Github Top Repositories

⚡ 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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🔥 OpenCut-app/OpenCut is trending — and it deserves your attention. 🔗 https://github.com/OpenCut-app/OpenCut 📝 The open-source CapCut alternative ────────────────────────────── OpenCut is a free and open source video editor designed for web, desktop, and mobile use. It's currently being rewritten from the ground up, with exciting new features on the horizon, including an Editor API, support for third-party plugins, and a plugin-first architecture. The new version will also feature a Rust core, allowing for seamless deployment across desktop, mobile, and browser platforms. To get started with development, you'll need to install proto and run it from the repo root. You can then use commands like moon run web:dev or moon run desktop:dev to start the application in different modes. The project is currently not accepting outside contributions, but you can join the Discord community to follow along, ask questions, or show your support. OpenCut is sponsored by companies like fal.ai, which believe in open source creator tools. One key takeaway: OpenCut is poised to revolutionize video editing with its open source approach and innovative features - the future of video editing is open. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 HKUDS/DeepTutor just hit the trending charts — here's why it matters. 🔗 https://github.com/HKUDS/DeepTutor 📝 DeepTutor: Lifelong Personalized Tutoring.https://deeptutor.info/. ────────────────────────────── The DeepTutor project is a lifelong personalized tutoring system designed to provide a comprehensive learning experience. Its key features include a knowledge graph, thinking models, and learning pathways to help users achieve their learning goals. To get started, users can access the project's README file, which provides a step-by-step guide on how to install and use the system. The project also offers a CLI for easy interaction and a web-based interface for a more visual experience. From a technical standpoint, DeepTutor is built using Python 3.11+ and Next.js 16, and it supports various learning models and integrations with other tools and platforms. The project has a large and active community, with many contributors and maintainers who help to ensure its continued development and improvement. Whether you're a student, a teacher, or simply a lifelong learner, DeepTutor has something to offer. So why not join the community today and start exploring the many features and benefits that this powerful tutoring system has to offer? The DeepTutor system is constantly evolving, with new releases and updates being added all the time, so be sure to check back often to see what's new. Get started with DeepTutor and discover a whole new world of personalized learning - your future self will thank you! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 RyanCodrai/turbovec caught my eye on GitHub Trending today. 🔗 https://github.com/RyanCodrai/turbovec 📝 A vector index built on TurboQuant, written in Rust with Python bindings ────────────────────────────── Turbovec is a fast and memory-efficient vector index built on Google Research's TurboQuant algorithm. It allows for online ingest, fast SIMD search, and filtering at search time, making it perfect for applications where privacy, memory, or latency matters. Key features include: - Online ingest: add vectors without training or rebuilding the index - Fast SIMD search: optimized kernels for ARM and x86 architectures - Filter at search time: pass an id allowlist to search and get results from the allowed set - Pure local: no managed service, no data leaving your machine or VPC The library is written in Rust and has Python bindings, making it accessible to a wide range of users. It also has integrations with popular frameworks like LangChain, LlamaIndex, Haystack, and Agno. Turbovec achieves 10-19% faster search times than FAISS on ARM and is memory-efficient, using only 4 GB of RAM for a 10 million document corpus. If you need a fast, private, and memory-efficient vector search solution, Turbovec is the way to go: it's the ultimate game-changer for applications where speed and efficiency matter. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔥 openinterpreter/openinterpreter is trending — and it deserves your attention. 🔗 https://github.com/openinterpreter/openinterpreter 📝 A coding agent for open models like Kimi K3 ────────────────────────────── The Open Interpreter is a coding agent optimized for low-cost models, built as a fork of OpenAI's Codex. Its primary purpose is to provide a flexible and efficient way to interact with various models and providers. The key features of Open Interpreter include its ability to run commands inside native sandboxing, switch providers and models from the terminal, and inspect or switch Rust-native model harnesses. To use Open Interpreter, you can install it on macOS, Linux, or Windows using a simple command, and then start a session by typing `i` or `interpreter` in your terminal. The project also supports Agent Client Protocol and is compatible with OpenAI's Codex SDK, making it easy to integrate with existing tools and workflows. From a technical standpoint, Open Interpreter is built using Rust and provides a high-performance and efficient way to interact with models. It also includes a range of harnesses that can be used to optimize performance for specific models and providers. The Open Interpreter is designed for developers and researchers who want to work with low-cost models and need a flexible and efficient way to interact with them. Overall, the Open Interpreter is a powerful tool for anyone looking to work with coding agents and low-cost models. Get ready to code with the Open Interpreter - it's the ultimate coding sidekick! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ docusealco/docuseal is making waves. Here's the full picture. 🔗 https://github.com/docusealco/docuseal 📝 Open source DocuSign alternative. Create, fill, and sign digital documents ✍️ ────────────────────────────── DocuSeal is an open source platform for secure and efficient digital document signing and processing. Its key features include a WYSIWYG PDF form fields builder, 12 field types such as signature, date, and file, automated emails, and files storage on disk or cloud services like AWS S3. The platform is mobile-optimized, supports 7 UI languages, and offers API and Webhooks for integrations. To use DocuSeal, simply create a PDF form, share it with others, and have them fill and sign it online. The platform also offers a live demo and a cloud trial for easy testing. From a technical standpoint, DocuSeal can be easily deployed using Docker or Docker Compose, and supports various databases like SQLite, PostgreSQL, and MySQL. DocuSeal is perfect for businesses looking to integrate seamless document signing into their web or mobile apps, particularly in industries like banking, healthcare, and real estate. One-liner takeaway: DocuSeal makes digital document signing and processing a breeze, so you can focus on what matters most - your business! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

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🎯 tirth8205/code-review-graph landed on trending. Worth a proper look. 🔗 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. ────────────────────────────── Code Review Simplified: The code-review-graph repository offers an innovative solution to optimize AI coding tool performance by creating a structural map of your codebase, tracking changes, and providing precise context. This approach significantly reduces the number of tokens read by AI assistants, resulting in a more efficient and smarter code review process. The key features of code-review-graph include: - Incremental updates that re-index large projects in under 2 seconds - Broad language coverage, including support for Jupyter notebooks - Blast-radius analysis to identify the minimal set of files affected by changes - Integration with various AI coding tools and platforms To get started, simply install code-review-graph using pip install code-review-graph, then run code-review-graph install to auto-detect and configure your platform. The initial build takes around 10 seconds for a 500-file project. The technical highlights of this repository include its use of Tree-sitter for parsing and MCP for providing context to AI assistants. The code is well-structured and includes detailed documentation, making it easy to understand and contribute to. This repository is perfect for developers and teams looking to improve their code review process and reduce the strain on their AI coding tools. With its robust features and broad language coverage, code-review-graph is an essential tool for any development workflow. In a nutshell, code-review-graph is a game-changer for code reviews - it helps AI assistants read less, understand more. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe