GitHub 1C Enterprise Daily Trending

Bayselonarrend/1Charts

2023-12-26T01:24:08Z

Библиотека формирования диаграмм ToastUI для 1С:Предприятие

elifgazioglu/doyouwannagooutwithme

2023-12-26T01:24:15Z

Community designed ERCF v2

Enraged Rabbit Community Project

ERCF v2 - RC1

An expandable MMU for Klipper based 3D-Printers

This is a community born project and major update to the Voron ERCF MMU that was started a couple of years ago by Ette. It is endorsed by Ette and the guiding philosophy wasn't to start again with a new MMU design but to refine what has already proven to be a very capable machine and push it to be the best it can be by simplifying problematic construction, improving reliability and aligning as close as possible to v1.1 BOM. However the project includes an all new optional integrated filament buffer system (ERCT), filament cutter option (ERF), a collection of recommended toolhead sensor modifications and a bit of Bling! It fully leverages the Happy Hare firmware MMU control software with Klipper Screen entensions.

There are a rapidly growing list of MMUs in the market place from the mass produced "Fords" who pioneered the market to the "Toyota" that are more recent efficient engineering feat but somehow lack soul. We consider ERCFv2 the "BMW" - a little over engineered perhaps but distinctively cool and you feel good driving it. We hope you enjoy! Videos: Teaser Release

Table of Content

ERCF
ERCT
ERF
Toolhead Sensor Modifications
Firmware
Documentation
BOM
CAD
FAQ
Acknowledgements
Vendors
Changelog
Build Photos
Showroom

Enraged Rabbit Carrot Feeder (ERCF)

An MMU or Multimaterial Unit/Upgrade allows for the automatic change of filaments on your 3D printer. You can use it to create beautiful multi-colored prints or, if you're lazy, simply to avoid loading filament by hand. If you are familar with ERCF v1.1 this will serve as an overview of updates:

Sturdy backbone - no more flex
Reliable (and custom) encoder design
Sprung servo instead of adjustable top hats
Innovative Filament trap in blocks instead of magnetic gates
Formal filament bypass
Reinforced gearbox assembly
Beautifully illustrated Manual
High Quality Step-by-step CAD
New integrated passive buffer system (Cotton Tail)
Perfect tips with Filametrix Filament cutter
Functional and asthetic LED status indication

Optional Enraged Rabbit Components

Enraged Rabbit Cotton Tail (ERCT)

When an MMU changes tool, the unloaded filament needs to be thoughtfully managed so that it doesn't tangle. The Enraged Rabbit Cotton Tail (ERCT) buffer system is designed to attach directly to ERCF V2. It is a passive system that optimizes space and is also designed to reduce resistance in the filament path, creating a consistent system for calibration.

ERCT includes a pregate filament sensor to more reliably manage endless spools. It also incorporates a NEOpixel on each gate that, when driven by the Happy Hare firmware, provides functional feedback and the necessary "bling!" Enjoy!

Enraged Rabbit Filametrix (ERF)

Before the MMU can unload a filament it must prepare the tip so that it can be cleanly loaded next time. This tip forming process is very difficult to tune and varies based on material type, temperature, hotend type and even weather! Introducing Enraged Rabbit Filametrix (ERF) filament cutting system. This lightweight addition to your Stealthburner toolhead adds a cutting blade. When retracting the problematic tip of the filament is simply cut off for perfect tips and no jams.

ERF also supports an optional servo operated ganrtry activation pin so no print area is lost with this addition. ERF designs also include the recommended integrated toolhead sensor

Toolhead Sensor Modifications

ERCF can be operated without a toolhead sensor (filament detection) in the toolhead but it is **not recommended**. A toolhead sensor provides an accurate homing point very close to the nozzle but also adds reliability to the tool change process. ERCF includes a set of toolhead sensor modifications for popular extruders. These work reliably through coupling a microswitch to the filament path.

Purge System (ERPS)

Pellet purge system to remove the need for the wipe tower. Stay tuned.

Firmware

ERCF is designed to be used with the Happy Hare MMU firmware for Klipper which adds a set of klipper extensions for configuration setup, testing and operation of ERCF. These commands are available through the command line or macros but are perhaps best operated with an interactive UI with the optional KlipperScreen extension.

Happy Hare provides an easy installation script which has knowledge of recommended settings and will greatly accelarate the setup process.

Happy Hare KlipperScreen

Documentation

Building something as complex as an MMU is a challenging undertaking but ERCFv2 project contains an amazingly detailed and illustrated manual with step-by-step instructions. We have tried to make the process similar to fitting together a jigsaw puzzle, albeit with a few optional pieces.

ERCFv2 PDF Manual

BOM

You can find a Bill of Materials and a convenient printed parts tracker for the project and options here. Note that the BOM also contains an upgrade list for those of you wanting to use your existing ERCF v1.1 kits. Please make a copy and edit the "Filament Blocks #" to be the number of gates for your build. This can be any number but we encourage kit vendors to use 4/8/12 as size variations. Note that there are separate columns for core ERCF, the optional ERCT and ERF options as well as the suggested "extras"

BOM Printed Parts Tracker

CAD

A lot of work has gone into creating a quality CAD model of the project carefully organized into folders that match the documentation! It is hightly recommended that you open the CAD and hide every folder and then expose them one at a time as you work through the build.

Master CAD

FAQ

ERCF v2 is currently in the RC1 phase. That means that we hope the BOM and parts are complete but there are still some areas of polish needed: documentation, kit availability, etc. Therefore we're sure there will be lots of questions. To avoid repetition on the various support channels you can find a list of frequently asked questions here. If something isn't answered the best place to go is the primary Voron #ercf_questions Discord server

Acknowledgements

Most importantly let me introduce the development, test and doc team. A project like this doesn't happen without many hundreds of hours of volunteer effort and all of these folks are truely awesome. Please give some 👏 👏 👏

@moggieuk V0.1503 | V2.4088 (Mr Happy Hare & Chief whip)
@gneu V2.5345 (Filament block & bling innovator)
@sneakytreesnake V2.3804 (The project backbone!)
@mneuhaus VT.483 (Mr Binky)
@Miriax (Designer & Doc Demon)
@kinematicdigit (Mr Cotton Tail & Doc Illustrator)
@ningpj (Tester, Breaker & Documenter)
@fizzy (King of CAD)
@gsx8299 (Test Builder Extraordinaire)
@sorted (Filametix "don't get enraged" filament cutting system)
@kierantheman (Mr ThumperBlocks)
@Fragmon (Videographer)

Vendors

These kits and specialty parts have been checked by us and meet good quality standards:

On the way... stay tuned!

Manufacturers: If you want to be included, please contact us. We are happy to validate your kit/parts and then add you to the list...

Changelog

v2.0 rc1 - Initial Release (Merry Christmas!)

CAD Design Guidelines used in this project (in case you were interested) can be found: here.

There once was a printer so keen,
To print in red, yellow, and green.
It whirred and it spun,
Mixing colors for fun,
The most vibrant prints ever seen!

Build Photos

User Print Showroom

apple/ml-ferret

2023-12-26T01:24:15Z

完全免费开源，基于 Requests 模块实现：TikTok 主页/视频/图集/原声；抖音主页/视频/图集/收藏/直播/原声/合集/评论/账号/搜索/热榜数据采集工具

TikTokDownloader

🔥 TikTok 主页/视频/图集/原声；抖音主页/视频/图集/收藏/直播/原声/合集/评论/账号/搜索/热榜数据采集工具：完全开源，基于 Requests 模块实现的免费工具；批量下载抖音账号发布、喜欢、收藏作品；批量下载 TikTok 账号主页作品；下载抖音链接或 TikTok 链接作品；获取抖音直播推流地址；下载抖音直播视频；采集抖音作品评论数据；批量下载抖音合集作品；采集抖音账号详细数据；采集抖音用户 / 作品 / 直播搜索结果；采集抖音热榜数据。

⭐ Windows 10 及以上用户可前往 Releases 下载已编译的 exe 程序，开箱即用！

❤️ 作者仅在 GitHub 发布 TikTokDownloader，未与任何个人或网站合作，且没有任何收费计划！

📝 功能清单(Function)

✅ 下载抖音无水印视频/图集
✅ 下载 TikTok 无水印视频/图集
✅ 批量下载抖音账号发布/喜欢/收藏作品
✅ 批量下载 TikTok 账号发布/喜欢作品
✅ 采集抖音 / TikTok 详细数据
✅ 批量下载链接作品
✅ 多账号批量下载作品
✅ 自动跳过已下载的文件
✅ 持久化保存采集数据
✅ 下载动态/静态封面图
✅ 获取抖音直播推流地址
✅ 调用 ffmpeg 下载直播
✅ Web UI 交互界面
✅ 采集抖音作品评论数据
✅ 批量下载抖音合集作品
✅ 记录点赞收藏等统计数据
✅ 筛选作品发布时间
✅ 支持账号作品增量下载
✅ 支持使用代理采集数据
✅ 支持局域网远程访问
✅ 采集抖音账号详细数据
✅ 作品统计数据更新
✅ 自动更新账号昵称
✅ 部署至私有服务器
✅ 部署至公开服务器
✅ 采集抖音搜索数据
✅ 采集抖音热榜数据
✅ 记录已下载作品 ID
✅ 扫码登陆获取 Cookie
✅ 支持 Web API 调用
✅ 支持多线程下载作品
✅ 文件完整性处理机制
✅ 自定义规则筛选作品

💻 程序界面(Screenshot)

终端命令行模式：

Web UI 交互模式：

Web API 接口模式：

📽 运行演示(Example)

批量下载账号发布作品

🎥 点击图片观看演示视频，建议通过配置文件管理账号，更多介绍请查阅文档

📈 项目状态(Status)

🟢 Releases 发布的源码已完成测试，所有功能均可正常使用
🟢 正在重构项目代码，优化项目结构
🟢 即将使用协程技术优化项目代码
🟡 未来可能新增可视化编辑配置文件功能
🟡 未来可能支持更多抖音热榜类型
🟡 未来可能新增终端文本用户界面(TUI)模式
🟡 未来可能支持全功能版的 Web UI 交互模式
🟡 未来可能新增监听剪贴板下载作品功能
🟡 未来可能新增账号新作品监测功能
🟡 未来可能新增合集新作品监测功能
🟡 未来可能新增直播开播监测功能
🟡 未来可能新增调用 API 下载作品文件功能
🟡 未来可能新增获取账号关注列表功能
🟡 未来可能新增获取账号收藏合集列表功能
🟡 未来可能优化 TikTok 平台批量下载功能
🔴 最新版本的源码可能存在不稳定的 Bug
🔴 如果在使用过程中发现程序 Bug，请及时告知作者修复
🔴 发现 Cookie 会影响下载的视频作品文件分辨率

📋 项目说明(Instructions)

快速入门

下载 EXE 程序 或者 配置运行环境
1. 下载 Releases 发布的 EXE 程序压缩包
2. 解压后打开程序文件夹，双击运行 main.exe
1. 安装不低于 3.12 版本的 Python 解释器
2. 下载最新的源码或 Releases 发布的源码至本地
3. 运行 python -m venv venv 命令创建虚拟环境（可选）
4. 运行 .\venv\Scripts\activate.ps1 或者 venv\Scripts\activate 命令激活虚拟环境（可选）
5. 运行 pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt 命令安装程序所需模块
6. 运行 python .\main.py 或者 python main.py 命令启动 TikTokDownloader
阅读 TikTokDownloader 的免责声明，根据提示输入内容
将 Cookie 信息写入配置文件
1. 参考 Cookie 提取教程，复制所需 Cookie 至剪贴板
2. 选择 复制粘贴写入 Cookie 选项，按照提示将 Cookie 写入配置文件
1. 选择 扫码登陆写入 Cookie 选项，程序会显示登录二维码图片，并使用默认应用打开图片
2. 使用抖音 APP 扫描二维码并登录账号
3. 按照提示操作，将 Cookie 写入配置文件
返回程序界面，依次选择 终端命令行模式 -> 批量下载链接作品
输入抖音或 TikTok 作品链接即可下载作品文件
更多详细说明请查看 项目文档

⭐ 推荐使用 Windows 终端（Windows 11 自带默认终端）

关于 Cookie

点击查看 Cookie 获取教程

程序功能	是否需要登录
下载账号发布作品	⭕建议登录
下载账号喜欢作品	⭕建议登录
下载链接作品	⭕建议登录
获取直播推流地址	❌无需登录
下载直播视频	❌无需登录
获取作品评论数据	⭕建议登录
下载合集作品	⭕建议登录
获取账号数据	⭕建议登录
采集搜索结果	⭕建议登录
采集热榜数据	❌无需登录
下载账号收藏作品	✔️需要登录

Cookie 仅需在失效后重新写入配置文件，并非每次运行程序都要写入配置文件！

程序获取数据失败时，可以尝试更新 Cookie 或者使用已登录的 Cookie！

其他说明

程序提示用户输入时，直接回车代表返回上级菜单，输入 Q 或 q 代表结束运行
由于获取账号喜欢作品和收藏作品数据仅返回喜欢 / 收藏作品的发布日期，不返回操作日期，因此程序需要获取全部喜欢 / 收藏作品数据再进行日期筛选；如果作品数量较多，可能会花费较长的时间；可通过 max_pages 参数控制请求次数
获取私密账号的发布作品数据需要登录后的 Cookie，且登录的账号需要关注该私密账号
批量下载账号作品或合集作品时，如果对应的昵称或标识发生变化，程序会自动更新已下载作品文件名称中的昵称和标识
程序下载文件时会先将文件下载至临时文件夹，下载完成后再移动至储存文件夹；程序运行结束时会清空临时文件夹
批量下载收藏作品模式 目前仅支持下载当前已登录 Cookie 对应账号的收藏作品，暂不支持多账号
如果想要程序使用代理，必须在 settings.json 设置 proxies 参数，否则程序不会使用代理
部分使用者反馈，新发布的作品过早下载会下载到低分辨率的文件，一段时间后才能下载到高分辨率文件，但时间规律尚不明确
退出程序时，请以正常方式结束运行或者按下 Ctrl + C 结束运行，不要直接点击终端窗口的关闭按钮结束运行，否则会导致数据丢失
如果您的计算机没有合适的程序编辑 JSON 文件，建议使用 JSON 在线工具编辑配置文件内容
当程序请求用户输入内容或链接时，请注意避免输入的内容或链接包含换行符，这可能会导致预期之外的问题

EXE 更新

方案一： 下载并解压文件，将旧版本的 cache 文件夹和 settings.json 文件复制到 _internal 文件夹。
方案二： 下载并解压文件，复制全部文件，直接覆盖旧版本文件。

⚠️ 免责声明(Disclaimers)

使用者对本项目的使用由使用者自行决定，并自行承担风险。作者对使用者使用本项目所产生的任何损失、责任、或风险概不负责。
本项目的作者提供的代码和功能是基于现有知识和技术的开发成果。作者尽力确保代码的正确性和安全性，但不保证代码完全没有错误或缺陷。
使用者在使用本项目时必须严格遵守 GNU General Public License v3.0 的要求，并在适当的地方注明使用了 GNU General Public License v3.0 的代码。
使用者在任何情况下均不得将本项目的作者、贡献者或其他相关方与使用者的使用行为联系起来，或要求其对使用者使用本项目所产生的任何损失或损害负责。
使用者在使用本项目的代码和功能时，必须自行研究相关法律法规，并确保其使用行为合法合规。任何因违反法律法规而导致的法律责任和风险，均由使用者自行承担。
本项目的作者不会提供 TikTokDownloader 项目的付费版本，也不会提供与 TikTokDownloader 项目相关的任何商业服务。
基于本项目进行的任何二次开发、修改或编译的程序与原创作者无关，原创作者不承担与二次开发行为或其结果相关的任何责任，使用者应自行对因二次开发可能带来的各种情况负全部责任。

在使用本项目的代码和功能之前，请您认真考虑并接受以上免责声明。如果您对上述声明有任何疑问或不同意，请不要使用本项目的代码和功能。如果您使用了本项目的代码和功能，则视为您已完全理解并接受上述免责声明，并自愿承担使用本项目的一切风险和后果。

✉️ 联系作者(Contact)

QQ: 2437596031（联系请说明来意）
QQ Group: 点击扫码加入群聊
Email: yonglelolu@gmail.com

TikTokDownloader 是我个人独立维护的一个开源项目，鉴于个人精力有限，请理解项目进展可能较为缓慢，我会尽力保持更新和维护，以确保项目的稳定性和功能的不断改进。

如果您通过 Email 联系我，我可能无法及时查看并回复信息，我会尽力在七天内回复您的邮件；如果有紧急事项或需要更快的回复，请通过其他方式与我联系，谢谢理解！

如果您对小红书感兴趣，可以了解一下我的另一个开源项目 XHS-Downloader

♥️ 支持项目(Support)

如果 TikTokDownloader 对您有帮助，请考虑为它点个 Star ⭐，感谢您的支持！

微信(WeChat)	支付宝(Alipay)

如果您愿意，可以考虑提供资助为 TikTokDownloader 提供额外的支持！

💡 代码参考(Refer)

Coder-World04/Complete-System-Design

2023-12-26T01:24:15Z

This repository contains everything you need to become proficient in System Design

Complete System Design with Implemented Case Studies and Code

This repository contains everything you need to become proficient in System Design .

Complete Cheat Sheet for Tech Interviews - How to prepare efficiently

Mega Launch - 200+ System Design Case Studies

System Design Most Important Terms

System Design Template

Complete System Design Case Studies

How to solve any System Design Question ( approach that you should take)

ML System Design Case Studies Series

For Data Structures and Algorithms, start here : Day 1 of 30 days of Data Structures and Algorithms and System Design Simplified : DSA and System Design made Easy

Topics you should know in System Design ---

System Design Case Studies

Case Study Name	Link to the Case Study
Design Instagram	Link
Design Messenger App	Link
Design Twitter	Link
Design URL Shortner	Link
Design Dropbox	Link
Design Youtube	Link
Design Tinder	Link
Design Google Drive	Link
Design Messenger App	Link
Design Instagram	Link
Design Twitter	Link
Design Ticketmaster	Link
Design Quora	Link
Design Flipkart	Link
Design Flickr	Link
Design TikTok	Link
Design Netflix	Link
Design Foursquare	Link
Design Uber	Link
Design Youtube	Link
Design Reddit	Link
Design Facebook’s Newsfeed	Link
Design Amazon Prime Video	Link
Design Web Crawler	Link
Design Web Crawler	Link
Design API Rate Limiter	Link
Design Dropbox	Link
Design Yelp	Link
Design Whatsapp	Link
Design URL Shortener	Link
Design Bookmyshow	Link
Design Linkedin	Link
Design Telegram	Link
Design Snapchat	Link
Design One Drive	Link
Design BookmyShow	Link
Design Google Maps	Link
Design Quora	Link
Design Foursquare	Link
Design Tiny URL	Link
Design Flipkart	Link

Design Facebook’s Newsfeed

Design Amazon Prime Video

Design Web Crawler

Design API Rate Limiter

Part 1 of System Design Made Easy Series

In the part 1, we covered what and why of System Design and the important topics that you should know. System Design is mostly an open ended concept and most of the questions can be answered in different degrees and aptitudes. In layman’s language, system design is about —

Architecture + Data + Applications

Learn what is system design and why it’s important, how they work and how to use them in your system design interviews in part 1

Part 2 of System Design Made Easy Series

In the part 2, we covered—

System design basics
Horizontal and Vertical Scaling with an example

In technical words, scalability is a the technique/process of adding/removing infrastructure/resources required by applications to better serve/accommodate increased/decreased demand/growth. We also covered the tradeoffs of horizontal and vertical scaling.

