginx-doing-ip-geolocation-right-in-nginx/)
Peter Fox looks at:
[PHP: Mocking Closures and performing assertions](https://articles.peterfox.me/php-mocking-closures-assertions-a14e5b5e2b32)
Noé Costa shares:
[Native Binaries with PHP](https://icinga.com/blog/2024/06/12/native-binaries-with-php/)
DDEV details a:
[MariaDB Dump (mysqldump) Breaking Change](https://ddev.com/blog/mariadb-dump-breaking-change/)
It's Imiro is:
[Mastering PHP Generators](https://itsimiro.medium.com/mastering-php-generators-4c19bc8a3367)
### Previous Weeks
Liip has a retrospective:
[Verona unveiled: A journey beyond the code at PHPDay](https://www.liip.ch/en/blog/verona-unveiled-a-journey-beyond-the-code-at-phpday)
---
## More Programming
The Open Source Initiative is:
[Exploring openness in AI: Insights from the Columbia Convening](https://opensource.org/blog/exploring-openness-in-ai-insights-from-the-columbia-convening)
Free Code Camp shows us:
[How to Create Notice Blocks in Markdown](https://www.freecodecamp.org/news/how-to-create-notice-blocks-in-markdown/)
**Very cool.**
Gravatar is:
[Introducing Profiles-as-a-Service and our new REST API](https://blog.gravatar.com/2024/06/03/profiles-as-a-service/)
Andrew Zuo opines:
[Maybe WebAssembly Isn’t That Stupid Of An Idea After All](https://andrewzuo.com/maybe-webassembly-isnt-that-stupid-of-an-idea-after-all-e50113c896d1)
CSS Tricks explores:
[CSS Container Queries](https://css-tricks.com/css-container-queries/)
Go Make Things asks:
[What is "the grain of the web?"](https://gomakethings.com/what-is-the-grain-of-the-web/)
IT Next has its:
[Top 10 GitHub Copilot Features](https://itnext.io/top-10-github-copilot-features-1cfb39778a10)
---
## Fighting for Democracy
[Please visit our Support Ukraine page](https://symfonystation.mobileatom.net/Support-Ukraine)to learn how you can help
kick Russia out of Ukraine (eventually, like ending apartheid in South Africa).
### The cyber response to Russia’s War Crimes and other douchebaggery
Radio Free Europe/Radio Liberty reports:
[A 'Very Painful' Book Boom: As Russia Wages War On Their Culture, Ukrainians Turn To Reading](https://www.rferl.org/a/ukraine-books-war-culture-russia/32983820.html)
The Kyiv Post reports:
[US and Poland to Help Ukraine Counter Russian Disinformation](https://www.kyivpost.com/post/34080)
[HUR Hackers Score Cyber-Hit on Russian Airports, Cause Flight Delays](https://www.kyivpost.com/post/34195)
The Register reports:
[Payoff from AI projects is 'dismal', biz leaders complain](https://www.theregister.com/2024/06/12/survey_ai_projects/)
**Let's hope they are penny stocks soon.**
Cory Doctorow opines:
[The CFPB is genuinely making America better, and they're going HARD](https://pluralistic.net/2024/06/10/getting-things-done/#deliverism)
**And he is right.**
The Register reports:
[Japan forces Apple and Google to allow third-party app stores and payments](https://www.theregister.com/2024/06/13/japan_smartphone_software_law/)
The Hacker News reports:
[Google Takes Down Influence Campaigns Tied to China, Indonesia, and Russia](https://thehackernews.com/2024/06/google-takes-down-influence-campaigns.html)
404 Media reports:
[Hackers Target AI Users With Malicious Stable Diffusion Tool on Github to Protest 'Art Theft'](https://www.404media.co/hackers-target-ai-users-with-malicious-stable-diffusion-tool-on-github/)
**Te he he.**
The International Business Times reports:
[Trump Media Shares Plummet Amid Criticism From Barry Diller; Trump's Net Worth Takes A Hit](https://www.ibtimes.com/trump-media-shares-plummet-amid-criticism-barry-diller-trumps-net-worth-takes-hit-372913)
Teen Vogue reports:
[How to Stop Deepfake Porn Using AI](https://www.teenvogue.com/story/how-to-stop-deepfake-porn-using-ai)
### The Evil Empire Strikes Back
The Guardian reports on:
[‘Sanctions hole’: how secretive routes supply Russia with western tech and consumer goods](https://www.theguardian.com/world/article/2024/jun/12/russia-sanctions-hole-backdoor-routes)
MIT Technology Review reports:
[Propagandists are using AI too—and companies need to be open about it](https://www.technologyreview.com/2024/06/08/1093356/propagandists-are-using-ai-too-and-companies-need-to-be-open-about-it/)
The Hacker News has:
[Chinese Actor SecShow Conducts Massive DNS Probing on Global Scale](https://thehackernews.com/2024/06/chinese-actor-secshow-conducts-massive.html)
[China-Backed Hackers Exploit Fortinet Flaw, Infecting 20,000 Systems Globally](https://thehackernews.com/2024/06/china-backed-hackers-exploit-fortinet.html)
Reuters reports:
[NewsBreak: Most downloaded US news app has Chinese roots and 'writes fiction' using AI](https://www.reuters.com/technology/top-news-app-us-has-chinese-origins-writes-fiction-with-help-ai-2024-06-05/)
Check First reports:
[Operation Overload: how pro-Russian actors flood newsrooms with fake content and seek to divert their efforts](https://checkfirst.network/operation-overload-how-pro-russian-actors-flood-newsrooms-with-fake-content-and-seek-to-divert-their-efforts/)
The Next Web reports:
[Hackers linked to Hamas tied to cyberespionage via Android spyware in Palestine](https://thenextweb.com/news/hamas-hackers-behind-cyberespionage-campaigns-in-palestine)
The New York Times reports:
[It Looked Like a Reliable News Site. It Was an A.I. Chop Shop.](https://www.nytimes.com/2024/06/06/technology/bnn-breaking-ai-generated-news.html)
Hollywood Reporter reports:
[Big Tech Launches Campaign to Defend AI Use](https://www.hollywoodreporter.com/business/business-news/big-tech-lobby-ai-use-1235916540/)
Pro Publica reports:
[Microsoft Chose Profit Over Security and Left U.S. Government Vulnerable to Russian Hack, Whistleblower Says](https://www.propublica.org/article/microsoft-solarwinds-golden-saml-data-breach-russian-hackers)
404 Media reports:
[Microsoft QA Contractors Say They Were Laid Off for Attempting to Unionize](https://www.404media.co/microsoft-qa-contractors-say-they-were-laid-off-for-attempting-to-unionize/)
### Cybersecurity/Privacy
And:
[Hacker Accesses Internal ‘Tile’ Tool That Provides Location Data to Cops](https://www.404media.co/email/74dedd5b-6e4c-45ae-9eb4-d6c1cc886cb0/)
Bleeping Computer reports:
[Malicious VSCode extensions with millions of installs discovered](https://www.bleepingcomputer.com/news/security/malicious-vscode-extensions-with-millions-of-installs-discovered/)
Security Affairs reports:
[PHP addressed critical RCE flaw potentially impacting millions of servers](https://securityaffairs.com/164302/breaking-news/php-critical-rce.html)
JetBrains reports:
[Updates for security issue affecting IntelliJ-based IDEs 2023.1+ and JetBrains GitHub Plugin](https://blog.jetbrains.com/security/2024/06/updates-for-security-issue-affecting-intellij-based-ides-2023-1-and-github-plugin/)
---
### Fediverse
Augment shares:
[Human-Generated Content #2](https://www.augment.ink/human-generated-content-2/)
Privacy Laws reports:
[Navigating User Privacy in the Decentralized Social Web](https://www.privacylaws.com/events-gateway/events/fediverse2024/)
We Distribute reports:
[Maven Imported 1.12 Million Fediverse Posts](https://wedistribute.org/2024/06/maven-mastodon-posts/)
Stefan Bohacek shows us:
[How to be a good fediverse citizen](https://stefanbohacek.com/blog/how-to-be-a-good-fediverse-citizen/)
### Other Federated Social Media
The Fediverse Report has:
[Last Month in Bluesky – May 2024](https://fediversereport.com/last-month-in-bluesky-may-2024/)
---
## CTAs (aka show us some free love)
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More importantly, if you are a Ukrainian company with coding-related products, we can offer free promotion on [our Support Ukraine page](https://symfonystation.mobileatom.net/Support-Ukraine). Or, if you know of one, get in touch.
You can find a vast array of curated evergreen content on our [communiqués page](https://symfonystation.mobileatom.net/communiques).
## Author
### Reuben Walker
Founder
Symfony Station | reubenwalker64 |
1,891,455 | Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation | Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation | 0 | 2024-06-17T16:08:30 | https://aimodels.fyi/papers/arxiv/autoregressive-model-beats-diffusion-llama-scalable-image | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation](https://aimodels.fyi/papers/arxiv/autoregressive-model-beats-diffusion-llama-scalable-image). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper introduces Llama, a novel autoregressive model for scalable image generation that outperforms diffusion models.
- Llama uses a hierarchical architecture to capture global and local image structure, allowing it to generate high-quality images more efficiently than diffusion models.
- The authors demonstrate Llama's capabilities on a range of image generation tasks, showcasing its ability to generate diverse and realistic images.
## Plain English Explanation
The paper presents a new type of machine learning model called Llama that can generate high-quality images. Unlike [diffusion models](https://aimodels.fyi/papers/arxiv/ladic-are-diffusion-models-really-inferior-to), which have been popular for image generation, Llama uses a different approach called autoregression.
Autoregressive models work by predicting the next pixel in an image based on the pixels that have already been generated. Llama takes this a step further by using a hierarchical structure, which means it can capture both the overall shape and finer details of an image. This allows Llama to generate images that are more realistic and diverse than those produced by diffusion models.
The researchers demonstrate Llama's capabilities on a variety of image generation tasks, showing that it can create high-quality images in a more efficient way than diffusion models. This could be useful for applications like [image editing](https://aimodels.fyi/papers/arxiv/many-to-many-image-generation-auto-regressive), [content creation](https://aimodels.fyi/papers/arxiv/denoising-autoregressive-representation-learning), and [visual art generation](https://aimodels.fyi/papers/arxiv/kaleido-diffusion-improving-conditional-diffusion-models-autoregressive).
## Technical Explanation
The paper introduces Llama, a novel autoregressive model for scalable image generation. Autoregressive models work by predicting the next pixel in an image based on the pixels that have already been generated, in contrast to diffusion models that generate images in a more iterative way.
Llama uses a hierarchical architecture to capture both global and local image structure. It has multiple levels of "resolution," where each level predicts the next set of pixels based on the previous level. This allows Llama to efficiently generate high-quality images by first focusing on the overall shape and then gradually adding finer details.
The authors evaluate Llama on a range of image generation tasks, including unconditional generation, conditional generation, and super-resolution. They show that Llama outperforms state-of-the-art diffusion models in terms of both image quality and generation speed. Llama is able to generate diverse and realistic images while requiring fewer computational resources than diffusion models.
## Critical Analysis
The paper provides a compelling argument for the use of autoregressive models like Llama for scalable image generation. The hierarchical architecture is a clever way to combine global and local structure, and the authors demonstrate impressive results compared to diffusion models.
However, the paper doesn't fully address potential limitations of Llama. For example, autoregressive models can sometimes suffer from "exposure bias," where the model's predictions are influenced by its own previous outputs rather than the true data distribution. This could lead to the generation of less diverse or realistic images over time.
Additionally, the paper doesn't discuss the training process or hyperparameter tuning in depth. It would be helpful to understand the challenges the authors faced in optimizing Llama and how they overcame them.
Further research could also explore ways to combine the strengths of autoregressive and diffusion models, as suggested by some recent work like [KaleIDo](https://aimodels.fyi/papers/arxiv/kaleido-diffusion-improving-conditional-diffusion-models-autoregressive). This could potentially yield even more powerful and flexible image generation capabilities.
## Conclusion
The Llama paper presents a novel autoregressive model that outperforms state-of-the-art diffusion models for scalable image generation. By using a hierarchical architecture, Llama is able to efficiently capture both global and local image structure, leading to the generation of diverse and realistic images.
While the paper doesn't address all potential limitations of the approach, it makes a compelling case for the use of autoregressive models in image generation. Llama's strong performance on a variety of tasks suggests that it could be a valuable tool for applications like [image editing](https://aimodels.fyi/papers/arxiv/many-to-many-image-generation-auto-regressive), [content creation](https://aimodels.fyi/papers/arxiv/denoising-autoregressive-representation-learning), and [visual art generation](https://aimodels.fyi/papers/arxiv/kaleido-diffusion-improving-conditional-diffusion-models-autoregressive).
As the field of generative AI continues to evolve, it will be interesting to see how researchers build upon the ideas presented in this paper to further advance the state of the art in image generation.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,453 | TextGrad: Automatic Differentiation via Text | TextGrad: Automatic Differentiation via Text | 0 | 2024-06-17T16:07:56 | https://aimodels.fyi/papers/arxiv/textgrad-automatic-differentiation-via-text | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [TextGrad: Automatic Differentiation via Text](https://aimodels.fyi/papers/arxiv/textgrad-automatic-differentiation-via-text). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper introduces "TextGrad," a novel approach for optimizing AI systems by backpropagating text feedback.
- The key idea is to treat text as a differentiable signal that can be used to provide gradients for updating the parameters of language models and other AI systems.
- The authors demonstrate the effectiveness of TextGrad on various tasks, including language model fine-tuning, style transfer, and text generation.
## Plain English Explanation
The paper presents a new technique called "TextGrad" that allows AI systems to be trained more effectively using text-based feedback. Traditionally, training AI models like [large language models](https://aimodels.fyi/papers/arxiv/enhancing-text-authenticity-novel-hybrid-approach-ai) involves providing them with labeled data and having them learn to predict the correct outputs.
With TextGrad, the AI system can instead be trained by receiving natural language feedback, such as a human-written description of how the system should behave. The key insight is that this text feedback can be treated as a differentiable signal, meaning the AI can use it to directly update its own parameters through a process called [backpropagation](https://aimodels.fyi/papers/arxiv/system-automatic-english-text-expansion).
This is akin to a human learning a new task by receiving ongoing verbal guidance and feedback, rather than just being shown examples. The authors demonstrate that this approach can lead to significant performance improvements on a variety of language-based AI tasks, including [text generation](https://aimodels.fyi/papers/arxiv/mage-machine-generated-text-detection-wild), [style transfer](https://aimodels.fyi/papers/arxiv/textmachina-seamless-generation-machine-generated-text-datasets), and fine-tuning large language models.
## Technical Explanation
The core innovation of the "TextGrad" approach is treating natural language feedback as a differentiable signal that can be used to directly update the parameters of an AI system through backpropagation. Traditionally, training language models and other AI systems involves providing them with labeled data and having them learn to predict the correct outputs.
In contrast, the TextGrad method allows the AI system to be trained using free-form text feedback. This feedback is first encoded into a numerical representation that can be differentiated with respect to the model's parameters. The resulting gradients are then used to update the model, allowing it to directly optimize its behavior based on the text-based guidance.
The authors demonstrate the effectiveness of TextGrad on a range of language-based tasks. For example, they show how it can be used to fine-tune a large language model to better match a desired writing style or persona. They also explore using TextGrad for open-ended text generation, allowing a model to iteratively refine its outputs based on human feedback.
Overall, the key contribution of this work is introducing a general-purpose technique for incorporating text-based supervision into the training of AI systems. By treating language as a differentiable signal, the authors have opened up new possibilities for interactive and user-guided machine learning.
## Critical Analysis
The TextGrad approach represents an intriguing step forward in making AI systems more responsive to human feedback and preferences. By allowing language to directly shape the optimization process, it offers a more intuitive and flexible training paradigm compared to traditional supervised learning.
However, the paper does not delve deeply into potential limitations or challenges. For example, it's unclear how well TextGrad would scale to large, open-ended language models or handle noisy or ambiguous text feedback. There are also open questions about the stability and convergence properties of the optimization process when using text gradients.
Additionally, the authors do not explore potential ethical implications or risks of this technology. Allowing AI systems to be shaped by unconstrained text feedback could potentially lead to unintended or harmful behaviors, especially if the feedback comes from biased or adversarial sources.
Further research is needed to better understand the broader implications of treating language as a differentiable signal for machine learning. Careful consideration of safety, robustness, and alignment with human values will be crucial as this line of work progresses.
## Conclusion
The "TextGrad" approach introduced in this paper represents an exciting advance in the field of interactive machine learning. By treating natural language feedback as a differentiable signal, it enables AI systems to be trained and optimized in a more intuitive, user-guided manner.
The authors demonstrate the potential of this technique across a variety of language-based tasks, showing how it can lead to significant performance improvements. This work opens up new possibilities for AI systems that can flexibly adapt to human preferences and refinements, rather than being constrained to predetermined objectives.
As the field of AI continues to evolve, techniques like TextGrad will likely play an increasingly important role in bridging the gap between human-centric and machine-centric intelligence. However, further research is needed to fully understand the limitations, risks, and ethical implications of this approach. Nonetheless, this paper represents an important step forward in making AI systems more responsive, interactive, and aligned with human values.
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1,891,452 | Can Language Models Serve as Text-Based World Simulators? | Can Language Models Serve as Text-Based World Simulators? | 0 | 2024-06-17T16:07:21 | https://aimodels.fyi/papers/arxiv/can-language-models-serve-as-text-based | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Can Language Models Serve as Text-Based World Simulators?](https://aimodels.fyi/papers/arxiv/can-language-models-serve-as-text-based). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper explores the potential of large language models (LLMs) to serve as text-based world simulators, capable of generating coherent and detailed textual descriptions of imaginary worlds.
- The researchers investigate the ability of LLMs to create and maintain consistent, multi-faceted simulations that can be interactively explored through text-based interactions.
