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🎯 MoonshotAI/kimi-cli landed on trending. Worth a proper look.
🔗 https://github.com/MoonshotAI/kimi-cli
📝 Kimi Code CLI is your next CLI agent.
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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
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.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/MoonshotAI/kimi-cli
📝 Kimi Code CLI is your next CLI agent.
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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.
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🧠 Channel: https://t.iss.one/GithubRe
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Github Top Repositories
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🌟 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.
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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
The
Developers can add their own languages without forking the repository by creating a
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.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 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.
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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 installcommand 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.
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🧠 Channel: https://t.iss.one/GithubRe
Github Top Repositories
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⚡ 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
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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
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
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.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/kvcache-ai/ktransformers
📝 A Flexible Framework for Experiencing Heterogeneous LLM Inference/Fine-tune Optimizations
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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.
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🧠 Channel: https://t.iss.one/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.
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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
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.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/rohitg00/ai-engineering-from-scratch
📝 Learn it. Build it. Ship it for others.
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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.
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🧠 Channel: https://t.iss.one/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.
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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,
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.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/jamiepine/voicebox
📝 The open-source AI voice studio. Clone, dictate, create.
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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.
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🧠 Channel: https://t.iss.one/GithubRe
Github Top Repositories
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🔥 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.
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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:
-
-
-
-
Technical highlights:
- Runs on
- Supports various platforms, including macOS, Linux, and Windows
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-
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.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 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.
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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:
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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-LDTechnical highlights:
- Runs on
Node ≥ 20 with ~1.5 GB of free disk space- Supports various platforms, including macOS, Linux, and Windows
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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 setupAudience:
- 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.
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🧠 Channel: https://t.iss.one/GithubRe
🌟 andrewrabert/jellium-desktop caught my eye on GitHub Trending today.
🔗 https://github.com/andrewrabert/jellium-desktop
📝 An unofficial desktop client for Jellyfin
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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
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:
The app is designed for Jellyfin users looking for a seamless desktop experience.
Jellium Desktop: stream your media, simplified.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/andrewrabert/jellium-desktop
📝 An unofficial desktop client for Jellyfin
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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.
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🧠 Channel: https://t.iss.one/GithubRe