Learn why good understanding of system design basics and horizontal and vertical scaling are important, how they work and how to use them in your system design interviews in part 2

Part 3 of System Design Made Easy Series

In the part 3, we covered system design’s most important concepts —

Load Balancing
Message Queues

Load balancing is a technique of distributing tasks over a set of servers/machines to improve the performance,throughput, high availability, redundancy and reliability of the system. Not just it enables horizontal scaling but also dynamic resizing/scaling.

Message queues are nothing bit temporary buffers placed between users/applications and servers to store the message requests and process them in FIFO order asynchronously until the requests/messages are delivered to the desired server.

We also covered the tradeoffs of the different techniques.

Learn why load balancing and message queues are important, how they work and how to use them in your system design interviews in part 3

Part 4 of System Design Made Easy Series

In the part 4, we covered-

High level design and Low level design
Monolithic and microservices architecture and which one to choose and when ?
Consistent Hashing

High level Design(HLD) describes the overall architecture of the application and covers functionality of each module of the system very briefly. LLD details the functional logic of the each module in the system.

Monolithic architecture — consists of single code base with multiple modules and it’s easier and faster to deploy. Microservices architecture — consists of individual service units with each service being responsible for exactly one functionality. It’s relatively complex and time taking to deploy.

Consistent hashing is a technique to divide keys/data between multiple servers/machines using a hash function ( key — value).

Learn why high level design and low level design, Monolith and microservices architecture and consistent hashing are important, how they work and how to use them in your system design interviews in part 4

Part 5 of System Design Made Easy Series

In the part 5, we covered —

Caching
Indexing
Proxies

Caching is a technique which is based on the principal of locality — stores the copies of most frequently used/accessed data in a small and faster memory to improve Data retrieval times, Compute costs, User Experience and Throughput.

Indexing helps in Improving the speed of data access/retrieval, Reducing the number of expensive I/O operations, Providing better organization and management of multilevel data records.

Proxies play an important role of coordinating user requests, handling concurrent requests, filtering user request, transforming user requests by adding an additional layer of encryption or header information or compression information and then forwarding the user request to the server.

Learn why Caching, Indexing and Proxies are important, how they work and how to use them in your system design interviews in part 5

Part 6 of System Design Made Easy Series

In the part 6, we covered —

Networking
How Browsers work
Content Network Delivery ( CDN)

Networking is nothing but interconnected devices that can exchange data-messages and share resources amongst themselves/with outside world based in the system protocols/rules, technologies and algorithms that govern these devices inner workings.

Browsers are used to present the website/resource you would like to visit say, for example google.com by sending the request to the server and displaying it on their browser window.

Content Network Delivery caters to the users by serving their requests by quickly transferring the data back and forth.

Learn why networking, browsers working and CDN are important, how they work and how to use them in your system design interviews in part 6

Part 7 of System Design Made Easy Series

In the part 7, we covered —

Database Sharding
CAP Theorem
Database schema Design

Sharding is the technique to database partitioning that separates large and complex databases into smaller, faster and distributed databases for higher throughput operations.

CAP theorem lets you determine how you want to handle your distributed databases with there is possibility of inconsistencies, unavailability and connection errors/failures/outrage.

Database schema Design lets you organize data into separate entities and establish and organize the relationships between different entities.

Learn why database sharding, CAP theorem and Database Schema Design are important, how they work and how to use them in your system design interviews in part 7

**Part 8 of System Design Made Easy Series **

In the part 8 , we covered —

Concurrency
API
Components + OOP + Abstraction

Concurrency is the process in which multiple computations/operations/process happen/execute in parallel/concurrently.

API is an acronym for application programming interface which provides a way to two or more programs to communicate, work together despite different configurations, architectures, resources etc

Components in the system design are building blocks designed to coordinate, cooperate, reuse and work well with other components of the same/different systems. They can be as simple as visual components or internal components/backend components.

Learn why concurrency, API and Components + OOP + Abstraction are important, how they work and how to use them in your system design interviews in part 8

Part 9 of System Design Made Easy Series

In the part 9 , we covered —

Planning and Estimation
Performance

Planning and estimation( numbers) and performance are very important concepts ( concept that you should be able to demonstrate well when asked).

Learn why Planning, estimation( numbers) and performance are important, how they work and how to use them in your system design interviews in part 9

Part 10 of System Design Made Easy Series

In the part 10, we covered —

Map Reduce
Patterns and Microservices

In system design, map reduce ( Hadoop systems) is a batch processing technique in which the engine takes huge amounts of data, processes ( map and reduce) and gives the output.

In system design, microservices architecture is used to build enterprise level applications which helps in structuring the whole application as a collection of tiny autonomous, self contained services for each task ( service) that you want/are allowed to perform.

Learn why map reduce, patterns and microservices are important, how they work and how to use them in your system design interviews in part 10

Most Popular System Design Questions — Mega Compilation

In this post. we covered the most popular/important system design questions that you should practice to build a thorough understanding of how large systems are designed.

Some of the other best Series -

Complete 60 Days of Data Science and Machine Learning Series

30 days of Machine Learning Ops

30 Days of Natural Language Processing ( NLP) Series

Data Science and Machine Learning Research ( papers) Simplified **

30 days of Data Engineering with projects Series

60 days of Data Science and ML Series with projects

100 days : Your Data Science and Machine Learning Degree Series with projects

23 Data Science Techniques You Should Know

Tech Interview Series — Curated List of coding questions

Complete System Design with most popular Questions Series

Complete Data Visualization and Pre-processing Series with projects

Complete Python Series with Projects

Complete Advanced Python Series with Projects

Kaggle Best Notebooks that will teach you the most

Complete Developers Guide to Git

Exceptional Github Repos — Part 1

Exceptional Github Repos — Part 2

All the Data Science and Machine Learning Resources

210 Machine Learning Projects

6 Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) -

Complete Data Scientist : https://bit.ly/3wiIo8u

Learn to run data pipelines, design experiments , build recommendation systems, and deploy solutions to the cloud.

Complete Data Engineering : https://bit.ly/3A9oVs5

Learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets

Complete Machine Learning Engineer : https://bit.ly/3Tir8ub

Learn advanced machine learning techniques and algorithms - including how to package and deploy your models to a production environment.

Complete Data Product Manager : https://bit.ly/3QGUtwi

Leverage data to build products that deliver the right experiences, to the right users, at the right time. Lead the development of data-driven products that position businesses to win in their market.

Complete Natural Language Processing : https://bit.ly/3T7J8qY

Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more.

Complete Deep Learning: https://bit.ly/3T5ppIo

Learn to implement Neural Networks using the deep learning framework PyTorch

vcian/laravel-code-insights

2023-12-26T01:25:36Z

Code insight for Laravel applications

Pagghiu/SaneCppLibraries

2023-12-26T01:26:08Z

Sane C++ Libraries

Sane C++

Sane C++ is a set of C++ platform abstraction libraries for macOS and Windows

Principles:

✅ Fast compile times
✅ Bloat free
✅ Simple readable code
✅ Easy to integrate
⛔️ No C++ Standard Library / Exceptions / RTTI
⛔️ No third party dependencies (prefer OS API)

Visit the documentation website for more information.

An introductory blog post has been written too.

Libraries

Library	Description
Algorithms	🟥 Placeholder library templated algorithms will be placed
Async	🟨 Async I/O (files, sockets, timers, processes, fs events, threads wake-up)
Build	🟥 Minimal build system where builds are described in C++
Containers	🟨 Generic containers (SC::Vector, SC::SmallVector, SC::Array etc.)
File	🟩 Synchronous Disk File I/O
File System	🟨 File System operations { exists
File System Iterator	🟨 Enumerates files and directories inside a given path
File System Watcher	🟨 Notifications {add, remove, rename, modified} for files and directories
Foundation	🟩 Primitive types, asserts, limits, Function, Span, Result, Tagged Union
Hashing	🟩 Compute `MD5`, `SHA1` or `SHA256` hashes for a stream of bytes
Http	🟥 HTTP parser, client and server
Plugin	🟥 Minimal dependency based plugin system with hot-reload
Process	🟨 Create child processes and chain them (also usable with Async library)
Reflection	🟩 Describe C++ types at compile time for serialization
Serialization Binary	🟨 Serialize to and from a binary format using Reflection
Serialization Text	🟨 Serialize to / from text formats (JSON) using Reflection
Socket	🟨 Synchronous socket networking and DNS lookup
Strings	🟩 String formatting / conversion / manipulation (ASCII / UTF8 / UTF16)
Testing	🟨 Simple testing framework used by all of the other libraries
Threading	🟥 Atomic, thread, mutex, condition variable
Time	🟨 Time handling (relative, absolute, high resolution)

License

Sane C++ is licensed under the MIT License, see LICENSE.txt for more information.

keep-starknet-strange/unruggable.meme

2023-12-26T01:26:14Z

☣️ A framework for building safer memecoins 💸

fmw/sfg

2023-12-26T01:26:37Z

jetmonk/cl-pigpio

2023-12-26T01:26:46Z

Common Lisp interface to the pigpio Raspberry Pi hardware library

CL-PIGPIO

A Common Lisp interface to the Rapsberry Pi pigpio library (pigpio on github)

Description

pigpio is a library to provide a simplified interface to the Raspberry Pi's hardware devices, like SPI, I2C, Serial, and GPIO.

pigpio has 1) a direct hardware version (linking with libpigpio.so) that requires the program using it to run as root; and 2) a pigpiod daemon (linked with libpigpiod_if2.so) that allows user-land programs to accesss hardware via network calls to the daemon.

CL-PIGPIO links with both libraries, and allows the user to select whether the hardware or the daemon form is called. If the hardware version is activated but the user's program is not running with root privileges, an error is generated.

Only one of the two (hardware or daemon) can be run on the same RPi, but the daemon on other RPis can be accessed remotely.

Installing

Install in your usual Lisp package tree and run

(asdf:load-system "cl-pigpio")

or, for just the low level FFI, run

(asdf:load-system "pigpio-ffi-universal")

Low level FFI interface

The unified pigpio/pigpiod interface is in the pigpio-ffi-universal package.

Here, the routines are generally the same as documented in the C pigpio interface but with an additional keyword device specifier.

For example, if the C library has

  int i2cOpen(unsigned i2cBus, unsigned i2cAddr, unsigned i2cFlags);

then the Lisp version has

  (pigpio-ffi-universal:pigpio-i2copen
         i2cbus i2caddr i2cflags
         &key (pidev pigpio-ffi-unversal:*default-pidev*))

The pidev keyword argument can be set as

  ;; daemon form, setting *default-pidev* to this device
  (pigpio-ffi-universal:open-pidev/daemon :host "localhost" :port "8888"
                                          :set-default t)
  ;; OR open hardware device, this time not setting the default variable
  (defparameter *my-pidev*
     (pigpio-ffi-universal:open-pidev/hardware :set-default nil))

It is necessary to open a device before making any pigpio/pigpiod calls.

Higher level interface

The cl-pigpio package contains a higher level interface to SPI, I2C, and GPIO.

This interface is not documented in detail, but the exported functions are visible in cl-pigpio-package.lisp, and the functions generally have good documentation strings. I2C, SPI, and GPIO have separate source files.

The purpose of the higher level interface is to translate opaque data like I2C flags into more easily understable Lisp keywords, and to perform FFI calls using Lisp buffers rather than using CFFI foreign pointers.

The high level routines also throw a descriptive Lisp error if the pigpio library returns an error.

Glitches and gotchas

pigpio does not work for RPi5 (yet?) because hardware interface is not documented
signals are turned off in the hardware version using gpioCfgGetInternals() to prevent clashes with any Lisp use of signals.

Dependencies

Tested with 64 bit SBCL, on Bookworm.

cffi
pigpio, which can be installed as a standard RPi Linux package.

mit-plv/kami

2023-12-26T01:26:53Z

A Platform for High-Level Parametric Hardware Specification and its Modular Verification

intellij-dlanguage/intellij-dlanguage

2023-12-26T01:27:17Z

Intellij Plugin for the D Programming Language

KabKebab/GS13

2023-12-26T01:27:40Z

Videogames

qdm12/latexdevcontainer

2023-12-26T01:27:47Z

LaTeX development container for Visual Studio Code Remote Containers Development

Latex Dev Container

Ultimate Latex development container for Visual Studio Code

Features

Fastest way to code LaTex and produce a pdf file when saving your .tex file
Uses texlive 2023
Based on Debian Bullseye Slim, using qmcgaw/basedevcontainer
Compatible with amd64 and aarch64
Two Docker images:
- qmcgaw/latexdevcontainer:latest (default) which uses the TexLive basic scheme and has an uncompressed image size of 467MB
- qmcgaw/latexdevcontainer:latest-full which uses the TexLive full scheme and has an uncompressed image size of 4.2GB - most packages you would need are already installed.
Comes with tlmgr to install more LaTex packages as needed
Latex compilation to pdf with latexmk
Formatting on save using latexindent
Latex linting using chktex built from source
Using the LaTex-Workshop VScode extension
Compatible with amd64, ~~ARM 64 bit, ARM 32 bit v6 and v7~~ (TeXLive not available on ARM, create an issue if you need it)
Cross platform
- Easily bind mount your SSH keys to use with git
- Manage your host Docker from within the dev container, more details at qmcgaw/basedevcontainer
Extensible with docker-compose.yml
Minimal uncompressed image size of 467MB

Requirements

Docker installed and running
- If you don't use Linux, share the directories ~/.ssh and the directory of your project with Docker Desktop
Docker Compose installed
VS code installed
VS code remote containers extension installed

Setup for a project

Setup your configuration files
- With style 💯
```
docker run -it --rm -v "/yourrepopath:/repository" qmcgaw/devtainr:v0.4.0 -dev latex -path /repository -name projectname
```
  Or use the built binary
- Or manually: download this repository and put the .devcontainer directory in your project.
Open the command palette in Visual Studio Code (CTRL+SHIFT+P) and select Remote-Containers: Open Folder in Container... and choose your project directory

Install LaTex packages

If you need for example the package lastpage, open the integrated terminal in VS Code, select zsh and enter:

tlmgr install lastpage
texhash

Update the Latex Docker image

Pull the docker image
```
docker pull qmcgaw/latexdevcontainer
```
Open the command palette in Visual Studio Code (CTRL+SHIFT+P) and select Remote-Containers: Rebuild and Reopen in Container.... ⚠️ this will erase your container shell history and custom latex packages you added on top of the base image.

devcontainer.json

You can change the "postCreateCommand" to be relevant to your situation. For example:
```
"postCreateCommand": "tlmgr install acronym pgf && texhash",
```
You can change the extensions installed in the Docker image within the "customizations/vscode/extensions" array
Other Latex settings can be changed or added in the "customizations/vscode/settings" object.

Development image

You can build the development image yourself:

docker build -t qmcgaw/latexdevcontainer -f Dockerfile https://github.com/qdm12/latexdevcontainer.git

You can extend the Docker image qmcgaw/latexdevcontainer with your own instructions.
1. Create a file .devcontainer/Dockerfile with FROM qmcgaw/latexdevcontainer
2. Append instructions to the Dockerfile created. For example:
```
FROM qmcgaw/latexdevcontainer
RUN tlmgr install lastpage
```
3. Modify .devcontainer/docker-compose.yml and add build: . in the vscode service.
4. Open the VS code command palette and choose Remote-Containers: Rebuild container
You can bind mount a shell script to /home/vscode/.welcome.sh to replace the current welcome script

TODOs

qmcgaw/basedevcontainer todos

License

This repository is under an MIT license unless indicated otherwise.

vagnercardosoweb/docker-php8

2023-12-26T01:27:47Z

Docker with php8.2+, apache, mysql, postgresql, phpmyadmin, redis, nginx and pgadmin

Docker with PHP 8.2.12

This repository aims to facilitate the creation of a development environment with php 8.2.12.

What's in the environment:

Prerequisites:

How to use:

Clone the repository
Enter the repository folder
Run the docker-compose up command
- if you want to run in background mode, run the command docker-compose up -d
Access the address http://localhost:8080 to access phpmyadmin
- user access
  - user: mysql
  - password: mysql
  - host: mysql
- root access
  - user: root
  - password: root
  - host: mysql
Access the address http://localhost:8081 to access pgadmin
- user: admin@localhost.com
- password: admin
Access the address http://localhost to access the project

Persistent data:

mysql data: ./docker/mysql/dbdata
postgresql data: ./docker/postgresql/dbdata
redis data: ./docker/redis

PHP INI Config:

Local php.ini configuration is located in the ./docker/php/php.ini file.

[PHP]
log_errors=On
xmlrpc_errors=On
html_errors=On
display_errors=On
display_startup_errors=On
report_memleaks=On
error_reporting=E_ALL
file_uploads=On
max_execution_time=120
max_input_time=120
session.gc_maxlifetime=1440
post_max_size=50M
upload_max_filesize=45M
max_file_uploads=20
variables_order="EGPCS"
max_input_vars=10000
max_input_nesting_level=64
date.timezone=UTC
memory_limit=512M
expose_php=On

[opcache]
opcache.enable=true
opcache.enable_cli=true
opcache.jit=tracing

[intl]
intl.default_locale=en_utf8

[xdebug]
xdebug.client_host=host.docker.internal
xdebug.client_port=9003
xdebug.discover_client_host=0
xdebug.start_with_request=yes
xdebug.remote_handler=dbgp
xdebug.idekey=PHPSTORM
xdebug.mode=debug,develop
xdebug.cli_color=1

PHP Modules:

[PHP Modules]
  apcu
  bcmath
  Core
  ctype
  curl
  date
  dom
  exif
  fileinfo
  filter
  ftp
  gd
  gmp
  hash
  iconv
  imap
  intl
  json
  libxml
  mbstring
  mongodb
  mysqli
  mysqlnd
  openssl
  pcntl
  pcre
  PDO
  pdo_mysql
  pdo_pgsql
  pdo_sqlite
  pgsql
  Phar
  posix
  random
  readline
  redis
  Reflection
  session
  SimpleXML
  soap
  sockets
  sodium
  SPL
  sqlite3
  ssh2
  standard
  sysvmsg
  sysvsem
  sysvshm
  tokenizer
  xdebug
  xml
  xmlreader
  xmlwriter
  xsl
  Zend OPcache
  zip
  zlib

[Zend Modules]
  Xdebug
  Zend OPcache

Comments:

The project starts the services of nginx, php, mysql, postgresql, phpmyadmin, pgadmin and redis by default, if you want to use apache2, mariadb you need to comment the services that are being used and enable the services you want to use on the docker-compose.yml file.

License:

MIT

feelingsurf/docker-viewer

2023-12-26T01:27:47Z

Official FeelingSurfViewer Docker image

The official FeelingSurf app in a container is available on Docker Hub.

FeelingSurfViewer is also available as a regular Desktop app at FeelingSurf.

Usage

docker run -d -e access_token=<YOUR_SECRET_ACCESS_TOKEN> feelingsurf/viewer:stable

Disclaimer: never share your access token as it allows full access to your FeelingSurf account.

adoptoposs/mjml_nif

2023-12-26T01:28:26Z

Elixir NIF bindings for the MJML Rust implementation (mrml)

eventrelay/eventrelay

2023-12-26T01:28:26Z

An event streaming/storage system for the rest of us

GDQuest/godot-tours

2023-12-26T01:29:12Z

A framework for interactive step-by-step tutorials in the Godot editor

freehuntx/godot-matcha

2023-12-26T01:29:12Z

Easy webrtc matchmaking using WebTorrent tracker

mathaou/termdbms

2023-12-26T01:29:31Z

A TUI for viewing and editing database files.

termdbms

A TUI for viewing and editing databases, written in pure Go

Installation Instructions

Go Install

go install github.com/mathaou/termdbms@latest

Arch Linux

// pacman
sudo pacman -S termdbms-git
// yay
yay -S termdbms-git

Database Support

SQLite
CSV* (see note below)

made with modernc.org/sqlite, charmbracelet/bubbletea, and charmbracelet/lipgloss

Works with keyboard and mouse!

Navigate tables with any number of columns!

Navigate tables with any number of rows!

Serialize your changes as a copy or over the database original! (SQLite only)

Query your database!

Other Features

Run SQL queries and display the results!
Save SQL queries to a clipboard!
Update, delete, or insert with SQL, with undo/redo supported for SQLite
Automatic JSON formatting in selection/format mode
Edit multi-line text with vim-like controls
Undo/Redo of changes (SQLite only)
Themes (press T in table mode)
Output query results as a csv
Convert .csv to SQLite database! Export as a SQLite database or .csv file again!

Roadmap

MySQL/ PostgreSQL support
Line wrapping / horizontal scroll for format/SQL mode + revamped (faster format mode)

How To Build

Linux

GOOS=linux GOARCH=amd64/386 go build

ARM (runs kind of slow depending on the specs of the system)

GOOS=linux GOARCH=arm GOARM=7 go build

Windows

GOOS=windows GOARCH=amd64/386 go build

OSX

GOOS=darwin GOARCH=amd64 go build

Terminal settings

Whatever terminal emulator used should support ANSI escape sequences. If there is an option for 256 color mode, enable it. If not available, try running program in ascii mode (-a).

Known Issues

Using termdbms over a serial connection works very poorly. This is due to ANSI sequences not being supported natively. Maybe putty/mobaxterm have settings to allow this?
The headers wig out sometimes in selection mode
Line wrapping is not yet implemented, so text in format mode should be less than the maximum number of columns available per line for best use. It's in the works!
Weird combinations of newlines + tabs can break stuff. Tabs at beginning of line and mid-line works in a stable manner.

Help:

-p / database/.csv path
-d / specifies which database driver to use (sqlite/mysql)
-a / enable ascii mode
-h / prints this message
-t / starts app with specific theme (default, nord, solarized)

Controls:

MOUSE

Scroll up + down to navigate table/text
Move cursor to select cells for full screen viewing

KEYBOARD

[WASD] to move around cells, and also move columns if close to edge
[ENTER] to select selected cell for full screen view
[UP/K and DOWN/J] to navigate schemas
[LEFT/H and RIGHT/L] to navigate columns if there are more than the screen allows.
    Also to control the cursor of the text editor in edit mode
[BACKSPACE] to delete text before cursor in edit mode
[M(scroll up) and N(scroll down)] to scroll manually
[Q or CTRL+C] to quit program
[B] to toggle borders!
[C] to expand column
[T] to cycle through themes!
[P] in selection mode to write cell to file, or to print query results as CSV.
[R] to redo actions, if applicable
[U] to undo actions, if applicable
[ESC] to exit full screen view, or to enter edit mode
[PGDOWN] to scroll down one views worth of rows
[PGUP] to scroll up one views worth of rows

EDIT MODE (for quick, single line changes and commands)

[ESC] to enter edit mode with no pre-loaded text input from selection
When a cell is selected, press [:] to enter edit mode with selection pre-loaded
The text field in the header will be populated with the selected cells text. Modifications can be made freely
[ESC] to clear text field in edit mode
[ENTER] to save text. Anything besides one of the reserved strings below will overwrite the current cell
[:q] to exit edit mode/ format mode/ SQL mode
[:s] to save database to a new file (SQLite only)
[:s!] to overwrite original database file (SQLite only). A confirmation dialog will be added soon
[:h] to display help text
[:new] opens current cell with a blank buffer
[:edit] opens current cell in format mode
[:sql] opens blank buffer for creating an SQL statement
[:clip] to open clipboard of SQL queries. [/] to filter, [ENTER] to select.
[HOME] to set cursor to end of the text
[END] to set cursor to the end of the text

FORMAT MODE (for editing lines of text)

[ESC] to move between top control bar and format buffer
[HOME] to set cursor to end of the text
[END] to set cursor to the end of the text
[:wq] to save changes and quit to main table view
[:w] to save changes and remain in format view
[:s] to serialize changes, non-destructive (SQLite only)
[:s!] to serialize changes, overwriting original file (SQLite only)

SQL MODE (for querying database)

[ESC] to move between top control bar and text buffer
[:q] to quit out of statement
[:exec] to execute statement. Errors will be displayed in full screen view.
[:stow <NAME>] to create a snippet for the clipboard with an optional name. A random number will be used if no name is specified.

QUERY MODE (specifically when viewing query results)

[:d] to reset table data back to original view
[:sql] to query original database again

overflowy/spongebob-cli

2023-12-26T01:29:31Z

SpongeBob delivered straight from your terminal

spongebob-cli

SpongeBob delivered straight from your terminal

Why?

Why not.

Features

List all available episodes
Stream episodes directly with minimal user interaction
Customize the video player used for streaming
Download all episodes asynchronously

Usage

Running spongebob-cli without any flags will prompt the user to select the episode number.

Usage of spongebob-cli:
  -d int
        download all episodes asynchronously but max [d] episodes at a time (default -1)
  -l    list episodes and quit
  -p int
        play the wanted episode without any user interaction (default -1)
  -vp string
        use another video player [default=mpv] (default "mpv")

Disclaimer

This tool is for educational purposes only. The maintainers do not own the rights to any of the content streamed by this application. It is the user's responsibility to ensure they have the right to watch the streamed content.

mislav/hub

2023-12-26T01:29:31Z

Fastest golang in-memory cache library based on S3-FIFO algorithm. many times faster than Ristretto and friends

High performance in-memory cache

📖 Contents

💡 Motivation

I once came across the fact that none of the Golang cache libraries are truly contention-free. All of them are just a standard map with mutex and some eviction policy. Unfortunately, these are not able to reach the speed of caches in other languages (such as Caffeine). For example, the fastest cache from Dgraph labs called Ristretto, which was faster than competitors by 30% at best (Otter is many times faster) and had a disgusting hit ratio even though README says otherwise. This can be a problem in different applications because no one wants to bump the performance of a cache library and its bad hit ratio 🙂. As a result, I wanted to get the fastest, easiest-to-use cache with excellent hit ratio and support from the authors and Otter is designed to correct this unfortunate misunderstanding.

Please leave a ⭐ as motivation if you liked the idea 😄

🗃 Related works

Otter is based on the following papers:

✨ Features

This library has lots of features such as:

Simple API: Just set the parameters you want in the builder and enjoy
Autoconfiguration: Otter is automatically configured based on the parallelism of your application
Generics: You can safely use any comparable types as keys and any types as values
TTL: Expired values will be automatically deleted from the cache
Cost-based eviction: Otter supports eviction based on the cost of each item
Excellent performance: Otter is currently the fastest cache library with a huge lead over the competition
Great hit ratio: New S3-FIFO algorithm is used, which shows excellent results

📚 Usage

📋 Requirements

Go 1.18+

🛠️ Installation

go get -u github.com/maypok86/otter

✏️ Examples

Builder

Otter uses a builder pattern that allows you to conveniently create a cache object with different parameters

package main

import (
    "github.com/maypok86/otter"
)

func main() {
    // NewBuilder creates a builder and sets the future cache capacity to 1000 elements.
    // Returns an error if capacity <= 0.
    builder, err := otter.NewBuilder[string, string](1000)
    if err != nil {
        panic(err)
    }

    // StatsEnabled determines whether statistics should be calculated when the cache is running.
    // By default, statistics calculating is disabled.
    builder.StatsEnabled(true)

    // Cost sets a function to dynamically calculate the weight of a key-value pair.
    // By default this function always returns 1.
    builder.Cost(func(key string, value string) uint32 {
        return uint32(len(value))
    })

    // Build creates a new cache object or
    // returns an error if invalid parameters were passed to the builder.
    cache, err := builder.Build()
    if err != nil {
        panic(err)
    }

    cache.Close()
}

Cache

package main

import (
    "fmt"
    "time"

    "github.com/maypok86/otter"
)

func main() {
    // create a cache with capacity equal to 10000 elements
    cache, err := otter.MustBuilder[string, string](10_000).Build()
    if err != nil {
        panic(err)
    }

    // set key-value pair with ttl (1 hour) 
    cache.SetWithTTL("key", "value", time.Hour)

    // get value from cache
    value, ok := cache.Get("key")
    if !ok {
        panic("not found key")
    }
    fmt.Println(value)

    // delete key-value pair from cache
    cache.Delete("key")

    // delete data and stop goroutines
    cache.Close()
}

📊 Benchmarks

The benchmark code can be found here

🚀 Performance

Read (100%)

Read (75%) / Write (25%)

Write (100%)

🎯 Hit ratio

Zipf

S3

This trace is described as "disk read accesses initiated by a large commercial search engine in response to various web search requests."

DS1

This trace is described as "a database server running at a commercial site running an ERP application on top of a commercial database."

P3

The trace P3 was collected from workstations running Windows NT by using Vtrace which captures disk operations through the use of device filters

P8

The trace P8 was collected from workstations running Windows NT by using Vtrace which captures disk operations through the use of device filters

LOOP

This trace demonstrates a looping access pattern.

OLTP

This trace is described as "references to a CODASYL database for a one hour period."

In summary, we have that S3-FIFO (otter) is inferior to W-TinyLFU (theine) on lfu friendly traces (databases, search, analytics), but has a greater or equal hit ratio on web traces.

👏 Contribute

Contributions are welcome as always, before submitting a new PR please make sure to open a new issue so community members can discuss it. For more information please see contribution guidelines.

Additionally, you might find existing open issues which can help with improvements.

This project follows a standard code of conduct so that you can understand what actions will and will not be tolerated.

📄 License

This project is Apache 2.0 licensed, as found in the LICENSE.

Asuk1/Glados_mirror

2023-12-26T01:30:06Z

Glados Mirror

Glados EPITECH Project

Qu'est-ce que Glados ?

Le projet Glados est un projet de programmation fonctionnelle en Haskell. Le but de ce projet est d'implémenter un langage de programmation de notre propre conception, en Haskell.

Il est divisé en deux parties:

Une partie LISP qui consiste à implémenter un interpreteur pour un sous-ensemble du langage LISP. Et une partie Glados où on doit implémenter notre propre langage de programmation.

Réparition des tâches du projet

Parser
CI/CD
Tests unitaires
Un début d'AST (Abstract Syntax Tree)
Un début d'évaluateur

Overview Parser

Le parser du projet Glados est un outil conçu pour analyser et transformer le code source écrit dans un langage de programmation spécifique en une représentation structurée. Cette représentation peut être utilisée pour une variété de tâches, telles que l'interprétation, la compilation ou l'analyse statique du code.

USAGE

./glados

Remplacez par le chemin du fichier contenant le code source que vous souhaitez parser.

Exemple de fichier d'entrée

(define x 42) (* 42 x)

Sortie du Parser

["(", "define", "x", "42", ")", "(", "*", "42", "x", ")"]

Overview CI/CD

La CI/CD dans ce projet est composé de plusieurs parties, tout d'abord une partie build qui compile le projet et qui vérifie que la compilation s'est bien déroulée. Ensuite une partie test qui vérifie que les tests unitaires se sont bien déroulés. Après une partie mirror qui permet de mettre à jour le dépôt github d'EPITECH. Et enfin une partie release qui permet de créer une release sur le github mirror.

Overview Tests unitaires

Les tests unitaires dans ce projet sont composés de plusieurs parties: Pour lancer les tests unitaires il faut utiliser la commande make tests_run

On à des tests sur:

la tokenisation
les opérations d'évaluation
l'identification des variables

Comment fonctionne le projet

On fait cette commmande:

./glados

DinmaMerciBoi/MyProjectApp

2023-12-26T01:30:02Z

rahulwagh/terraform-jenkins

2023-12-26T01:30:02Z

Azure-Samples/terraform-github-actions

2023-12-26T01:30:02Z

A reference implementation of using GitHub Actions to deploy infrastructure to Azure using Terraform

elifgazioglu/doyouwannagooutwithme

2023-12-26T01:30:26Z

✨doyouwannagooutwithme.com

A website to invite your lover for a date 🥰

0atman/noboilerplate

2023-12-26T01:30:26Z

Code for my talks on the No Boilerplate channel

Code for my talks on the No Boilerplate channel is in scripts/

I build these using obsidian.md and the advanced-slides plugin: I take each slide, printscreen it, and splice it with my audio using reaper.fm.

Spandan-Madan/DeepLearningProject

2023-12-26T01:30:26Z

Material for The Mathematical Engineering of Deep Learning. See https://deeplearningmath.org

MathematicalEngineeringDeepLearning

Material for The Mathematical Engineering of Deep Learning. See the actual book content on deeplearningmath.org or (when it is out) purchase the book from CRC press.

This repository contains general supporting material for the book.

Below is a detailed list of the source code used for creating figures and tables in the book. We use Julia, Python, or R and the code is sometimes in stand alone files, sometimes in Jupyter notebooks, sometimes as R Markdown, and sometimes in Google Colab. Many of our static illustrations were created using TikZ by Ajay Hemanth and Vishnu Prasath with the source of their illustrations also available so you can adapt it for purposes.

Chapter 1

Figure	Topic	Source Code
1.1	Fast.ai example	Python Google Colab
1.3	Architectures	TikZ(a), TikZ(b), TikZ(c), TikZ(d), TikZ(e), TikZ(f)
1.4	Neurons	TikZ(b), TikZ(d)
1.5	Data on earth	Julia

Chapter 2

Figure	Topic	Source Code
2.1	Supervised Learning	TikZ
2.2	Unsupervised Learning	TikZ
2.3	Simple regression	R
2.4	Breast Cancer ROC curves	R
2.5	Least Squares	TikZ
2.6	Loss functions	Julia
Table 2.1	Linear MNIST classification	Julia
2.7	Gradient Descent Learning Rate	Python
2.8	Loss Landscape	R
2.9	Generalization and Training	TikZ or Julia
2.10	Polynomial fit	R
2.11	K-fold cross validation	TikZ
2.12	K-means clustering	R
2.13	K-means image segmentation	R
2.14	Breast Cancer PCA	R
2.15	SVD Compression	Julia

Chapter 3 (Figures under construction)

Figure	Topic	Source Code
3.1	Logistic regression model curves	R(a) R(b)
3.2	Linear decision boundary for logistic regression	R
3.3	Components of an artificial neuron	TikZ
3.4	Loss landscape of MSE vs. CE on logistic regression	Python
3.5	Evolution of gradient descent learning in logistic regression	R
3.6	Shallow multi-output neural network with softmax	TikZ
3.7	Multinomial regression for classification	R
Table 3.1	Different approaches for creating an MNIST digit classifier.	Julia
3.8	Feature engineering in simple logistic regression	R
3.9	Non-linear classification decision boundaries with feature engineering in logistic regression	R
3.10	Non-linear classification decision boundaries with feature engineering in multinomial regression	R
3.11	Single hidden layer autoencoder	TikZ
3.12	Autoencoder projections of MNIST including using PCA	R TikZ
3.13	Manifolds and autoencoders	R TikZ
3.14	MNIST using autoencoders	R
3.15	Denoising autoencoder	TikZ
3.16	Interpolations with autoencoders	Julia

Chapter 4 (Figures)

Figure	Topic	Source Code
4.1	Convexity and local/global extrema	Python
4.2	Gradient descent with a time dependent learning rate	Python
4.3	Stochastic gradient descent	Python
4.4	Early stopping in deep learning	Julia
4.5	Non-convex loss landscapes	Python
4.6	Momentum enhancing gradient descent	Python
4.7	The computational graph for automatic differentiation	TikZ
4.8	Line search concepts	Python
4.9	The zig-zagging property of line search (zoom in)	Python
4.10	Newton's method in one dimension	Python

Chapter 5 (Figures under construction)

Figure	Topic	Source Code
5.1	Fully Connected Feedforward Neural Networks	TikZ(a), TikZ(b)
5.2	Arbitrary function approximation with neural nets	Julia
5.3	Binary classification with increasing depth	R
5.4	A continuous multiplication gate with 4 hidden units	TikZ
5.5	Several common scalar activation functions	Julia
5.6	Flow of information in general back propagation	TikZ
5.7	Simple neural network hypothetical example	TikZ
5.8	Flow of information in standard neural network back propagation	TikZ
5.9	Computational graph for batch normalization	TikZ
5.10	The effect of dropout	TikZ

Chapter 6 (Figures under construction)

Figure	Topic	Source Code
6.1	TBD

Chapter 7 (Figures under construction)

Figure	Topic	Source Code
7.1	TBD

Chapter 8 (Figures under construction)

Figure	Topic	Source Code
8.1	TBD

Chapter 9 (Figures under construction)

Figure	Topic	Source Code
9.1	TBD

Chapter 10 (Figures under construction)

Figure	Topic	Source Code
10.1	TBD

brunodev85/winlator

2023-12-26T01:31:40Z

Android application for running Windows applications with Wine and Box86/Box64

Winlator

Winlator is an Android application that lets you to run Windows (x86_64) applications with Wine and Box86/Box64.

Installation

Download and install the APK (Winlator_3.2.apk) from GitHub Releases
Download the OBB file (main.3.com.winlator.obb) and put it into the directory /storage/emulated/0/Android/obb/com.winlator (create it if it doesn't exist) or install from settings
Launch the app and wait for the installation process to finish

Credits and Third-party apps

Ubuntu RootFs (Focal Fossa)
Wine (winehq.org)
Box86/Box64 by ptitseb
PRoot (proot-me.github.io)
Mesa3D (mesa3d.org)
DXVK (github.com/doitsujin/dxvk)
D8VK (github.com/AlpyneDreams/d8vk)
CNC DDraw (github.com/FunkyFr3sh/cnc-ddraw)

Thanks to alexvorxx for the Mesa mods and build instructions

kovacsv/Online3DViewer

2023-12-26T01:31:15Z

A solution to visualize and explore 3D models in your browser.

Online 3D Viewer

Online 3D Viewer (https://3dviewer.net) is a free and open source web solution to visualize and explore 3D models in your browser. This repository contains the source code of the website and the library behind it.