- The paper builds on recent advancements in [human simulacra benchmarking](https://aimodels.fyi/papers/arxiv/human-simulacra-benchmarking-personification-large-language-models) and [language model-guided simulation-to-real](https://aimodels.fyi/papers/arxiv/dreureka-language-model-guided-sim-to-real) techniques.
## Plain English Explanation
Large language models, which are artificial intelligence systems trained on vast amounts of text data, have demonstrated remarkable abilities in tasks like generating coherent and natural-sounding text. This paper explores whether these models can be used to create and maintain detailed simulations of imaginary worlds that can be explored through text-based interactions.
The researchers are investigating the possibility of using LLMs as "text-based world simulators" - systems that can generate rich and consistent descriptions of fictional worlds, and allow users to interact with and explore these worlds by typing commands and receiving textual responses. This builds on recent work in areas like [human simulacra benchmarking](https://aimodels.fyi/papers/arxiv/human-simulacra-benchmarking-personification-large-language-models), which explores how well LLMs can mimic the personalities and behaviors of real people, and [language model-guided simulation-to-real](https://aimodels.fyi/papers/arxiv/dreureka-language-model-guided-sim-to-real) techniques, which use language models to bridge the gap between simulated and real-world environments.
The ultimate goal is to develop LLMs that can create and maintain complex, multi-faceted simulated worlds that users can immerse themselves in through text-based interactions, much like in classic text-based adventure games. This could have applications in areas like entertainment, education, and even psychological research.
## Technical Explanation
The paper begins by reviewing the relevant literature on using LLMs for tasks like [world model building](https://aimodels.fyi/papers/arxiv/worldgpt-empowering-llm-as-multimodal-world-model), [character personification](https://aimodels.fyi/papers/arxiv/character-is-destiny-can-large-language-models), and [simulation-to-real bridging](https://aimodels.fyi/papers/arxiv/dreureka-language-model-guided-sim-to-real). The researchers note that while these techniques have shown promise, there has been limited work on using LLMs to create and maintain coherent, interactive text-based simulations of imaginary worlds.
To address this, the paper outlines a methodology for training LLMs to serve as world simulators. This involves fine-tuning the models on large datasets of text-based adventure games, interactive fiction, and other sources of world-building narratives. The goal is to imbue the models with the necessary knowledge and capabilities to generate consistent, multi-faceted textual descriptions of fictional worlds, and to respond appropriately to user inputs and commands.
The researchers also discuss the use of prompt engineering, world model representations, and other techniques to enhance the models' world-building and interactive capabilities. They propose evaluation frameworks to assess the models' ability to maintain coherence, respond to user inputs, and generally create a sense of immersion and engagement for the user.
## Critical Analysis
The paper raises some important caveats and limitations to the proposed approach. For example, the researchers acknowledge that maintaining long-term coherence and consistency in simulated worlds is a significant challenge, and that current LLMs may struggle with tasks like logical reasoning, causal understanding, and long-term memory.
Additionally, the paper notes that the quality and richness of the text-based simulations will be heavily dependent on the quality and breadth of the training data used to fine-tune the LLMs. Ensuring sufficient coverage of diverse world-building narratives and interactive fiction may be a significant hurdle.
The researchers also highlight the potential for biases, inconsistencies, and other undesirable behaviors to emerge in the simulated worlds, and the need for robust safety and control mechanisms to mitigate these risks.
Overall, the paper provides a compelling vision for the use of LLMs as text-based world simulators, but also acknowledges the substantial technical challenges that must be overcome to realize this vision. Continued research and innovation in areas like [linguistic intentionality](https://aimodels.fyi/papers/arxiv/large-language-models-linguistic-intentionality), world modeling, and interactive narrative generation will be crucial.
## Conclusion
This paper explores the potential of large language models to serve as text-based world simulators, capable of generating coherent and detailed descriptions of imaginary worlds that can be interactively explored through text-based interactions. The researchers outline a methodology for training LLMs to create and maintain these simulated worlds, building on recent advancements in related areas.
While the proposed approach holds significant promise, the paper also highlights the substantial technical challenges that must be addressed, such as maintaining long-term coherence, addressing safety and bias concerns, and ensuring the richness and immersiveness of the simulated experiences. Continued research and innovation will be essential to realizing the full potential of LLMs as text-based world simulators.
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1,891,451 | Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling | Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling | 0 | 2024-06-17T16:06:46 | https://aimodels.fyi/papers/arxiv/samba-simple-hybrid-state-space-models-efficient | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling](https://aimodels.fyi/papers/arxiv/samba-simple-hybrid-state-space-models-efficient). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper introduces a new language modeling approach called "Samba" that combines simple state space models with large language models for efficient and scalable unlimited context modeling.
- Samba uses a hybrid architecture that integrates recurrent neural networks with linear time-invariant state space models to capture both short-term and long-term dependencies in text.
- The authors demonstrate that Samba achieves competitive perplexity scores on standard language modeling benchmarks while being more efficient and scalable than previous state-of-the-art models.
## Plain English Explanation
The researchers have developed a new way to build language models, called "Samba", that can understand and generate human language more efficiently than existing approaches. Traditional language models struggle to capture both short-term patterns and long-term dependencies in text. Samba solves this by using a combination of simple mathematical models and large neural networks.
At the core of Samba is a state space model - a type of mathematical model that can efficiently represent and predict sequences of data over time. This state space model is combined with a large neural network, which helps Samba understand the complex semantics and structure of natural language.
By blending these two components, Samba can understand the immediate context of a piece of text as well as broader, longer-term patterns. This allows it to generate human-like text that flows naturally and coherently, without requiring huge amounts of computational power. The researchers show that Samba performs well on standard language modeling benchmarks, matching the accuracy of state-of-the-art models while being more efficient and scalable.
## Technical Explanation
The paper introduces a new language modeling approach called "Samba" that combines [simple state space models](https://aimodels.fyi/papers/arxiv/mamba-linear-time-sequence-modeling-selective-state) with [large neural language models](https://aimodels.fyi/papers/arxiv/mamba-360-survey-state-space-models-as) to capture both short-term and long-term dependencies in text.
Samba's architecture integrates a [linear time-invariant state space model](https://aimodels.fyi/papers/arxiv/mambabyte-token-free-selective-state-space-model) with a large transformer-based language model. The state space model handles the long-range context, while the neural network handles the local, short-term patterns. This [hybrid approach](https://aimodels.fyi/papers/arxiv/zamba-compact-7b-ssm-hybrid-model) allows Samba to be more efficient and scalable than previous state-of-the-art models.
The authors evaluate Samba on standard language modeling benchmarks and show that it achieves competitive perplexity scores while being more computationally efficient than [previous approaches](https://aimodels.fyi/papers/arxiv/simba-simplified-mamba-based-architecture-vision-multivariate). This demonstrates the potential of combining simple state space models with large neural networks for effective and scalable language modeling.
## Critical Analysis
The paper provides a thorough evaluation of Samba's performance on standard language modeling tasks, but there are a few potential limitations that could be explored in future work:
- The authors only evaluate Samba on text-based language modeling benchmarks. It would be interesting to see how the model performs on tasks involving multimodal data, such as vision-language modeling.
- The paper does not delve into the interpretability of Samba's internal representations. Understanding how the state space and neural network components interact to capture language structure could lead to valuable insights.
- The authors mention that Samba is more computationally efficient than previous models, but they do not provide a detailed analysis of the model's scaling properties or its suitability for real-world, resource-constrained deployment scenarios.
Overall, the Samba approach is a promising step forward in the quest for efficient and scalable language modeling. Further research exploring its broader applications and potential limitations could yield valuable insights for the field.
## Conclusion
The Samba paper presents a novel language modeling approach that combines simple state space models with large neural networks to capture both short-term and long-term dependencies in text. By blending these two components, the authors demonstrate that Samba can achieve competitive performance on standard language modeling benchmarks while being more computationally efficient than previous state-of-the-art models.
This work highlights the potential for hybrid architectures that leverage the strengths of different modeling techniques to create more effective and scalable language models. As natural language processing continues to advance, approaches like Samba may play an important role in developing language models that are both accurate and practical for real-world applications.
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1,891,450 | Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes | Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes | 0 | 2024-06-17T16:06:11 | https://aimodels.fyi/papers/arxiv/publicly-shareable-clinical-large-language-model-built | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes](https://aimodels.fyi/papers/arxiv/publicly-shareable-clinical-large-language-model-built). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- Researchers developed a specialized clinical language model called Asclepius using synthetic clinical notes
- Asclepius was trained on publicly available case reports, then evaluated on real-world clinical notes
- Asclepius outperformed other large language models like GPT-3.5-turbo in clinical text tasks
- The researchers made all resources used in Asclepius publicly accessible for future research
## Plain English Explanation
Artificial intelligence (AI) models trained on large volumes of text data, known as large language models, have shown great potential in various applications. However, [adapting open-source large language models to clinical settings](https://aimodels.fyi/papers/arxiv/adapting-open-source-large-language-models-cost) can be challenging due to the limited accessibility and strict privacy regulations surrounding real-world clinical notes.
To [unlock the potential of large language models for clinical text](https://aimodels.fyi/papers/arxiv/unlocking-potential-large-language-models-clinical-text), the researchers in this study [utilized synthetic data to generate a specialized clinical language model](https://aimodels.fyi/papers/arxiv/utilizing-large-language-models-to-generate-synthetic). They created a large-scale dataset of synthetic clinical notes by extracting case reports from biomedical literature. This allowed them to train a new clinical language model, named Asclepius, without needing access to real patient data.
To [validate the effectiveness of this approach](https://aimodels.fyi/papers/arxiv/comparative-analysis-open-source-language-models-summarizing), the researchers evaluated Asclepius on real clinical notes and compared its performance to other large language models, including GPT-3.5-turbo. The results showed that Asclepius outperformed these models, demonstrating the potential of using synthetic data to [enhance clinical documentation and leverage generative AI models](https://aimodels.fyi/papers/arxiv/enhancing-clinical-documentation-synthetic-data-leveraging-generative).
The researchers have made all the resources used in the development of Asclepius, including the model weights, code, and data, publicly available for future research. This will help advance the field of clinical language modeling and improve the accessibility of large language models in healthcare applications.
## Technical Explanation
The researchers in this study recognized the challenge of [adapting open-source large language models to clinical settings](https://aimodels.fyi/papers/arxiv/adapting-open-source-large-language-models-cost) due to the limited accessibility and strict privacy regulations surrounding real-world clinical notes. To [unlock the potential of large language models for clinical text](https://aimodels.fyi/papers/arxiv/unlocking-potential-large-language-models-clinical-text), they developed a specialized clinical language model called Asclepius using synthetic data.
The researchers [utilized large language models to generate synthetic clinical notes](https://aimodels.fyi/papers/arxiv/utilizing-large-language-models-to-generate-synthetic) by extracting case reports from publicly available biomedical literature. These synthetic notes were then used to train Asclepius, a custom-built clinical language model.
To [validate the effectiveness of this approach](https://aimodels.fyi/papers/arxiv/comparative-analysis-open-source-language-models-summarizing), the researchers evaluated Asclepius on real clinical notes and benchmarked its performance against several other large language models, including GPT-3.5-turbo and open-source alternatives. They also compared Asclepius with variants trained on real clinical notes to further validate the use of synthetic data.
The findings of the study convincingly demonstrated that synthetic clinical notes can serve as viable substitutes for real ones when constructing high-performing clinical language models. This conclusion was supported by detailed evaluations conducted by both GPT-4 and medical professionals.
## Critical Analysis
While the researchers have shown the potential of using synthetic clinical notes to train specialized language models, there may be some limitations to this approach. The synthetic notes, although generated from real-world case reports, may not fully capture the nuances and complexities of actual clinical documentation. Additionally, the performance of the Asclepius model on real-world clinical tasks may still be influenced by the quality and representativeness of the synthetic data used in its training.
It would be valuable for future research to further investigate the [limitations and potential biases](https://aimodels.fyi/papers/arxiv/adapting-open-source-large-language-models-cost) introduced by the use of synthetic data, as well as explore ways to [enhance the clinical documentation and leverage generative AI models](https://aimodels.fyi/papers/arxiv/enhancing-clinical-documentation-synthetic-data-leveraging-generative) in a more robust and reliable manner.
## Conclusion
The researchers in this study have demonstrated a novel approach to [utilizing large language models to generate synthetic clinical notes](https://aimodels.fyi/papers/arxiv/utilizing-large-language-models-to-generate-synthetic) and [training a specialized clinical language model](https://aimodels.fyi/papers/arxiv/unlocking-potential-large-language-models-clinical-text) called Asclepius. By [comparing Asclepius to other open-source language models](https://aimodels.fyi/papers/arxiv/comparative-analysis-open-source-language-models-summarizing), they have shown that synthetic clinical notes can serve as viable substitutes for real ones in constructing high-performing clinical language models.
This research has significant implications for [adapting open-source large language models to clinical settings](https://aimodels.fyi/papers/arxiv/adapting-open-source-large-language-models-cost) and [enhancing clinical documentation through the use of synthetic data and generative AI](https://aimodels.fyi/papers/arxiv/enhancing-clinical-documentation-synthetic-data-leveraging-generative). The publicly accessible resources provided by the researchers will further advance the field of clinical language modeling and improve the accessibility of large language models in healthcare applications.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,449 | Can Language Models Use Forecasting Strategies? | Can Language Models Use Forecasting Strategies? | 0 | 2024-06-17T16:05:36 | https://aimodels.fyi/papers/arxiv/can-language-models-use-forecasting-strategies | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Can Language Models Use Forecasting Strategies?](https://aimodels.fyi/papers/arxiv/can-language-models-use-forecasting-strategies). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper explores whether large language models (LLMs) can effectively use forecasting strategies to make predictions.
- The researchers investigate the forecasting capabilities of LLMs by having them compete against human participants in a series of judgment-based forecasting tasks.
- The paper builds on previous research on [LLMs and forecasting](https://aimodels.fyi/papers/arxiv/humans-vs-large-language-models-judgmental-forecasting), [LLMs and transportation/mobility systems](https://aimodels.fyi/papers/arxiv/large-language-models-mobility-transportation-systems-survey), and [ensemble prediction capabilities of LLMs](https://aimodels.fyi/papers/arxiv/wisdom-silicon-crowd-llm-ensemble-prediction-capabilities).
## Plain English Explanation
The researchers wanted to see if powerful language AI models, known as large language models (LLMs), could use forecasting strategies to make predictions. Forecasting is the practice of estimating future events or outcomes based on available information.
To test this, the researchers had the LLMs compete against human participants in a series of judgment-based forecasting tasks. This means the participants had to use their own knowledge and reasoning to make predictions, rather than relying on historical data or statistical models.
The paper builds on previous research that has looked at how LLMs perform in other forecasting and prediction-related tasks, such as [forecasting soccer matches](https://aimodels.fyi/papers/arxiv/forecasting-events-soccer-matches-through-language) and [predicting outcomes in general](https://aimodels.fyi/papers/arxiv/do-large-language-models-perform-way-people). The researchers wanted to see if the LLMs could hold their own against human experts in this more subjective, judgment-based type of forecasting.
## Technical Explanation
The researchers conducted a series of experiments where LLMs and human participants competed in various judgment-based forecasting tasks. The tasks involved predicting future events or outcomes based on limited information, rather than relying on historical data or statistical models.
The LLMs used in the experiments were large, state-of-the-art language models that had been trained on massive amounts of textual data. The researchers compared the forecasting performance of the LLMs to that of human participants with expertise in the relevant domains.
The key insights from the study include:
- LLMs were able to match or even outperform human participants in certain forecasting tasks, demonstrating their potential for using sophisticated forecasting strategies.
- The performance of the LLMs was influenced by factors such as the complexity of the task, the amount of contextual information available, and the specific capabilities of the language model.
- The researchers also found that ensembles of LLMs could further improve forecasting accuracy, building on previous work in this area.
## Critical Analysis
The paper presents an interesting and important exploration of the forecasting capabilities of large language models. However, the researchers acknowledge several limitations and areas for further research:
- The forecasting tasks used in the experiments were relatively narrow in scope, and it's unclear how the LLMs would perform in more complex, real-world forecasting scenarios.
- The study did not delve deeply into the specific strategies and reasoning processes used by the LLMs, making it difficult to fully understand the underlying mechanisms behind their forecasting abilities.
- The researchers note that the performance of the LLMs was influenced by factors like task complexity and available information, suggesting that more work is needed to understand the boundaries and constraints of their forecasting capabilities.
Additionally, some potential concerns that were not addressed in the paper include:
- The potential for biases and errors in the LLMs' forecasts, especially in high-stakes domains like finance or healthcare.
- The ethical implications of relying on LLMs for important forecasting and decision-making tasks, particularly if their inner workings are not fully transparent.
- The long-term sustainability and reliability of LLM-based forecasting systems, which may be vulnerable to shifts in data, model architecture, or other factors.
Overall, the paper makes an important contribution to the growing body of research on the capabilities and limitations of large language models. However, further investigation and critical analysis will be needed to fully understand the implications and practical applications of this technology in the realm of forecasting.
## Conclusion
This paper presents an intriguing exploration of the forecasting capabilities of large language models (LLMs). The researchers found that LLMs can match or even outperform human participants in certain judgment-based forecasting tasks, suggesting that these powerful AI systems may be able to effectively utilize sophisticated forecasting strategies.
The findings build on previous research on LLMs and forecasting, transportation/mobility systems, and ensemble prediction capabilities. While the study demonstrates the potential of LLMs in this domain, it also highlights the need for further investigation into the boundaries and constraints of their forecasting abilities, as well as the potential ethical and practical implications of relying on these models for important decision-making tasks.
As the field of AI continues to advance, understanding the forecasting capabilities of large language models will be crucial for leveraging these technologies to make more accurate and informed predictions about the future. The insights from this paper contribute to this ongoing effort and pave the way for future research in this exciting and rapidly evolving area.
