标签：#git

GitHub热门项目 | 🥧 Savoury implementation of the QUIC transport protocol and HTTP/3 | Stars: 11,540 | 7 stars today | 语言: Rust

GitHub热门项目 | A set of beautifully-designed, accessible components and a code distribution platform. Works with your favorite frameworks. Open Source. Open Code. | Stars: 115,842 | 73 stars today | 语言: TypeScript

GitHub热门项目 | Storybook is the industry standard workshop for building, documenting, and testing UI components in isolation | Stars: 90,212 | 11 stars today | 语言: TypeScript

GitHub热门项目 | Vite plugin that reimplements the Next.js API surface — deploy anywhere | Stars: 8,149 | 8 stars today | 语言: TypeScript

GitHub热门项目 | The swiss army knife of lossless video/audio editing | Stars: 40,995 | 67 stars today | 语言: TypeScript

GitHub热门项目 | the full-stack Vue framework | Stars: 60,358 | 14 stars today | 语言: TypeScript

GitHub热门项目 | Next generation frontend tooling. It's fast! | Stars: 81,082 | 71 stars today | 语言: TypeScript

GitHub热门项目 | Free, open-source and self-hosted CAPTCHA alternative to reCAPTCHA. Privacy-first and powered by proof-of-work and instrumentation challenges. | Stars: 6,767 | 59 stars today | 语言: JavaScript

GitHub热门项目 | A full-stack AI helpdesk platform that uses machine learning, NLP, and OCR to automatically analyze support requests, detect similar incidents, and help teams resolve technical issues faster. | Stars: 139 | 4 stars today | 语言: JavaScript

GitHub热门项目 | use ai analysis Performance issue with perfetto | Stars: 491 | 1 star today | 语言: JavaScript

GitHub热门项目 | A bundler for javascript and friends. Packs many modules into a few bundled assets. Code Splitting allows for loading parts of the application on demand. Through "loaders", modules can be CommonJs, AMD, ES6 modules, CSS, Images, JSON, Coffeescript, LESS, ... and your custom stuff. | Stars: 65,757 | 2 stars today | 语言: JavaScript

GitHub热门项目 | Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free. | Stars: 34,899 | 51 stars today | 语言: Python

GitHub热门项目 | Open-Source Frontier Voice AI | Stars: 48,317 | 219 stars today | 语言: Python

GitHub热门项目 | Robust Speech Recognition via Large-Scale Weak Supervision | Stars: 101,694 | 116 stars today | 语言: Python

GitHub热门项目 | The Go programming language | Stars: 134,434 | 24 stars today | 语言: Go

GitHub热门项目 | The official NGINX Open Source repository. | Stars: 30,602 | 39 stars today | 语言: C

GitHub热门项目 | Agentic AI Infrastructure for magnifying HUMAN capabilities. | Stars: 14,794 | 63 stars today | 语言: TypeScript

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built After being abandoned for several months, I have come back to build and complete Claude with LeetCode, which is a DSA learning system that automates daily algorithm education with Claude code directly inside GitHub repo. Every time I submit an accepted solution on Leetcode, the Github workflow fetches my Leetcode account data and commit the problem with the solution to the repo. Claude will then run on a fixed schedule and automatically generates a full structured lecture, covering the DSA topic, brute force through optimal solutions in Python, complexity analysis, and a YouTube video packaged in a GitHub Issue. This project means a lot to me because it merges two things I care about daily: now not only can I solve Leetcode problem, my solution is automatically analyzed by a powerful AI agent mentor. Demo Link to my project: https://github.com/Stewie-pixel/claude-with-leetcode.git Link to my application walkthrough: https://youtu.be/ClWdW3v9JJ0 The Comeback Story At first this was only a project to store the Leetcode questions I have solved. The process required manual pushing the problem to the repo and nothing special. Later I have added the automation workflow to fetch data from my Leetcode account, Claude will be prompted like an experienced dsa mentor from Claude and skill.md file to give a thorough analysis on that problem. And at the end of the day, Github Copilot workflow will give a daily summary report to cover my daily progress. My Experience with GitHub Copilot I built a DSA Mentor skill that gives Copilot the full context of what a lecture should contain: topic identification, the brute force to optimal approach structure, complexity analysis requirements, and the YouTube search step. Without Copilot, writing the dsaMentor.js orchestration logic and getting the agent to consistently produce structured markdown output would have taken significantly longer. I then use Copilot cli

This is a submission for the GitHub Finish-Up-A-Thon Challenge OpsPilot AI: Reviving an Unfinished AI-Powered Operations Platform with GitHub Copilot What I Built OpsPilot AI is an AI-powered operations assistant designed to help DevOps engineers, SREs, and operations teams investigate incidents, monitor service health, and gain actionable operational insights. The project originally started as a side project inspired by my experience working in production support and monitoring environments. I built an initial version to validate the idea but never fully completed it. The core concept was promising, but several important features and usability improvements were still missing. Through the GitHub Finish-Up-A-Thon Challenge, I revisited the project and transformed it into a much more complete and polished MVP. Key features include: AI-powered incident analysis Root cause investigation assistance MTTR analytics dashboard Service health monitoring Incident trend analysis Executive reporting insights Modern responsive user interface Demo Live Application GitHub Repository OpsPilot AI helps operations teams reduce investigation time and improve operational visibility through AI-powered workflows and analytics. The Comeback Story When I first started OpsPilot AI, it was mainly an experiment to explore how AI could assist operations teams during incident investigations. Although the foundation was built, the project was left unfinished because of limited time and competing priorities. The original version lacked: Incident analytics Meaningful operational insights Root cause investigation workflows Executive reporting capabilities A polished user experience For this challenge, I focused on completing the project and turning it into a usable MVP. What I Added AI Incident Analysis Enhanced the platform with AI-powered incident summaries and investigation assistance. Operations Analytics Added dashboards to track: Mean Time To Resolution (MTTR) Incident frequency Service health

Created a Google Chrome extension that instantly turns any highlighted text on a webpage into a Google Calendar event