💡 Lum1104/Understand-Anything just hit the trending charts — here's why it matters.

🔗 https://github.com/Lum1104/Understand-Anything
📝 Graphs that teach > graphs that impress. Turn any code into an interactive knowledge graph you can explore, search, and ask questions about. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more.
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Understand Anything is an innovative tool that turns any codebase, knowledge base, or documentation into an interactive knowledge graph. This graph can be explored, searched, and questioned, making it easier to understand complex systems. The tool works with various platforms, including Claude Code, Codex, Cursor, Copilot, and Gemini CLI.

Key features include:
- Interactive knowledge graph: visualize your codebase as a graph, with every file, function, and class as a node that can be clicked, searched, and explored.
- Guided tours: auto-generated walkthroughs of the architecture, ordered by dependency.
- Fuzzy and semantic search: find anything by name or meaning.
- Diff impact analysis: see which parts of the system your changes affect before you commit.

Technical highlights:
- Multi-agent pipeline: analyzes your project and builds a knowledge graph.
- Tree-sitter + LLM hybrid: combines static analysis and LLMs for parsing and semantic analysis.

Audience: developers, teams, and organizations looking to improve their understanding of complex codebases and knowledge bases.

To get started, simply install the plugin and analyze your codebase. Then, explore the dashboard and start gaining insights into your system.

Understand Anything is a game-changer for anyone looking to simplify complex systems and improve their productivity. Try it out and start understanding anything!

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🧠 Channel: https://t.me/GithubRe

🎯 anthropics/knowledge-work-plugins landed on trending. Worth a proper look.

🔗 https://github.com/anthropics/knowledge-work-plugins
📝 Open source repository of plugins primarily intended for knowledge workers to use in Claude Cowork
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The anthropics/knowledge-work-plugins GitHub repository offers a collection of plugins designed to enhance Claude's capabilities, making it a specialist for various roles, teams, and companies. These plugins are compatible with Claude Cowork and Claude Code, allowing users to customize how work is done, which tools to use, and how to handle critical workflows.

The repository includes 11 open-sourced plugins, each catering to a specific job function, such as productivity, sales, customer-support, and more. These plugins bundle skills, connectors, slash commands, and sub-agents, providing a strong starting point for Claude to assist users in their respective roles.

To get started, users can install plugins directly from Claude Cowork or Claude Code using the following command:

claude plugin install sales@knowledge-work-plugins

Each plugin follows a standard structure, consisting of a plugin.json manifest, .mcp.json tool connections, commands directory, and skills directory. The plugins are file-based, using markdown and JSON, and require no coding, infrastructure, or build steps.

The true power of these plugins lies in their customizability. Users can modify the plugins to fit their company's specific tools, terminology, and processes, making Claude an integral part of their team. By contributing to the repository, users can also share their custom plugins and help others benefit from their expertise.

In summary, the anthropics/knowledge-work-plugins repository offers a powerful way to enhance Claude's capabilities, making it an indispensable tool for teams and companies - and with customization, Claude becomes a specialist that understands your world.

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🧠 Channel: https://t.me/GithubRe

💡 rohitg00/ai-engineering-from-scratch just hit the trending charts — here's why it matters.

🔗 https://github.com/rohitg00/ai-engineering-from-scratch
📝 Learn it. Build it. Ship it for others.
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The GitHub repository rohitg00/ai-engineering-from-scratch is a comprehensive, open-source curriculum designed to teach AI engineering from scratch. The purpose of this repository is to provide a structured approach to learning AI, covering 20 phases and 435 lessons that span ~320 hours of content.

Key features of this curriculum include its focus on building AI systems from raw math, using languages like Python, TypeScript, Rust, and Julia. Each lesson follows a consistent structure, starting with the problem, deriving the math, writing the code, running the test, and keeping the resulting artifact.

The curriculum is designed for individuals who want to understand how AI actually works, not just call APIs. It's suitable for those who can write code in any language, with some familiarity with Python being helpful.

To get started, users can choose from three options: reading completed lessons on the website, cloning and running the repository, or finding their level using a placement quiz.

Every lesson ships with a reusable tool, such as prompts, skills, agents, or MCP servers, which can be installed or pasted into daily workflows.

The takeaway: Master AI engineering by building it from scratch, and become a part of a community that's shaping the future of artificial intelligence.

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🧠 Channel: https://t.me/GithubRe

🚀 Meet affaan-m/ECC: a gem from today's GitHub trending list.

🔗 https://github.com/affaan-m/ECC
📝 The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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The ECC (Efficient Computing Companion) repository is a comprehensive system for agentic work, built on top of various AI agent harnesses like Claude Code, Codex, and OpenCode. With over 182K stars and 28K forks, it's a widely-recognized project that's been developed over 10 months of intensive daily use. ECC provides a range of features, including skills, instincts, memory optimization, continuous learning, and security scanning. The system is production-ready and works across multiple harnesses, making it a versatile tool for developers.

The project is constantly evolving, with new features and updates being added regularly. The most recent version, v2.0.0-rc.1, includes a public surface refresh, operator workflows, and an ECC 2.0 alpha. The system has a large community of contributors and users, with over 170 contributors and 12 language ecosystems supported.

Whether you're a developer, researcher, or simply interested in AI, ECC is definitely worth checking out. With its comprehensive guides and extensive documentation, you'll be able to get started quickly and easily. So why wait? Dive into the world of ECC today and discover the power of efficient computing companions.
The ECC repository is a one-stop-shop for all your agentic work needs - it's the ultimate tool for anyone looking to streamline their workflow and boost productivity.

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🧠 Channel: https://t.me/GithubRe

🚀 Meet mukul975/Anthropic-Cybersecurity-Skills: a gem from today's GitHub trending list.

🔗 https://github.com/mukul975/Anthropic-Cybersecurity-Skills
📝 754 structured cybersecurity skills for AI agents · Mapped to 5 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND & NIST AI RMF · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 26 security domains · Apache 2.0
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The Anthropic Cybersecurity Skills repository provides a comprehensive library of 754 production-grade cybersecurity skills across 26 security domains. This open-source project aims to equip AI agents with the skills of a senior security analyst, enabling them to perform tasks such as threat hunting, incident response, and vulnerability management.

The repository includes five framework mappings, including MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, and NIST AI RMF, making it a unique and valuable resource for the cybersecurity community.

To get started, users can clone the repository or use the npx skills add mukul975/Anthropic-Cybersecurity-Skills command. The skills are designed to be used with various AI platforms, including Claude Code, GitHub Copilot, and OpenAI Codex CLI.

The project is not affiliated with Anthropic PBC and is an independent, community-created initiative. The repository is licensed under Apache-2.0 and is open to contributions.

Overall, the Anthropic Cybersecurity Skills repository has the potential to significantly enhance the capabilities of AI agents in the cybersecurity domain, and its open-source nature makes it an exciting development for the community.

Give your AI agent the security skills of a senior analyst — and watch it become a game-changer in cybersecurity.

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🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: colbymchenry/codegraph — let's break it down.

🔗 https://github.com/colbymchenry/codegraph
📝 Pre-indexed code knowledge graph for Claude Code, Codex, Cursor, OpenCode, and Hermes Agent — fewer tokens, fewer tool calls, 100% local
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Introducing CodeGraph, a revolutionary tool that supercharges your development workflow with semantic code intelligence. It integrates seamlessly with popular agents like Claude Code, Cursor, Codex CLI, opencode, and Hermes Agent, providing a pre-indexed knowledge graph that enables instant code exploration and search.

To get started, simply run the installer with npx @colbymchenry/codegraph or use the provided scripts for macOS, Linux, or Windows. Then, initialize your project with codegraph init -i to build the knowledge graph index.

Key Features include smart context building, full-text search, impact analysis, and framework-aware routes. CodeGraph supports 19+ languages and is 100% local, with no data leaving your machine.

The benefits are clear: 35% cheaper, 57% fewer tokens, 46% faster, and 71% fewer tool calls. With CodeGraph, you can supercharge your coding experience. Try it today and discover a whole new world of coding efficiency - CodeGraph: because coding just got a whole lot smarter!

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🧠 Channel: https://t.me/GithubRe

💡 manaflow-ai/cmux just hit the trending charts — here's why it matters.

🔗 https://github.com/manaflow-ai/cmux
📝 Ghostty-based macOS terminal with vertical tabs and notifications for AI coding agents
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cmux is a macOS terminal designed for AI coding agents, built on top of Ghostty and offering features like vertical tabs, notifications, and an in-app browser.

Key features include:
- Notification rings and a notification panel for managing agent notifications
- An with a scriptable API
- Vertical and horizontal tabs for organizing workspaces
- SSH support for remote machine workspaces
- Claude Code Teams integration
- Custom commands and scriptable functionality via the CLI and socket API

To get started, you can download the DMG or install via Homebrew.

Audience: Developers working with AI coding agents, particularly those using Claude Code and other similar tools.

Technical highlights: Native macOS app built with Swift and AppKit, GPU-accelerated using libghostty.

Give a million developers composable primitives like cmux and they'll build the most efficient workflows - faster than any product team.

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🧠 Channel: https://t.me/GithubRe

🔥 multica-ai/andrej-karpathy-skills is trending — and it deserves your attention.

🔗 https://github.com/multica-ai/andrej-karpathy-skills
📝 A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
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The Andrej Karpathy Skills GitHub repository provides guidelines for improving Claude Code behavior, inspired by Andrej Karpathy's observations on LLM coding pitfalls. The project addresses issues like wrong assumptions, overcomplication, and lack of clarity in code.

Key features include four principles:
1. Think Before Coding - explicit reasoning and questioning assumptions,
2. Simplicity First - minimal code and no overengineering,
3. Surgical Changes - touching only necessary code,
4. Goal-Driven Execution - defining success criteria and looping until verified.

These principles are designed to be used by developers working with LLMs, particularly those using Claude Code. The guidelines can be installed as a plugin or added to a project's CLAUDE.md file.

Technical highlights include the use of success criteria to transform imperative tasks into verifiable goals and the emphasis on simplicity and caution over speed.

To get started, users can install the guidelines as a Claude Code plugin or add them to their project's CLAUDE.md file.

The target audience is developers and teams working with LLMs and Claude Code, looking to improve their coding workflow and reduce mistakes.

In summary: Follow these guidelines to make your LLMs loop until they get it right - success criteria and verification are key to efficient coding with LLMs.

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🧠 Channel: https://t.me/GithubRe