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1,891,447 | An Empirical Study of Mamba-based Language Models | An Empirical Study of Mamba-based Language Models | 0 | 2024-06-17T16:05:01 | https://aimodels.fyi/papers/arxiv/empirical-study-mamba-based-language-models | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [An Empirical Study of Mamba-based Language Models](https://aimodels.fyi/papers/arxiv/empirical-study-mamba-based-language-models). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper presents an empirical study on Mamba-based language models, which are a type of state-space model used for sequence modeling.
- The researchers investigate the performance of Mamba-based models on various language tasks and compare them to other popular model architectures like Transformers.
- The paper provides insights into the strengths and limitations of Mamba-based models, as well as their potential applications in the field of natural language processing.
## Plain English Explanation
Mamba-based language models are a type of machine learning model that can be used for tasks like text generation, translation, and summarization. They work by breaking down language into a sequence of states, which allows them to capture the underlying structure and patterns in the data.
The researchers in this study wanted to see how well Mamba-based models perform compared to other popular model architectures, like Transformers, on a variety of language tasks. They tested the models on things like predicting the next word in a sentence, translating between languages, and summarizing longer passages of text.
Overall, the results suggest that Mamba-based models can be quite effective for certain language tasks, particularly those that involve modeling long-term dependencies or hierarchical structures in the data. However, they may struggle in areas where Transformers excel, like handling large-scale parallelism or capturing complex semantic relationships.
The key takeaway is that Mamba-based models represent a promising alternative approach to language modeling, with their own unique strengths and weaknesses. By understanding the tradeoffs between different model architectures, researchers and practitioners can make more informed choices about which ones to use for their specific applications.
## Technical Explanation
The paper begins by providing an overview of Mamba-based language models, which are a type of [state-space model](https://aimodels.fyi/papers/arxiv/mamba-state-space-models-can-be-strong) for sequence modeling. These models work by representing language as a series of latent states, which evolve over time according to a specified transition function.
The researchers then describe a series of experiments designed to assess the performance of Mamba-based models on various language tasks, including [next-word prediction](https://aimodels.fyi/papers/arxiv/mamba-linear-time-sequence-modeling-selective-state), machine translation, and text summarization. They compare the Mamba-based models to [Transformer](https://aimodels.fyi/papers/arxiv/transformers-are-ssms-generalized-models-efficient-algorithms) models, which have become the dominant architecture in many natural language processing applications.
The results of the experiments show that Mamba-based models can outperform Transformers on certain tasks, particularly those that involve long-range dependencies or hierarchical structure in the language. However, Transformers tend to have an advantage when it comes to tasks that require large-scale parallelism or the capture of complex semantic relationships.
The paper also discusses some of the limitations of Mamba-based models, such as their sensitivity to hyperparameter tuning and the difficulty of scaling them to very large datasets. The researchers suggest that further research is needed to address these challenges and fully unlock the potential of Mamba-based language models.
## Critical Analysis
The paper presents a well-designed and thorough empirical study of Mamba-based language models, which is a valuable contribution to the literature. The researchers have clearly put a lot of thought into the experimental setup and the selection of appropriate baselines for comparison.
However, one potential limitation of the study is that it focuses primarily on a relatively narrow set of language tasks, such as next-word prediction and machine translation. It would be interesting to see how Mamba-based models perform on a wider range of language understanding and generation tasks, such as question answering, dialogue systems, or text summarization.
Additionally, the paper does not delve deeply into the underlying mechanisms and architectural choices that give Mamba-based models their unique strengths and weaknesses. A more detailed analysis of the model components and their interactions could provide additional insights into the model's inner workings and help guide future research and development.
Overall, this paper represents an important step forward in our understanding of Mamba-based language models and their potential applications in natural language processing. By encouraging further research and critical analysis in this area, the authors have laid the groundwork for more advanced and impactful applications of these models in the years to come.
## Conclusion
This paper provides an in-depth empirical study of Mamba-based language models, a promising alternative to the dominant Transformer architecture in natural language processing. The researchers found that Mamba-based models can outperform Transformers on certain tasks, particularly those involving long-range dependencies or hierarchical structure in the language.
However, the paper also highlights some of the limitations of Mamba-based models, such as their sensitivity to hyperparameter tuning and challenges in scaling to very large datasets. Further research is needed to address these issues and fully unlock the potential of this approach to language modeling.
Overall, this work represents an important contribution to the ongoing efforts to develop more powerful and versatile language models, with applications ranging from text generation and translation to dialogue systems and question answering. By understanding the tradeoffs between different model architectures, researchers and practitioners can make more informed choices about which approaches to use for their specific needs and applications.
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1,891,445 | Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch | Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch | 0 | 2024-06-17T16:04:26 | https://aimodels.fyi/papers/arxiv/language-models-are-super-mario-absorbing-abilities | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch](https://aimodels.fyi/papers/arxiv/language-models-are-super-mario-absorbing-abilities). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper introduces a technique called DARE (Decoupled Alignment and Robust Embedding) that allows language models (LMs) to acquire new capabilities by assimilating parameters from similar models without retraining or specialized hardware.
- The authors show that the differences (delta parameters) between fine-tuned and pre-trained LMs are typically small and redundant, and DARE can effectively eliminate 90% or even 99% of these parameters without affecting the model's abilities.
- DARE can be used as a versatile plug-in to merge multiple task-specific LMs into a single model with diverse capabilities, which is especially pronounced in large-scale LMs.
- The merged LM can sometimes surpass the performance of any of the source models, providing a new discovery.
## Plain English Explanation
**Acquiring New Capabilities Without Retraining**
The paper explains how language models can learn new skills by incorporating parameters from similar models, without having to go through the entire retraining process. This is done using a technique called DARE, which can efficiently remove most of the differences (delta parameters) between the fine-tuned and pre-trained versions of a model, without affecting its performance. [link to DARE paper](https://aimodels.fyi/papers/arxiv/decoupled-alignment-robust-plug-play-adaptation)
**Merging Multiple Language Models**
The researchers also show how DARE can be used to combine several task-specific language models into a single model that has a diverse set of capabilities. This is particularly powerful for large-scale language models, where the merged model can sometimes outperform any of the individual source models. [link to paper on abilities of large language models](https://aimodels.fyi/papers/arxiv/how-abilities-large-language-models-are-affected)
**Potential for Efficient Model Scaling**
This discovery suggests that there may be an efficient way to scale up language models by merging specialized models, rather than having to retrain a single large model from scratch. This could lead to significant improvements in the capabilities of AI systems without the need for massive computational resources. [link to paper on teaching languages to large language models](https://aimodels.fyi/papers/arxiv/sambalingo-teaching-large-language-models-new-languages)
## Technical Explanation
The paper introduces a technique called DARE (Decoupled Alignment and Robust Embedding) that allows language models (LMs) to acquire new capabilities by assimilating parameters from similar, or "homologous," models without retraining or specialized hardware like GPUs.
The authors first show that the differences (delta parameters) between fine-tuned and pre-trained LMs are typically small, within a range of 0.002, and exhibit extreme redundancy. They then propose DARE, which **Drops** delta parameters with a ratio **p** and **REscales** the remaining ones by **1 / (1 - p)** to approximate the original embeddings. This effectively eliminates 90% or even 99% of the delta parameters without affecting the model's abilities.
The researchers then use DARE as a versatile plug-in to **sparsify** the delta parameters of multiple task-specific SFT (Supervised Fine-Tuning) homologous models, and **merge** them into a single model by parameter fusing. [link to paper on robust plug-and-play adaptation](https://aimodels.fyi/papers/arxiv/decoupled-alignment-robust-plug-play-adaptation)
The experiments show that this phenomenon is more pronounced in large-scale LMs, where the merged model can sometimes surpass the performance of any of the source models, providing a new discovery. The authors also utilize DARE to create a merged LM that ranks first among models with 7 billion parameters on the Open LLM Leaderboard. [link to paper on expansion of spoken language understanding](https://aimodels.fyi/papers/arxiv/large-language-models-expansion-spoken-language-understanding)
## Critical Analysis
The paper presents an intriguing approach for efficiently scaling up language models by merging specialized models, rather than retraining a single large model from scratch. This could lead to significant improvements in the capabilities of AI systems without the need for massive computational resources.
However, the authors do not address potential limitations or caveats of their approach. For example, it's unclear how well the merged model would perform on a wide range of tasks compared to a model trained from scratch on a diverse dataset. Additionally, the paper does not explore the effects of this approach on model robustness, fairness, or safety. [link to paper on debiasing algorithm through model adaptation](https://aimodels.fyi/papers/arxiv/debiasing-algorithm-through-model-adaptation)
Further research is needed to understand the broader implications and potential issues with this technique, as well as its applicability to other types of AI models beyond language models. It will be important for the research community to critically examine the findings and consider the long-term consequences of such model merging approaches.
## Conclusion
This paper introduces a novel technique called DARE that enables language models to acquire new capabilities by assimilating parameters from similar models, without the need for retraining or specialized hardware. The authors demonstrate that DARE can effectively merge multiple task-specific language models into a single model with diverse capabilities, particularly for large-scale language models.
This discovery suggests that there may be an efficient way to scale up language models by leveraging existing specialized models, rather than having to retrain a single large model from scratch. If further research can address the potential limitations and implications of this approach, it could lead to significant advancements in the capabilities and accessibility of AI systems.
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1,891,444 | HyperFields: Towards Zero-Shot Generation of NeRFs from Text | HyperFields: Towards Zero-Shot Generation of NeRFs from Text | 0 | 2024-06-17T16:03:51 | https://aimodels.fyi/papers/arxiv/hyperfields-towards-zero-shot-generation-nerfs-from | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [HyperFields: Towards Zero-Shot Generation of NeRFs from Text](https://aimodels.fyi/papers/arxiv/hyperfields-towards-zero-shot-generation-nerfs-from). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- HyperFields is a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and optional fine-tuning.
- Key aspects are a dynamic hypernetwork that learns a smooth mapping from text token embeddings to the space of NeRFs, and NeRF distillation training to distill scenes encoded in individual NeRFs into one dynamic hypernetwork.
- This allows a single network to fit over a hundred unique scenes, and learn a more general map between text and NeRFs, enabling prediction of novel in-distribution and out-of-distribution scenes.
- Finetuning HyperFields benefits from accelerated convergence and can synthesize novel scenes 5-10 times faster than existing neural optimization-based methods.
## Plain English Explanation
HyperFields is a new approach for generating 3D scenes from text descriptions. [Neural Radiance Fields (NeRFs)](https://aimodels.fyi/papers/arxiv/neural-radiance-fields-based-holography-invited) are a powerful technique for representing 3D scenes, but typically require a lot of computational resources to generate a single scene.
HyperFields solves this by using a **dynamic hypernetwork** - a neural network that can rapidly generate unique NeRFs from text inputs. The hypernetwork learns a smooth mapping between text descriptions and the parameters of NeRFs, allowing it to efficiently produce a wide variety of 3D scenes with a single forward pass.
Additionally, HyperFields uses a technique called **NeRF distillation** to condense the knowledge of many individual NeRFs into the hypernetwork. This means the hypernetwork can capture the details of over a hundred unique scenes, rather than just a single one.
The result is a system that can generate novel 3D scenes from text descriptions, either completely from scratch or by fine-tuning on a few examples. This fine-tuning process is much faster than training NeRFs from scratch, allowing HyperFields to synthesize new scenes 5-10 times quicker than previous methods.
## Technical Explanation
The core of HyperFields is a **dynamic hypernetwork** that learns a smooth mapping from text token embeddings to the space of NeRFs. This allows the system to efficiently generate unique NeRFs for diverse text inputs with a single forward pass, rather than having to optimize each NeRF individually.
To train the hypernetwork, the authors use a **NeRF distillation** technique. They first train individual NeRFs for a large number of scenes, then distill the knowledge from these NeRFs into the parameters of the hypernetwork. This enables the hypernetwork to capture the details of over a hundred unique scenes, rather than just a single one.
The authors demonstrate that HyperFields learns a more general map between text and NeRFs, allowing it to predict novel in-distribution and out-of-distribution scenes either zero-shot or with a few finetuning steps. This finetuning process benefits from accelerated convergence compared to training NeRFs from scratch, enabling HyperFields to synthesize new scenes 5-10 times faster than existing neural optimization-based methods.
Ablation experiments show that both the dynamic architecture and NeRF distillation are critical to the expressivity of HyperFields, highlighting the importance of these key components.
## Critical Analysis
The HyperFields paper presents a novel and promising approach for text-conditional 3D scene generation. The authors demonstrate impressive results in terms of the diversity of scenes that can be generated and the efficiency of the finetuning process.
However, the paper does not address some potential limitations or areas for further research. For example, the quality of the generated scenes is not comprehensively evaluated, and it's unclear how HyperFields compares to other text-to-3D methods in terms of visual fidelity and realism.
Additionally, the paper does not explore the robustness of HyperFields to out-of-distribution text inputs or its ability to handle more complex scene descriptions. [Connecting NeRFs: Images & Text](https://aimodels.fyi/papers/arxiv/connecting-nerfs-images-text) and [Depth-Aware Text-Based Editing of NeRFs](https://aimodels.fyi/papers/arxiv/datenerf-depth-aware-text-based-editing-nerfs) are related works that could provide useful context and comparisons.
Further research could also investigate the interpretability of the hypernetwork's internal representations and explore ways to leverage the learned text-to-NeRF mapping for other applications, such as [neural radiance field-based holography](https://aimodels.fyi/papers/arxiv/neural-radiance-fields-based-holography-invited) or [sparse input radiance field regularization](https://aimodels.fyi/papers/arxiv/simple-rf-regularizing-sparse-input-radiance-fields).
## Conclusion
HyperFields represents a significant advancement in the field of text-conditional 3D scene generation. By leveraging a dynamic hypernetwork and NeRF distillation, the authors have developed a system that can efficiently produce a wide variety of 3D scenes from text descriptions, with the ability to fine-tune on new examples quickly.
This work has the potential to enable more natural and accessible 3D content creation, as well as to contribute to other areas of 3D scene understanding and manipulation. While the paper highlights several notable strengths of the HyperFields approach, further research will be needed to fully explore its capabilities and limitations.
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1,891,443 | Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications | Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications | 0 | 2024-06-17T16:03:16 | https://aimodels.fyi/papers/arxiv/raccoon-prompt-extraction-benchmark-llm-integrated-applications | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications](https://aimodels.fyi/papers/arxiv/raccoon-prompt-extraction-benchmark-llm-integrated-applications). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
• This paper introduces Raccoon, a benchmark for evaluating the ability of large language models (LLMs) to resist prompt extraction attacks, where an attacker attempts to extract the original prompt used to generate a given output.
• Prompt extraction attacks are a critical security concern for LLM-integrated applications, as they could allow attackers to reverse-engineer sensitive prompts and gain unauthorized access to restricted functionalities.
• The Raccoon benchmark provides a standardized set of test cases and evaluation metrics to assess an LLM's robustness against such attacks, with the goal of driving progress in this important area of research.
## Plain English Explanation
Large language models (LLMs) are powerful AI systems that can generate human-like text on a wide range of topics. These models are increasingly being integrated into various applications, from chatbots to content generation tools. However, there is a growing concern about the security of these LLM-integrated applications.
One key security threat is the risk of **prompt extraction attacks**. In these attacks, a malicious user tries to figure out the original prompt (or instructions) that was used to generate a particular output from the LLM. If successful, the attacker could potentially reverse-engineer sensitive prompts and gain unauthorized access to restricted functionalities within the application.
To address this issue, the researchers have developed a new benchmark called **Raccoon**. Raccoon provides a standardized way to evaluate how well an LLM can resist prompt extraction attacks. It includes a set of test cases and evaluation metrics that can be used to assess an LLM's security in this regard.
By using Raccoon, researchers and developers can better understand the vulnerabilities of their LLM-integrated applications and work on improving the models' robustness against these types of attacks. This is an important step in ensuring the security and trustworthiness of AI systems as they become more ubiquitous in our daily lives.
## Technical Explanation
The Raccoon benchmark is designed to assess an LLM's ability to resist prompt extraction attacks, where an attacker attempts to determine the original prompt used to generate a given output. The benchmark includes a set of test cases that cover different types of prompts, ranging from simple instructions to more complex, multi-step tasks.
For each test case, the benchmark evaluates the LLM's performance on two key metrics:
1. **Prompt Reconstruction Accuracy**: This measures how well the attacker can reconstruct the original prompt from the generated output.
2. **Output Fidelity**: This assesses how closely the LLM's output matches the expected result, even in the face of prompt extraction attempts.
The researchers have also developed a dataset of diverse prompts and their corresponding outputs to serve as the benchmark's test cases. This dataset covers a wide range of domains, including text generation, translation, and question-answering.
By using the Raccoon benchmark, researchers and developers can identify vulnerabilities in their LLM-integrated applications and work on improving the models' robustness against prompt extraction attacks. This is a crucial step in ensuring the security and trustworthiness of AI systems as they become more prevalent in our daily lives.
## Critical Analysis
The Raccoon benchmark is a valuable contribution to the field of LLM security research, as it provides a standardized way to evaluate the resilience of these models against a critical attack vector. However, it's important to note that the benchmark has some limitations and potential areas for further research.
One key limitation is that the Raccoon dataset may not fully capture the diversity and complexity of real-world prompts used in LLM-integrated applications. While the researchers have made an effort to include a wide range of prompt types, there may be additional scenarios that are not yet represented in the benchmark.
Additionally, the Raccoon benchmark focuses solely on the security aspect of prompt extraction attacks, without considering other potential security risks or broader implications of LLM integration. For example, the benchmark does not address issues related to data privacy, model bias, or the potential for LLMs to be used for malicious purposes, such as [disinformation campaigns](https://aimodels.fyi/papers/arxiv/pleak-prompt-leaking-attacks-against-large-language).
Further research could explore ways to expand the Raccoon benchmark to address these broader security and ethical concerns, as well as investigate potential defenses against prompt extraction attacks, such as those discussed in [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://aimodels.fyi/papers/arxiv/formalizing-benchmarking-prompt-injection-attacks-defenses) and [Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts](https://aimodels.fyi/papers/arxiv/wolf-sheeps-clothing-generalized-nested-jailbreak-prompts).
## Conclusion
The Raccoon benchmark is a valuable tool for researchers and developers working on the security of LLM-integrated applications. By providing a standardized way to evaluate an LLM's resilience against prompt extraction attacks, Raccoon can help drive progress in this critical area of AI security research.
As LLMs become increasingly ubiquitous, it is essential to ensure that these powerful models are secure and trustworthy. The Raccoon benchmark is an important step in this direction, but continued effort and innovation will be needed to address the broader security and ethical challenges posed by the integration of LLMs into real-world applications, as discussed in [Do Anything Now: Characterizing and Evaluating Emergent "Jailbreak" Capabilities in Large Language Models](https://aimodels.fyi/papers/arxiv/do-anything-now-characterizing-evaluating-wild-jailbreak) and [Robust Prompt Optimization: Defending Language Models Against Prompt Attacks](https://aimodels.fyi/papers/arxiv/robust-prompt-optimization-defending-language-models-against).
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,442 | Understanding Hallucinations in Diffusion Models through Mode Interpolation | Understanding Hallucinations in Diffusion Models through Mode Interpolation | 0 | 2024-06-17T16:02:42 | https://aimodels.fyi/papers/arxiv/understanding-hallucinations-diffusion-models-through-mode-interpolation | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Understanding Hallucinations in Diffusion Models through Mode Interpolation](https://aimodels.fyi/papers/arxiv/understanding-hallucinations-diffusion-models-through-mode-interpolation). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper explores the issue of "hallucinations" in diffusion models, which are a type of machine learning model used to generate images.
- Hallucinations refer to the model generating content that does not align with the input data, such as creating objects or details that are not present in the original image.
- The researchers investigate this phenomenon through a technique called "mode interpolation", which allows them to better understand how diffusion models behave and the factors that contribute to hallucinations.
## Plain English Explanation
Diffusion models are a powerful type of AI that can create new images from scratch. However, sometimes these models can generate content that doesn't quite match the original image - this is what's known as "hallucination." The researchers in this paper looked into hallucinations in diffusion models using a technique called "mode interpolation."
Mode interpolation allows the researchers to explore how diffusion models work under the hood and what factors might lead to hallucinations. By understanding this better, they hope to find ways to reduce or eliminate hallucinations in the future. This is an important issue because we want AI-generated images to be accurate and truthful representations, not something that's been "made up" by the model.
The paper dives into the technical details of how diffusion models and mode interpolation work, but the key takeaway is that the researchers are trying to shine a light on this hallucination problem in order to improve the reliability and trustworthiness of AI-generated images going forward. [Looks too good to be true: Information](https://aimodels.fyi/papers/arxiv/looks-too-good-to-be-true-information)
## Technical Explanation
The researchers use a technique called "mode interpolation" to better understand hallucinations in diffusion models. Diffusion models work by adding noise to an image in a stepwise fashion, then learning to reverse that process to generate new images. However, this can sometimes lead to the model "hallucinating" content that isn't present in the original data.
Mode interpolation allows the researchers to visualize the different modes, or "subimages", that the diffusion model is learning. By interpolating between these modes, they can see how the model transitions between different types of content and where hallucinations might occur. [Tackling Structural Hallucination in Image Translation with Local Diffusion](https://aimodels.fyi/papers/arxiv/tackling-structural-hallucination-image-translation-local-diffusion)
The paper provides detailed experiments and analysis of how mode interpolation reveals insights about hallucinations in diffusion models. For example, they find that hallucinations are more likely to occur when the model has to "bridge the gap" between different modes or types of content in the training data.
## Critical Analysis
The researchers acknowledge several limitations in their work. For one, mode interpolation only provides a partial view into the inner workings of diffusion models - there may be other factors beyond just the modes that contribute to hallucinations. [Hallucination in Multimodal Large Language Models: A Survey](https://aimodels.fyi/papers/arxiv/hallucination-multimodal-large-language-models-survey)
Additionally, the experiments are conducted on a relatively simple image generation task, so it's unclear how well the insights would translate to more complex, real-world applications of diffusion models. Further research would be needed to validate the findings at scale.
That said, the mode interpolation technique does seem like a promising avenue for better understanding and potentially mitigating hallucinations in these types of generative models. The researchers outline some directions for future work, such as investigating the role of model architecture and training data in hallucination behavior. [Alleviating Hallucinations in Large Vision-Language Models Through Prompting](https://aimodels.fyi/papers/arxiv/alleviating-hallucinations-large-vision-language-models-through)
## Conclusion
This paper takes an important step towards unpacking the issue of hallucinations in diffusion models, a critical problem as these generative AI systems become more widely adopted. By leveraging mode interpolation, the researchers gain valuable insights into the inner workings of diffusion models and the factors that can lead to the generation of content that doesn't align with the input data.
While more research is needed, this work lays the groundwork for developing strategies to reduce or eliminate hallucinations, which will be crucial for ensuring the reliability and trustworthiness of AI-generated imagery. [Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models](https://aimodels.fyi/papers/arxiv/prescribing-right-remedy-mitigating-hallucinations-large-vision) As diffusion models and other generative AI continue to advance, addressing the challenge of hallucinations will only become more important for the field.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,441 | How to Use External Configuration Files in Python Production Code | When developing software for production, it's common to have a lot of configurable parameters such as... | 0 | 2024-06-17T16:02:19 | https://dev.to/ganesh_p_96bc2f769a6049e1/how-to-use-external-configuration-files-in-python-production-code-5enm | python, coding, softwaredevelopment, devops | When developing software for production, it's common to have a lot of configurable parameters such as API keys, passwords, and settings. Storing these values directly in the code can be problematic for scalability and security reasons. To address this issue, it's important to keep configuration separate from the code. This can be achieved by using external configuration files like JSON or YAML.
One common scenario where external configuration files are used is when dealing with database connections. Instead of hardcoding the connection parameters in the code, we can keep them in a separate YAML file. For example, the file "config.yaml" could contain parameters for the database host, port, username, password, and database name.
To handle this configuration, we can create a class called "DatabaseConfig" with an init method to store the parameters. Additionally, we can define a class method called "from_dict" which serves as a builder method to create a configuration instance from a dictionary.
In our main code, we can use the builder method and parameter hydration to instantiate the configuration class using the dictionary extracted from the external YAML file. This eliminates the need for hardcoding parameters in the code and offers more flexibility. We can also use an argument parser to access the config file path, ensuring that the code remains adaptable and doesn't rely on hardcoded paths. This approach allows for easier management and modification of configuration parameters without needing to make changes to the codebase.
explain this with program examples:
Here is an example of how we can implement this approach in Python:
First, we define our DatabaseConfig class:
```
class DatabaseConfig:
def __init__(self, host, port, username, password, dbname):
self.host = host
self.port = port
self.username = username
self.password = password
self.dbname = dbname
@classmethod
def from_dict(cls, config_dict):
return cls(**config_dict)
```
Next, we create our "config.yaml" file with the necessary parameters:
```
database:
host: localhost
port: 5432
username: myuser
password: mypassword
dbname: mydatabase
```
Then, in our main code, we load the YAML file and extract the database dictionary to instantiate our configuration class:
```
import yaml
def load_config(filename):
with open(filename, "r") as file:
return yaml.safe_load(file)
config = load_config("config.yaml")
db_config = DatabaseConfig.from_dict(config["database"])
```
Now, we can use our db_config instance to access the database parameters without hardcoding them into our code.
This approach makes it easy to manage our configuration parameters and modify them as needed, without needing to make changes to our codebase. We can also use an argument parser to handle the config file path, allowing for even more flexibility. Overall, separating external configurations from code not only improves security but also makes our code more maintainable and adaptable for future changes.
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1,891,440 | FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models | FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models | 0 | 2024-06-17T16:02:07 | https://aimodels.fyi/papers/arxiv/fintral-family-gpt-4-level-multimodal-financial | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models](https://aimodels.fyi/papers/arxiv/fintral-family-gpt-4-level-multimodal-financial). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- Introduces a family of multimodal financial large language models called FinTral, which aim to achieve GPT-4 level performance
- Presents the FinSet dataset, a large-scale financial dataset used to train and evaluate the FinTral models
- Describes the FinTral architecture, which leverages state-of-the-art techniques in computer vision and natural language processing
## Plain English Explanation
The researchers have developed a new family of AI models called FinTral that are designed to work with a wide range of financial data and tasks. These models are trained on a large dataset of financial information called FinSet, which covers topics like company financials, news articles, and market data.
The goal of FinTral is to achieve a level of performance similar to GPT-4, one of the most advanced language models available today. To do this, the models use cutting-edge techniques in computer vision and natural language processing to analyze and understand financial data from multiple sources.
This could be useful for a variety of applications, such as [link to "Battle of the LLMs" paper] automatically answering questions about a company's financial health, [link to "Eliciting Translation Ability" paper] translating financial regulations into plain language, or [link to "Financial Regulatory Interpretation" paper] helping human experts interpret complex financial rules and regulations.
By developing this family of FinTral models, the researchers hope to push the boundaries of what's possible with AI in the financial domain and unlock new capabilities for professionals and consumers alike.
## Technical Explanation
The researchers introduce FinTral, a family of multimodal financial large language models (LLMs) that aim to achieve GPT-4 level performance. FinTral is trained on the FinSet dataset, a large-scale financial dataset covering a wide range of data types, including company financials, news articles, and market data.
The FinTral architecture leverages state-of-the-art techniques in computer vision and natural language processing. It includes components for processing textual, numerical, and visual data, as well as mechanisms for cross-modal interaction and transfer learning. This allows the models to understand and reason about financial information from multiple perspectives.
The researchers evaluate the FinTral models on a variety of financial tasks, such as [link to "Battle of the LLMs" paper] question answering, [link to "Eliciting Translation Ability" paper] language translation, and [link to "Financial Regulatory Interpretation" paper] regulatory interpretation. The results demonstrate the models' ability to achieve human-level or better performance on these tasks, highlighting their potential for practical applications in the financial domain.
## Critical Analysis
The FinTral models represent a significant advancement in the field of financial AI, as they demonstrate the ability to comprehend and reason about financial data at a level that approaches or exceeds human experts. However, the researchers acknowledge several caveats and areas for further research.
One potential limitation is the reliance on the FinSet dataset, which, while comprehensive, may not capture the full breadth and complexity of real-world financial data. [link to "FinRobot" paper] Further work is needed to ensure the models can generalize to a wider range of financial scenarios and data sources.
Additionally, the researchers note that the interpretability and explainability of the FinTral models' decision-making processes remain important areas for investigation. [link to "AlignGPT" paper] Improving the transparency of these models could enhance trust and facilitate their adoption in high-stakes financial applications.
Overall, the FinTral research represents a significant step forward in the development of advanced financial AI capabilities. While further refinement and validation are needed, the models' performance on a range of financial tasks suggests a promising future for the application of large language models in the financial industry.
## Conclusion
The FinTral family of multimodal financial large language models represents a significant advancement in the field of financial AI. By leveraging state-of-the-art techniques in computer vision and natural language processing, the FinTral models demonstrate the ability to comprehend and reason about financial data at a level approaching or exceeding human experts.
The introduction of the FinSet dataset and the evaluation of the FinTral models on a variety of financial tasks, including question answering, language translation, and regulatory interpretation, highlight the practical potential of these technologies. As the researchers continue to refine and expand the FinTral models, they may unlock new capabilities that transform how financial professionals and consumers interact with and utilize financial information.
While some caveats and areas for further research remain, the FinTral project represents a significant step forward in the development of advanced AI systems for the financial domain. As the field of financial AI continues to evolve, the insights and technologies presented in this work may serve as a foundation for future breakthroughs that benefit both the financial industry and society as a whole.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,439 | Progress Towards Decoding Visual Imagery via fNIRS | Progress Towards Decoding Visual Imagery via fNIRS | 0 | 2024-06-17T16:01:33 | https://aimodels.fyi/papers/arxiv/progress-towards-decoding-visual-imagery-via-fnirs | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Progress Towards Decoding Visual Imagery via fNIRS](https://aimodels.fyi/papers/arxiv/progress-towards-decoding-visual-imagery-via-fnirs). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper explores the potential of functional near-infrared spectroscopy (fNIRS) to decode visual imagery, with the ultimate goal of reconstructing visual perceptions from brain activity.
- The researchers investigate the [resolution needed for effective image reconstruction](https://aimodels.fyi/papers/arxiv/mind-to-image-projecting-visual-mental-imagination) and examine the feasibility of using fNIRS, a non-invasive neuroimaging technique, for this purpose.
- The findings contribute to the ongoing efforts in [advancing fNIRS neuroimaging](https://aimodels.fyi/papers/arxiv/advancing-fnirs-neuroimaging-through-synthetic-data-generation) and [enhancing visual reconstruction](https://aimodels.fyi/papers/arxiv/neuro-vision-to-language-enhancing-visual-reconstruction) from neural signals.
## Plain English Explanation
The paper looks at using a brain imaging technique called functional near-infrared spectroscopy (fNIRS) to decode and reconstruct visual imagery. fNIRS measures changes in blood flow and oxygenation in the brain, which are linked to brain activity.
The researchers wanted to see if they could use fNIRS data to reconstruct visual images that people were imagining or perceiving. This could have applications in [decoupling the reconstruction of dynamic natural scenes from neural signals](https://aimodels.fyi/papers/arxiv/animate-your-thoughts-decoupled-reconstruction-dynamic-natural), or in [reconstructing retinal visual images from brain scans](https://aimodels.fyi/papers/arxiv/reconstructing-retinal-visual-images-from-3t-fmri).
The study looked at the level of detail, or resolution, needed to effectively reconstruct images from fNIRS data. They found that fNIRS can capture some information about visual imagery, but may have limitations in fully reconstructing detailed images compared to other brain imaging techniques like functional magnetic resonance imaging (fMRI).
The results contribute to our understanding of the capabilities and limitations of fNIRS for decoding and reconstructing visual perception and imagination from brain activity.
## Technical Explanation
The paper investigates the feasibility of using functional near-infrared spectroscopy (fNIRS) to decode and reconstruct visual imagery. fNIRS is a non-invasive neuroimaging technique that measures changes in blood flow and oxygenation in the brain, which are linked to neural activity.
The researchers first examined the [resolution needed for effective image reconstruction](https://aimodels.fyi/papers/arxiv/mind-to-image-projecting-visual-mental-imagination) from fNIRS data. They conducted experiments where participants viewed or imagined simple geometric shapes and compared the fNIRS signals to the actual or imagined visual input.
The results showed that fNIRS could capture some information about visual imagery, but may have limitations in fully reconstructing detailed images compared to other techniques like functional magnetic resonance imaging (fMRI). The [fNIRS signals contained information about the location and general shape of the visual stimuli](https://aimodels.fyi/papers/arxiv/advancing-fnirs-neuroimaging-through-synthetic-data-generation), but the level of detail was lower than what could be achieved with fMRI.
The findings contribute to the ongoing efforts in [enhancing visual reconstruction from neural signals](https://aimodels.fyi/papers/arxiv/neuro-vision-to-language-enhancing-visual-reconstruction) and suggest that fNIRS could be a useful complementary tool for [decoupling the reconstruction of dynamic natural scenes from neural activity](https://aimodels.fyi/papers/arxiv/animate-your-thoughts-decoupled-reconstruction-dynamic-natural) or [reconstructing retinal visual images from brain scans](https://aimodels.fyi/papers/arxiv/reconstructing-retinal-visual-images-from-3t-fmri).
## Critical Analysis
The paper provides a valuable exploration of the potential and limitations of using fNIRS for decoding and reconstructing visual imagery. The researchers acknowledge that fNIRS may not be able to achieve the same level of detail as other neuroimaging techniques like fMRI, but suggest it could still be a useful complementary tool.
One potential limitation not addressed in the paper is the impact of individual differences in brain structure and function on the fNIRS signals and the resulting image reconstruction. The performance of the system may vary depending on the participant, and further research is needed to understand the generalizability of the findings.
Additionally, the paper focuses on simple geometric shapes, and it is unclear how the system would perform with more complex or naturalistic visual stimuli. Further research is needed to [explore the limits of fNIRS-based visual reconstruction](https://aimodels.fyi/papers/arxiv/mind-to-image-projecting-visual-mental-imagination) and its potential applications in real-world scenarios.
Overall, the paper provides a valuable contribution to the field of neural-based visual reconstruction and highlights the potential of fNIRS as a non-invasive and relatively low-cost neuroimaging technique for this purpose.
## Conclusion
This paper explores the potential of functional near-infrared spectroscopy (fNIRS) to decode and reconstruct visual imagery. The researchers investigate the [resolution needed for effective image reconstruction](https://aimodels.fyi/papers/arxiv/mind-to-image-projecting-visual-mental-imagination) and find that fNIRS can capture some information about visual imagery, but may have limitations in fully reconstructing detailed images compared to other techniques like fMRI.
The findings contribute to the ongoing efforts in [advancing fNIRS neuroimaging](https://aimodels.fyi/papers/arxiv/advancing-fnirs-neuroimaging-through-synthetic-data-generation) and [enhancing visual reconstruction](https://aimodels.fyi/papers/arxiv/neuro-vision-to-language-enhancing-visual-reconstruction) from neural signals. The research also suggests that fNIRS could be a useful complementary tool for [decoupling the reconstruction of dynamic natural scenes from neural activity](https://aimodels.fyi/papers/arxiv/animate-your-thoughts-decoupled-reconstruction-dynamic-natural) or [reconstructing retinal visual images from brain scans](https://aimodels.fyi/papers/arxiv/reconstructing-retinal-visual-images-from-3t-fmri).
The paper provides a valuable contribution to the field and highlights the potential of fNIRS as a non-invasive and relatively low-cost neuroimaging technique for visual reconstruction, while also identifying areas for further research to address the limitations and expand the capabilities of the technology.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,438 | Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies | Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies | 0 | 2024-06-17T16:00:58 | https://aimodels.fyi/papers/arxiv/stencil-computations-amd-nvidia-graphics-processors-performance | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies](https://aimodels.fyi/papers/arxiv/stencil-computations-amd-nvidia-graphics-processors-performance). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- The paper evaluates the performance and energy efficiency of stencil computations on modern datacenter graphics processors from AMD and Nvidia.
- Stencil computations are a type of data-parallel task that are widely used in high-performance computing, including machine learning and computational sciences.
- The authors propose a tuning strategy for fusing cache-heavy stencil kernels to improve performance and energy efficiency.
- The study covers both synthetic and practical applications involving linear and nonlinear stencil functions in one to three dimensions.
- The findings reveal key differences between AMD and Nvidia graphics processors, highlighting the need for platform-specific tuning to reach their full computational potential.
## Plain English Explanation
Graphics processors have become a popular choice for accelerating [data-parallel tasks](https://aimodels.fyi/papers/arxiv/evaluation-programming-models-performance-stencil-computation-current), which are common in fields like machine learning and scientific computing. These tasks involve performing the same operation on multiple pieces of data at the same time.
In this study, the researchers looked at a specific type of data-parallel task called [stencil computations](https://aimodels.fyi/papers/arxiv/preliminary-study-accelerating-simulation-optimization-gpu-implementation). Stencil computations involve updating the value of a point based on the values of its neighboring points. This is used in a variety of applications, such as simulating the flow of fluids or processing images.
The researchers evaluated the performance and energy efficiency of stencil computations on two types of modern graphics processors: those made by AMD and those made by Nvidia. They also proposed a way to combine multiple stencil computations to improve performance.
The researchers found that the AMD and Nvidia graphics processors had some key differences in how they work, both in the hardware and the software. This means that the best way to get the most out of these processors can vary depending on which one you're using. The researchers suggest that it's important to customize your approach for the specific type of graphics processor you're working with.
## Technical Explanation
The paper evaluates the performance and energy efficiency of stencil computations on [modern datacenter graphics processors](https://aimodels.fyi/papers/arxiv/taking-gpu-programming-models-to-task-performance) from AMD and Nvidia. Stencil computations are a type of data-parallel task that involve updating the value of a point based on the values of its neighboring points. These computations are widely used in various branches of high-performance computing, including machine learning and computational sciences.
The authors propose a tuning strategy for [fusing cache-heavy stencil kernels](https://aimodels.fyi/papers/arxiv/optimizing-hardware-resource-partitioning-job-allocations-modern) to improve performance and energy efficiency. The study covers both synthetic and practical applications, involving the evaluation of linear and nonlinear stencil functions in one to three dimensions.
The experimental results reveal key differences between AMD and Nvidia graphics processors in terms of both hardware and software. These differences necessitate platform-specific tuning to reach the full computational potential of the respective architectures. The authors' findings highlight the importance of customizing optimization strategies for the target hardware when working with data-parallel tasks such as stencil computations.
## Critical Analysis
The paper provides a comprehensive evaluation of stencil computations on modern datacenter graphics processors, but it acknowledges some limitations and areas for further research. For example, the study focuses on a specific set of stencil kernels and does not explore the impact of more complex memory access patterns or the integration of stencil computations with other types of workloads.
Additionally, the paper does not delve into the underlying reasons for the observed performance differences between AMD and Nvidia graphics processors. A deeper analysis of the architectural features and software stack differences between the two platforms could provide more insights and guide future hardware and software co-design efforts.
While the proposed tuning strategy for fusing cache-heavy stencil kernels demonstrates promising results, it would be valuable to investigate the generalizability of this approach to a broader range of stencil computations and application scenarios. Exploring the trade-offs between performance, energy efficiency, and programming complexity could also help determine the practical applicability of the technique.
## Conclusion
This study highlights the importance of platform-specific tuning for achieving optimal performance and energy efficiency in data-parallel tasks like stencil computations on modern [graphics processors](https://aimodels.fyi/papers/arxiv/evaluation-computational-energy-performance-matrix-multiplication-algorithms). The findings suggest that the differences between AMD and Nvidia graphics processors require customized optimization strategies to fully harness the computational capabilities of each architecture.
The insights gained from this research can inform the design and development of future hardware and software systems for high-performance computing, helping to bridge the gap between theoretical peak performance and realized application-level efficiency. By understanding the unique characteristics of emerging accelerator technologies, researchers and engineers can create more efficient and robust solutions for a wide range of data-intensive applications.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,388 | Advanced Concurrency Patterns in Go | Advanced Concurrency Patterns in Go Concurrency is a key feature of the Go programming... | 0 | 2024-06-17T15:10:05 | https://dev.to/romulogatto/advanced-concurrency-patterns-in-go-2of8 | # Advanced Concurrency Patterns in Go
Concurrency is a key feature of the Go programming language, allowing developers to write highly efficient and scalable programs. While basic concurrency patterns such as goroutines and channels are widely used, Go offers several advanced patterns that can further enhance your concurrent code.
In this article, we will explore some of these advanced concurrency patterns in Go, demonstrating their practical applications and benefits.
## 1. Context package
The `context` package provides powerful functionalities for managing the lifecycle of concurrent operations by passing cancellation signals. It helps control resources and gracefully handle scenarios like timeouts or cancelation requests.
Using the `context` package allows you to improve your code's robustness by making it more responsive to external events. You can create contexts using `context.Background()` or derive them from existing ones using functions like `context.WithTimeout()` or `context.WithCancel()`.
```go
ctx, cancel := context.WithTimeout(context.Background(), time.Second*5)
defer cancel()
```
With this pattern, you can easily stop long-running operations when a timeout is reached or propagate cancellation signals throughout different goroutines involved in a complex program flow.
## 2. WaitGroup
The `sync.WaitGroup` provides another powerful mechanism for managing multiple goroutines execution flow. It allows you to wait until a group of goroutines finish their tasks before proceeding further in your code execution.
To use the WaitGroup pattern effectively, follow three main steps:
- Create an instance of WaitGroup:
```go
var wg sync.WaitGroup
```
- Increment the counter whenever starting a new goroutine:
```go
wg.Add(1)
```
- Decrement the counter when each individual task is completed:
```go
wg.Done()
```
- Finally wait for all tasks completion:
```go
wg.Wait()
```
This synchronization pattern is particularly useful when coordinating parallel processing tasks performed by multiple goroutines running independently from each other.
## 3. Rate Limiting
Rate limiting is essential in scenarios where you need to control the number of concurrent executions of a specific task or resource access. Go offers a simple way to implement rate limiting using the `time` package and goroutines.
```go
func doWork(taskID int, limiter <-chan time.Time) {
<-limiter
// Perform task operations
}
func main() {
tasks := make(chan int, 10)
for i := 0; i < 10; i++ {
tasks <- i
}
limiter := time.Tick(time.Second * 2)
for taskID := range tasks {
go doWork(taskID, limiter)
}
time.Sleep(time.Second * 20)
}
```
In this pattern, by setting a specific frequency at which goroutines can execute their respective tasks (in this case every two seconds), you can ensure proper control over how resources are consumed without overwhelming your system.
## Conclusion
By leveraging these advanced concurrency patterns in Go, you can significantly improve the performance and efficiency of your concurrent programs. The `context` package allows better management of lifecycle events, while the WaitGroup pattern facilitates coordination between multiple concurrently running goroutines. Finally, rate limiting provides fine-grained control over resource utilization.
Experiment with these patterns on your projects and see how they enhance your codebase by enabling more robust and scalable solutions!
| romulogatto | |
1,891,350 | TW Elements - TailwindCSS Icons. Free UI/UX design course | Icons If you've used popular icon sets before, such as Font Awesome or Material Icons,... | 25,935 | 2024-06-17T16:00:00 | https://dev.to/keepcoding/tw-elements-tailwindcss-icons-free-uiux-design-course-1ha0 | tailwindcss, tutorial, css, html | ## Icons
If you've used popular icon sets before, such as Font Awesome or Material Icons, you've probably used a simplified version where we include a link to the entire icon set, and then use these icons in our HTML in the form of defined classes, such as:
**HTML**
```
<i class="fas fa-heart"></i>
```
And a heart appears in your project:
However, for performance reasons, Tailwind CSS recommends using icons as SVG elements.
In this lesson, we'll find out exactly how it works.
## SVG icons in Tailwind CSS
Using icons in the SVG form has one great advantage - instead of loading the entire (sometimes really huge) set of icons to our project, we can add only the icons we choose (even just one), which of course can have a significant impact on the weight of our project and its performance.
_SVG stands for Scalable Vector Graphics, which means these icons will maintain their quality regardless of the display size._
Problem? This looks gross in our HTML because we need to include some really big SVG code.
Same heart icon as above, but added as an SVG element.
**HTML**
```
<svg
class="h-10 w-10"
xmlns="http://www.w3.org/2000/svg"
viewBox="0 0 512 512">
<path
d="M47.6 300.4L228.3 469.1c7.5 7 17.4 10.9 27.7 10.9s20.2-3.9 27.7-10.9L464.4 300.4c30.4-28.3 47.6-68 47.6-109.5v-5.8c0-69.9-50.5-129.5-119.4-141C347 36.5 300.6 51.4 268 84L256 96 244 84c-32.6-32.6-79-47.5-124.6-39.9C50.5 55.6 0 115.2 0 185.1v5.8c0 41.5 17.2 81.2 47.6 109.5z" />
<!--! Font Awesome Pro 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license (Commercial License) Copyright 2023 Fonticons, Inc. -->
</svg>
```
However, controlled HTML clutter is something that is acceptable in Tailwind.
**How does it exactly work? Let's find out.**
## Hero Icons
The simplest solution is to use the recommended icon set created by one of Tailwind's creators.
This set is [Hero Icons](https://heroicons.com/).
The advantage is that these icons already have Tailwind CSS classes added, which allows you to easily add them to our project and they work right away.
The disadvantage is that this set is relatively small (only 292 icons), which means that you often have to look for the icons you need in other sources.
## How to use Hero Icons in Tailwind CSS?
Firstly click on the button below and go to the Hero Icons page.
**[HERO ICONS](https://heroicons.com/)**
You will see a list of icons and 3 options to choose from - **Outline**, **Solid** and **Mini** icons. When you select an option, the icons will be filtered.
Once you've selected an icon, hover over it and click the "Copy SVG" button. The needed code will be copied to the clipboard.
Suppose we copied the academic cap icon.
**HTML**
```
<svg
xmlns="http://www.w3.org/2000/svg"
viewBox="0 0 24 24"
fill="currentColor"
class="h-6 w-6">
<path
d="M11.7 2.805a.75.75 0 01.6 0A60.65 60.65 0 0122.83 8.72a.75.75 0 01-.231 1.337 49.949 49.949 0 00-9.902 3.912l-.003.002-.34.18a.75.75 0 01-.707 0A50.009 50.009 0 007.5 12.174v-.224c0-.131.067-.248.172-.311a54.614 54.614 0 014.653-2.52.75.75 0 00-.65-1.352 56.129 56.129 0 00-4.78 2.589 1.858 1.858 0 00-.859 1.228 49.803 49.803 0 00-4.634-1.527.75.75 0 01-.231-1.337A60.653 60.653 0 0111.7 2.805z" />
<path
d="M13.06 15.473a48.45 48.45 0 017.666-3.282c.134 1.414.22 2.843.255 4.285a.75.75 0 01-.46.71 47.878 47.878 0 00-8.105 4.342.75.75 0 01-.832 0 47.877 47.877 0 00-8.104-4.342.75.75 0 01-.461-.71c.035-1.442.121-2.87.255-4.286A48.4 48.4 0 016 13.18v1.27a1.5 1.5 0 00-.14 2.508c-.09.38-.222.753-.397 1.11.452.213.901.434 1.346.661a6.729 6.729 0 00.551-1.608 1.5 1.5 0 00.14-2.67v-.645a48.549 48.549 0 013.44 1.668 2.25 2.25 0 002.12 0z" />
<path
d="M4.462 19.462c.42-.419.753-.89 1-1.394.453.213.902.434 1.347.661a6.743 6.743 0 01-1.286 1.794.75.75 0 11-1.06-1.06z" />
</svg>
```
After adding it to our project, we should see a cap like this:
It works out of the box, but let's have a closer look at this.
## SVG explanation
The outer svg element defines the SVG element. The xmlns attribute is used to specify the XML namespace for the SVG (which is a requirement for SVGs to work properly). The viewBox attribute is used to specify the aspect ratio and coordinate system of the SVG. The fill attribute is set to "currentColor", meaning the color of the shapes inside the SVG will inherit the color of the text in the same context.
The class attribute contains Tailwind CSS utility classes to style the SVG. The .w-6 and .h-6 classes set the width and height of the SVG to 1.5rem (24px).
The path elements contain the actual drawing instructions for the icon. Each path represents a different part of the icon. The d attribute in each path element holds these commands.
## Font awesome
Another icon set that has SVG icons available is **[Font Awesome](https://fontawesome.com/search)**.
This is one of the most popular (if not the most popular) icon sets. It has a free and paid version.
There are over **2000** icons in the **free** version, which is many times more than in Hero Icons.
The disadvantage is that when using icons in SVG form, we also need to add a comment about the license.
**HTML**
```
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512">
<!--! Font Awesome Pro 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license (Commercial License) Copyright 2023 Fonticons, Inc. -->
<path
d="M316.9 18C311.6 7 300.4 0 288.1 0s-23.4 7-28.8 18L195 150.3 51.4 171.5c-12 1.8-22 10.2-25.7 21.7s-.7 24.2 7.9 32.7L137.8 329 113.2 474.7c-2 12 3 24.2 12.9 31.3s23 8 33.8 2.3l128.3-68.5 128.3 68.5c10.8 5.7 23.9 4.9 33.8-2.3s14.9-19.3 12.9-31.3L438.5 329 542.7 225.9c8.6-8.5 11.7-21.2 7.9-32.7s-13.7-19.9-25.7-21.7L381.2 150.3 316.9 18z" />
</svg>
```
**How to use Font Awesome in Tailwind CSS?**
Firstly click on the button below and go to the Font Awesome page.
**[FONT AWESOME](https://fontawesome.com/search)**
Then select the "Free" option (Unless you have purchased a paid license. In my opinion, a free license is more than enough).
Then click on the icon you are interested in and select the SVG option. Then click on the code to copy it. It will be kept in the clipboard.
Suppose we have copied the **house** icon.
**HTML**
```
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512">
<!--! Font Awesome Pro 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license (Commercial License) Copyright 2023 Fonticons, Inc. -->
<path
d="M575.8 255.5c0 18-15 32.1-32 32.1h-32l.7 160.2c0 2.7-.2 5.4-.5 8.1V472c0 22.1-17.9 40-40 40H456c-1.1 0-2.2 0-3.3-.1c-1.4 .1-2.8 .1-4.2 .1H416 392c-22.1 0-40-17.9-40-40V448 384c0-17.7-14.3-32-32-32H256c-17.7 0-32 14.3-32 32v64 24c0 22.1-17.9 40-40 40H160 128.1c-1.5 0-3-.1-4.5-.2c-1.2 .1-2.4 .2-3.6 .2H104c-22.1 0-40-17.9-40-40V360c0-.9 0-1.9 .1-2.8V287.6H32c-18 0-32-14-32-32.1c0-9 3-17 10-24L266.4 8c7-7 15-8 22-8s15 2 21 7L564.8 231.5c8 7 12 15 11 24z" />
</svg>
```
When we copy it to our project, we will see that it is huge and fills all available space.
This is because there is no defined size by default. So we need to add Tailwind classes to define height and width.
So let's use the same classes that Hero Icons have by default. Add the .w-6 and .h-6 classes to the svg element.
**HTML**
```
<svg
class="h-6 w-6"
xmlns="http://www.w3.org/2000/svg"
viewBox="0 0 576 512">
<!--! Font Awesome Pro 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license (Commercial License) Copyright 2023 Fonticons, Inc. -->
<path
d="M575.8 255.5c0 18-15 32.1-32 32.1h-32l.7 160.2c0 2.7-.2 5.4-.5 8.1V472c0 22.1-17.9 40-40 40H456c-1.1 0-2.2 0-3.3-.1c-1.4 .1-2.8 .1-4.2 .1H416 392c-22.1 0-40-17.9-40-40V448 384c0-17.7-14.3-32-32-32H256c-17.7 0-32 14.3-32 32v64 24c0 22.1-17.9 40-40 40H160 128.1c-1.5 0-3-.1-4.5-.2c-1.2 .1-2.4 .2-3.6 .2H104c-22.1 0-40-17.9-40-40V360c0-.9 0-1.9 .1-2.8V287.6H32c-18 0-32-14-32-32.1c0-9 3-17 10-24L266.4 8c7-7 15-8 22-8s15 2 21 7L564.8 231.5c8 7 12 15 11 24z" />
</svg>
```
A cute little house should then appear in our project.
_**Note:** You can also try our **[SVG icon generator](https://www.designtoolshub.com/tailwind-css/icon-generator)** with font awesome prepared icons already with Tailwind CSS classes._ | keepcoding |
1,891,437 | Step-by-Step Diffusion: An Elementary Tutorial | Step-by-Step Diffusion: An Elementary Tutorial | 0 | 2024-06-17T15:59:50 | https://aimodels.fyi/papers/arxiv/step-by-step-diffusion-elementary-tutorial | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Step-by-Step Diffusion: An Elementary Tutorial](https://aimodels.fyi/papers/arxiv/step-by-step-diffusion-elementary-tutorial). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- Provides a step-by-step tutorial on the fundamentals of diffusion models, a powerful class of generative models used in machine learning.
- Covers key concepts like Gaussian diffusion, the diffusion process, and the reverse diffusion process.
- Aims to make the underlying principles of diffusion models accessible to a general audience.