Live website - Website documentation - Developer documentation - Discord server

Example

Check the live version!

Supported file formats

Import: obj, 3ds, stl, ply, gltf, glb, off, 3dm, fbx, dae, wrl, 3mf, amf, ifc, brep, step, iges, fcstd, bim.
Export: obj, stl, ply, gltf, glb, off, 3dm, bim.

External Libraries

Online 3D Viewer uses these wonderful libraries: three.js, pickr, fflate, draco, rhino3dm, web-ifc, occt-import-js.

zeon-studio/hugoplate

2023-12-26T01:31:15Z

Hugoplate is a free starter template built with Hugo and TailwindCSS that will save you hours of work.

Hugo + Tailwind CSS Starter and Boilerplate

Hugoplate is a free starter template built with Hugo, and TailwindCSS, providing everything you need to jumpstart your Hugo project and save valuable time.

Made with ♥ by Zeon Studio

If you find this project useful, please give it a ⭐ to show your support.

👀 Demo | Page Speed (95+)🚀

🎁 What's Included

We have included almost everything you need to start your Hugo project. Let's see what's included in this template:

📌 Key Features

👥 Multi-Authors
🎯 Similar Posts Suggestion
🔍 Search Functionality
🌑 Dark Mode
🏷️ Tags & Categories
🔗 Netlify setting pre-configured
📞 Support contact form
📱 Fully responsive
📝 Write and update content in Markdown
💬 Disqus Comment
🔳 Syntax Highlighting

📄 15+ Pre-designed Pages

🏠 Homepage
👤 About
📞 Contact
👥 Authors
👤 Author Single
📝 Blog
📝 Blog Single
🚫 Custom 404
💡 Elements
📄 Privacy Policy
🏷️ Tags
🏷️ Tag Single
🗂️ Categories
🗂️ Category Single
🔍 Search

📦 Tech Stack

🚀 Getting Started

First you need to clone or download the template repository, and then let's get started with the following process:

⚙️ Prerequisites

To start using this template, you need to have some prerequisites installed on your machine.

👉 Project Setup

We build this custom script to make your project setup easier. It will create a new Hugo theme folder, and clone the Hugoplate theme into it. Then move the exampleSite folder into the root directory. So that you can start your Hugo server without going into the exampleSite folder. Use the following command to setup your project.

npm run project-setup

👉 Install Dependencies

Install all the dependencies using the following command.

npm install

👉 Development Command

Start the development server using the following command.

npm run dev

🎬 Still Confused? Watch a Quick Video

https://github.com/zeon-studio/hugoplate/assets/58769763/c260c0ae-91be-42ce-b8db-aa7f11f777bd

📝 Customization

This template has been designed with a lot of customization options in mind. You can customize almost anything you want, including:

👉 Site Config

You can change the site title, base URL, language, theme, plugins, and more from the hugo.toml file.

👉 Site Params

You can customize all the parameters from the config/_default/params.toml file. This includes the logo, favicon, search, SEO metadata, and more.

👉 Colors and Fonts

You can change the colors and fonts from the data/theme.json file. This includes the primary color, secondary color, font family, and font size.

👉 Social Links

You can change the social links from the data/social.json file. Add your social links here, and they will automatically be displayed on the site.

🛠 Advanced Usage

We have added some custom scripts to make your life easier. You can use these scripts to help you with your development.

👉 Update Modules

We have added a lot of modules to this template. You can update all the modules using the following command.

npm run update-modules

👉 Remove Dark Mode

If you want to remove dark mode from your project, then you have to do it manually from everywhere. So we build a custom script to do it for you. you can use the following command to remove dark mode from your project.

npm run remove-darkmode

Note: This command will work before running project-setup script. If you already run the project-setup command, then you have to run npm run theme-setup first, and then you can run this command. afterward, you can run npm run project-setup again.

🚀 Build And Deploy

After you finish your development, you can build or deploy your project almost everywhere. Let's see the process:

👉 Build Command

To build your project locally, you can use the following command. It will purge all the unused CSS and minify all the files.

npm run build

👉 Deploy Site

We have provided 5 different deploy platform configurations with this template, so you can deploy easily.

And if you want to Host some other hosting platforms. then you can build your project, and you will get a public folder. that you can copy and paste on your hosting platform.

Note: You must change the baseURL in the hugo.toml file. Otherwise, your site will not work properly.

🔒 Guide to Staying Compliant

🐞 Reporting Issues

We use GitHub Issues as the official bug tracker for this Template. Please Search existing issues. It’s possible someone has already reported the same problem. If your problem or idea has not been addressed yet, feel free to open a new issue.

📝 License

Code License: Released under the MIT license.

Image license: The images are only for demonstration purposes. They have their license, we don't have permission to share those images.

🖼️ Showcase

List of projects people are building with Hugoplate! Have you built a project with Hugoplate? Submit it by creating a pull request and we'll feature it here!


Open Neuromorphic	AI Models	Hugobricks

nexusflowai/NexusRaven-V2

2023-12-26T01:32:14Z

NexusRaven-V2

Pushing The Boundaries of Open Source Function Calling Models

Nexusflow HF - Nexusflow Discord - NexusRaven-V2 blog post - Prompting Notebook CoLab - Leaderboard - Read-World Demo - NexusRaven-V2-13B HF

Welcome to NexusRaven-V2! We want to provide the evaluation data, evaluation code, and a prompting guide to help you reproduce our results, as well as use NexusRaven-V2 to the best of its ability!

Introducing NexusRaven-V2

NexusRaven-V2 is an open-source and commercially viable function calling LLM that surpasses the state-of-the-art in function calling capabilities. It has 13B parameters. NexusRaven-V2 is capable of doing single function calls, parallel function calls (where multiple "disconnected" function calls are necessary to answer the user query), and nested function calls (where the argument for the necessary function requires a chain of other function calls).

💪 Versatile Function Calling Capability: NexusRaven-V2 is capable of generating single function calls, nested calls, and parallel calls in many challenging cases.

🤓 Fully Explainable: NexusRaven-V2 is capable of generating very detailed explanations for the function calls it generates. This behavior can be turned off, to save tokens during inference.

📊 Performance Highlights: NexusRaven-V2 surpasses GPT-4 by up to 7% in function calling success rates in human-generated use cases involving nested and composite functions.

🔧 Generalization to the Unseen: NexusRaven-V2 has never been trained on the functions used in evaluation.

🔥 Commercially Permissive: The training of NexusRaven-V2 does not involve any data generated by proprietary LLMs such as GPT-4. You have full control of the model when deployed in commercial applications.

Please checkout the following links!

NexusRaven-V2 model usage

NexusRaven-V2 accepts a list of python functions. These python functions can do anything (including sending GET/POST requests to external APIs!). The two requirements include the python function signature and the appropriate docstring to generate the function call.

NexusRaven-V2's Capabilities

NexusRaven-V2 is capable of generating deeply nested function calls, parallel function calls, and simple single calls. It can also justify the function calls it generated. If you would like to generate the call only, please set a stop criteria of "<bot_end>". Otherwise, please allow NexusRaven-V2 to run until its stop token (i.e. "</s>").

Quick Start Prompting Guide

Please refer to our notebook, How-To-Prompt.ipynb, for more advanced tutorials on using NexusRaven-V2!

Quickstart

You can run the model on a GPU using the following code.

# Please `pip install transformers accelerate`
from transformers import pipeline


pipeline = pipeline(
    "text-generation",
    model="Nexusflow/NexusRaven-V2-13B",
    torch_dtype="auto",
    device_map="auto",
)

prompt_template = \
'''
Function:
def get_weather_data(coordinates):
    """
    Fetches weather data from the Open-Meteo API for the given latitude and longitude.

    Args:
    coordinates (tuple): The latitude of the location.

    Returns:
    float: The current temperature in the coordinates you've asked for
    """

Function:
def get_coordinates_from_city(city_name):
    """
    Fetches the latitude and longitude of a given city name using the Maps.co Geocoding API.

    Args:
    city_name (str): The name of the city.

    Returns:
    tuple: The latitude and longitude of the city.
    """

User Query: {query}<human_end>

'''

prompt = prompt_template.format(query="What's the weather like in Seattle right now?")

result = pipeline(prompt, max_new_tokens=2048, return_full_text=False, do_sample=False, temperature=0.001)[0]["generated_text"]
print (result)

This should generate the following:

Call: get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle'))<bot_end>
Thought: The function call `get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle'))` answers the question "What's the weather like in Seattle right now?" by following these steps:

1. `get_coordinates_from_city(city_name='Seattle')`: This function call fetches the latitude and longitude of the city "Seattle" using the Maps.co Geocoding API.
2. `get_weather_data(coordinates=...)`: This function call fetches the current weather data for the coordinates returned by the previous function call.

Therefore, the function call `get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle'))` answers the question "What's the weather like in Seattle right now?" by first fetching the coordinates of the city "Seattle" and then fetching the current weather data for those coordinates.

If you would like to prevent the generation of the explanation of the function call (for example, to save on inference tokens), please set a stopping criteria of "<bot_end>".

Please follow this prompting template to maximize the performance of RavenV2.

Using with OpenAI FC Schematics

If you currently have a workflow that is built around OpenAI's function calling and you want to try NexusRaven-V2, we have a package that helps you drop in NexusRaven-V2.

Please give it a try!

NexusRaven-V2 on Nexus Function Calling Benchmark

Benchmarks

We curated 9 tasks within the Nexus Function Calling Benchmark specifically around function calling. We release 8 of them, but keep one internally to avoid situations where new community models overfit to these benchmarks.

They are based on real-world APIs. Here are the 8 datasets for evaluation:

The function definitions for all benchmarks can be found here.

We report accuracy on The Stack API but do not release the data. We keep it internal as a means of benchmarking new models to ensure generalizability.

Benchmark Classifications

We classify the benchmarks above into three categories, based on the type of API usage they require to get the answer correctly. These include: single calls, parallel calls, and nested calls.

Single Calls

Single Calls are defined as API usage that only require a single function call. Here is an example of this:

Nested Calls

Nested Calls are defined as API usage that require multiple calls all at once to get the right answer, where the argument for the outer call depends on the call graph. Here is an example of this:

Parallel Calls

Parallel calls are when seperate calls are generated, but they do not connect into each other.

Task Classification

We classify the tasks within Nexus Function Calling Benchmark into the following categories:

API	Category
OTX	Single Calls
NVDLibrary	Single Calls
VirusTotal	Single Calls
VirusTotal-Nested Calls	Nested Calls
Climate	Nested and Parallel Calls
VirusTotal-Parallel Calls	Parallel Calls
Places API	Nested Calls
NVDLibrary-Nested Calls	Nested Calls
The Stack API	Mostly Single Calls

NexusRaven-V2 Capability Breakdown

We benchmark NexusRaven-V2 against GPT4 primarily. Our benchmarking effort consists of several categories of benchmarks. This includes: single calls, parallel calls, and nested calls.

Capability Performance

The performance of NexusRaven-V2 on each category is reflected below:

Overall Scores

Reproducing The Results

Please see the Benchmarking Notebook for reproducing Raven's results. The cells have already been run and the output has been recorded in the notebook itself. The output of each cell is what we report in the leaderboard. However, you are welcome to rerun the cells to verify. The NexusRaven-V2 endpoint will be accessible for this effort, and the link is present in the notebook itself.

NexusRaven-V2's benchmark performance should be identical to the reported accuracy in leaderboard.

IMPORTANT NOTE: However, it is currently impossible to generate deterministic outputs for GPT4, so we see WILD swings in the per-task accuracy for GPT4 (but, the average accuracy across task categories are relatively stable within a few percent). Please note this when rerunning the GPT4 cells in the notebook.

License

The code in this repository for running the NexusRaven-V2 model, the evaluation notebook, the prompting notebook, and the evaluation data are licensed under Apache 2.0.

References

We thank the CodeLLaMa Team for their great foundational models that made NexusRaven-V2 possible.

@misc{rozière2023code,
      title={Code Llama: Open Foundation Models for Code}, 
      author={Baptiste Rozière and Jonas Gehring and Fabian Gloeckle and Sten Sootla and Itai Gat and Xiaoqing Ellen Tan and Yossi Adi and Jingyu Liu and Tal Remez and Jérémy Rapin and Artyom Kozhevnikov and Ivan Evtimov and Joanna Bitton and Manish Bhatt and Cristian Canton Ferrer and Aaron Grattafiori and Wenhan Xiong and Alexandre Défossez and Jade Copet and Faisal Azhar and Hugo Touvron and Louis Martin and Nicolas Usunier and Thomas Scialom and Gabriel Synnaeve},
      year={2023},
      eprint={2308.12950},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Citation

@misc{nexusraven,
      title={NexusRaven-V2: Surpassing GPT-4 for Zero-shot Function Calling},
      author={Nexusflow.ai team},
      year={2023},
      url={https://nexusflow.ai/blogs/ravenv2}
}

Contact

Please join our Discord Channel to reach out for any issues and comments!

ytzfhqs/AAAMLP-CN

2023-12-26T01:32:14Z

Approaching (Almost) Any Machine Learning Problem中译版，在线文档地址：https://ytzfhqs.github.io/AAAMLP-CN/

AAAMLP-CN

新特性

2023.10.25

😎完成全部翻译
📝计划对kaggle游乐园系列优秀解决方案代码进行解析

2023.09.07

⚡修正部分已知文字错误和代码错误
🤗添加在线文档

简介

Abhishek Thakur，很多 kaggler 对他都非常熟悉，2017 年，他在 Linkedin 发表了一篇名为Approaching (Almost) Any Machine Learning Problem的文章，介绍他建立的一个自动的机器学习框架，几乎可以解决任何机器学习问题，这篇文章曾火遍 Kaggle。

Abhishek 在 Kaggle 上的成就：

Competitions Grandmaster（17 枚金牌，世界排名第 3）
Kernels Expert （Kagglers 排名前 1％）
Discussion Grandmaster（65 枚金牌，世界排名第 2）

目前，Abhishek 在挪威 boost 公司担任首席数据科学家的职位，这是一家专门从事会话人工智能的软件公司。

本文对Approaching (Almost) Any Machine Learning Problem进行了中文翻译，由于本人水平有限，且未使用机器翻译，可能有部分言语不通顺或本土化程度不足，也请大家在阅读过程中多提供宝贵意见。另附上书籍原项目地址，转载请一定标明出处！

本项目支持在线阅读，方便您随时随地进行查阅。

因为有几章内容太过基础，所以未进行翻译，详细情况请参照书籍目录：

准备环境（已翻译）
无监督和有监督学习（已翻译）
交叉检验（已翻译）
评估指标（已翻译） -
组织机器学习（已翻译）
处理分类变量（已翻译）
特征工程（已翻译）
特征选择（已翻译）
超参数优化（已翻译）
图像分类和分割方法（已翻译）
文本分类或回归方法（已翻译）
组合和堆叠方法（已翻译）
可重复代码和模型方法（已翻译）

我将会把完整的翻译版 Markdown 文件上传到 GitHub，以供大家免费下载和阅读。为了最佳的阅读体验，推荐使用 PDF 格式或是在线阅读进行查看

若您在阅读过程中发现任何错误或不准确之处，非常欢迎通过提交 Issue 或 Pull Request 来协助我进行修正。

如果您觉得这个项目对您有帮助，请不吝给予 Star 或者进行关注。

rohan-paul/LLM-FineTuning-Large-Language-Models

2023-12-26T01:32:14Z

LLM (Large Language Model) FineTuning

Multiple LLM (Large Language Models) FineTuning Projects

For almost all of these I have detailed video in my YouTube Channel

Find me here..

🐦 TWITTER: https://twitter.com/rohanpaul_ai
🟠 YouTube: https://www.youtube.com/@RohanPaul-AI/featured
👨🏻‍💼 LINKEDIN: https://www.linkedin.com/in/rohan-paul-b27285129/
👨‍🔧 KAGGLE: https://www.kaggle.com/paulrohan2020

Other Language Models (Which are technically NOT LLM )

BasemNasr/StoreKMP

2023-12-26T01:32:34Z

Compose Multiplatform Store App ( android , ios )

Store KMP

Compose Multiplatform Project ( android , ios )

🔍 Features Snapshot:

Login, Sign Up ✅
Profile and update Profile✅
Home ✅
Products And ProductDetails✅
Categories And Category Details ✅
Search, Cart ,Setting ,Logout ✅

💻 Tech Stack Highlights:

Clean Archetecture with MVI
Kotlin Multiplatform
Kotlin Coroutines
Compose Multiplatform
Material3
Ktor
Datastore
Precompose
Koin
Voyager
Moko

MaterialCleaner/MaterialCleaner

2023-12-26T01:32:34Z

MaterialCleaner

由于以下原因，此项目停止开发：

这个项目自在 GitHub 发布第一个版本已经两年多了，其中一年多的时间都是在按上班强度开发。我知道这是一个过于小众的需求，注定无法进入大众视野。然而如果所有人都在做大众项目，小的需求谁来满足呢？总有人要做吃亏的那一个。

是的，我明白这个就是从一开始就注定要亏本的项目。虽然安卓的设计没有非常强调“文件”的概念（苹果系统的早期版本甚至根本没有“文件”的概念），然而受 Windows 影响，我还是会经常使用文件管理器，一个自由定制文件存放的功能非常令我着迷。以普遍理性而言，找一个程序员定制开发需要每月付至少 2000 多元（按照国内法定最低工资标准），这个功能逻辑较为复杂，不知需要多久才能好用，定制的价格是普通人难以承受的，找一个现成的产品显然更加划算。没错，现成的产品就是存储空间隔离，起初我认为这个应用有着良好的提示和完善的逻辑，然而我使用一段时间后发现操作有些繁琐，而且最令我难以接受的是强制要求导出在二级目录和不能重定向私有目录，所以我决定还是自己做一个。

两年多时间过去了，我自己的软件终于有了完善的逻辑，也接入了 Google Play 的支付系统。虽然项目收入很低，总收入约 8600 元（相关资料已放在最后的 release 中），平均下来连低保水平都不到，但是看着与我有同样需求的用户付费支持、在群内积极反馈应用问题，自己的需求也得到了满足，我还是非常开心。然而在我项目巨大亏损的情况下，却被酷安大神带节奏，质疑定价太贵。虽然这个应用的功能也许确实会让多数用户认为不值得（也许这也就是手机厂商都不愿意做的原因吧，性价比太低），但是我认为考虑到做小众项目更加困难，70 人民币永久的价格还算合理。然而即便如此，当我看到评论区一些用户也说太贵，我决定还是开源并放出一个完全免费的版本。然而在得罪两百多个付费用户之后，得到的结果是评论区把带节奏的人当做英雄，带节奏之人的回复是“既然有能力改变”，有能力说的是我吗？恐怕是在炫耀有能力轻松地让一个弱小项目破产的你。

现在这个项目已经大概率要停更了，如果你还在寻找替代品，我还是会推荐存储空间隔离，第一是因为据用户反馈它对各种定制系统有良好的兼容性（这是质感清理现在还没有做到的），第二是因为我不觉得谁还有动力和实力克服众多困难开发下一个替代品了。

Spritetm/mn812_module

2023-12-26T01:32:56Z

Driver and documentation for an mn812 audio mixer module

arichorn/YouTubeEnhanced

2023-12-26T01:32:57Z

YouTube+ is a modified version of YouTube with additional features and mainly made for non jailbroken users!

climateseed/services-health

2023-12-26T01:33:21Z

📈 Uptime monitor and status page for ClimateSeed, powered by @upptime

kustomer/kustomer-ios

2023-12-26T01:34:30Z

Kustomer Chat iOS SDK

Embed Kustomer in your own products with our chat SDK for iOS.

Documentation and guides

iOS Docs and guides

Kustomer Developer Portal

Requirements

A Kustomer.com API Key (How to get an API key)
Xcode 14.0+
iOS 12.0+

Installation

Note: Starting with iOS version 2.6.0, the PubNub library is downloaded as a dependency if installing the SDK using the Swift Package Manager or Cocoapods.

Swift Package Manager

Create or open your project inside of Xcode
Navigate to File > Swift Packages > Add Package Dependency
Enter https://github.com/kustomer/kustomer-ios in the URL field
Use the Up to Next Major Version rule spanning from 2.0.0 < 3.0.0, and hit the Next button

Cocoapods

Add pod 'KustomerChat', :git => 'https://github.com/kustomer/kustomer-ios.git' to your Podfile
Run pod update or pod install on your project

Manual

Add KustomerChat.xcframework to your project's Frameworks, Libraries, and Embedded Content section.
Check Embed & Sign for both frameworks.
Add PubNub using your preferred method. You can find details in their documentation here

CALLOUT: If you add PubNub as a git submodule, you must install the PubNub version required by our podspec/package.swift.

Manual - Static version

The release page for each version contains a static version of the SDK. To install, download and unzip libKustomerChatStatic.xcframework and KustomerAssets.bundle, and add them to your project. You will also need to install the version of PubNub required by our podspec/package.swift. You can find details in their documentation here

Post-Install

Add photo and camera privacy descriptions to your Info.plist.
In the application(_:didFinishLaunchingWithOptions:) method of your AppDelegate add

Kustomer.configure(apiKey: "API_KEY_HERE", options: nil, launchOptions: launchOptions)

Call Kustomer.show() whenever and wherever you want to open the Kustomer chat interface.

See Installation for a full guide.

Example app

Open Example/Example.xcodeproj
Set your API key in AppDelegate.swift
- Your API key must have only the org.tracking role. You can get your API key from the Kustomer website.
Run the app

inner-active/InneractiveAdSDK-iOS

2023-12-26T01:34:30Z

DT Exchange iOS SDK

Digital Turbine Exchange iOS SDK

Useful resources:

leostera/atacama

2023-12-26T01:34:42Z

Modern, pure OCaml socket pool for Riot

xapi-project/xcp-rrd

2023-12-26T01:34:42Z

OCaml RRD library for use with XCP

ssg/sozluk-cgi

2023-12-26T01:35:05Z

one of the first versions of ekşi sözlük code as of may 1999

acdphp/laravel-schedule-police

2023-12-26T01:35:14Z

Laravel Schedule Police

Use this if you need to:

✅ Stop and start scheduled commands without redeploying.
✅ Execute commands without going into server console.
✅ Keep the visibility, control, and reviewability of the schedule configurations in your codebase.

Installation

Install the package

composer require acdphp/laravel-schedule-police

Run the migration.
```
php artisan migrate
```

Publish assets

php artisan vendor:publish --tag=schedule-police-assets --force

Update your Console Kernel to extend Acdphp\SchedulePolice\Console\Kernel instead of Illuminate\Foundation\Console\Kernel.

namespace App\Console;

use Acdphp\SchedulePolice\Console\Kernel as ConsoleKernel;

class Kernel extends ConsoleKernel
...

Config

You may override the config by publishing it.

php artisan vendor:publish --tag=schedule-police-config

You may also just define environment variables if you don't need to publish the config.

Disable command execution in the dashboard.

SCHEDULE_POLICE_ALLOW_EXECUTE_CMD=false

Add prefix to routes.

SCHEDULE_POLICE_URL_PREFIX=your-prefix

Dashboard

After installation, you may access the dashboard via the /schedule-police route.

Authorization

By default, you will only be able to access this dashboard in the local environment. However, you may specify authorization for non-local environments by defining viewSchedulePolice gate, typically within the boot method of the App\Providers\AuthServiceProvider class.

public function boot(): void
{
    Gate::define('viewSchedulePolice', function (User $user) {
        // return true or false
    });
}

Screenshots

Events list page

Execute page

License

The MIT License (MIT). Please see License File for more information.

YosfanEilay/AuthLogParser

2023-12-26T01:35:38Z

AuthLogParser is a powerful DFIR tool designed specifically for analyzing Linux authentication logs, commonly known as auth.log

AuthLogParser v1.0

Stop wasting time, let AuthLogParser do the work!

What is AuthLogParser ?

AuthLogParser is a powerful Digital Forensics and Incident Response tool designed specifically for analyzing Linux authentication logs, commonly known as auth.log. This tool serves as an invaluable asset for Incident Responders, streamlining the process of investigating security incidents on Linux systems. AuthLogParser meticulously scans the auth.log log file, extracting key information such as SSH logins, user creations, event names, IP addresses, and more. The generated summary provides a clear and concise overview of the activities recorded in the authentication logs, presenting the data in an easily readable format. By enhancing efficiency and accessibility, AuthLogParser significantly contributes to the effectiveness of incident response efforts, enabling practitioners to quickly and comprehensively assess security events on Linux platforms. While it proves indispensable for Incident Responders, its utility extends beyond DFIR teams, making it a valuable asset for the entire InfoSec and IT community.

What The Tool Can Do ?

Below is a comprehensive list of features that AuthLogParser can analyze:

Summary Report features

Hostname
Log Size
Start Time
End Time
Duration

Statistics Table

Event Names Table
IP Addresses Table
Failed SSH Table
Not Found Elements Table

Raw Event Categorizing

Successful SSH Password Authentication
Successful SSH Public key Authentication
New User Creation Activity
User Deletion Activity
User Password Change Activity
New Group Creation Activity
Group Deletion Activity
User Added To A Group Activity
User Removed From A Group Activity
Session Opened For User root

Feature Requests:

If you wish to propose the addition of a new feature, kindly submit your request by creating an issue here: https://github.com/YosfanEilay/AuthLogParser/issues/new

How To Use ?

How To Use - Text Guide

From this GitHub repository press on "<> Code" and then press on "Download ZIP".
From "AuthLogParser-main.zip" export the folder "AuthLogParser-main" to you Desktop.
Open a PowerSehll terminal and navigate to the "AuthLogParser-main" folder.

# How to navigate to "AuthLogParser-main" folder from the PS terminal
PS C:\> cd "C:\Users\{UserName}\Desktop\AuthLogParser-main\"

From the "AuthLogParser-main" path, execute the tool on your Auth.Log file like this:

# Example
PS C:\Users\{UserName}\Desktop\AuthLogParser-main> .\AuthLogParser.ps1 "PATH\TO\YOUR\AUTH.LOG"

Thats it, enjoy the tool!

How To Use - Video Guide

https://github.com/YosfanEilay/AuthLogParser/assets/132997318/43649759-8f47-40c6-a340-69c74c8af536

microsoft/go-images

2023-12-26T01:35:38Z

Docker image packaging and publishing for the Microsoft build of Go

Docker images containing the Microsoft build of Go

This repository creates Docker images that contain the Microsoft build of Go produced by the microsoft/go repository. The tags are published on the Microsoft Artifact Registry (MAR), formerly Microsoft Container Registry (MCR), in the oss/go/microsoft/golang repository.

The images produced by this repository are for general use within Microsoft and to produce FIPS-compliant Go apps. For other purposes, we recommend using the Docker Hub golang official images.

For more information about building FIPS-compatible Go apps with the Microsoft Go tools, visit the FIPS readme and user guide in the microsoft/go repository.

Support

GitHub issues for microsoft/go-images are maintained in the microsoft/go project. For help and questions about the Microsoft Go images, please file an issue in microsoft/go.

Recommended tags

In general, the microsoft/go-images tag names match those available for the official images. To switch from the official image to one on MCR, it may be possible to simply prepend mcr.microsoft.com/oss/go/microsoft/ to the official image you would normally use.

This tag is recommended for general build scenarios:

mcr.microsoft.com/oss/go/microsoft/golang:1.20-bullseye

If you need to build a FIPS-compliant app, use a fips tag, such as:

mcr.microsoft.com/oss/go/microsoft/golang:1.20-fips-cbl-mariner2.0

When building a containerized FIPS-compliant app, in general we recommend using a multi-stage Dockerfile that uses our fips tag in the builder stage and copies the built Go app into a minimal CBL-Mariner container to produce the final image.

To view the full list of available Go tags in MAR:

Visit golang on the MAR Discovery Portal (Microsoft internal auth required)
- You must be currently signed into the MAR discovery portal with Microsoft internal auth to see the golang image. If you see a "not found" page, make sure you are signed in at the top-right corner of the MAR page, then close the tab and click the golang link again.
- Go to the Tags tab to find a filterable list of tags and expand one to see the command to use to pull it.
Use the Microsoft Artifact Registry API
- The full tag URL is mcr.microsoft.com/{name}:{tag}

See Tags of microsoft/go-images for more information about tag support, more tag names, and the purpose of each image.

We don't build any Alpine images. See microsoft/go#446.

Is this repository a fork?

We think it's accurate to call this repository a fork of the official Golang image repository, docker-library/golang. The branches here do not share Git ancestry with docker-library/golang. However, the repository serves the same purpose as a Git fork: maintaining a modified version of the Go source code over time.

The submodule named go contains the official image source code. The templates in go are used to create the Dockerfiles in this repo, at src/microsoft. See the eng README file for more information about this repository's infrastructure.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

rithinskaria/kodekloud-az104-v1

2023-12-26T01:35:38Z

Azure scripts

kodekloud-azure

Azure scripts

Hillobar/Rope

2023-12-26T01:36:11Z

Ferret: Refer and Ground Anything Anywhere at Any Granularity

An End-to-End MLLM that Accept Any-Form Referring and Ground Anything in Response. [Paper]

Haoxuan You*, Haotian Zhang*, Zhe Gan, Xianzhi Du, Bowen Zhang, Zirui Wang, Liangliang Cao, Shih-Fu Chang, Yinfei Yang [*: equal contribution]

Overview

Diagram of Ferret Model.

Key Contributions:

Ferret Model - Hybrid Region Representation + Spatial-aware Visual Sampler enable fine-grained and open-vocabulary referring and grounding in MLLM.
GRIT Dataset (~1.1M) - A Large-scale, Hierarchical, Robust ground-and-refer instruction tuning dataset.
Ferret-Bench - A multimodal evaluation benchmark that jointly requires Referring/Grounding, Semantics, Knowledge, and Reasoning.

Release

[12/14] 🔥 We released the checkpoints(7B, 13B).
[10/30] 🔥 We released the code of FERRET model and Ferret-Bench.

Usage and License Notices: The data, and code is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaMA, Vicuna and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.

Install

Clone this repository and navigate to FERRET folder

git clone https://github.com/apple/ml-ferret
cd ml-ferret

Install Package

conda create -n ferret python=3.10 -y
conda activate ferret
pip install --upgrade pip  # enable PEP 660 support
pip install -e .
pip install pycocotools
pip install protobuf==3.20.0

Install additional packages for training cases

pip install ninja
pip install flash-attn --no-build-isolation

Train

FERRET is trained on 8 A100 GPUs with 80GB memory. To train on fewer GPUs, you can reduce the per_device_train_batch_size and increase the gradient_accumulation_steps accordingly. Always keep the global batch size the same: per_device_train_batch_size x gradient_accumulation_steps x num_gpus.

Hyperparameters

We use a similar set of hyperparameters as LLaVA(Vicuna) in finetuning.

Hyperparameter	Global Batch Size	Learning rate	Epochs	Max length	Weight decay
FERRET-7B	128	2e-5	3	2048	0
FERRET-13B	128	2e-5	3	2048	0

Prepare Vicuna checkpoint and LLaVA's projector

Before you start, prepare our base model Vicuna, which is an instruction-tuned chatbot. Please download its weights following the instructions here. Vicuna v1.3 is used in FERRET.

Then download LLaVA's first-stage pre-trained projector weight (7B, 13B).

FERRET Training

The scripts are provided (7B, 13B).

Evaluation

Please see this doc for the details.

Checkpoints

We extracted the delta between our pre-trained model and Vicuna. Please first download weights of Vicuna following the previous instruction. Then download our prepared offsets of weights: 7B, 13B using wget or curl, and unzip the downloaded offsets. Lastly, apply the offset to the Vicuna's weight by running the following script:

# 7B
python3 -m ferret.model.apply_delta \
    --base ./model/vicuna-7b-v1-3 \
    --target ./model/ferret-7b-v1-3 \
    --delta path/to/ferret-7b-delta
# 13B
python3 -m ferret.model.apply_delta \
    --base ./model/vicuna-13b-v1-3 \
    --target ./model/ferret-13b-v1-3 \
    --delta path/to/ferret-13b-delta

Notices: Apple's rights in the attached weight differentials are hereby licensed under the CC-BY-NC license. Apple makes no representations with regards to LLaMa or any other third party software, which are subject to their own terms.

Please refer to the next section about how to set up a local demo with pre-trained weight.

Demo

To run our demo, you need to train FERRET and use the checkpoints locally. Gradio web UI is used. Please run the following commands one by one.

Launch a controller

python -m ferret.serve.controller --host 0.0.0.0 --port 10000

Launch a gradio web server.

python -m ferret.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload --add_region_feature

Launch a model worker

This is the worker that load the ckpt and do the inference on the GPU. Each worker is responsible for a single model specified in --model-path.

CUDA_VISIBLE_DEVICES=0 python -m ferret.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path ./checkpoints/FERRET-13B-v0 --add_region_feature

Wait until the process finishes loading the model and you see "Uvicorn running on ...". Now, refresh your Gradio web UI, and you will see the model you just launched in the model list.

Example of Ferret Interactive Demo.

Citation

If you find Ferret useful, please cite using this BibTeX:

@article{you2023ferret,
  title={Ferret: Refer and Ground Anything Anywhere at Any Granularity},
  author={You, Haoxuan and Zhang, Haotian and Gan, Zhe and Du, Xianzhi and Zhang, Bowen and Wang, Zirui and Cao, Liangliang and Chang, Shih-Fu and Yang, Yinfei},
  journal={arXiv preprint arXiv:2310.07704},
  year={2023}
}

Acknowledgement

LLaVA: the codebase we built upon.
Vicuna: the LLM codebase.

atomicals/atomicals-electrumx

2023-12-26T01:36:11Z

Wall of Flippers is designed to find Flipper Zero devices using BLE (Bluetooth Low Energy)

Wall of Flippers (WoF)

                            YAao,                            
                             Y8888b, 
                           ,oA8888888b,      
                     ,aaad8888888888888888bo,   
                  ,d888888888888888888888888888b,               
                ,888888888888888888888888888888888b,            
               d8888888888888888888888888888888888888,           
              d888888888888888888888888888888888888888b                 
             d888888P'                    `Y88888888Ꙩ \,             
             88888P'                    Ybaaaa888888  Ꙩ l          
            a8888'                      `Y8888P' `V888888    
          d8888888a                                `Y8888           
         AY/'' `\Y8b                                 ``Y8b
         Y'      `YP                                    ~~
 _       __      ____         ____   _________                           
| |     / /___ _/ / /  ____  / __/  / ____/ (_)___  ____  ___  __________
| | /| / / __ `/ / /  / __ \/ /_   / /_  / / / __ \/ __ \/ _ \/ ___/ ___/
| |/ |/ / /_/ / / /  / /_/ / __/  / __/ / / / /_/ / /_/ /  __/ /  (__  ) 
|__/|__/\__,_/_/_/   \____/_/    /_/   /_/_/ .___/ .___/\___/_/  /____/ 
                                          /_/   /_/

💡 Introduction

Wall of Flippers (WoF) is a Python based project designed for Bluetooth Low Energy (BTLE) exploration. Its primary functionality involves the discovery of the Flipper Zero device and the identification of potential BTLE based attacks. Please keep in mind this is a work in progress and will still continue to get updates.

🛠️ Features

Flipper Zero Detection (BT Must be Enabled)
Flipper Archiving (Saving Past Data)
Bluetooth Low Energy Attacks
- iOS Crash and Popup BTLE Detection
- Android Crash and Popup BTLE Detection
- Windows Swift Pair BTLE Detection
- LoveSpouse BTLE Detection

💡 Future Improvements

GoLang Support
hcidump / hcitool support
Capture the Flippers??? 👀
Built-in BLE Exploitation
Auto-install Functionality
Suspicious BLE Advertisements

📚 Documentation

Requirements

A few things are required to properly run WoF. We Recommend the Raspberry Pi as its compact and portable! Additionally, it's also required to have a chipset or a USB adapter that supports BTLE. Additionally, we also now have limited support for Windows and full support for linux at this time.

Linux Install Guide

Another requirement is Python. Debian based install:

$ sudo apt-get install python3

Additionally, bluepy requires the libglib2.0-dev library to be installed. Debian based install:

$ sudo apt-get install python3-pip libglib2.0-dev

Finally, bluepy is required. This can be installed with the following command:

$ sudo pip3 install bluepy

Your install may look different depending if python3 is used
Additionally, if you are having trouble. Feel free to visit this repo for better documentation: https://github.com/IanHarvey/bluepy/

Git Clone and Running

Alright, it's fun for the fun install process. Downloading WoF is quite straightforward as it's a few commands. I'd recommend using git as this command can be easily used to retrieve the repository. Otherwise, just donwload via GitHub.

$ git clone https://github.com/K3YOMI/Wall-of-Flippers

After installing, navigate to the Wall of Flippers directory

$ cd ./Wall\ of\ Flippers

Next, run under sudo as this part is required to properly use the pyblue functionality.

$ sudo python3 WallofFlippers.py

Windows Install Guide

Another requirement is Python. Windows Based Below:
https://www.python.org/

bleak is required. This can be installed with the following command:

$ pip install bleak

Additional commands may need to be ran for pip to run properly. see: https://stackoverflow.com/questions/23708898/pip-is-not-recognized-as-an-internal-or-external-command

Your install may look different depending if py is used
Additionally, if you are having trouble. Feel free to visit this repo for better documentation: https://github.com/hbldh/bleak

Git Clone and Running

$ git clone https://github.com/K3YOMI/Wall-of-Flippers

After installing, navigate to the Wall of Flippers directory

$ cd ./Wall\ of\ Flippers

Next, run this command below to start the python script and wallah! You are now running Wall of Flippers!

$ py WallofFlippers.py

Conclusion

Tad-ah! You are now properly running WoF on your device. Hopefully this small guide works and gets you started on collecting flippers or checking for BTLE based attacks. Feel free to report bugs as this can help improve WoF. You can modify, release, or use Wall of Flippers in any way you want as long as proper credit is given to emilia (jbohack) and k3yomi (kiyomi). Thank you!

Notice (Please Read)

This project isn't the solution for catching every Bluetooth Low Energy (BLE) attack or Flipper Zero device. Users can easily dodge detection by changing their device's MAC address or name, or simply turning off Bluetooth. The main purpose here is keeping track of activity to catch a few "script kiddies" now and then. It's not foolproof, but it's a useful tool for identifying issues and figuring out how often devices are being misused. The real goal is to encourage updates for vulnerable devices, so they can't be exploited in the future. While this project doesn't stop attacks, logging is a crucial step in understanding device issues and abuse patterns. If you have any questions or concerns about bluetooth or the project itself, don't hesitate to reach out to me!

Support Kiyomi (Developer)

Support Emilia (Contributor)

microsoft/promptbench

2023-12-26T01:36:11Z

Fast and Simple Face Swap Extension Node for ComfyUI

^{!!! Important Update !!!
Don't forget to add the Node again in existing workflows}
^{Support This Project}

English | Русский

ReActor Node for ComfyUI

The Fast and Simple Face Swap Extension Node for ComfyUI, based on ReActor SD-WebUI Face Swap Extension

This Node goes without NSFW filter (uncensored, use it on your own responsibility)

What's new in the latest update

0.4.1 _^BETA1

Face Restore Visibility and CodeFormer Weight (Fidelity) options are now available! Don't forget to reload the Node in your existing workflow

0.4.0

Input "input_image" goes first now, it gives a correct bypass and also it is right to have the main input first;
You can now save face models as "safetensors" files (ComfyUI\models\reactor\faces) and load them into ReActor implementing different scenarios and keeping super lightweight face models of the faces you use:

Ability to build and save face models directly from an image:

Both the inputs are optional, just connect one of them according to your workflow; if both is connected - image has a priority.
Different fixes making this extension better.

Thanks to everyone who finds bugs, suggests new features and supports this project!

Installation

SD WebUI: AUTOMATIC1111 or SD.Next

Close (stop) your SD-WebUI/Comfy Server if it's running
(For Windows Users):
- Install Visual Studio 2022 (Community version - you need this step to build Insightface)
- OR only VS C++ Build Tools and select "Desktop Development with C++" under "Workloads -> Desktop & Mobile"
- OR if you don't want to install VS or VS C++ BT - follow this steps (sec. I)
Go to the extensions\sd-webui-comfyui\ComfyUI\custom_nodes
Open Console or Terminal and run git clone https://github.com/Gourieff/comfyui-reactor-node
Go to the SD WebUI root folder, open Console or Terminal and run (Windows users).\venv\Scripts\activate or (Linux/MacOS)venv/bin/activate
python -m pip install -U pip
cd extensions\sd-webui-comfyui\ComfyUI\custom_nodes\comfyui-reactor-node
python install.py
Please, wait until the installation process will be finished
(From the version 0.3.0) Download facerestorers models from the links below and put them into the extensions\sd-webui-comfyui\ComfyUI\custom_nodes\comfyui-reactor-node\models\facerestore_models directory:
- CodeFormer: https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth
- GFPGAN: https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth
Run SD WebUI and check console for the message that ReActor Node is running:

Go to the ComfyUI tab and find there ReActor Node inside the menu ReActor or by using a search:

Standalone (Portable) ComfyUI for Windows

Do the following:
- Install Visual Studio 2022 (Community version - you need this step to build Insightface)
- OR only VS C++ Build Tools and select "Desktop Development with C++" under "Workloads -> Desktop & Mobile"
- OR if you don't want to install VS or VS C++ BT - follow this steps (sec. I)
Go to the ComfyUI\custom_nodes directory
Open Console and run git clone https://github.com/Gourieff/comfyui-reactor-node
Run install.bat
(From the version 0.3.0) Download facerestorers models from the links below and put them into the ComfyUI\models\facerestore_models directory:
- CodeFormer: https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth
- GFPGAN: https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth
Run ComfyUI and find there ReActor Node inside the menu ReActor or by using a search

Usage

You can find ReActor Nodes inside the menu ReActor or by using a search (just type "ReActor" in the search field)

List of Nodes:

ReActorFaceSwap (Main Node)
ReActorLoadFaceModel (Load Face Model)
ReActorSaveFaceModel (Save Face Model)

Connect all required slots and run the query.

Main Node Inputs

input_image - is an image to be processed (target image, analog of "target image" in the SD WebUI extension);
- Supported Nodes: "Load Image", "Load Video" or any other nodes providng images as an output;
source_image - is an image with a face or faces to swap in the input_image (source image, analog of "source image" in the SD WebUI extension);
- Supported Nodes: "Load Image";
face_model - is the input for the "Load Face Model" Node or another ReActor node to provide a face model file (face embedding) you created earlier via the "Save Face Model" Node;
- Supported Nodes: "Load Face Model";

Main Node Outputs

IMAGE - is an output with the resulted image;
- Supported Nodes: any nodes which have images as an input;
FACE_MODEL - is an output providng a source face's model being built during the swapping process;
- Supported Nodes: "Save Face Model", "ReActor";

Face Restoration

Since version 0.3.0 ReActor Node has a buil-in face restoration.
Just download the models you want (see Installation instruction) and select one of them to restore the resulting face(s) during the faceswap. It will enhance face details and make your result more accurate.

Face Indexes

ReActor detects faces in images in the following order:
left->right, top->bottom

And if you need to specify faces, you can set indexes for source and input images.

Index of the first detected face is 0.

You can set indexes in the order you need.
E.g.: 0,1,2 (for Source); 1,0,2 (for Input).
This means: the second Input face (index = 1) will be swapped by the first Source face (index = 0) and so on.

Genders

You can specify the gender to detect in images.
ReActor will swap a face only if it meets the given condition.

Face Models

Since version 0.4.0 you can save face models as "safetensors" files (stored in ComfyUI\models\reactor\faces) and load them into ReActor implementing different scenarios and keeping super lightweight face models of the faces you use.

To make new models appear in the list of the "Load Face Model" Node - just refresh the page of your ComfyUI web application.
(I recommend you to use ComfyUI Manager - otherwise you workflow can be lost after you refresh the page if you didn't save it before that).

Troubleshooting

I. (For Windows users) If you still cannot build Insightface for some reasons or just don't want to install Visual Studio or VS C++ Build Tools - do the following:

(ComfyUI Portable) From the root folder check the version of Python:
run CMD and type python_embeded\python.exe -V

Download prebuilt Insightface package for Python 3.10 or for Python 3.11 (if in the previous step you see 3.11) and put into the stable-diffusion-webui (A1111 or SD.Next) root folder (where you have "webui-user.bat" file) or into ComfyUI root folder if you use ComfyUI Portable
From the root folder run:
- (SD WebUI) CMD and .\venv\Scripts\activate
- (ComfyUI Portable) run CMD
Then update your PIP:
- (SD WebUI) python -m pip install -U pip
- (ComfyUI Portable) python_embeded\python.exe -m pip install -U pip
Then install Insightface:
- (SD WebUI) pip install insightface-0.7.3-cp310-cp310-win_amd64.whl (for 3.10) or pip install insightface-0.7.3-cp311-cp311-win_amd64.whl (for 3.11)
- (ComfyUI Portable) python_embeded\python.exe -m pip install insightface-0.7.3-cp310-cp310-win_amd64.whl (for 3.10) or python_embeded\python.exe -m pip install insightface-0.7.3-cp311-cp311-win_amd64.whl (for 3.11)
Enjoy!

II. "AttributeError: 'NoneType' object has no attribute 'get'"

This error may occur if there's smth wrong with the model file inswapper_128.onnx

Try to download it manually from here and put it to the ComfyUI\models\insightface replacing existing one

III. "reactor.execute() got an unexpected keyword argument 'reference_image'"

This means that input points have been changed with the latest update
Remove the current ReActor Node from your workflow and add it again

IV. ControlNet Aux Node IMPORT failed error when using with ReActor Node

Close ComfyUI if it runs
Go to the ComfyUI root folder, open CMD there and run:
- python_embeded\python.exe -m pip uninstall -y opencv-python opencv-contrib-python opencv-python-headless
- python_embeded\python.exe -m pip install opencv-python==4.7.0.72
That's it!

V. "ModuleNotFoundError: No module named 'basicsr'" or "subprocess-exited-with-error" during future-0.18.3 installation

Download https://github.com/Gourieff/Assets/raw/main/comfyui-reactor-node/future-0.18.3-py3-none-any.whl

Put it to ComfyUI root And run:

python_embeded\python.exe -m pip install future-0.18.3-py3-none-any.whl

Then:

python_embeded\python.exe -m pip install basicsr

VI. "fatal: fetch-pack: invalid index-pack output" when you try to `git clone` the repository"

Try to clone with --depth=1 (last commit only):

 git clone --depth=1 https://github.com/Gourieff/comfyui-reactor-node

Then retrieve the rest (if you need):

 git fetch --unshallow

Updating

Just put .bat or .sh script from this Repo to the ComfyUI\custom_nodes directory and run it when you need to check for updates

Disclaimer

This software is meant to be a productive contribution to the rapidly growing AI-generated media industry. It will help artists with tasks such as animating a custom character or using the character as a model for clothing etc.

The developers of this software are aware of its possible unethical applicaitons and are committed to take preventative measures against them. We will continue to develop this project in the positive direction while adhering to law and ethics.

Users of this software are expected to use this software responsibly while abiding the local law. If face of a real person is being used, users are suggested to get consent from the concerned person and clearly mention that it is a deepfake when posting content online. Developers and Contributors of this software are not responsible for actions of end-users.

By using this extension you are agree not to create any content that:

violates any laws;
causes any harm to a person or persons;
propogates (spreads) any information (both public or personal) or images (both public or personal) which could be meant for harm;
spreads misinformation;
targets vulnerable groups of people.

This software utilizes the pre-trained models buffalo_l and inswapper_128.onnx, which are provided by InsightFace. These models are included under the following conditions:

From insighface licence: The InsightFace’s pre-trained models are available for non-commercial research purposes only. This includes both auto-downloading models and manually downloaded models.

Users of this software must strictly adhere to these conditions of use. The developers and maintainers of this software are not responsible for any misuse of InsightFace’s pre-trained models.

Please note that if you intend to use this software for any commercial purposes, you will need to train your own models or find models that can be used commercially.

Models Hashsum

Safe-to-use models have the folowing hash:

inswapper_128.onnx

MD5:a3a155b90354160350efd66fed6b3d80
SHA256:e4a3f08c753cb72d04e10aa0f7dbe3deebbf39567d4ead6dce08e98aa49e16af

1k3d68.onnx

MD5:6fb94fcdb0055e3638bf9158e6a108f4
SHA256:df5c06b8a0c12e422b2ed8947b8869faa4105387f199c477af038aa01f9a45cc

2d106det.onnx

MD5:a3613ef9eb3662b4ef88eb90db1fcf26
SHA256:f001b856447c413801ef5c42091ed0cd516fcd21f2d6b79635b1e733a7109dbf

det_10g.onnx

MD5:4c10eef5c9e168357a16fdd580fa8371
SHA256:5838f7fe053675b1c7a08b633df49e7af5495cee0493c7dcf6697200b85b5b91

genderage.onnx

MD5:81c77ba87ab38163b0dec6b26f8e2af2
SHA256:4fde69b1c810857b88c64a335084f1c3fe8f01246c9a191b48c7bb756d6652fb

w600k_r50.onnx

MD5:80248d427976241cbd1343889ed132b3
SHA256:4c06341c33c2ca1f86781dab0e829f88ad5b64be9fba56e56bc9ebdefc619e43

Please check hashsums if you download these models from unverified (or untrusted) sources

Note!

If you encounter any errors when you use ReActor Node - don't rush to open an issue, first try to remove current ReActor node in your workflow and add it again

ReActor Node gets updates from time to time, new functions appears and old node can work with errors or not work at all

fiverrhellotinah/youtubeproject

2023-12-26T01:36:58Z

templatus/templatus-hotwire

2023-12-26T01:37:16Z

Opinionated template for starting new web applications with Ruby on Rails and Hotwire

Templatus (Hotwire edition)

Templatus is an opinionated template to build web applications with Ruby on Rails and Hotwire. It simplifies the process of setting up a new application while following best practices.

Live demo available at https://templatus-hotwire.ledermann.dev

Similar projects

There are two sister repositories:

For using Vue.js instead of Hotwire: https://github.com/templatus/templatus-vue/
For using Inertia and Svelte.js instead of Hotwire: https://github.com/templatus/templatus-inertia/

Features / Technology stack

Backend

Ruby 3.2
Ruby on Rails 7.1
ActionCable for WebSocket communication
PostgreSQL for using as SQL database
Sidekiq for background processing
Redis for Caching, ActionCable, and Sidekiq

Frontend

Hotwire for building the frontend without using much JavaScript by sending HTML instead of JSON over the wire
TypeScript for static type checking in Stimulus controllers and other script code
ViewComponent for creating reusable, testable & encapsulated view components
Slim for writing templates instead of ERB
Tailwind CSS 3 to not have to write CSS at all
Heroicons for beautiful hand-crafted SVG icons
Vite for bundling JavaScript and CSS with Hot Module Replacement (HMR) in development

Development

Puma-dev for using .test-domain and HTTPS in development
Overmind for starting up the application locally (Procfile handling like Foreman)
dotenv to load environment variables from .env into ENV
Prettier for auto-formatting JavaScript and Ruby code in Visual Studio Code
Lookbook as development UI for ViewComponent
annotate for annotating models and routes
Live reloading

Linting and testing

RuboCop for Ruby static code analysis
ESLint for JavaScript static code analysis
RSpec for Ruby testing
Factory Bot for setting up Ruby objects as test data
Cypress for E2E testing

Deployment

Docker for production deployment, NOT for development
DockerRailsBase for fast building an optimized Docker image based on Alpine Linux
GitHub Actions for testing, linting, and building Docker image
Dependabot configuration for updating dependencies (with auto-merge)
Ready for serving assets via CDN like CloudFront
Honeybadger for error tracking in Ruby and JavaScript
RorVsWild for performance monitoring
Plausible for privacy friendly analytics
Lockup to place a staging server behind a basic codeword

Production

Lograge for single-line logging
Gzip compression of dynamic responses (HTML, JSON) using Rack::Deflater
Fine-tuned Content Security Policy (CSP)
Ready for PWA (manifest, service-worker)

Metrics

This template is developed with optimized performance and security in mind. The following benchmarks are performed against the demo installation on production. It uses an inexpensive virtual server on the Hetzner Cloud behind a Traefik setup.

Lighthouse site performance

100% in all categories.

Security headers

What's the red Permissions-Policy badge? This seems to be fixed with one of the next Rails update: https://github.com/rails/rails/pull/41994

Mozilla Observatory

WebPageTest

GTmetrix

Check-your-website

JavaScript size

159 KB of compiled JavaScript (minified, uncompressed). The largest parts are:

Turbo with ActionCable (72 KB)
Stimulus (32 KB)
Honeybadger (25 KB)

$ RAILS_ENV=production bin/rails assets:precompile
yarn install v1.22.21
[1/5] 🔍  Resolving packages...
success Already up-to-date.
✨  Done in 0.08s.
Building with Vite ⚡️
vite v5.0.10 building for production...
transforming...
✓ 47 modules transformed.
rendering chunks...
computing gzip size...
../../public/vite/.vite/manifest-assets.json         0.10 kB │ gzip:  0.09 kB
../../public/vite/assets/logo-3amqgDdM.svg           0.50 kB │ gzip:  0.30 kB
../../public/vite/.vite/manifest.json                0.79 kB │ gzip:  0.28 kB
../../public/vite/assets/application-khfDISII.css   23.42 kB │ gzip:  5.03 kB
../../public/vite/assets/application-AGZF-dBE.js     3.65 kB │ gzip:  1.44 kB │ map:   9.85 kB
../../public/vite/assets/index-GVA0YpUy.js           9.48 kB │ gzip:  3.02 kB │ map:  31.38 kB
../../public/vite/assets/vendor-EJSvyBgX.js        178.04 kB │ gzip: 48.05 kB │ map: 598.94 kB
✓ built in 785ms
Build with Vite complete: /Users/ledermann/Projects/templatus-hotwire/public/vite

Network transfer

Small footprint: The demo application transfers only 62 KB of (compressed) data on the first visit.

Docker build time

With multi-stage building and using DockerRailsBase the build of the Docker image takes very little time. Currently, the build job requires about 1,5 minutes on GitHub Actions (see https://github.com/templatus/templatus-hotwire/actions)

Docker image size

The Docker image is based on Alpine Linux and is optimized for minimal size (currently 117 MB uncompressed disk size). It includes just the bare minimum - no build tools like Node.js, no JS sources (just the compiled assets), no tests.

$ container-diff analyze ghcr.io/templatus/templatus-hotwire -n

-----Size-----

Analysis for ghcr.io/templatus/templatus-hotwire:
IMAGE                                      DIGEST       SIZE
ghcr.io/templatus/templatus-hotwire        sha256:... 116.7M

Getting started

Install for development

Clone the repo locally:

git clone git@github.com:templatus/templatus-hotwire.git
cd templatus-hotwire

Install PostgreSQL, Redis, and puma-dev (if not already present). On a Mac with HomeBrew, run this to install from the Brewfile:

brew bundle

Install and set up puma-dev to use HTTPS for development. Do this on macOS:

sudo puma-dev -setup
puma-dev -install
puma-dev link

# Use Vite via puma-dev proxy
# Adopted from https://github.com/puma/puma-dev#webpack-dev-server
echo 3036 > ~/.puma-dev/vite.templatus-hotwire

Setup the application to install gems and NPM packages and create the database:

bin/setup

Start the application locally:

bin/dev

Then open https://templatus-hotwire.test in your browser.

Preview components in LookBook

bin/dev

Then open https://templatus-hotwire.test/lookbook/ in your browser.

Running linters

RuboCop:

bin/rubocop

ESLint:

bin/yarn lint

Running tests locally

Ruby tests:

bin/rspec
open coverage/index.html

JavaScript unit tests:

bin/yarn test

E2E tests with Cypress:

bin/cypress open

This opens Cypress and starts Rails in development environment, but with CYPRESS=true, so the test database is used. This allows code editing without class reloading and recompiling assets.

To run Cypress in headless mode:

bin/cypress run

Test deployment locally

docker network create public
docker-compose up

volcengine/volcengine-specs

2023-12-26T01:37:16Z

Volcengine CocoaPods specs.

volcengine-specs

Volcengine CocoaPods specs.

火山引擎 iOS CocoaPods 仓库。

Usage

source 'https://cdn.cocoapods.org/'
source 'https://github.com/volcengine/volcengine-specs.git'

pod 'XXXSDK', '~> 1.0.0'

paketo-buildpacks/php-dist

2023-12-26T01:37:16Z

PHP Distribution Cloud Native Buildpack

The PHP Distribution CNB provides the PHP binary distribution. The buildpack installs the PHP binary distribution onto the $PATH which makes it available for subsequent buildpacks. These buildpacks can then use that distribution to run PHP tooling. The PHP Web CNB is an example of a buildpack that utilizes the PHP binary.

Integration

The PHP Distribution CNB provides php as a dependency. Downstream buildpacks, like PHP Composer CNB can require the php dependency by generating a Build Plan TOML file that looks like the following:

[[requires]]

  # The name of the PHP dependency is "php". This value is considered
  # part of the public API for the buildpack and will not change without a plan
  # for deprecation.
  name = "php"

  # The PHP buildpack supports some non-required metadata options.
  [requires.metadata]

    # Setting the build flag to true will ensure that the PHP
    # depdendency is available on the $PATH for subsequent buildpacks during
    # their build phase. If you are writing a buildpack that needs to run PHP
    # during its build process, this flag should be set to true.
    build = true

    # Setting the launch flag to true will ensure that the PHP
    # dependency is available on the $PATH for the running application. If you are
    # writing an application that needs to run PHP at runtime, this flag should
    # be set to true.
    launch = true

    # Optional. If not provided, the buildpack will provide the default version from buildpack.toml.
    # To request a specific version, you can specify a semver constraint such as "8.*", "8.0.*",
    # or even "8.0.4".
    version = "8.0.4"

Usage

To package this buildpack for consumption:

$ ./scripts/package.sh

This builds the buildpack's Go source using GOOS=linux by default. You can supply another value as the first argument to package.sh.

License

This buildpack is released under version 2.0 of the Apache License.

Run Tests

To run all unit tests, run:

./scripts/unit.sh

To run all integration tests, run:

./scripts/integration.sh

Buildpack Configurations

PHP Version

Specifying the PHP Dist version through buildpack.yml configuration is deprecated.

To migrate from using buildpack.yml please set the $BP_PHP_VERSION environment variable at build time either directly (ex. pack build my-app --env BP_PHP_VERSION=7.3.*) or through a project.toml file

# this allows you to specify a version constraint for the `php` depdendency
# any valid semver constaints (e.g. 7.*) are also acceptable
$BP_PHP_VERSION="7.3.*"

PHP library directory

The PHP library directory is available to PHP via an include path in the PHP configuration. By default it is set to /workspace/lib and can be overriden by setting the BP_PHP_LIB_DIR environment variable at build-time.

$BP_PHP_LIB_DIR="some-directory"

Provide custom `.ini` files

Custom .ini files can be provided from users to amend the default php.ini file. This can be done by placing an ini-type configuration file inside <application directory>/.php.ini.d/. Its path will be made available via the PHP_INI_SCAN_DIR.

Debug Logs

For extra debug logs from the image build process, set the $BP_LOG_LEVEL environment variable to DEBUG at build-time (ex. pack build my-app --env BP_LOG_LEVEL=DEBUG or through a project.toml file.

informalsystems/apalache

2023-12-26T01:37:32Z

APALACHE: symbolic model checker for TLA+ and Quint

APALACHE

A symbolic model checker for TLA+

Apalache translates TLA+ into the logic supported by SMT solvers such as Microsoft Z3. Apalache can check inductive invariants (for fixed or bounded parameters) and check safety of bounded executions (bounded model checking). To see the list of supported TLA+ constructs, check the supported features. In general, Apalache runs under the same assumptions as TLC.

To learn more about TLA+, visit Leslie Lamport's page on TLA+ and his Video course.

Releases

Check the releases page for our latest release.

For a stable release, we recommend that you pull the latest docker image with docker pull ghcr.io/informalsystems/apalache:main, use the jar from the most recent release, or checkout the source code from the most recent release tag.

To try the latest cool features, check out the head of the main branch.

You can also find Apalache packaged via Nix at cosmos.nix

For more information on installation options, see the manual.

Getting started

Read the Beginner's tutorial.
Check the material at TLA-Apalache workshop.
Read the Apalache user manual.
Consult the (WIP) Idioms for writing better TLA+.
Consult the (WIP) TLA+ language manual for engineers.

Website

Our current website is served at https://apalache.informal.systems .

The site is hosted by github, and changes can be made through PRs into the gh-pages branch of this repository. See the README.md on that branch for more information.

The user documentation is automatically deployed to the website branch as per the CI configuration.

Our old website is still available at https://forsyte.at/research/apalache/ .

Community

Join the chat in the Apalache zulip stream.
Contribute to the development of Apalache.

Help wanted

Want to contribute? Here is a list of issues that could be solved without knowing too much about the internals of Apalache. Solving these issues would improve usability! Please comment in the relevant issue, if you are going to solve it.

Writing annotations in the JSON format: #804
Add support for VIEW in the TLC config: #851
Translate \E x \in STRING: P and \A x \in STRING: P: #844
Interval analysis for a..b: #446

Industrial examples

Check Light client specs and Model-based testing of the Tendermint light client.
Check Tendermint specs to see how we use TLA+ and Apalache at Informal to design and specify protocols for the Tendermint blockchain.
To see more examples, check the standard repository of TLA+ examples.

Talks

Informal Systems Tutorial: TLA+ Basics
Extended version of the Apalache tutorial. TLA+ tutorial at DISC 2021 (October 2021).
How TLA+ and Apalache Helped Us to Design the Tendermint Light Client. Interchain Conversations 2020 (December 2020).
Model-based testing with TLA+ and Apalache. TLA+ Community Event 2020 (October 2020).
Type inference for TLA+ in Apalache. TLA+ Community Event 2020 (October 2020).
Formal Spec and Model Checking of the Tendermint Blockchain Synchronization Protocol 2nd Workshop on Formal Methods for Blockchains (July 2020).
Showing safety of Tendermint Consensus with TLA+ and Apalache. Dev session at Informal Systems (May 2020).
TLA+ model checking made symbolic OOPSLA 2019 (October 2019).
Bounded model checking of TLA+ specifications with SMT TLA+ Community Event 2018 (July 2018).

Performance

We are collecting apalache benchmarks. See the Apalache performance when checking inductive invariants and running bounded model checking. Versions 0.6.0 and 0.7.2 are a major improvement over version 0.5.2 (the version reported at OOPSLA19).

Academic papers

To read an academic paper about the theory behind Apalache, check our paper at OOPSLA19. For the details of our novel encoding using SMT arrays, check our paper at TACAS23. Related reports and publications can be found at the Apalache page at TU Wien.

Apalache is developed at Informal Systems.

With additional funding from
.

Past funding from Der Wiener Wissenschafts-, Forschungs- und Technologiefonds.

dialvarezs/db2-full-stack

2023-12-26T01:37:40Z

hypnoglow/helm-s3

2023-12-26T01:37:40Z

⎈ Helm plugin that adds support for AWS S3 as a chart repository.

awesome-jellyfin/awesome-jellyfin

2023-12-26T01:37:45Z

A collection of awesome Jellyfin Plugins, Themes. Guides and Companion Software (Not affiliated with Jellyfin)

Awesome Jellyfin

A List of Awesome Jellyfin Themes and Plugins

[ Plugins • Metadata Providers • Themes • Other • Snippets • Related • Contribute ]

🧩 Plugins

AnilistSync 📅 - Automatically tracks anime watching progress on Anilist.
jellyfin-plugin-media-analyzer - Fingerprint audio to automatically detect intro and outro segments in Jellyfin.
jellyfin-ani-sync - Synchronize anime watch status between Jellyfin and anime tracking sites.
Jellyfin Ignore 🔸 - Ignore filename patterns on library scans.
Jellyfin-Newsletter-Plugin 🔸 - Newsletters for recently added media.
jellyfin-plugin-ldapauth - Allows the use of ldap as an auth provider.
jellyfin-plugin-skin-manager 📅 - Helps you to download and install skins.
jellyfin-plugin-sso 🔸 - Allows users to sign in through an SSO provider.
jellyscrub - Smooth mouse-over video scrubbing previews.
Themerr-jellyfin - Adds theme songs to movies using ThemerrDB.

🏷️ Metadata Providers

jellyfin-youtube-metadata-plugin - YouTube Metadata Plugin.

🖌️ Themes

👉 Moved themes to THEMES.md

📺 Clients

👉 Moved clients to CLIENTS.md

👾 Other

autoscan - Replaces the default Plex and Emby behaviour for picking up changes on the file system.
Fixarr 🔸 - Media Renamer and Backup tool. (Backup still WIP)
jellyfin-mods - A collection of things you can do to personalize Jellyfin.
jellyfin-rewind - A Spotify Wrapped-like app made for people who use Jellyfin for listening to music.
jellyseerr - A request management and media discovery tool.
Jellystat - A free and open source Statistics App for Jellyfin.
jfa-go - User- / Invite-Management system for Jellyfin.
Playlifin - Converts YouTube Music playlists to Jellyfin playlists.
reiverr 🔸 - Combined interface for JF, TMDB, Radarr and Sonarr.
subgen - Autogenerate subtitles using OpenAI Whisper Model via Jellyfin.
TitleCardMaker - Automated title card maker for Plex, Jellyfin, and Emby.
wizarr - Advanced user invitation and management system.
ytdl-sub - Automate downloading and metadata generation with YoutubeDL.
xsrv.jellyfin - Ansible role to deploy and configure Jellyfin.

📜 Snippets

snippets/language-overlay - A script to add flags to movie posters.

🌌 Related

This section includes software, guides and tools that are not specifically designed to work with Jellyfin, but can be useful for tasks related to media management or to enhance Jellyfin's functionality.

calibre2jellyfin - Python script to construct a Jellyfin ebook library from a Calibre library.
ErsatzTV 🔸 - Stream custom live channels using your own media.
locatarr - A list of tools used to automate the downloading and organization of media files.
- radarr - Automates downloading & management of movies 🟣 TRaSH Guide.
- sonarr - Automates downloading & management of TV series 🟣 TRaSH Guide.
- tdarr 📛 - Distributed transcode automation + Library analytics + Video health checking.
- recyclarr - Automatically sync TRaSH guides to Sonarr and Radarr instances.
MediaTracker - Media tracker and user rating platform with Jellyfin integration.
Movary 🔸 - Media tracker and user rating platform.
Multi Scrobbler 🔸 - Scrobbles music from many sources to many clients.
rffmpeg - A remote FFmpeg wrapper, commonly used for transcoding media on more capable machines.
Threadfin - M3U proxy for Jellyfin (Based on xTeVe).
TRaSH Guides - Easy-to-understand guides for Sonarr, Radarr, and Bazarr, along with related tools.
watchstate - Sync play state between different media servers.

Contribute

Contributions welcome! But please read the contribution guidelines first.
You can also create a new issue.

Early Release / BETA software 🔸
Closed-Source Software 📛
Mobile App 📱
Stale / Inactive / May not work anymore 📅

OpenBMB/ChatDev

2023-12-26T01:37:45Z

Create Customized Software using Natural Language Idea (through LLM-powered Multi-Agent Collaboration)

Communicative Agents for Software Development

【📚 Wiki | 🚀 Visualizer | 👥 Community Built Software | 🔧 Customization | 👾 Discord】

📖 Overview

ChatDev stands as a virtual software company that operates through various intelligent agents holding different roles, including Chief Executive Officer , Chief Product Officer , Chief Technology Officer , programmer , reviewer , tester , art designer . These agents form a multi-agent organizational structure and are united by a mission to "revolutionize the digital world through programming." The agents within ChatDev collaborate by participating in specialized functional seminars, including tasks such as designing, coding, testing, and documenting.
The primary objective of ChatDev is to offer an easy-to-use, highly customizable and extendable framework, which is based on large language models (LLMs) and serves as an ideal scenario for studying collective intelligence.

🎉 News

November 15, 2023: We launched ChatDev as a SaaS platform that enables software developers and innovative entrepreneurs to build software efficiently at a very low cost and barrier to entry. Try it out at https://chatdev.modelbest.cn/.
November 2, 2023: ChatDev is now supported with a new feature: incremental development, which allows agents to develop upon existing codes. Try --config "incremental" --path "[source_code_directory_path]" to start it.
October 26, 2023: ChatDev is now supported with Docker for safe execution (thanks to contribution from ManindraDeMel). Please see Docker Start Guide.

September 25, 2023: The Git mode is now available, enabling the programmer to utilize Git for version control. To enable this feature, simply set "git_management" to "True" in ChatChainConfig.json. See guide.
September 20, 2023: The Human-Agent-Interaction mode is now available! You can get involved with the ChatDev team by playing the role of reviewer and making suggestions to the programmer ; try python3 run.py --task [description_of_your_idea] --config "Human". See guide and example.
September 1, 2023: The Art mode is available now! You can activate the designer agent to generate images used in the software; try python3 run.py --task [description_of_your_idea] --config "Art". See guide and example.
August 28, 2023: The system is publicly available.
August 17, 2023: The v1.0.0 version was ready for release.
July 30, 2023: Users can customize ChatChain, Phase, and Role settings. Additionally, both online Log mode and replay mode are now supported.
July 16, 2023: The preprint paper associated with this project was published.
June 30, 2023: The initial version of the ChatDev repository was released.

❓ What Can ChatDev Do?

https://github.com/OpenBMB/ChatDev/assets/11889052/80d01d2f-677b-4399-ad8b-f7af9bb62b72

⚡️ Quickstart

💻️ Quickstart with Web

Access the web page for visualization and configuration use: https://chatdev.modelbest.cn/

🖥️ Quickstart with terminal

To get started, follow these steps:

Clone the GitHub Repository: Begin by cloning the repository using the command:
```
git clone https://github.com/OpenBMB/ChatDev.git
```
Set Up Python Environment: Ensure you have a version 3.9 or higher Python environment. You can create and activate this environment using the following commands, replacing ChatDev_conda_env with your preferred environment name:
```
conda create -n ChatDev_conda_env python=3.9 -y
conda activate ChatDev_conda_env
```
Install Dependencies: Move into the ChatDev directory and install the necessary dependencies by running:
```
cd ChatDev
pip3 install -r requirements.txt
```
Set OpenAI API Key: Export your OpenAI API key as an environment variable. Replace "your_OpenAI_API_key" with your actual API key. Remember that this environment variable is session-specific, so you need to set it again if you open a new terminal session. On Unix/Linux:
```
export OPENAI_API_KEY="your_OpenAI_API_key"
```
On Windows:
```
$env:OPENAI_API_KEY="your_OpenAI_API_key"
```
Build Your Software: Use the following command to initiate the building of your software, replacing [description_of_your_idea] with your idea's description and [project_name] with your desired project name: On Unix/Linux:
```
python3 run.py --task "[description_of_your_idea]" --name "[project_name]"
```
On Windows:
```
python run.py --task "[description_of_your_idea]" --name "[project_name]"
```
Run Your Software: Once generated, you can find your software in the WareHouse directory under a specific project folder, such as project_name_DefaultOrganization_timestamp. Run your software using the following command within that directory: On Unix/Linux:
```
cd WareHouse/project_name_DefaultOrganization_timestamp
python3 main.py
```
On Windows:
```
cd WareHouse/project_name_DefaultOrganization_timestamp
python main.py
```

🐳 Quickstart with Docker

We thank ManindraDeMel for providing Docker support. Please see Docker Start Guide.

✨️ Advanced Skills

For more detailed information, please refer to our Wiki, where you can find:

An introduction to all command run parameters.
A straightforward guide for setting up a local web visualizer demo, which can visualize real-time logs, replayed logs, and ChatChain.
An overview of the ChatDev framework.
A comprehensive introduction to all advanced parameters in ChatChain configuration.
Guides for customizing ChatDev, including:
- ChatChain: Design your own software development process (or any other process), such as DemandAnalysis -> Coding -> Testing -> Manual.
- Phase: Design your own phase within ChatChain, like DemandAnalysis.
- Role: Defining the various agents in your company, such as the Chief Executive Officer.

🤗 Share Your Software

Code: We are enthusiastic about your interest in participating in our open-source project. If you come across any problems, don't hesitate to report them. Feel free to create a pull request if you have any inquiries or if you are prepared to share your work with us! Your contributions are highly valued. Please let me know if there's anything else you need assistance!

Company: Creating your own customized "ChatDev Company" is a breeze. This personalized setup involves three simple configuration JSON files. Check out the example provided in the CompanyConfig/Default directory. For detailed instructions on customization, refer to our Wiki.

Software: Whenever you develop software using ChatDev, a corresponding folder is generated containing all the essential information. Sharing your work with us is as simple as making a pull request. Here's an example: execute the command python3 run.py --task "design a 2048 game" --name "2048" --org "THUNLP" --config "Default". This will create a software package and generate a folder named /WareHouse/2048_THUNLP_timestamp. Inside, you'll find:

All the files and documents related to the 2048 game software
Configuration files of the company responsible for this software, including the three JSON config files from CompanyConfig/Default
A comprehensive log detailing the software's building process that can be used to replay (timestamp.log)
The initial prompt used to create this software (2048.prompt)

See community contributed software here!

👨‍💻‍ Contributors

Made with contrib.rocks.

🔎 Citation

@misc{qian2023communicative,
      title={Communicative Agents for Software Development},
      author={Chen Qian and Xin Cong and Wei Liu and Cheng Yang and Weize Chen and Yusheng Su and Yufan Dang and Jiahao Li and Juyuan Xu and Dahai Li and Zhiyuan Liu and Maosong Sun},
      year={2023},
      eprint={2307.07924},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}

⚖️ License

Source Code Licensing: Our project's source code is licensed under the Apache 2.0 License. This license permits the use, modification, and distribution of the code, subject to certain conditions outlined in the Apache 2.0 License.
Project Open-Source Status: The project is indeed open-source; however, this designation is primarily intended for non-commercial purposes. While we encourage collaboration and contributions from the community for research and non-commercial applications, it is important to note that any utilization of the project's components for commercial purposes necessitates separate licensing agreements.
Data Licensing: The related data utilized in our project is licensed under CC BY-NC 4.0. This license explicitly permits non-commercial use of the data. We would like to emphasize that any models trained using these datasets should strictly adhere to the non-commercial usage restriction and should be employed exclusively for research purposes.

🌟 Star History

🤝 Acknowledgments

📬 Contact

If you have any questions, feedback, or would like to get in touch, please feel free to reach out to us via email at chatdev.openbmb@outlook.com

opencodeco/phpctl

2023-12-26T01:37:45Z

🐳 It is a Docker (containers) based development environment for PHP 🐘

phpctl

🐳 A Docker-based development environment for PHP 🐘

Heavily inspired by opencodeco/hfctl.

Introduction

Frictionless PHP Development! Seamlessly run and switch between different versions of PHP, with different installed extensions, thanks to the power of containers. Take the advantage of goodie commands like phpctl create to start a new project, phpctl repl to start a REPL, phpclt init to initialize a new configuration file and a lot more.

Getting started

Requirements

Git
Docker

Install

sh <(wget -qO- https://raw.githubusercontent.com/opencodeco/phpctl/main/installer.sh)

Usage

Then you can use phpctl or pctl with a subcommand:

phpctl <command> [arguments]

For example

phpctl php -m # To show built-in modules

phpctl sh echo 'Hello, World!' # To run arbitrary sh commands inside the container

Command list

Developing

Command	Description
`php`	Runs PHP commands (`phpctl php -v` or `phpctl php -m`).
`composer`	Runs Composer commands (`phpctl composer install` or `pctl composer validate`).
`at [port] [command]`	Runs commands within a bound port (default port is `80`).
`server [port] [directory]`	Runs PHP's built-in web-server (default port is `80` and default directory is current `.`).

