Github Top Repositories

🌟 tirth8205/code-review-graph caught my eye on GitHub Trending today. 🔗 https://github.com/tirth8205/code-review-graph 📝 Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows. ────────────────────────────── The code-review-graph GitHub repository offers a solution to optimize code review tasks for AI coding tools. It creates a structural map of your codebase using Tree-sitter, tracks changes, and provides context to AI assistants via the Model Context Protocol (MCP), reducing the amount of code they need to read. The tool supports various platforms, including Codex, Claude Code, and GitHub Copilot, and can be installed with a single command. The

code-review-graph install

command auto-detects and configures supported platforms, and the tool can be used with Python 3.10+. It also features incremental updates, blast-radius analysis, and broad language coverage, including support for Jupyter notebooks. Developers can add their own languages without forking the repository by creating a languages.toml file. The repository also includes a GitHub Action for risk-scored PR reviews in CI. The tool has been benchmarked across six real repositories, with a median per-question token reduction of ~82x. The code-review-graph is a valuable resource for developers looking to optimize their code review workflow. One-liner takeaway: code-review-graph revolutionizes code reviews by giving AI assistants precise context, slashing token waste, and making the development process smarter and more efficient. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 MoonshotAI/kimi-cli landed on trending. Worth a proper look. 🔗 https://github.com/MoonshotAI/kimi-cli 📝 Kimi Code CLI is your next CLI agent. ────────────────────────────── Kimi CLI is an AI agent that runs in the terminal, helping you complete software development tasks and terminal operations. It can read and edit code, execute shell commands, search and fetch web pages, and autonomously plan and adjust actions during execution. Key features include a shell command mode, VS Code extension, IDE integration via ACP, Zsh integration, and MCP support. You can use Kimi CLI with various tools and editors, such as Visual Studio Code, Zed, and JetBrains, to enhance your development experience. To get started, you can install Kimi CLI and refer to the getting started guide. For development, you can clone the repository, prepare the environment, and run Kimi CLI using uv run kimi. Technical highlights include support for MCP tools, ad-hoc MCP configuration, and a range of sub-commands for managing MCP servers. The project is evolving into Kimi Code CLI, which will automatically migrate your configuration and sessions. This project is suitable for developers and users who want to leverage AI capabilities in their terminal and development workflow. One-liner takeaway: Kimi CLI is an AI-powered terminal agent that streamlines your development workflow, and it's evolving into Kimi Code CLI for even more capabilities. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🚀 Meet codecrafters-io/build-your-own-x: a gem from today's GitHub trending list. 🔗 https://github.com/codecrafters-io/build-your-own-x 📝 Master programming by recreating your favorite technologies from scratch. ────────────────────────────── The codecrafters-io/build-your-own-x GitHub repository is a treasure trove of step-by-step guides for building a wide range of technologies from scratch. With the motto "What I cannot create, I do not understand" from Richard Feynman, this project encourages learning through hands-on creation. The repository offers tutorials and guides on building various technologies, including 3D renderers, AI models, augmented reality, blockchains, bots, command-line tools, databases, and many more. These guides cover a range of programming languages, such as C, C++, Java, Python, and JavaScript, making it accessible to developers with different skill sets. The guides are well-structured and provide a comprehensive overview of the technology being built, including the technical aspects and implementation details. They also offer a unique opportunity for developers to learn by doing and gain a deeper understanding of the technologies they use. The target audience for this repository appears to be developers and programmers who want to learn and improve their skills by building real-world projects from scratch. Whether you're a beginner or an experienced developer, this repository has something to offer. In short, the codecrafters-io/build-your-own-x repository is an excellent resource for anyone looking to learn and build new skills, and its motto says it all: build it to understand it. Don't just use it, build it! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 KnockOutEZ/wigolo just hit the trending charts — here's why it matters. 🔗 https://github.com/KnockOutEZ/wigolo 📝 The go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta. ────────────────────────────── Meet wigolo, a local-first web intelligence platform designed for AI agents. It provides a durable surface for everything web-related, including search, fetch, crawl, extract, cache, and research, all without requiring API keys or incurring metered bills. Key features include multi-engine web search, tiered routing for fetching pages, structured data extraction, and a memory that compounds with each query. wigolo runs wherever your agent runs, whether as an MCP server, a REST/MCP endpoint, or embedded through an SDK. To get started, simply run npx wigolo init --agents=<your-agent> to set up the local engine. You can then use various tools like search, fetch, and research to gather information. Technical highlights include on-device models, direct adapters for public engines, and transparent per-result scoring. wigolo is designed for agents, not humans, and provides honest output with surfaced degradation and self-flagged junk results. Audience: This platform is ideal for developers and users of AI agents, including those using Claude Code, Cursor, Codex, and other popular AI tools. In short, wigolo is a powerful, private, and free web intelligence platform that empowers your AI agents to gather information without incurring costs or relying on third-party services. The takeaway: with wigolo, your AI agents can search, fetch, and research the web without breaking the bank or sacrificing privacy. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 lyogavin/airllm just hit the trending charts — here's why it matters. 🔗 https://github.com/lyogavin/airllm 📝 AirLLM 70B inference with single 4GB GPU ────────────────────────────── AirLLM is a game-changer for large language models, enabling you to run massive models like 405B Llama 3.1 on a single 8GB GPU and 671B DeepSeek-V3 on ~12GB. This is achieved without quantization, distillation, or pruning. The key to this magic is that AirLLM only keeps one layer on the GPU at a time, reducing the required VRAM significantly. To get started, you can install the airllm package using pip install airllm. Then, initialize the model using AutoModel.from_pretrained(), passing in the Hugging Face repo ID or local path of the model. You can also enable model compression for up to 3x inference speedup by specifying the compression argument. AirLLM supports a wide range of models, including Llama, Qwen, DeepSeek, Mistral, and many more. It's perfect for developers and researchers who want to work with large language models without breaking the bank on GPU hardware. In short, AirLLM is a powerful tool that makes large language models accessible to everyone. With its ease of use and impressive performance, you can now run huge models on relatively small GPUs - unlocking new possibilities in NLP research and development. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 elder-plinius/G0DM0D3 just hit the trending charts — here's why it matters. 🔗 https://github.com/elder-plinius/G0DM0D3 📝 LIBERATED AI CHAT ────────────────────────────── The G0DM0D3 project is a fully open-source, privacy-transparent, multi-model chat interface that pushes the limits of post-training layer capabilities. It's designed for hackers, philosophers, and system tinkerers, offering features like multi-provider support with 60 listed OpenRouter models, GODMODE CLASSIC for racing model combos in parallel, and ULTRAPLINIAN for multi-model evaluation. Key features include: - Parseltongue for input perturbation and red-teaming research - AutoTune for context-adaptive sampling parameters - Local History for conversation storage and export/import support - Responsive design for desktop and mobile use To get started, visit the hosted site at godmod3.ai or self-host the standalone interface by cloning the repository and opening index.html in your browser. For local model support, configure OpenRouter, Venice, or your own models in Settings. The project emphasizes privacy controls, with metadata-only app telemetry that can be disabled with No-Log or Local-only mode. Chat history is stored in browser storage, and there's no account system or cloud history sync. G0DM0D3 is perfect for those who want to explore the frontiers of AI interaction without sacrificing control or privacy. The punchy one-liner takeaway: Experience liberated AI cognition with G0DM0D3, where you're in control, not the AI overlords! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ tirth8205/code-review-graph is making waves. Here's the full picture. 🔗 https://github.com/tirth8205/code-review-graph 📝 Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows. ────────────────────────────── Stop burning tokens and start reviewing smarter with code-review-graph, a tool that builds a structural map of your code and tracks changes incrementally, providing your AI assistant with precise context. The key features of code-review-graph include: - Building a structural map of your code with Tree-sitter - Tracking changes incrementally - Providing precise context to your AI assistant via MCP To use code-review-graph, simply install it with pip install code-review-graph, then run code-review-graph install to configure it for your platform. From a technical perspective, code-review-graph uses Tree-sitter to parse your code, stores the result as a graph of nodes and edges, and queries this graph at review time to find the minimal set of files your AI assistant needs to read. This tool is suitable for developers and teams who use AI coding tools and want to optimize their code review process. The takeaway: code-review-graph helps you review smarter, not harder, by reducing the number of tokens your AI assistant needs to read. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 rohitg00/ai-engineering-from-scratch caught my eye on GitHub Trending today. 🔗 https://github.com/rohitg00/ai-engineering-from-scratch 📝 Learn it. Build it. Ship it for others. ────────────────────────────── The GitHub repo "rohitg00/ai-engineering-from-scratch" is a comprehensive, free, and open-source curriculum designed to teach AI engineering from scratch. With 503 lessons and 20 phases, this curriculum covers everything from math foundations to autonomous systems and ethics & alignment. Key features include: - Four programming languages: Python, TypeScript, Rust, and Julia. - Reusable artifacts: Each lesson produces a prompt, skill, agent, or MCP server that can be used in real-world projects. - Linear progression: Phases build on top of each other, allowing learners to progress from basic math concepts to complex AI systems. To get started, users can read lessons online, clone and run the code, or use the /find-your-level skill to determine their starting point. The curriculum is suitable for anyone who can write code and wants to understand how AI actually works. The takeaway: Build AI from scratch and ship reusable tools with this comprehensive curriculum. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: ibelick/ui-skills — let's break it down. 🔗 https://github.com/ibelick/ui-skills 📝 Skills for Design Engineers ────────────────────────────── Here's a summary of the ui-skills GitHub repository. The purpose of this repo is to provide skills for design engineers, specifically helping them choose the right UI skill set for a task. Key features include a command-line interface (CLI) that allows users to run various commands, such as npx ui-skills start, to route their agent through the right UI skill set. To use ui-skills, simply run npx ui-skills in your terminal, and explore the different options available, including listing categories and getting specific skills like baseline-ui. From a technical standpoint, the repo is licensed under the MIT license, making it accessible for anyone to use and contribute. The target audience appears to be design engineers or anyone interested in UI skills. In short, ui-skills is a handy tool for design engineers - and with its easy-to-use CLI, you can supercharge your UI skills in no time! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🚀 Meet PostHog/posthog: a gem from today's GitHub trending list. 🔗 https://github.com/PostHog/posthog 📝 🦔 PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error tracking, logs, and more – capture all the context agents need to diagnose problems, uncover opportunities, and ship fixes. Steer it all from Slack, web, desktop, or the MCP. ────────────────────────────── The PostHog platform is an open-source solution for building self-driving products, offering a wide range of tools to capture product data and automate diagnosis, fixes, and improvements. Key features include self-driving mode, product analytics, web analytics, session replays, feature flags, and error tracking. You can get started with PostHog by signing up for the PostHog Cloud or self-hosting the open-source version using Docker. The platform supports various programming languages, including JavaScript, Python, and React. With a generous free tier and transparent pricing, PostHog is suitable for developers, product managers, and businesses looking to optimize their products. As a developer, you can contribute to the project, and the company is also hiring! Overall, PostHog is a powerful tool for building and optimizing products - and it's free to get started, so why not give it a try? ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔍 Deep-diving into apache/ossie — fresh off the trending list. 🔗 https://github.com/apache/ossie 📝 Apache Ossie, industry wide specification effort to standardize how we exchange semantic metadata across analytics, AI and BI platforms, providing a vendor neutral, single source of truth for semantic data ────────────────────────────── Apache Ossie is a collaborative, open-source effort to standardize semantic model exchange and utilization across data analytics, AI, and BI tools. Its key feature is a JSON- and YAML-based specification that ensures unparalleled interoperability and efficiency. To get started, explore the core-spec/, converters/, and examples/ directories in the repository. Developers can contribute by proposing specification changes or contributing code, and join the conversation on GitHub Discussions or Slack. The project's technical highlight is its ability to eliminate inconsistencies across different tools. Apache Ossie is for data scientists and developers seeking a vendor-agnostic semantic model specification. With Ossie, your data's definitions and value remain consistent - that's the power of a single, consistent source of truth! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 Robbyant/lingbot-map caught my eye on GitHub Trending today. 🔗 https://github.com/Robbyant/lingbot-map 📝 A feed-forward 3D foundation model for reconstructing scenes from streaming data ────────────────────────────── LingBot-Map is a cutting-edge, feed-forward 3D foundation model designed for streaming 3D reconstruction. Its key features include a Geometric Context Transformer that unifies various components for a single, efficient framework, High-Efficiency Streaming Inference for stable and fast processing, and State-of-the-Art Reconstruction capabilities that outperform existing methods. To use lingbot-map, users can follow a straightforward installation process that involves setting up a conda environment, installing PyTorch and the necessary dependencies, and then installing the lingbot-map package itself. The model can be downloaded from Hugging Face or ModelScope repositories. The demo.py script provides an interactive way to test the model with various scenes and options. It supports features like streaming with keyframe intervals for longer sequences and sky masking for improved outdoor scene visualization. For longer sequences, windowed inference mode can be used. Audience: This project is primarily aimed at researchers and developers in the field of computer vision and 3D reconstruction who are looking for a robust and efficient solution for streaming 3D reconstruction tasks. Technical Highlights include the use of paged KV cache attention for efficient streaming inference, support for various input formats, and the ability to handle long sequences. In summary, lingbot-map is a powerful tool for 3D reconstruction, offering state-of-the-art performance, efficiency, and flexibility, making it an excellent choice for a wide range of applications - Experience the future of 3D reconstruction with LingBot-Map! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

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