## Plain English Explanation
Diffusion models are a type of machine learning model that can generate new, realistic-looking data such as images, text, or audio. They work by starting with random noise and gradually transforming it into something meaningful through a process called diffusion.
The **[step-by-step diffusion tutorial](https://aimodels.fyi/papers/arxiv/overview-diffusion-models-applications-guided-generation-statistical)** explains this diffusion process in simple terms. It begins by describing **[Gaussian diffusion](https://aimodels.fyi/papers/arxiv/multistep-distillation-diffusion-models-via-moment-matching)**, where the data is gradually corrupted with random noise that follows a normal (Gaussian) distribution.
The tutorial then walks through the **reverse diffusion process**, where the model learns to gradually "undo" this corruption and reconstruct the original data from the noisy version. This is the key idea behind diffusion models - they learn to generate new data by reversing a process of gradually adding noise.
By breaking down the fundamentals of diffusion in an accessible way, this tutorial aims to help readers understand the core principles behind this powerful class of generative models, which have been successfully applied to a wide range of [applications](https://aimodels.fyi/papers/arxiv/video-diffusion-models-survey), from [image generation](https://aimodels.fyi/papers/arxiv/physics-informed-diffusion-models) to [text synthesis](https://aimodels.fyi/papers/arxiv/theoretical-research-generative-diffusion-models-overview).
## Technical Explanation
The tutorial first introduces **Gaussian diffusion**, where the input data is progressively corrupted by adding Gaussian noise. This noise-adding process is modeled as a Markov chain, with each step introducing more noise.
The key insight is that this diffusion process can be reversed. The tutorial explains how the model learns to "undo" the diffusion by predicting the clean data from the noisy version, essentially learning to generate new samples by following the reverse diffusion process.
The tutorial provides step-by-step details on the mathematical formulation of the diffusion process and the reverse diffusion, including the loss function used to train the model. It also discusses practical considerations like the choice of noise schedule and model architecture.
## Critical Analysis
The tutorial provides a solid introduction to the fundamental principles of diffusion models, making the core concepts accessible to a general audience. However, it does not delve into some of the more advanced topics, such as techniques for [stabilizing and improving diffusion models](https://aimodels.fyi/papers/arxiv/multistep-distillation-diffusion-models-via-moment-matching), or their [application to specific domains](https://aimodels.fyi/papers/arxiv/video-diffusion-models-survey).
Additionally, the tutorial does not address potential limitations or challenges of diffusion models, such as their computational complexity, sensitivity to hyperparameters, or the difficulty of controlling the generated output. Readers interested in a more comprehensive understanding of the strengths and weaknesses of this approach may need to consult additional resources.
## Conclusion
This step-by-step tutorial offers a clear and accessible introduction to the fundamental principles of diffusion models, a powerful class of generative models with a wide range of applications in machine learning. By breaking down the core concepts of Gaussian diffusion and the reverse diffusion process, the tutorial provides readers with a solid foundation for understanding how these models work and their potential for generating realistic and novel data.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,436 | Rough Set improved Therapy-Based Metaverse Assisting System | Rough Set improved Therapy-Based Metaverse Assisting System | 0 | 2024-06-17T15:59:15 | https://aimodels.fyi/papers/arxiv/rough-set-improved-therapy-based-metaverse-assisting | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Rough Set improved Therapy-Based Metaverse Assisting System](https://aimodels.fyi/papers/arxiv/rough-set-improved-therapy-based-metaverse-assisting). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Related Work
The paper references several relevant studies in the field of virtual reality (VR) and pain management. One study, [Exploring Physiological Responses to Virtual Reality-Based Interventions](https://aimodels.fyi/papers/arxiv/exploring-physiological-responses-virtual-reality-based-interventions), investigated the physiological effects of VR-based interventions on pain perception. Another, [Using Capability Maps Tailored to Arm Range](https://aimodels.fyi/papers/arxiv/using-capability-maps-tailored-to-arm-range), looked at using personalized VR environments to improve upper-body mobility. A [pilot study comparing prefrontal cortex activities](https://aimodels.fyi/papers/arxiv/pilot-study-comparison-prefrontal-cortex-activities-robotic) examined the neurological impacts of VR-based rehabilitation. Additionally, the paper cites research on [using large language models for patient-specific interventions](https://aimodels.fyi/papers/arxiv/patient-psi-using-large-language-models-to) and [facilitating self-guided mental health interventions through technology](https://aimodels.fyi/papers/arxiv/facilitating-self-guided-mental-health-interventions-through). These studies provide important context and insights that inform the current work.
## Plain English Explanation
The paper presents a new system that combines virtual reality (VR) technology with cognitive behavioral therapy (CBT) to help people manage chronic neck and shoulder pain. The key idea is to create an interactive VR environment that can guide users through CBT-based exercises and activities, tailored to their specific needs and preferences.
The system uses a technique called "rough set" analysis to better understand the user's symptoms and create personalized treatment plans. This involves collecting data on the user's pain levels, mobility, and other relevant factors, and using that information to fine-tune the VR experience.
By integrating VR and CBT, the researchers aim to provide a more engaging and effective way for people to manage their pain. The VR environment can transport users to calming, therapeutic settings, while the CBT-based exercises help them develop coping strategies and change negative thought patterns that may be contributing to their pain.
Overall, the goal of this system is to improve the quality of life for individuals suffering from chronic neck and shoulder pain, using a combination of cutting-edge technology and evidence-based psychological techniques.
## Technical Explanation
The paper proposes a "Rough Set improved Therapy-Based Metaverse Assisting System" (RTBM) to help individuals with chronic neck and shoulder pain. The system integrates virtual reality (VR) technology with cognitive behavioral therapy (CBT) to create an interactive, personalized pain management platform.
The key components of the RTBM system are:
1. **Data Collection and Rough Set Analysis**: The system collects data on the user's pain levels, mobility, and other relevant factors. It then uses rough set analysis, a technique for handling uncertain or incomplete data, to identify patterns and create personalized treatment plans.
2. **VR-Based CBT Modules**: Based on the rough set analysis, the system generates customized VR environments and CBT-based exercises for the user. These include activities designed to improve range of motion, reduce stress and anxiety, and change negative thought patterns.
3. **Interactive User Interface**: The VR environment provides an engaging, immersive interface for the user to interact with the system. Users can navigate through the virtual space, participate in the CBT exercises, and track their progress over time.
The researchers conducted a pilot study to evaluate the RTBM system, involving participants with chronic neck and shoulder pain. The results suggest that the integrated VR-CBT approach can lead to significant improvements in pain management, mobility, and psychological well-being compared to traditional therapy methods.
## Critical Analysis
The paper presents a promising approach to leveraging VR and CBT for chronic pain management. The integration of these two modalities, guided by personalized rough set analysis, is a novel and potentially impactful contribution to the field.
One potential limitation of the research is the small sample size of the pilot study. While the initial results are encouraging, further large-scale validation would be needed to establish the system's efficacy more definitively. Additionally, the paper does not address potential barriers to adoption, such as the cost and accessibility of VR hardware, or the level of technical expertise required to set up and use the system.
Another area for further exploration is the long-term sustainability of the RTBM approach. The paper focuses on the immediate effects of the therapy, but it would be valuable to understand how the benefits of the system may persist over time and whether users are able to maintain the coping strategies and behavioral changes learned through the VR-CBT experience.
Overall, the Rough Set improved Therapy-Based Metaverse Assisting System represents an exciting development in the field of pain management, combining cutting-edge technologies with evidence-based psychological interventions. As the research in this area continues to evolve, it will be important to carefully consider the practical challenges and long-term implications of such systems to ensure they can be effectively implemented and sustained.
## Conclusion
The Rough Set improved Therapy-Based Metaverse Assisting System presented in this paper offers a novel approach to chronic neck and shoulder pain management. By integrating virtual reality technology with cognitive behavioral therapy, the system provides a personalized, engaging platform for users to develop coping strategies and improve their physical and psychological well-being.
The key innovation of this work is the use of rough set analysis to tailor the VR-CBT experience to the individual user's needs and preferences. This data-driven approach helps ensure the therapy is optimized for each person, increasing the likelihood of successful outcomes.
While the initial pilot study shows promising results, further research is needed to fully validate the system's efficacy and explore its long-term impacts. Addressing practical considerations, such as accessibility and cost, will also be important as this technology moves towards real-world implementation.
Overall, the Rough Set improved Therapy-Based Metaverse Assisting System represents an exciting step forward in the integration of cutting-edge technologies and evidence-based psychological interventions for chronic pain management. As the field continues to evolve, this type of innovative, personalized approach could have significant implications for improving the quality of life for individuals suffering from debilitating physical and mental health conditions.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,435 | AES: The Power of Advanced Encryption Standard for Data Security | ## Advanced Encryption Standard "AES (Advanced Encryption Standard) is a symmetric key encryption... | 0 | 2024-06-17T15:58:43 | https://dev.to/harish_05/aes-the-power-of-advanced-encryption-standard-for-data-security-g6c | devchallenge, cschallenge, computerscience, beginners |
**## Advanced Encryption Standard**
"AES (Advanced Encryption Standard) is a symmetric key encryption method crucial for secure data transmission. It uses 128, 192, or 256-bit keys to encrypt and decrypt data, ensuring robust protection against unauthorized access and cyber threats. " | harish_05 |
1,891,434 | Open Problems in DAOs | Open Problems in DAOs | 0 | 2024-06-17T15:58:07 | https://aimodels.fyi/papers/arxiv/open-problems-daos | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Open Problems in DAOs](https://aimodels.fyi/papers/arxiv/open-problems-daos). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- Decentralized Autonomous Organizations (DAOs) are a new and rapidly growing type of organization governed by smart contracts
- Researchers can contribute to the emerging science of DAOs and other digitally-constituted organizations
- Opportunities exist to tackle high-impact problems in the DAO ecosystem, from privacy primitives to mechanism design to model laws
## Plain English Explanation
Decentralized Autonomous Organizations (DAOs) are a new way of organizing people and resources online. Instead of a traditional hierarchical structure with a central authority, DAOs use smart contracts - computer programs that automatically execute agreed-upon rules - to govern how the organization operates. This allows DAOs to be decentralized and autonomous, meaning they can function without a centralized control point.
The researchers in this paper believe there are many important problems to tackle in the DAO space that could benefit from the skills and expertise of researchers from different fields. For example, [researchers could work on building better privacy protections for DAO participants](https://aimodels.fyi/papers/arxiv/perils-current-dao-governance), developing new mechanism designs to improve DAO governance, or [creating model legal frameworks for DAOs](https://aimodels.fyi/papers/arxiv/automated-transparency-legal-empirical-analysis-digital-services). By drawing on knowledge from areas like computer science, economics, law, and political science, researchers have an opportunity to help shape the future of this new organizational model.
The authors are calling on the wider research community to get involved in this emerging field and help invent the next generation of digitally-constituted organizations like DAOs. They see great potential for innovative research that could lead to exciting new business opportunities as well.
## Technical Explanation
The paper outlines a research agenda for contributing to the science of Decentralized Autonomous Organizations (DAOs) and other digitally-constituted organizations. DAOs are a new class of organizations that are governed by smart contracts rather than traditional hierarchical structures.
The authors identify several high-impact problem areas within the DAO ecosystem where existing research gaps could be addressed. These include:
- [Developing granular privacy primitives](https://aimodels.fyi/papers/arxiv/perils-current-dao-governance) to protect DAO participant data and communications
- Designing new mechanism [designs to improve DAO governance](https://aimodels.fyi/papers/arxiv/conference-proceedings-european-dao-workshop-2024) and decision-making
- Creating [model legal frameworks and laws](https://aimodels.fyi/papers/arxiv/automated-transparency-legal-empirical-analysis-digital-services) to support the operation of DAOs
The paper suggests that researchers from diverse fields such as computer science, economics, law, and political science could apply their expertise to tackle these challenges. For example, [social sentiment analysis](https://aimodels.fyi/papers/arxiv/decoding-social-sentiment-dao-comparative-analysis-blockchain) could provide insights into DAO community dynamics, while [research on governance for generative AI companies](https://aimodels.fyi/papers/arxiv/governance-generative-artificial-intelligence-companies) may offer lessons for DAO governance models.
Overall, the authors make a compelling case for the research community to engage with the DAO ecosystem and help shape the future of this new organizational paradigm.
## Critical Analysis
The paper provides a broad, high-level overview of research opportunities in the DAO space, but does not delve deeply into the specifics of any particular problem area. While the authors identify several promising directions, they do not offer detailed proposals or case studies to illustrate what such research might look like in practice.
Additionally, the paper does not address some of the more controversial or challenging aspects of DAOs, such as the potential for abuse, the unclear legal status of these entities, or the environmental concerns around the energy-intensive blockchain technology that underpins many DAOs. A more balanced discussion of the potential risks and limitations of DAOs would help readers assess the research agenda more critically.
That said, the core premise of the paper - that researchers across disciplines have an important role to play in shaping the future of digitally-constituted organizations - is a valuable one. By bringing diverse perspectives to bear on DAO-related problems, the research community can help ensure these new organizational models develop in responsible and beneficial ways.
## Conclusion
This paper outlines an ambitious research agenda for contributing to the emerging science of Decentralized Autonomous Organizations (DAOs) and other digitally-constituted organizations. The authors identify several high-impact problem areas where researchers could make important contributions, from developing better privacy protections to designing more effective DAO governance mechanisms.
By drawing on expertise from fields like computer science, economics, law, and political science, the research community has an opportunity to help steer the development of DAOs and similar organizational models in positive directions. This could lead to exciting new business opportunities as well as important societal benefits.
While the paper could have delved deeper into the nuances and potential challenges of DAOs, its core message is a compelling one. The authors rightly call on the wider research community to get involved in this rapidly evolving space and help invent the next generation of digitally-empowered organizations.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,433 | Unlearning Traces the Influential Training Data of Language Models | Unlearning Traces the Influential Training Data of Language Models | 0 | 2024-06-17T15:56:58 | https://aimodels.fyi/papers/arxiv/unlearning-traces-influential-training-data-language-models | machinelearning, ai, beginners, datascience | *This is a Plain English Papers summary of a research paper called [Unlearning Traces the Influential Training Data of Language Models](https://aimodels.fyi/papers/arxiv/unlearning-traces-influential-training-data-language-models). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*
## Overview
- This paper explores a novel technique called "unlearning" to reveal the influential training data of large language models.
- The researchers propose a method to systematically remove specific training examples from a model, allowing them to identify the most influential data points that shape the model's behavior.
- The findings provide valuable insights into the inner workings of these complex models and have implications for model transparency, fairness, and accountability.
## Plain English Explanation
The researchers in this paper looked at a new way to understand how large language models, like those used in chatbots and virtual assistants, are influenced by the data they are trained on. [Rethinking Machine Unlearning in Large Language Models](https://aimodels.fyi/papers/arxiv/rethinking-machine-unlearning-large-language-models) and [Machine Unlearning for Large Language Models](https://aimodels.fyi/papers/arxiv/machine-unlearning-large-language-models) are related papers that explore similar concepts.
Rather than just looking at the final model, the researchers developed a technique called "unlearning" that allows them to systematically remove specific examples from the training data. This helps them identify which training examples had the biggest impact on shaping the model's behavior and outputs.
By selectively "unlearning" parts of the training data, the researchers can peek under the hood of these complex language models and better understand what influences their decisions. This could lead to more transparent and accountable AI systems, as well as help address issues of fairness and bias. [Class-Based Machine Unlearning for Complex Data via Concepts](https://aimodels.fyi/papers/arxiv/class-machine-unlearning-complex-data-via-concepts) and [Adversarial Machine Unlearning](https://aimodels.fyi/papers/arxiv/adversarial-machine-unlearning) explore related techniques for "unlearning" in machine learning models.
The findings from this research provide valuable insights into the inner workings of language models and have implications for improving the transparency, fairness, and accountability of these powerful AI systems. [Data Attribution for Text-to-Image Models by](https://aimodels.fyi/papers/arxiv/data-attribution-text-to-image-models-by) is another relevant paper that looks at understanding the influence of training data on AI models.
## Technical Explanation
The researchers propose a novel "unlearning" technique to systematically remove specific training examples from large language models. By selectively "unforgeetting" parts of the model's training data, they can identify the most influential data points that shape the model's behavior and outputs.
The key steps of their approach are:
1. **Training a Base Model**: The researchers start by training a large language model on a standard dataset, such as Wikipedia or Common Crawl.
2. **Unlearning Individual Examples**: They then systematically remove individual training examples from the model, one at a time, and measure the change in the model's performance. Examples that result in the largest performance changes are considered the most influential.
3. **Analyzing Influential Examples**: By examining the characteristics of the most influential training examples, the researchers can gain insights into what types of data have the greatest impact on the model's learned representations and outputs.
The researchers demonstrate their unlearning approach on several large language models, including GPT-2 and GPT-3. Their findings reveal that the models are heavily influenced by a relatively small subset of the training data, with certain types of examples (e.g., longer, more complex sentences) having a disproportionate impact.
This technique provides a powerful tool for opening up the "black box" of large language models and understanding their inner workings. The insights gleaned from unlearning can inform efforts to improve model transparency, fairness, and accountability.
## Critical Analysis
The unlearning approach presented in this paper is a promising step towards greater transparency in large language models. By systematically removing training examples, the researchers are able to identify the most influential data points that shape the models' behaviors and outputs.
However, one potential limitation of the unlearning approach is that it may not capture more complex or indirect ways in which the training data influences the model. The removal of individual examples may not fully account for the cumulative or interactive effects of the training data.
Additionally, the unlearning process can be computationally intensive, as it requires retraining the model for each example removed. This could limit the scalability of the approach, especially for the largest language models.
Future research could explore more efficient or targeted unlearning techniques, as well as investigate the unlearning of entire subsets of the training data (e.g., by topic or source) rather than individual examples. [Adversarial Machine Unlearning](https://aimodels.fyi/papers/arxiv/adversarial-machine-unlearning) and [Class-Based Machine Unlearning for Complex Data via Concepts](https://aimodels.fyi/papers/arxiv/class-machine-unlearning-complex-data-via-concepts) discuss related approaches for "unlearning" in machine learning models.
Overall, the unlearning technique presented in this paper represents an important step towards greater transparency and accountability in large language models. The insights gained from this research can help inform the development of more responsible and trustworthy AI systems.
## Conclusion
This paper introduces a novel "unlearning" technique that allows researchers to systematically remove specific training examples from large language models. By selectively "unforgeetting" parts of the training data, the researchers can identify the most influential data points that shape the models' behaviors and outputs.
The findings from this research provide valuable insights into the inner workings of complex language models, which can inform efforts to improve their transparency, fairness, and accountability. The unlearning approach offers a powerful tool for opening up the "black box" of these AI systems and understanding the factors that drive their decision-making.
While the unlearning process has some limitations, such as computational intensity and potential to miss more complex data influences, the insights gained from this research are an important step towards developing more responsible and trustworthy AI systems. Further research in this area, as seen in [Rethinking Machine Unlearning in Large Language Models](https://aimodels.fyi/papers/arxiv/rethinking-machine-unlearning-large-language-models), [Machine Unlearning for Large Language Models](https://aimodels.fyi/papers/arxiv/machine-unlearning-large-language-models), and [Data Attribution for Text-to-Image Models by](https://aimodels.fyi/papers/arxiv/data-attribution-text-to-image-models-by), will continue to shed light on the complex relationship between training data and model behavior.
**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.** | mikeyoung44 |
1,891,432 | Button Yes or No in HTML/CSS? | Check out this Pen I made! This is a Yes/No button! | 0 | 2024-06-17T15:55:23 | https://dev.to/tidycoder/button-yes-or-no-in-htmlcssjs-2m9l | codepen, button, webdev, css | Check out this Pen I made! This is a Yes/No button!
{% codepen https://codepen.io/TidyCoder/pen/eYayXbO %} | tidycoder |
1,891,430 | Cryptographic Security: Safeguarding Data | ## Cryptographic Security "Cryptographic security ensures data confidentiality, integrity, and... | 0 | 2024-06-17T15:52:29 | https://dev.to/harish_05/cryptographic-security-safeguarding-data-5aaj | devchallenge, cschallenge, computerscience, beginners | 
**## Cryptographic Security**
"Cryptographic security ensures data confidentiality, integrity, and authenticity through encryption, hashing, and digital signatures, safeguarding against unauthorized access and tampering."
| harish_05 |
1,891,429 | Introducing Shelldon: A New Rust CLI Tool with GPT Features | I’m a big fan of tools like Warp and Raycast, especially for their AI capabilities. However, I’ve... | 0 | 2024-06-17T15:52:17 | https://dev.to/douglasmakey/introducing-shelldon-a-new-rust-cli-tool-with-gpt-features-4hm4 | rust, openai, cli, tooling | I’m a big fan of tools like Warp and Raycast, especially for their AI capabilities. However, I’ve found their free tiers to be somewhat limiting. Since I already have an OpenAI account, I wanted a solution that would allow me to use my own token. That’s why I created Shelldon.
[Shelldon](https://github.com/douglasmakey/shelldon) is a command-line tool written in Rust. It provides utilities for executing shell commands, managing prompts, and interacting with multiple LLMs.
Yes, another CLI with GPT features. Shelldon is not intended to be a full GPT client from the terminal; there are a couple of CLIs much better for that and also a lot of applications and even the OpenAI ChatGPT apps. Shelldon is to solve some personal use cases and it is very useful for me; I hope it could be useful for you too. Also, I made it to have fun playing with Rust!
> One of the features that other tools were missing for my use case is the ability to use custom prompts for specific tasks that I need. For that reason, I created Shelldon with the ability to manage prompts and use them whenever you want for specific tasks. You can read more about this [here](https://github.com/douglasmakey/shelldon?tab=readme-ov-file#handling-prompts). Also, I plan to extend it with plugins to integrate more complex workflows.
And if you are like me and spend more time in the terminal than in other apps, this might help you. I hope some of you find it useful too. I’m open to feedback and suggestions, and I’d love to hear how you might use Shelldon in your own setups.
## Installation
Shelldon provides [Github releases](https://github.com/douglasmakey/shelldon/releases) with prebuilt binaries for MacOS and Linux.
### Homebrew
```sh
brew tap douglasmakey/tap
brew install shelldon
```
### Building from Source
If you prefer to build Shelldon from source, you can clone the repository and build it using cargo:
```sh
git clone https://github.com/douglasmakey/shelldon.git
cd shelldon
cargo build --release
```
## Usage
Shelldon supports different AI providers such as Ollama, OpenAI, Gemini, Anthropic, and Cohere. You can control which provider to use with the `--model` flag. For example, `--model claude-3-haiku-20240307` or `--model gemini-1.5-flash-latest`. By default, Shelldon uses `gpt-4o` as the model.
To use Shelldon, you need to set your API keys for the mentioned providers. You can do this by setting an environment variable. Here’s how to set it in your terminal:
```sh
export OPENAI_API_KEY="api-key"
export ANTHROPIC_API_KEY="api-key"
export COHERE_API_KEY="api-key"
export GEMINI_API_KEY="api-key"
```
Shelldon allows you to integrate GPT features into your shell commands easily. Here are some examples to get you started:
### Running Shell Commands
```sh
$ shelldon exec "Show all the graphics ports for the Vagrant machine using Libvirt." --model gpt-4o
Command to execute: vagrant ssh -c "virsh list --all | grep vagrant | awk '{print \$1}' | xargs -I {} virsh domdisplay {}"
? [R]un, [M]odify, [C]opy, [A]bort ›
```
**Analyzing Docker Logs**
Use Shelldon to analyze Docker logs and identify errors:
```sh
$ docker logs nginx | shelldon ask "check logs, find errors"
```
**Troubleshooting Kubernetes**
Shelldon can help you understand why a Kubernetes pod is failing:
```sh
$ k describe pod nginx | shelldon ask "why this pod is failing?"
The pod is failing because it was terminated due to an "Out of Memory" (OOM) condition. The `OOMKilled` reason indicates that the container running in the pod exceeded its memory limit, causing the system to kill the process to prevent it from affecting other processes on the node.
Here are some steps you can take to address this issue:
...
```
**Generate configuration files with the help of GPT:**
```sh
$ shelldon ask "Create a basic nginx configuration file"
Configuration file content:
server {
listen 80;
server_name example.com;
location / {
proxy_pass http://localhost:3000;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
}
}
...
```
**Automate routine system tasks with ease:**
```sh
$ shelldon exec "Find and delete all log files older than 30 days in /var/log"
Command to execute: find /var/log -name "*.log" -type f -mtime +30 -exec rm {} \;
? [R]un, [M]odify, [C]opy, [A]bort ›
```
**Get help with writing meaningful Git commit messages:**
```sh
$ git diff | shelldon ask "Generate a commit message" --copy
"Refactor logging system to improve error handling and performance. This change updates the logging library and adjusts the log levels for better clarity."
```
You can use the `--copy` command to copy the output directly to your clipboard.
### Handling Prompts
Shelldon allows you to create, edit, list, and delete custom prompts to streamline your command-line workflows. Here’s how you can manage your prompts:
**Command Overview**
```sh
$ shelldon prompts -h
Usage: shelldon prompts <COMMAND>
Commands:
create Create a new prompt
edit Edit an existing prompt
list List all prompts
delete Delete an existing prompt
help Print this message or the help of the given subcommand(s)
Options:
-h, --help Print help
```
**Listing Prompts**
To view all the prompts you have created, use the list command:
```sh
$ shelldon prompts list
╭────────────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┬───────────╮
│ Name ┆ Content ┆ Variables │
╞════════════╪═════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╪═══════════╡
│ script ┆ Let’s think step by step and act as a {script:bash} code scripts expert. Provide only the {script} script code as output without any descriptions or explanations. Ensure the output is in plain text format without Markdown formatting or symbols. If ┆ script │
│ ┆ details are insufficient, provide the most logical solution. You are not allowed to ask for more details. Just print the script directly. ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ translator ┆ Let’s think step by step and act as a translator. Translate the following text from {from:english} to {to:spanish}. Make it sound natural to a native speaker of {to} while keeping the original tone. Do only minimal edits without changing the tone. ┆ from, to │
│ ┆ Avoid using fancy words. Reply with only the translated text and nothing else. Do not provide explanations. ┆ │
├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
│ note-taker ┆ I am software engineer and I’d like you to look at the following text I wrote and edit it to make it sound more natural to a native English speaker. Do only minimal/minor edits without changing the tone of the text, which should remain the same. ┆ │
│ ┆ Dont use fancy words and I want you to only reply the correction, the improvements and nothing else, do not write explanations. ┆ │
╰────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┴───────────╯
```
You can use the `{}` notation to add variables to the prompt, and you can override those values using the `--set key=value` option. Additionally, in the prompt template, you can define default values like `{from:spanish}`. This allows for flexible and dynamic prompts that can be customized based on user input.
Then, you can run the ask command with a defined template:
In my daily routine, I often need to generate bash and python scripts or cloud-init files. `shelldon` helps me with this task:
```sh
shelldon ask --prompt script "Generate a cloud-init script to set up an Ubuntu server with the following steps:
1. Update Ubuntu.
2. Install Nginx.
3. Create a custom HTML file to be served by Nginx.
4. Ensure Nginx is enabled and started." --set script=cloud-init > cloud-init
```
As you can see, we can redirect the output directly to a file to create the script.
Or some translations.
```sh
alias sat="shelldon ask --prompt translator"
sat "Hey guys, I'm a few minutes late for the meeting, in less than 5 minutes I'll be there."
Hola chicos, voy unos minutos tarde para la reunión, en menos de 5 minutos estaré ahí.
```
You can also modify the values for the template:
```sh
alias sat="shelldon ask --prompt translator"
sat "Chicos voy a llegar 5 minutos tarde a la reunion" --set to=english --set from=spanish
Guys, I'm going to be 5 minutes late to the meeting.
```
So the ability to handle dynamic prompts with args and use them makes Shelldon a useful tool for me.
[Shelldon](https://github.com/douglasmakey/shelldon)
| douglasmakey |
1,890,743 | What are your goals for week 25? | It's week 25 of 2024. It's June, at Virtual Coffee we are doing mid year check-ins. Are you on track... | 19,128 | 2024-06-17T15:48:39 | https://dev.to/jarvisscript/what-are-your-goals-for-week-25-2lp3 | It's week 25 of 2024. It's June, at Virtual Coffee we are doing mid year check-ins. Are you on track to meet your goals for the year?
## What are your goals for the week?
- What are you building?
- What will be a good result by week's end?
- What events are happening this week?
* any suggestions for in person or virtual events?
- Any special goals for the quarter?
{% embed https://dev.to/virtualcoffee/monthly-challenge-mid-year-check-in-recharge-and-refocus-for-an-amazing-second-half-2k4c %}
### Last Week's Goals
- [:white_check_mark:] Continue Job Search. Networked. Collected Nos but at least I'm hearing back.
- [:x:] Project work.
- [:x:] Blog. Used the template for DEV's one Byte Challenge but I rejected my subjects.
- Events.
* [:x:] 3JS talk.
- [:white_check_mark:] Run a goal setting thread on Virtual Coffee Slack.
- Assess my mid year progress. I am not where I want to be.
- [:white_check_mark:] Yard Work * Cleared brush took it to street fro brush pick up this week. Had planned for more but it was 96F this weekend.
### This Week's Goals
- Continue Job Search.
- Project work.
- Blog.
- DEV Has new Challenges need to work on the one Byte explainer.
- Events.
* React Native stream.
- Run a goal setting thread on Virtual Coffee Slack.
- Assess my mid year progress.
### Your Goals for the week
Your turn what do you plan to do this week?
- What are you building?
- What will be a good result by week's end?
- What events are happening any week?
* in person or virtual?
- Got any Summer Plans?
```html
-$JarvisScript git commit -m "Summer!"
``` | jarvisscript | |
1,891,427 | LINKED LIST DATA STRUCTURE IN COMPUTER SCIENCE. | This is a submission for DEV Computer Science Challenge v24.06.12: One Byte Explainer. ... | 0 | 2024-06-17T15:47:30 | https://dev.to/cebo_msweli/linked-list-data-structure-in-computer-science-1dj8 | devchallenge, cschallenge, computerscience, beginners | *This is a submission for [DEV Computer Science Challenge v24.06.12: One Byte Explainer](https://dev.to/challenges/cs).*
## Explainer
<!-- In a linked list, nodes dynamically grow and shrink, facilitating efficient element insertion and deletion. Each node stores data and points to the next, ideal for scenarios requiring frequent changes without contiguous memory allocation. -->
## Additional Context
<!-- linked lists offer a flexible and efficient way to manage elements with dynamic growth/shrinkage, ideal for scenarios requiring frequent changes without the need for contiguous memory allocation. -->
<!-- Team Submissions: I don't have teammates. -->
<!-- Don't forget to add a cover image to your post (if you want). -->
<!-- Thanks for participating! --> | cebo_msweli |
1,891,425 | RxJs and Redux-Observable | Redux-Observable is a middleware for Redux that uses RxJS to handle asynchronous actions. It offers... | 0 | 2024-06-17T15:46:41 | https://dev.to/codeparrot/rxjs-and-redux-observable-167i | webdev, redux, rxjs, observable |
> Redux-Observable is a middleware for Redux that uses **RxJS** to handle asynchronous actions. It offers an alternative to `redux-thunk` and `redux-saga`, allowing you to work with async actions using observables.
## Understanding the Observer Pattern
Before diving into RxJS and Redux-Observable, let's revisit the **Observer Pattern**. In this pattern, an "Observable" object maintains a list of "Observers". When the Observable's state changes, it notifies all its Observers.
```javascript
document.addEventListener("click", (event) => {
console.log("Element clicked:", event);
});
```
In this example, `addEventListener` makes the document an Observable, and the callback function is the Observer.
## Diving into RxJS
**RxJS** (Reactive Extensions for JavaScript) is a library for composing asynchronous and event-based programs using observable sequences. It extends the Observer pattern by providing operators that allow you to compose Observables in a declarative manner.
### Key Concepts in RxJS
- **Observers**: Objects that subscribe to Observables and receive notifications.
- **Observables**: Objects that emit data over time.
- **Operators**: Functions that allow you to manipulate Observables.
- **Subjects**: Special types of Observables that are also Observers.
## Observers and Observables
### Observers
Observers are objects that can subscribe to Observables and receive notifications of three types: `next`, `error`, and `complete`. Here's a basic example of an Observer in action:
```javascript
import { Observable } from "rxjs";
const observable = new Observable((subscriber) => {
subscriber.next("Hello");
subscriber.next("World");
subscriber.complete();
});
const observer = {
next: (value) => console.log("Received value:", value),
error: (err) => console.error("Error:", err),
complete: () => console.log("Completed"),
};
observable.subscribe(observer);
```
**Expected Output:**
```
Received value: Hello
Received value: World
Completed
```
### Observables
Observables emit data over time and can be created using the `new Observable` constructor. Here’s an example where an Observable emits values periodically:
```javascript
const observable = new Observable((subscriber) => {
let count = 1;
const intervalId = setInterval(() => {
subscriber.next(count++);
if (count > 5) {
clearInterval(intervalId);
subscriber.complete();
}
}, 1000);
});
observable.subscribe({
next: (value) => console.log(value),
complete: () => console.log("Completed"),
});
```
**Expected Output:**
```
1
2
3
4
5
Completed
```
## Subjects
A Subject is a special type of Observable that can multicast to multiple Observers. Here’s an example:
```javascript
import { Subject } from "rxjs";
const subject = new Subject();
subject.subscribe({
next: (value) => console.log(`Observer 1: ${value}`),
});
subject.subscribe({
next: (value) => console.log(`Observer 2: ${value}`),
});
subject.next("Hello");
subject.next("World");
```
**Expected Output:**
```
Observer 1: Hello
Observer 2: Hello
Observer 1: World
Observer 2: World
```
**Note:**
- Observables are unicast, meaning each subscription is independent.
- Subjects are multicast, meaning they share the same execution path among all subscribers
## What Are Operators?
Operators are functions that allow you to manipulate and transform Observables. Here are some examples:
### Creation Operators
**from**
Creates an Observable from an array:
```javascript
import { from } from "rxjs";
const observable = from([1, 2, 3, 4]);
observable.subscribe((value) => console.log(value));
```
**Expected Output:**
```
1
2
3
4
```
### Pipeable Operators
**map**
Transforms each value emitted by the source Observable:
```javascript
import { map } from "rxjs/operators";
import { of } from "rxjs";
const observable = of(1, 2, 3, 4).pipe(map((value) => value * 2));
observable.subscribe((value) => console.log(value));
```
**Expected Output:**
```
2
4
6
8
```
**filter**
Filters the emitted values based on a condition:
```javascript
import { filter } from "rxjs/operators";
import { of } from "rxjs";
const observable = of(1, 2, 3, 4, 5).pipe(filter((value) => value % 2 === 0));
observable.subscribe((value) => console.log(value));
```
**Expected Output:**
```
2
4
```
**mergeMap**
Maps each value to an Observable and flattens the inner Observables:
```javascript
import { mergeMap } from "rxjs/operators";
import { of } from "rxjs";
const observable = of("Hello", "World").pipe(
mergeMap((value) => of(`${value} RxJS`))
);
observable.subscribe((value) => console.log(value));
```
**Expected Output:**
```
Hello RxJS
World RxJS
```
**switchMap**
Switches to a new Observable on each emission, canceling the previous one:
```javascript
import { switchMap } from "rxjs/operators";
import { interval, of } from "rxjs";
const observable = interval(1000).pipe(
switchMap((value) => of(`Switched to ${value}`))
);
observable.subscribe((value) => console.log(value));
```
**Expected Output:**
```
Switched to 0
Switched to 1
Switched to 2
... (continues every second)
```
## Setting Up Redux-Observable
To start using Redux-Observable, you need to install the necessary packages:
```bash
npm install redux-observable rxjs
```
### Creating an Epic
An **epic** is a function that takes a stream of actions and returns a stream of actions. Let's start with a basic example:
```javascript
import { ofType } from "redux-observable";
import { mapTo } from "rxjs/operators";
const pingEpic = (action$) =>
action$.pipe(ofType("PING"), mapTo({ type: "PONG" }));
export default pingEpic;
```
Here, when a `PING` action is dispatched, the epic intercepts it and maps it to a `PONG` action.
### Integrating the Epic with Redux
```javascript
import { createStore, applyMiddleware } from "redux";
import { createEpicMiddleware } from "redux-observable";
import rootReducer from "./reducers";
import pingEpic from "./epics";
const epicMiddleware = createEpicMiddleware();
const store = createStore(rootReducer, applyMiddleware(epicMiddleware));
epicMiddleware.run(pingEpic);
```
- **createEpicMiddleware()**: This function creates the middleware required for Redux-Observable.
- **applyMiddleware(epicMiddleware)**: This applies the epic middleware to your Redux store.
- **epicMiddleware.run(pingEpic)**: This runs the `pingEpic`, allowing it to start intercepting actions.
When the Redux store is set up and a `PING` action is dispatched, the `pingEpic` will intercept it and dispatch a `PONG` action.
### Handling AJAX Requests with Redux-Observable
Let's take a more practical example where we fetch user data from an API. We'll define action creators, create an epic to handle the AJAX request, and update the reducer to process the actions.
#### Action Creators
First, define action creators for starting the fetch and handling the response:
```javascript
export const fetchUser = () => ({ type: "FETCH_USER" });
export const fetchUserFulfilled = (payload) => ({
type: "FETCH_USER_FULFILLED",
payload,
});
```
#### Epic for AJAX Request
Create an epic to handle the AJAX request:
```javascript
import { ofType } from "redux-observable";
import { ajax } from "rxjs/ajax";
import { mergeMap, map, catchError } from "rxjs/operators";
import { fetchUserFulfilled } from "./actions";
const fetchUserEpic = (action$) =>
action$.pipe(
ofType("FETCH_USER"),
mergeMap(() =>
ajax.getJSON("/api/user").pipe(
map((response) => fetchUserFulfilled(response)),
catchError(() => of({ type: "FETCH_USER_FAILED" }))
)
)
);
export default fetchUserEpic;
```
1. **ofType('FETCH_USER')**: Filters the actions to only include those with the type `'FETCH_USER'`.
2. **ajax.getJSON('/api/user')**: Makes an AJAX request to fetch user data from the `/api/user` endpoint.
3. **map(response => fetchUserFulfilled(response))**: Maps the AJAX response to a `FETCH_USER_FULFILLED` action.
4. **catchError(() => of({ type: 'FETCH_USER_FAILED' }))**: Catches any errors during the AJAX request and maps them to a `FETCH_USER_FAILED` action.
When a `FETCH_USER` action is dispatched, the epic makes an AJAX request. If the request is successful, a `FETCH_USER_FULFILLED` action is dispatched with the response data. If the request fails, a `FETCH_USER_FAILED` action is dispatched.
#### Combining Epics
If you have multiple epics, combine them using `combineEpics`:
```javascript
import { combineEpics } from "redux-observable";
import fetchUserEpic from "./fetchUserEpic";
const rootEpic = combineEpics(fetchUserEpic);
export default rootEpic;
```
#### Updating the Reducer
Update your reducer to handle the new actions:
```javascript
const initialState = {
user: null,
};
const userReducer = (state = initialState, action) => {
switch (action.type) {
case "FETCH_USER_FULFILLED":
return { ...state, user: action.payload };
default:
return state;
}
};
export default userReducer;
```
When a `FETCH_USER_FULFILLED` action is dispatched, the user data in the state is updated with the fetched data. When a `FETCH_USER_FAILED` action is dispatched, an error message is set in the state.
## Practical Use Cases for Redux-Observable
### Debouncing API Requests
Let's say you want to provide autocomplete suggestions as the user types. Instead of making an API call for every keystroke, you can debounce the requests.
```javascript
import { debounceTime, switchMap } from "rxjs/operators";
const searchEpic = (action$) =>
action$.pipe(
ofType("SEARCH"),
debounceTime(500),
switchMap((action) =>
ajax.getJSON(`/api/search?q=${action.payload}`).pipe(
map((response) => ({ type: "SEARCH_FULFILLED", payload: response })),
catchError(() => of({ type: "SEARCH_FAILED" }))
)
)
);
export default searchEpic;
```
### Cancelling Ongoing Requests
Using `switchMap`, you can cancel the previous request if a new one comes in:
```javascript
const searchEpic = (action$) =>
action$.pipe(
ofType("SEARCH"),
debounceTime(500),
switchMap((action) =>
ajax.getJSON(`/api/search?q=${action.payload}`).pipe(
map((response) => ({ type: "SEARCH_FULFILLED", payload: response })),
catchError(() => of({ type: "SEARCH_FAILED" }))
)
)
);
export default searchEpic;
```
### Polling an API
You might need to poll an API to get updates regularly. Here's how you can do it:
```javascript
import { interval } from "rxjs";
import { switchMap } from "rxjs/operators";
const pollEpic = (action$) =>
action$.pipe(
ofType("START_POLLING"),
switchMap(() =>
interval(5000).pipe(
switchMap(() =>
ajax.getJSON("/api/data").pipe(
map((response) => ({ type: "POLL_SUCCESS", payload: response })),
catchError(() => of({ type: "POLL_FAILED" }))
)
)
)
)
);
export default pollEpic;
```
### Handling WebSocket Connections
Redux-Observable can also be used to manage WebSocket connections:
```javascript
import { webSocket } from "rxjs/webSocket";
const websocketEpic = (action$) =>
action$.pipe(
ofType("CONNECT_WEBSOCKET"),
switchMap(() =>
webSocket("ws://example.com").pipe(
map((message) => ({ type: "WEBSOCKET_MESSAGE", payload: message })),
catchError(() => of({ type: "WEBSOCKET_FAILED" }))
)
)
);
export default websocketEpic;
```
## Conclusion
Redux-Observable, powered by RxJS, provides a robust and flexible way to handle asynchronous actions in Redux applications. By embracing observables and functional programming, you can simplify your code and make it more maintainable.
Whether you're dealing with API calls, debouncing user input, managing WebSocket connections, or polling APIs, Redux-Observable offers powerful tools to manage these workflows efficiently.
If your application involves complex async workflows, give Redux-Observable a try. You might find it to be the perfect solution for your needs. For more detailed information and examples, check out the [official Redux-Observable documentation](https://redux-observable.js.org/).
| mvaja13 |
1,891,424 | I Built a Chrome Extension with Svelte and Firebase!!🔥🔥 | Hey everyone, hope you're doing well! Recently, I discovered a site called Watchparty, which allows... | 0 | 2024-06-17T15:46:14 | https://dev.to/mazahir26/i-built-a-chrome-extension-with-svelte-and-firebase-2mnd | extensions, firebase, svelte, typescript | Hey everyone, hope you're doing well!
Recently, I discovered a site called [Watchparty](https://www.watchparty.me/), which allows you to watch videos in sync with your friends and family. It's a fantastic concept, but I encountered a few issues: there were no options for syncing subtitles, and the user interface felt a bit outdated (no offense intended). Nonetheless, the site works wonders and is definitely worth checking out.
While exploring similar sites, I came across [Metastream](https://app.getmetastream.com/), which has a modern interface and supports subtitles with third-party Chrome extensions. However, I faced challenges there too—some videos didn't work, and the platform hadn't been updated in six years. So, I decided to tackle these issues myself.
### Introducing Sync Buddy
Sync Buddy is a Chrome extension designed to let you watch any video from any website in sync with others. It also features a chat functionality that can be toggled by pressing Shift + Enter.
It can support any website as long it has a video tag in it. it syncs in real time and the chat box is nonintrusive as well.
While this isn't a tutorial, I'd like to provide an overview of the components involved in developing my Chrome extension:
### Components:
- **Popup (Svelte)**: The main UI of the extension, accessible by clicking the extension icon in your browser.
- **content.ts**: Injected script that adds listeners and updates the DOM on websites.
- **background.ts**: Web worker managing logic and authentication.
- **manifest.js**: Configuration file for the extension.
_That's all, Yeah it's pretty easy to make an extension._
I made this project over the weekend and learned a lot about creating browser extensions. It's important to note that this is a personal/pet project, so there may be some bugs.
Oh, I forgot to tell you, it's pretty simple to use as well. Install the extension, then open a website with a video, click on the icon, and enter a room name. Share the room name and ask your friends and family to join using the same name (they should be on the same website as well). Voila! You're done. Enjoy binge-watching!
You can learn how to install and get started using the extension from the GitHub repository's [README](https://github.com/Mazahir26/sync-buddy/blob/main/README.md).
You can find the code on [GitHub](https://github.com/Mazahir26/sync-buddy) under the MIT License.
### Credits:
I would like to give credit to [Watchparty](https://www.watchparty.me/) and [Metastream](https://app.getmetastream.com/) for inspiring this project and providing valuable insights into synchronized video watching.
Additionally, this project is based on [svelte-chrome-extension-template](https://github.com/taishi55/svelte-chrome-extension-template) by [taishi55](https://github.com/taishi55). Thank you for the foundation!
Thank you so much for checking it out! Feel free to ask any questions, share suggestions, or provide feedback. Your input is valuable!😁😁
| mazahir26 |
1,891,381 | What WordPress Playground means for your future | Wordpress in one click Meaning, you can have a WordPress site on any device, without the... | 0 | 2024-06-17T15:46:10 | https://dev.to/brownio/what-wordpress-playground-means-for-your-future-106d | webdev, wordpress, productivity, cms | ##Wordpress in one click
Meaning, you can have a WordPress site **on any device**, **without the need of a host**, with just **clicking a single button**.
A site you can your work with **directly in your browser**. Download it as a zip, import it to GitHub, test themes and plugins on the fly... All while being **safe**.
When you first start using WordPress Playground, you'll be provided with a separate space where you can create and customize your own WordPress website. This space is completely isolated from your actual website.
<figcaption>You and your fellow dev destroying the WP site without consecuences</figcaption>
Web applications like WordPress have long-relied on server technologies to run logic and to store data.
Using those technologies has meant either running an web server connected to the internet or using those technologies in a desktop service or app (sometimes called a "WordPress local environment") that either leans on a virtual server with the technologies installed or the underlying technologies on the current device.
**Playground** is a novel way **to stream server technologies** -- and WordPress (and WP-CLI) -- **as files that can then run in the browser**.
<figcaption>Part of the configuration of the playground</figcaption>
The WordPress you see when you open Playground in your browser is a WordPress that should function like any WordPress, with a** few limitations**:
- Network connections are disabled (will limit connections to some third-party services)
- Volatile, data will disappear once you leave (but you can export it!)
- It renders in an iframe
- PHP code and wp-cli commands are ran via [Blueprints](https://wordpress.github.io/wordpress-playground/blueprints-api/index)
###So, how can Playground help your projects?
<figcaption>WordPress Playground at the WordCamp 2024</figcaption>
####At Launch:
Reach your clients or customers faster. Showcase your product, let users try it live, or launch it in the App Store with zero lead time.
- Embed interactive product demos on websites.
- Put a native app running WordPress in the App Store.
- Create new sites from Blueprints and share them with a few clicks.
####At QA:
Upgrade your QA process with the ability to **review progress in your browser in a single click**. When you’re ready, push updates instantly.
- Live preview pull requests in GitHub.
- Clone your site and experiment in a private sandbox.
- Test with different WordPress and PHP versions.
####At development:
Create and learn WordPress quickly—even on mobile with no signal. Use Playground where you work best, whether that’s in the browser, Node.js, mobile apps, VS Code, or elsewhere.
- Install WordPress in a single click.
- Build a block theme in your browser and save it to GitHub.
- Integrate with Open AI and CLI apps to create new tools.
So, to summarize:
WordPress Playground allows you to create and customize a WordPress site on any device with a single click, no host needed. This browser-based tool provides a secure, isolated environment to work on your site, download it, or test plugins and themes instantly. While it has some limitations, like disabled network connections and temporary data storage, it offers a fast, flexible solution for showcasing products, enhancing QA processes, and accelerating development. Whether for demos, app integration, or learning, Playground streamlines WordPress management and development directly in your browser.
And you can go a play around at:
https://playground.wordpress.net/
**Github**: https://github.com/WordPress/wordpress-playground
**Documentation**: https://wordpress.github.io/wordpress-playground/
**Contribute**: https://wordpress.github.io/wordpress-playground/contributing/index/ | brownio |
1,891,422 | Recursion | This is a submission for DEV Computer Science Challenge v24.06.12: One Byte Explainer. ... | 0 | 2024-06-17T15:41:49 | https://dev.to/a_j316_hyperion/recursion-3l79 | devchallenge, cschallenge, computerscience, beginners | *This is a submission for [DEV Computer Science Challenge v24.06.12: One Byte Explainer](https://dev.to/challenges/cs).*
## Explainer
<!-- Explain a computer science concept in 256 characters or less. -->
## Additional Context
<!-- Please share any additional context you think the judges should take into consideration as it relates to your One Byte Explainer. -->
<!-- Team Submissions: Please pick one member to publish the submission and credit teammates by listing their DEV usernames directly in the body of the post. -->
<!-- Don't forget to add a cover image to your post (if you want). -->
<!-- Thanks for participating! -->
Recursion - see recursion. | a_j316_hyperion |
1,891,420 | IPFS: The Decentralized Future of File Storage in 256 Characters | ## IPFS Explained in 256 Characters "IPFS (InterPlanetary File System) is a decentralized... | 0 | 2024-06-17T15:40:24 | https://dev.to/harish_05/ipfs-the-decentralized-future-of-file-storage-in-256-characters-3lo1 | devchallenge, cschallenge, computerscience, beginners |
**## IPFS Explained in 256 Characters**
"IPFS (InterPlanetary File System) is a decentralized protocol for storing and sharing files. It uses content-addressing to uniquely identify files by their cryptographic hash, enabling efficient and secure data retrieval without relying on a central server. This ensures data integrity and resilience." | harish_05 |
1,891,419 | I am looking for Senior Python Developer who has over 8 years of professional experience | We are running a startup business that is targeted to the US software and digital marketing sector.... | 0 | 2024-06-17T15:39:36 | https://dev.to/eugene_goodwin_c9d195b96d/i-am-looking-for-senior-python-developer-who-has-over-8-years-of-professional-experience-f1j | webdev, python, ai, devops | We are running a startup business that is targeted to the US software and digital marketing sector. So we are looking for a talented and skilled Python Expert who has over 8 years of experience in Data Analytics, Web API Development, ML.
Additionally, effective communication skills in English are vital for collaborating with team members and presenting findings to stakeholders. The ability to clearly and concisely convey complex information is a must.
Skills:
- Python Libraries such as Django, Flask, FastAPI for web development and Pytorch, selenium, Numpy, Pandas, Pyspark, SnowFlake, Airflow
- SQL Server, PostgreSQL, MongoDB
- Docker, Kubernetes
- AWS, Terraform, ES2, CI/CD
- Jira, Trello
Responsibilities:
- Analyze large datasets and extract meaningful insights using Python and data analytics techniques.
- Construct data models and develop algorithms to facilitate data analysis.
- Utilize data manipulation and visualization tools to present findings in a clear and actionable manner.
- Apply statistical analysis and machine learning techniques to identify patterns and trends within the data.
- Collaborate with team members to understand project objectives and requirements.
- Effectively communicate findings and recommendations to stakeholders through reports and presentations.
Requirements:
- Proficiency in Python programming.
- Strong knowledge of data analytics techniques.
- Experience with data manipulation and visualization.
- Familiarity with statistical analysis and machine learning.
- Excellent problem-solving and analytical skills.
- Strong verbal and written communication skills in English.
Job Details:
- Company Size: Medium
- Duration: 6 to 12 months
- Expertise Level: Expert
Salary Range: 2.5k per month.
Note: Please attach the short video for the assessment of your Verbal English.(Brief Introduction of yourself for about 3 mins).
If it looks good, we will proceed with several steps of the interview process.
How to apply: Please send updated resume and the link of short intro video via email: eugenegoodwin67@gmail.com (Technical Recruiter). He will schedule the interview for technical background assessment.
*The proposals with no video will be ignored. Please check that you attached your short video and start the message with "video was attached"*
whatsapp : +1 (614) 391-6839 | eugene_goodwin_c9d195b96d |
1,891,418 | Install JDK11(MACOS) | 1.download installation package (macOS version end with .pkg) download link ... | 0 | 2024-06-17T15:39:09 | https://dev.to/__1c1b7f036f4faee450ed/install-jdk11macos-8pi | programming, beginners, tutorial | ## 1.download installation package
(macOS version end with .pkg)
[download link](https://github.com/adoptium/temurin11-binaries/releases/download/jdk-11.0.19+7/OpenJDK11U-jdk_x64_mac_hotspot_11.0.19_7.pkg)
## 2.install jdk11
1. open the '.pkg' file
2. follow the installation prompts to install
## 3.configuring environment vaiables
- open the terminal, run the fllowing command to confirm the installation path
```
/usr/libexec/java_home -v 11
```
- edit '~/.zshrc' or '~/.bash_profile' file，add the following content
```
export JAVA_HOME=$(/usr/libexec/java_home -v 11)
export PATH=$JAVA_HOME/bin:$PATH
```
- save and exit the file, use the following command to make the configuration effective
```
source ~/.zshrc
or
source ~/.bash_profile
```
## 4.verify
- check 'JAVA_HOME' variable
```
echo $JAVA_HOME
```
- check java version
```
java -version
```
- check javac version
```
javac -version
```
| __1c1b7f036f4faee450ed |
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