Github Top Repositories

🔍 Deep-diving into MoonshotAI/kimi-cli — fresh off the trending list. 🔗 https://github.com/MoonshotAI/kimi-cli 📝 Kimi Code CLI is your next CLI agent. ────────────────────────────── Kimi CLI is a terminal-based AI agent designed to assist with software development tasks and terminal operations. It can read and edit code, execute shell commands, and search the web. Key features include a built-in shell command mode, integration with Visual Studio Code and other IDEs via the Agent Client Protocol (ACP), and support for Zsh and Model Context Protocol (MCP) tools. To get started, users can install Kimi CLI and access its features through a simple command-line interface. The kimi command is used to interact with the agent, and various sub-commands are available for tasks like MCP server management. kimi mcp add --transport http context7 https://mcp.context7.com/mcp --header "CONTEXT7_API_KEY: ctx7sk-your-key" Kimi CLI is evolving into Kimi Code CLI, the next-generation terminal AI agent, and existing installations will be migrated automatically. Takeaway: Kimi CLI is a powerful tool for developers, but its evolution into Kimi Code CLI means it's time to upgrade and experience the latest advancements in terminal-based AI assistance. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: trycua/cua — let's break it down. 🔗 https://github.com/trycua/cua 📝 Scale computer-use 2.0 with open-source drivers, cross-OS fleets, and benchmarks for training, evaluation, and data generation. ────────────────────────────── Cua is an open-source tool that enables scalable computer-use 2.0 with cross-OS fleets, benchmarks, and data generation. It features background computer-use on macOS, Windows, and Linux, allowing agents to interact with native desktop apps without stealing the cursor or focus. The cua command-line interface and Multi-Computer Protocol (MCP) server provide a unified way to drive computers across different operating systems. Key features include: - Cua Drivers: Background computer-use on macOS, Windows, and Linux - Cua Sandbox: Agent-ready sandboxes for any OS, with a unified API for VMs or container images - Cua Bench: Benchmarks and RL environments for evaluating and training computer-use models - Lume: macOS virtualization on Apple Silicon with near-native performance Technical highlights include support for:

from cua import Sandbox, Image
async with Sandbox.ephemeral(Image.linux()) as sb:
    # Interact with the sandbox
    result = await sb.shell.run("echo hello")
    screenshot = await sb.screenshot()

Cua is designed for researchers, developers, and organizations working on AI, computer vision, and human-computer interaction. The project provides extensive documentation, a blog, and community support through Discord and GitHub Issues. Get started with Cua today and unlock the full potential of computer-use 2.0! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ 1jehuang/jcode is making waves. Here's the full picture. 🔗 https://github.com/1jehuang/jcode 📝 Coding Agent Harness ────────────────────────────── jcode is a cutting-edge coding agent designed to elevate your coding experience. Its primary purpose is to provide a next-generation coding agent harness that supports multi-session workflows, offers infinite customizability, and ensures high performance. To get started with jcode, you can install it using the following bash command:

curl -fsSL https://jcode.sh/install | bash

or using powershell:

irm https://jcode.sh/install.ps1 | iex

jcode boasts impressive technical highlights, including optimized RAM usage and fast boot-up times. Its performance is significantly better than other similar tools, making it an attractive choice for developers. The target audience for jcode appears to be developers looking for a high-performance, customizable coding agent that can handle multi-session workflows. In a nutshell, jcode is the ultimate coding sidekick that will revolutionize your coding experience - code smarter, not harder, with jcode! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 AstrBotDevs/AstrBot landed on trending. Worth a proper look. 🔗 https://github.com/AstrBotDevs/AstrBot 📝 AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature, and can be your openclaw alternative. ✨ ────────────────────────────── AstrBot is an open-source, all-in-one chatbot platform that integrates with popular instant messaging apps. It provides a reliable and scalable conversational AI infrastructure for individuals, developers, and teams. With AstrBot, you can quickly build production-ready AI applications within your IM platform workflows. Key features include AI LLM Conversations, multimodal support, agent platforms, and multi-platform compatibility with QQ, Telegram, Slack, and more. AstrBot also offers plugin extensions with over 1000 plugins available for one-click installation. To get started, you can choose from various deployment methods, including one-click deployment with uv, cloud deployment with RainYun, Docker deployment, or desktop application deployment. AstrBot supports multiple messaging platforms and model services, making it a versatile and powerful tool for building conversational AI applications. Whether you're building a personal AI companion or an enterprise knowledge base, AstrBot enables you to create custom AI solutions with ease. So why wait? Dive into the world of conversational AI with AstrBot and discover the endless possibilities it has to offer: AstrBot is the ultimate conversational AI platform to unlock your chatbot's full potential! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 microsoft/terminal just hit the trending charts — here's why it matters. 🔗 https://github.com/microsoft/terminal 📝 The new Windows Terminal and the original Windows console host, all in the same place! ────────────────────────────── The Windows Terminal is a modern, feature-rich terminal application for command-line users. It's designed to be fast, efficient, and highly customizable, with support for tabs, rich text, and theming. The terminal is built on top of a modernized Windows Console Host (`conhost.exe`) codebase, which has been overhauled to include key extensibility points and safer, more efficient containers. To get started with Windows Terminal, you can install it from the Microsoft Store, or manually download and install it from the GitHub repository. There are also unofficial install methods available via Chocolatey and Scoop. Some of the key features of Windows Terminal include: * Support for tabs and rich text * Globalization and configurability * Theming and styling * A new DirectWrite-based text layout and rendering engine * A text buffer capable of storing both UTF-16 and UTF-8 The Windows Terminal is designed to be highly customizable, with a wide range of settings and options available. It's also designed to be fast and efficient, with a focus on performance and reliability. Whether you're a developer, a power user, or just someone who wants a better command-line experience, Windows Terminal is definitely worth checking out. With its modern design, rich features, and high customizability, it's an excellent choice for anyone looking for a powerful and flexible terminal application. One-liner takeaway: Windows Terminal is a game-changer for command-line users, offering a modern, feature-rich, and highly customizable terminal experience that's fast, efficient, and reliable. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔥 PostHog/posthog is trending — and it deserves your attention. 🔗 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. ────────────────────────────── PostHog is an open-source platform for building self-driving products, providing a range of tools to help you understand user behavior and optimize your product. With PostHog, you can capture product analytics, web analytics, and session replays, as well as set up feature flags, experiments, and error tracking. The platform also includes a data warehouse, data pipelines, and AI observability features. To get started with PostHog, you can sign up for a free account on PostHog Cloud or self-host the platform using Docker. The platform offers a generous free tier and transparent pricing for its paid plan. PostHog has a large community of contributors and offers extensive documentation, including a company handbook, product guides, and developer resources. The platform is written in a variety of languages, including JavaScript, Python, and Node, and has SDKs and libraries for popular frameworks like React and Angular. Whether you're a product manager, developer, or data analyst, PostHog has the tools you need to build a successful product. So why not give it a try and see how PostHog can help you drive growth and revenue? With its powerful features and flexible pricing, PostHog is the perfect choice for anyone looking to take their product to the next level: build better products, faster. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 github/copilot-sdk landed on trending. Worth a proper look. 🔗 https://github.com/github/copilot-sdk 📝 Multi-platform SDK for integrating GitHub Copilot Agent into apps and services ────────────────────────────── The GitHub Copilot SDK allows you to embed Copilot's agentic workflows into your application. It exposes the same engine as the Copilot CLI, handling planning, tool invocation, and file edits. The SDK is available for Python, TypeScript, Go, .NET, Java, and Rust, with each implementation providing a production-tested agent runtime that can be invoked programmatically. To get started, you can install the SDK using the provided commands, then define agent behavior and let Copilot handle the rest. The SDK communicates with the Copilot CLI server via JSON-RPC, managing the CLI process lifecycle automatically. Key features include support for BYOK (Bring Your Own Key), custom agents, skills, and tools, as well as multiple authentication methods. The SDK is production-ready and follows semantic versioning. Whether you're a developer looking to streamline your workflow or a business seeking to automate tasks, the GitHub Copilot SDK is a powerful tool to consider. With its robust features and ease of use, it's an ideal solution for anyone looking to harness the power of Copilot in their application. The GitHub Copilot SDK is a game-changer for developers: it puts the power of AI directly into your code. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 andrewrabert/jellium-desktop caught my eye on GitHub Trending today. 🔗 https://github.com/andrewrabert/jellium-desktop 📝 An unofficial desktop client for Jellyfin ────────────────────────────── Jellium Desktop is an unofficial desktop client for Jellyfin, built on top of CEF and mpv. It provides an easy-to-use interface for managing and streaming media content. The app is available for download on Linux, macOS, and Windows platforms, with various installation options such as AppImage, Flatpak, and Arch Linux (AUR). To get started, users can download the app from the provided links and follow the installation instructions. On macOS, users need to remove the quarantine flag using the command sudo xattr -cr /Applications/Jellium\ Desktop.app. From a development perspective, the project utilizes just as a command runner, providing various recipes for building, testing, and maintaining the app. Some of the available recipes include:

just build
just run
just test
just fmt

The app is designed for Jellyfin users looking for a seamless desktop experience. Jellium Desktop: stream your media, simplified. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔥 KnockOutEZ/wigolo is trending — and it deserves your attention. 🔗 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. ────────────────────────────── wigolo is a local-first web intelligence tool for AI agents, offering a range of features like search, fetch, crawl, extract, cache, and find-similar. It's designed to work with various agents, including Claude Code, Cursor, and Codex, and can be used as an MCP server, REST endpoint, or embedded through an SDK. Key features include: - search: multi-engine web search with rank fusion and ML reranking - fetch: load URLs through a tiered router with auto-escalation to a headless browser engine - crawl: multi-page crawl with per-domain rate limits and robots.txt respect - extract: structured data extraction from pages, including tables, metadata, and JSON-LD Technical highlights: - Runs on Node ≥ 20 with ~1.5 GB of free disk space - Supports various platforms, including macOS, Linux, and Windows - npx wigolo init command sets up the local engine, downloading the browser engine and on-device models - npx wigolo doctor checks the health of the setup Audience: - AI agent developers - Users of Claude Code, Cursor, Codex, and other supported agents - Anyone looking for a local-first web intelligence solution Takeaway: With wigolo, you can empower your AI agents with a robust, local-first web intelligence layer, free from API keys, cloud dependencies, and metered bills. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🚀 Meet jamiepine/voicebox: a gem from today's GitHub trending list. 🔗 https://github.com/jamiepine/voicebox 📝 The open-source AI voice studio. Clone, dictate, create. ────────────────────────────── The Voicebox is an open-source AI voice studio that allows you to clone any voice, generate speech in 23 languages, dictate into any app, and give AI agents a voice of your choice. It's a local-first alternative to other cloud-based services, offering complete privacy as all models, voice data, and captures never leave your machine. Key features include 7 TTS engines, voice cloning and preset voices, post-processing effects, and unlimited generation length. The app also supports global dictation with a hotkey, voice input, and agent voice output. From a technical standpoint, Voicebox is built with Tauri (Rust) and has a REST API plus a built-in MCP server for integration into other apps and agents. The target audience for Voicebox appears to be developers, content creators, and anyone interested in AI-powered voice technology. In a nutshell, Voicebox is a powerful tool that puts you in control of your voice data and offers a wide range of features for voice cloning, generation, and input - and it's all free and open-source. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: rohitg00/ai-engineering-from-scratch — let's break it down. 🔗 https://github.com/rohitg00/ai-engineering-from-scratch 📝 Learn it. Build it. Ship it for others. ────────────────────────────── The AI Engineering from Scratch GitHub repository is a comprehensive curriculum designed to teach AI engineering skills from the ground up. It consists of 20 phases and 503 lessons, covering a wide range of topics from math foundations to autonomous systems. The curriculum is structured to provide a linear learning experience, with each lesson building on the previous one, and includes Python, TypeScript, Rust, and Julia code examples. The key features of this curriculum include: * A focus on building AI systems from scratch, rather than just using pre-built libraries and frameworks * A comprehensive coverage of AI topics, including math foundations, machine learning, deep learning, and autonomous systems * A linear learning structure, with each lesson building on the previous one * A variety of code examples in different programming languages To get started with the curriculum, you can choose from three options: * Read the lessons online * Clone the repository and run the code examples * Use the built-in agent skills to find your level and get personalized recommendations The curriculum is designed for anyone who wants to learn AI engineering, from beginners to experienced practitioners. The prerequisites are minimal, requiring only basic programming skills and a willingness to learn. The technical highlights of the curriculum include: * A focus on building reusable tools and artifacts, rather than just completing exercises * A comprehensive coverage of AI topics, including math foundations, machine learning, and deep learning * A variety of code examples in different programming languages Overall, the AI Engineering from Scratch curriculum is a valuable resource for anyone who wants to learn AI engineering skills from the ground up. With its comprehensive coverage of AI topics, linear learning structure, and focus on building reusable tools and artifacts, it provides a unique and valuable learning experience. Takeaway: With AI Engineering from Scratch, you don't just learn AI - you build it, from scratch, and ship reusable tools and artifacts that you can use in your daily workflow. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ kvcache-ai/ktransformers is making waves. Here's the full picture. 🔗 https://github.com/kvcache-ai/ktransformers 📝 A Flexible Framework for Experiencing Heterogeneous LLM Inference/Fine-tune Optimizations ────────────────────────────── The KTransformers project is a flexible framework for experiencing cutting-edge large language model (LLM) inference and fine-tuning optimizations. It focuses on efficient inference and fine-tuning through CPU-GPU heterogeneous computing. The project exposes two key capabilities: inference and fine-tuning with LLaMA-Factory integration. Inference is powered by the high-performance kt-kernel, which offers CPU-optimized kernel operations for heterogeneous LLM inference. This includes features like AMX/AVX acceleration, MoE optimization, and quantization support. For fine-tuning, KTransformers integrates with LLaMA-Factory, allowing for ultra-large MoE model fine-tuning. This integration supports multi-backend, ultra-large MoE models, and faster training speeds. The project is developed and maintained by several teams, including the MADSys Lab at Tsinghua University and community contributors. KTransformers welcomes contributions and provides support through GitHub issues and a WeChat group. To get started, users can follow the quick-start guides for both inference and fine-tuning, which include installing the necessary packages and launching the training process. KTransformers is ideal for researchers and developers working with large language models, particularly those interested in efficient inference and fine-tuning on various hardware configurations. In summary, KTransformers is a powerful tool for optimizing LLM inference and fine-tuning - experience the future of AI with KTransformers. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe