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⚡ 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.
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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
- Building a structural map of your code with Tree-sitter
- Tracking changes incrementally
- Providing precise context to your AI assistant via MCP
To use
From a technical perspective,
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.
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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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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.
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🧠 Channel: https://t.iss.one/GithubRe
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💡 elder-plinius/G0DM0D3 just hit the trending charts — here's why it matters.
🔗 https://github.com/elder-plinius/G0DM0D3
📝 LIBERATED AI CHAT
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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,
Key features include:
-
-
-
- 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
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!
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/elder-plinius/G0DM0D3
📝 LIBERATED AI CHAT
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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!
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🧠 Channel: https://t.iss.one/GithubRe
Github Top Repositories
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💡 lyogavin/airllm just hit the trending charts — here's why it matters.
🔗 https://github.com/lyogavin/airllm
📝 AirLLM 70B inference with single 4GB GPU
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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 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.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/lyogavin/airllm
📝 AirLLM 70B inference with single 4GB GPU
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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.
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🧠 Channel: https://t.iss.one/GithubRe
Github Top Repositories
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💡 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.
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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.
To get started, simply run
Technical highlights include on-device models, direct adapters for public engines, and transparent per-result scoring.
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,
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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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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.iss.one/GithubRe
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🚀 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.
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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
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!
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/codecrafters-io/build-your-own-x
📝 Master programming by recreating your favorite technologies from scratch.
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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!
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🧠 Channel: https://t.iss.one/GithubRe
Github Top Repositories
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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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