Useful

Command	Description
`phpunit`	Runs PHPUnit.
`sh [commands]`	Starts an interactive Shell session or runs `sh` commands.
`repl`	Starts a PHP REPL session (powered by PsySH).
`php-cs-fixer`	Runs PHP-CS-Fixer.
`phpstan`	Runs PHPStan.
`infection`	Runs Infection.
`box`	Runs Box.

Scaffolders

Command	Description
`create [framework] [dir]`	Creates a new project using the given framework (or package).
`init [skeleton]`	Initializes a skeleton configuration.

Skeletons

phpunit
php-cs-fixer
phpstan
infection
box

Helpers

Command	Description
`help` or `man`	Displays a help message.
`doctor`	Inspects the current `PHP_VERSION` and `PHPCTL_IMAGE`.
`build`	Builds the current `Dockerfile` (useful for custom images).
`images`	Shows local `phpctl` images.

The `.phpctlrc` file

You can also add a .phpctlrc file at project's root directory with some overrides like:

Environment variables

Variable	Description
`PHP_VERSION`	Values can be `82` and `83`
`PHPCTL_IMAGE`	Use to name your own custom image

For example:

PHP_VERSION=83

Docker behaviour

Run options

You can also provide any additional Docker run arguments using the args variable.

For example, suppose you want to bind the 9501 port from the running phpctl container to your host, you can add the following to your .phpctlrc file:

args=(-p 9501:9501)

Build options

You can also provide build options to the build command using the build variable:

build=(--build-arg APP_ENV=dev --label phprocks)

Podman

You can use Podman instead of Docker by setting the PHPCTL_RUNTIME variable to podman in your environment variables or at .phpctlrc file.

PHPCTL_RUNTIME=podman phpctl php -v

Modules

For the default Docker image we have the following modules installed (click to expand).

[PHP Modules]
Core
ctype
curl
date
dom
fileinfo
filter
hash
iconv
igbinary
json
libxml
mbstring
mongodb
msgpack
mysqlnd
openssl
pcntl
pcov
pcre
PDO
pdo_mysql
Phar
posix
random
rdkafka
readline
redis
Reflection
session
SimpleXML
sockets
SPL
standard
swoole
tokenizer
xml
xmlreader
xmlwriter
zlib

You can always use your custom image with the PHPCTL_IMAGE variable, but feel free to ask for more essential extensions in the issues.

Why it exists?

[!TIP] After some years struggling with different PHP distributions into different operating systems, dealing with different PHP versions and sets of extensions, I came out with phpctl to use the power of containers to seamlessly run PHP 🐘 for developement environments.

Contributing

Click here to read the contributing guidelines.

otwld/ollama-helm

2023-12-26T01:38:05Z

Helm chart for Ollama on Kubernetes

stacksimplify/cloud-helm-charts

2023-12-26T01:38:05Z

Cloud Helm Charts - AWS EKS, Azure AKS and Google GKE

huntabyte/advent-of-svelte-2023

2023-12-26T01:38:32Z

PureKFD/PureKFD

2023-12-26T01:38:38Z

A KFD Focused Package Manager that supports 14.0-16.6b1 (excluding 16.5.1) (also supports MDC)

PureKFD - The Purest iOS Customization Toolbox

Download • Discord • Twitter

Overview

PureKFD is a KFD Focused Package Manager for iOS, focusing on iOS versions 14.0 through 16.6b1 (excluding 16.5.1) using KFD (CVE-2023-32434) and MDC (CVE-2022-46689). PureKFD allows users to customize their iOS experience through a user-friendly interface, utilizing community-developed tweaks available from the community’s repositories.

Getting Started

Compatibility: Ensure your iOS device is running an iOS version 14.0 through 16.6b1 (excluding 16.5.1).
Installation: Download PureKFD from the github releases for the latest release or our discord for the latest betas.
Customize: Explore the vast array of tweaks, personalize your iOS experience, and make your device truly yours.

Credits

Lrdsnow - Main Developer
Nick Chan - Developer
leminlimez - Springboard Color Manager
icons8 - Plumpy Icons
emmikat - M1/M2 Fixes
dhinakg - M1/M2 Fixes
lilmayofuksu - M1/M2 Fixes
noxwell - M1/M2 Fixes
@dor4a - Icon/Tweak Creator/Translator
@hackzy - Icon/Tweak Creator
@dreelpoop_er - Icon/Tweak Creator
Oliver Tzeng - Translator
@lunginspector - Icon Creator
@k3wl.4id - Icon Creator
@severalpeople - Icon Creator
@mildpeppercat - Icon Creator
@modmenus - Icon Creator

Abregud/All-Chains-EthereumX-Sniping-Bot

2023-12-26T01:38:48Z

Ethereum sniping bot for the community. Sniping bot for Ethereum

OWASP/www-project-top-10-for-large-language-model-applications

2023-12-26T01:38:54Z

OWASP Foundation Web Respository

www-project-top-10-for-large-language-model-applications

OWASP Foundation Web Repository

OWASP Top 10 for Large Language Model Applications

Welcome to the official repository for the OWASP Top 10 for Large Language Model Applications!

Overview and Audience 🗣️

The OWASP Top 10 for Large Language Model Applications is a standard awareness document for developers and web application security. It represents a broad consensus about the most critical security risks to Large Language Model (LLM) applications. There are other ongoing frameworks both inside and outside of OWASP that are not to be confused with this project and is currently scoped towards only LLM Application Security.

Our primary audience is developers, data scientists, and security experts tasked with designing and building applications and plugins leveraging LLM technologies. We aim to provide practical, actionable, and concise security guidance to help these professionals navigate the complex and evolving terrain of LLM application security.

Key Focus 📖

The primary aim of this project is to provide a comprehensible and adoptable guide to navigate the potential security risks in LLM applications. Our Top 10 list serves as a starting point for developers and security professionals who are new to this domain, and as a reference for those who are more experienced.

Mission Statement 🚀

Our mission is to make application security visible, so that people and organizations can make informed decisions about application security risks related to LLMs. While our list shares DNA with vulnerability types found in other OWASP Top 10 lists, we do not simply reiterate these vulnerabilities. Instead, we delve into these vulnerabilities’ unique implications when encountered in applications utilizing LLMs.

Our goal is to bridge the divide between general application security principles and the specific challenges posed by LLMs. The group’s goals include exploring how conventional vulnerabilities may pose different risks or be exploited in novel ways within LLMs and how developers must adapt traditional remediation strategies for applications utilizing LLMs.

Contribution 👋

The first version of this list was contributed by Steve Wilson of Contrast Security. We encourage the community to contribute and help improve the project. If you have any suggestions, feedback or want to help improve the list, feel free to open an issue or send a pull request.

We have a working group channel on the OWASP Slack, so please sign up and then join us on the #project-top10-llm channel.

Please hop over to our wiki page to collaborate on the project and stay up to date with the latest meetings and current roadmap.

License

This project is licensed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.

gitdagray/csv-to-sql-insert

2023-12-26T01:39:21Z

Provide table data as a CSV (comma-separated values) file and output a SQL insert statement for a table with the same name as the file.

csv-to-sql-insert

Provide table data as a CSV (comma-separated values) file and output a SQL insert statement for a table with the same name as the file.

Usage ⚙

Confirm you have a directory named csv
Confirm you have a directory named sql
Save your input CSV file in the csv directory
In a terminal window, first run npm install to install dependencies and then run npm start YourFileName
Watch the terminal window for any error messages
Your SQL insert statement will be saved in sql/YourFileName.sql

Support 👨‍💻

Contributing 🛠

Please read CONTRIBUTING.md prior to contributing.

Code of Conduct

Please see CODE_OF_CONDUCT.md.

blacksev/Gemini-Next-Web

2023-12-26T01:39:21Z

A well-designed cross-platform Gemini UI (Web / PWA / Linux / Win / MacOS). 一键拥有你自己的跨平台 Gemini 应用。

Gemini Next Web

English / 简体中文

One-Click to get well-designed cross-platform Gemini web UI.

一键免费部署你的跨平台私人 Gemini 应用。

Web App / Desktop App / Telegram

网页版 / 客户端 / 反馈 / 电报

Features

Deploy for free with one-click on Vercel in under 1 minute
Compact client (~5MB) on Linux/Windows/MacOS, download it now
Privacy first, all data is stored locally in the browser
Markdown support: LaTex, mermaid, code highlight, etc.
Responsive design, dark mode and PWA
Fast first screen loading speed (~100kb), support streaming response
New in v2: create, share and debug your chat tools with prompt templates (mask)
Awesome prompts powered by awesome-chatgpt-prompts-zh and awesome-chatgpt-prompts
Automatically compresses chat history to support long conversations while also saving your tokens
I18n: English, 简体中文, 繁体中文, 日本語, Français, Español, Italiano, Türkçe, Deutsch, Tiếng Việt, Русский, Čeština, 한국어, Indonesia

Roadmap

System Prompt: pin a user defined prompt as system prompt #138
User Prompt: user can edit and save custom prompts to prompt list
Prompt Template: create a new chat with pre-defined in-context prompts #993
Share as image, share to ShareGPT #1741
Desktop App with tauri
Self-host Model: Fully compatible with RWKV-Runner, as well as server deployment of LocalAI: llama/gpt4all/rwkv/vicuna/koala/gpt4all-j/cerebras/falcon/dolly etc.
Plugins: support network search, calculator, any other apis etc. #165

What's New

🚀 v2.0 is released, now you can create prompt templates, turn your ideas into reality! Read this: ChatGPT Prompt Engineering Tips: Zero, One and Few Shot Prompting.
🚀 v2.7 let's share conversations as image, or share to ShareGPT!
🚀 v2.8 now we have a client that runs across all platforms!
🚀 v2.9.11 you can use azure endpoint now.

主要功能

在 1 分钟内使用 Vercel 免费一键部署
提供体积极小（~5MB）的跨平台客户端（Linux/Windows/MacOS）, 下载地址
完整的 Markdown 支持：LaTex 公式、Mermaid 流程图、代码高亮等等
精心设计的 UI，响应式设计，支持深色模式，支持 PWA
极快的首屏加载速度（~100kb），支持流式响应
隐私安全，所有数据保存在用户浏览器本地
预制角色功能（面具），方便地创建、分享和调试你的个性化对话
海量的内置 prompt 列表，来自中文和英文
自动压缩上下文聊天记录，在节省 Token 的同时支持超长对话
多国语言支持：English, 简体中文, 繁体中文, 日本語, Español, Italiano, Türkçe, Deutsch, Tiếng Việt, Русский, Čeština, 한국어, Indonesia
拥有自己的域名？好上加好，绑定后即可在任何地方无障碍快速访问

开发计划

为每个对话设置系统 Prompt #138
允许用户自行编辑内置 Prompt 列表
预制角色：使用预制角色快速定制新对话 #993
分享为图片，分享到 ShareGPT 链接 #1741
使用 tauri 打包桌面应用
支持自部署的大语言模型：开箱即用 RWKV-Runner ，服务端部署 LocalAI 项目 llama / gpt4all / rwkv / vicuna / koala / gpt4all-j / cerebras / falcon / dolly 等等，或者使用 api-for-open-llm
插件机制，支持联网搜索、计算器、调用其他平台 api #165

Get Started

简体中文 > 如何开始使用

Click , remember that CODE is your page password;
Enjoy :)

FAQ

简体中文 > 常见问题

English > FAQ

Keep Updated

简体中文 > 如何保持代码更新

If you have deployed your own project with just one click following the steps above, you may encounter the issue of "Updates Available" constantly showing up. This is because Vercel will create a new project for you by default instead of forking this project, resulting in the inability to detect updates correctly.

We recommend that you follow the steps below to re-deploy:

Delete the original repository;
Use the fork button in the upper right corner of the page to fork this project;
Choose and deploy in Vercel again, please see the detailed tutorial.

Enable Automatic Updates

If you encounter a failure of Upstream Sync execution, please manually sync fork once.

After forking the project, due to the limitations imposed by GitHub, you need to manually enable Workflows and Upstream Sync Action on the Actions page of the forked project. Once enabled, automatic updates will be scheduled every hour:

Manually Updating Code

If you want to update instantly, you can check out the GitHub documentation to learn how to synchronize a forked project with upstream code.

You can star or watch this project or follow author to get release notifications in time.

Access Password

简体中文 > 如何增加访问密码

This project provides limited access control. Please add an environment variable named CODE on the vercel environment variables page. The value should be passwords separated by comma like this:

code1,code2,code3

After adding or modifying this environment variable, please redeploy the project for the changes to take effect.

Environment Variables

简体中文 > 如何配置 api key、访问密码、接口代理

`CODE` (optional)

Access password, separated by comma.

`OPENAI_API_KEY` (required)

Your openai api key, join multiple api keys with comma.

`BASE_URL` (optional)

Default: https://api.openai.com

Examples: http://your-openai-proxy.com

Override openai api request base url.

`OPENAI_ORG_ID` (optional)

Specify OpenAI organization ID.

`AZURE_URL` (optional)

Example: https://{azure-resource-url}/openai/deployments/{deploy-name}

Azure deploy url.

`AZURE_API_KEY` (optional)

Azure Api Key.

`AZURE_API_VERSION` (optional)

Azure Api Version, find it at Azure Documentation.

`HIDE_USER_API_KEY` (optional)

Default: Empty

If you do not want users to input their own API key, set this value to 1.

`DISABLE_GPT4` (optional)

Default: Empty

If you do not want users to use GPT-4, set this value to 1.

`ENABLE_BALANCE_QUERY` (optional)

Default: Empty

If you do want users to query balance, set this value to 1, or you should set it to 0.

`DISABLE_FAST_LINK` (optional)

Default: Empty

If you want to disable parse settings from url, set this to 1.

`CUSTOM_MODELS` (optional)

Default: Empty Example: +llama,+claude-2,-gpt-3.5-turbo,gpt-4-1106-preview=gpt-4-turbo means add llama, claude-2 to model list, and remove gpt-3.5-turbo from list, and display gpt-4-1106-preview as gpt-4-turbo.

To control custom models, use + to add a custom model, use - to hide a model, use name=displayName to customize model name, separated by comma.

User -all to disable all default models, +all to enable all default models.

Requirements

NodeJS >= 18, Docker >= 20

Development

简体中文 > 如何进行二次开发

Before starting development, you must create a new .env.local file at project root, and place your api key into it:

Local Development

# 1. install nodejs and yarn first
# 2. config local env vars in `.env.local`
# 3. run
yarn install
yarn dev

Deployment

简体中文 > 如何部署到私人服务器

Docker (Recommended)

docker pull blacksev/gemini-next-web

docker run -d -p 3000:3000 \
   -e OPENAI_API_KEY=sk-xxxx \
   -e CODE=your-password \
   blacksev/gemini-next-web

You can start service behind a proxy:

docker run -d -p 3000:3000 \
   -e CODE=your-password \
   -e PROXY_URL=http://localhost:7890 \
   blacksev/gemini-next-web

If your proxy needs password, use:

-e PROXY_URL="http://127.0.0.1:7890 user pass"

Shell

bash <(curl -s https://raw.githubusercontent.com/blacksev/Gemini-Next-Web/main/scripts/setup.sh)

Synchronizing Chat Records (UpStash)

Documentation

Please go to the [docs][./docs] directory for more documentation instructions.

Screenshots

Translation

If you want to add a new translation, read this document.

Donation

Buy Me a Coffee

zai.chat The best ChatGPT Plus alternative and GPT4 API provider, only $6 per month.

Contributor

Contributors

LICENSE

MIT

flybywiresim/aircraft

2023-12-26T01:39:21Z

The A32NX & A380X Project are community driven open source projects to create free Airbus aircraft in Microsoft Flight Simulator that is as close to reality as possible.

FlyByWire Simulations A32NX

The A32NX Project is a community-driven open source project to create a free Airbus A320neo in Microsoft Flight Simulator that is as close to reality as possible.

The following aircraft configuration is currently simulated or targeted:

A320neo

Model       A320-251N
Engine      CFM LEAP 1A-26
APU         APS3200
FMS         Honeywell Release H3
FWC Std.    H2F13
RA          Honeywell ALA-52B
TAWS        Honeywell EGPWS
ACAS        Honeywell TPA-100B
ATC         Honeywell TRA-100B
MMR         Honeywell iMMR
WXR         Honeywell RDR-4000

Please note that this configuration may change in the future as the A32NX project evolves and changes.

How to download and use the aircraft

To download the aircraft please go to the FlyByWire Simulations website. Please be sure to thoroughly read the documentation on how to install and use the aircraft.

If you're still running into problems after reading through the documentation, feel free to jump into our Discord #support channel.

How to contribute

If you would like to contribute to the project, see Contributing.md

Known issues and bug reporting

Our known issues list contains the most commonly reported issues. Should you have an issue not found on this list, then please take a look at the reported issues within the issue tracker and report a new issue if your issue isn't found there. You can also use the issue tracker to request a new feature.

FAQ

What liveries are available?

Liveries for the A32NX can be found on Flightsim.to.

When is the next update?

We don't know, since it depends on many factors. We will announce each new stable version via Discord and our social media channels listed above.

How do I join the team?

Read Contributing.md and join our Discord to get started.

Is the A32NX payware?

No, it is a completely free aircraft, open-source.

How do we report bugs?

Please read the known Issues and bug reporting section.

Is this going to be "study-level"?

While many in the team dislike the term "study-level" as its use is mostly applicable to certified training devices, we are in fact very dedicated on bringing a high fidelity A320neo to the Microsoft Flight Simulator platform.

Is feature X from the A320neo going to be implemented?

Chances are, yes! While we do not guarantee every single detail of the aircraft will eventually be represented, our goal is to produce an extremely accurate simulation based on technical data and real-world testing. This means you can be almost certain every feature of the aircraft will eventually be simulated to the best of the simulator's ability as long as the technical data backs it up.

Why has feature X not been implemented yet?

It's very likely that the feature you are awaiting is already under development! However, we strive for the greatest accuracy possible when producing aircraft, and it is therefore likely that a great deal of time will be spent on developing a proper software design and gathering sufficient references before we are able to offer the feature.

Why is my version not the same as what I see others using?

We have two mainline versions: stable and development. The stable version is a 'snapshot' of the development which we regard as stable with the current version of the simulator. The developer build is updated daily and is a constant work in progress and although we test thoroughly each update, minor issues may occur from time to time.

You can read more about the differences between the versions we offer on our documentation portal.

Is there a list of features or a changelog available?

We do not keep a list of features implemented, but a lengthy changelog of what has been implemented, and their associated pull requests can be found here.

License

Original source code assets present in this repository are licensed under the GNU GPLv3. Original 3D assets are licensed under CC BY-NC 4.0.

Microsoft Flight Simulator © Microsoft Corporation. The FlyByWire Simulations A32NX was created under Microsoft's "Game Content Usage Rules" using assets from Microsoft Flight Simulator, and it is not endorsed by or affiliated with Microsoft.

The contents of distribution packages built from the sources in this repository are therefore licensed as follows:

in the case of original source code from FBW or compiled artifacts generated from it, under GPLv3.
in the case of original 3D assets from FBW, under CC BY-NC 4.0.
in the case of assets covered by the "Game Content Usage Rules", under the license granted by those rules.

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