💡 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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The Understand Anything GitHub repository is a powerful tool that helps developers navigate and understand complex codebases, knowledge bases, and documentation. Its key features include an interactive knowledge graph, business logic understanding, and analysis of knowledge bases. With guided tours, fuzzy and semantic search, and diff impact analysis, developers can easily explore and comprehend large codebases. The repository supports multiple platforms, including Claude Code, Codex, and VS Code, and is suitable for developers of all levels. To get started, simply install the plugin, analyze your codebase, and explore the interactive dashboard. As the creator says, the goal isn't a graph that wows you with how complex your codebase is — it's a graph that quietly teaches you how every piece fits together. Understand Anything is the ultimate tool for any developer looking to turn complexity into clarity - and that's a game-changer!

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🧠 Channel: https://t.me/GithubRe

🔥 rohitg00/ai-engineering-from-scratch is trending — and it deserves your attention.

🔗 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 curriculum is a comprehensive, free, and open-source resource designed to bridge the gap between AI education and professional readiness. With 435 lessons and 20 phases, it covers the full spectrum of AI engineering, from math foundations to autonomous systems, in four programming languages: Python, TypeScript, Rust, and Julia.

Key features include a linear progression from basic math to advanced AI concepts, hands-on coding in each lesson, and a focus on building reusable artifacts such as prompts, skills, agents, and MCP servers. The curriculum is designed for self-paced learning, with flexible entry points and a find-your-level quiz to help students determine their starting point.

Technical highlights include a strong emphasis on understanding AI algorithms from scratch, with implementations in raw math before using frameworks like PyTorch. The curriculum also covers production-ready skills like agent engineering, infrastructure, and ethics.

The intended audience is anyone interested in building a strong foundation in AI engineering, from beginners to experienced professionals looking to fill gaps in their knowledge. With its comprehensive scope, hands-on approach, and focus on practical skills, the AI Engineering from Scratch curriculum is an invaluable resource for anyone seeking to master the art of AI engineering.

Build it from scratch, and you'll never forget it: that's the power of AI Engineering from Scratch.

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🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: anthropics/claude-plugins-official — let's break it down.

🔗 https://github.com/anthropics/claude-plugins-official
📝 Official, Anthropic-managed directory of high quality Claude Code Plugins.
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The anthropics/claude-plugins-official GitHub repository is a curated directory of high-quality plugins for Claude Code. It features a collection of internal and third-party plugins, which can be easily installed via Claude Code's plugin system using /plugin install {plugin-name}@claude-plugins-official. The repository is structured into plugins and external_plugins directories, with each plugin following a standard structure that includes a plugin.json file for metadata. To contribute, developers can submit their plugins for review, and must adhere to quality and security standards. For those looking to get started, the repository provides a reference implementation and documentation. Whether you're a developer or a user, this repository is the go-to destination for extending Claude Code's capabilities. You can supercharge Claude Code with these plugins - install and unleash their power.

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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 the capabilities of Claude, a productivity tool. These plugins cater to various roles and industries, including sales, customer support, product management, and more.

Key features of these plugins include their ability to connect with external tools such as CRMs, project trackers, and data warehouses, and to provide domain-specific skills and workflows. The plugins are highly customizable, allowing users to tailor them to their company's specific needs and workflows.

To get started, users can install the plugins directly from Cowork or Claude Code using the following claude plugin install sales@knowledge-work-plugins command. Once installed, the plugins activate automatically, and users can invoke specific actions using slash commands.

The plugins follow a standardized structure, consisting of a manifest file, tool connections, slash commands, and skills. The skills component is particularly noteworthy, as it encodes domain expertise and best practices that Claude can draw upon automatically.

The repository is open-sourced, allowing users to contribute and customize the plugins to suit their needs. By doing so, users can create a tailored experience for their team, making Claude an even more effective tool for their workflow.

The punchy one-liner takeaway: Unlock Claude's full potential with customizable plugins that make it an expert in your company's unique workflows and tools.

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🧠 Channel: https://t.me/GithubRe

🌟 multica-ai/andrej-karpathy-skills caught my eye on GitHub Trending today.

🔗 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 multica-ai/andrej-karpathy-skills GitHub repository provides a set of guidelines to improve the behavior of large language models (LLMs) like Claude Code when it comes to coding tasks. Inspired by Andrej Karpathy's observations on the pitfalls of LLMs, these guidelines aim to address issues such as wrong assumptions, overcomplication, and lack of clarity.

The guidelines are based on four key principles: Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution. These principles encourage LLMs to state assumptions explicitly, prefer simplicity, make minimal changes, and define success criteria.

To use these guidelines, you can install them as a Claude Code plugin or add them to your project's CLAUDE.md file. The repository also includes a Cursor project rule for applying the guidelines in Cursor projects.

The target audience for these guidelines includes developers working with LLMs and those looking to improve the quality and reliability of their code. By following these principles, developers can reduce costly mistakes, improve code simplicity, and increase productivity.

In terms of technical highlights, the guidelines provide a CLAUDE.md file that directly addresses common issues with LLMs, and the Goal-Driven Execution principle allows LLMs to loop independently until verifiable success criteria are met.

One-liner takeaway: By applying the multica-ai/andrej-karpathy-skills guidelines, you can significantly improve the reliability and quality of your code generated by large language models.

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🧠 Channel: https://t.me/GithubRe

⚡ earendil-works/pi is making waves. Here's the full picture.

🔗 https://github.com/earendil-works/pi
📝 AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI libraries, Slack bot, vLLM pods
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The pi agent harness project is a self-extensible coding agent that combines a range of tools to enhance development workflows. At its core, the project includes three key packages: @earendil-works/pi-coding-agent for interactive coding, @earendil-works/pi-agent-core for agent runtime and state management, and @earendil-works/pi-ai for unified multi-provider large language model (LLM) APIs.

To get started, users can visit the project website for demos, read the documentation, or ask the agent to explain itself. The project encourages users to share their open-source coding agent sessions to improve the agents with real-world tasks and tool use.

From a technical perspective, the project is built using a range of technologies and includes features like differential rendering and a terminal UI library. The project also prioritizes security and supply-chain hardening through measures like pinning direct external dependencies and verifying pinned direct dependencies.

Audience: The project is geared towards developers and coding enthusiasts looking to enhance their workflows and explore the potential of self-extensible coding agents.

One-liner takeaway: Supercharge your coding workflow with the pi agent harness project, a cutting-edge self-extensible coding agent that's open-source and constantly evolving.

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🧠 Channel: https://t.me/GithubRe

🔍 Deep-diving into Alishahryar1/free-claude-code — fresh off the trending list.

🔗 https://github.com/Alishahryar1/free-claude-code
📝 Use claude-code for free in the terminal, VSCode extension or discord like OpenClaw (voice supported)
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The Free Claude Code repository is a drop-in proxy for Claude Code's Anthropic API calls, allowing users to choose from 17 different provider backends, including NVIDIA NIM, OpenRouter, and Google AI Studio. This proxy enables per-model routing, streaming, and local request optimizations, making it a versatile tool for developers.

The key features of this repository include:
- Seventeen provider backends to choose from, each with its own unique models and capabilities
- Per-model routing, allowing users to send different types of traffic to different providers
- Native Claude Code model picker support, making it easy to switch between models
- Optional integrations with Discord, Telegram, and VS Code, providing a range of use cases

To get started, users can follow the Quick Start guide, which includes installing the required dependencies, starting the proxy server, and configuring the proxy settings through the Admin UI.

From a technical standpoint, the repository uses

Python 3.14

and is tested with Pytest, ensuring that the code is reliable and stable. The repository also includes type checking with Ty and code formatting with Ruff, making it easy to contribute to and maintain.

The target audience for this repository includes developers who want to use Claude Code with different provider backends, as well as those who want to customize their AI workflows.

In summary, the Free Claude Code repository provides a flexible and customizable solution for working with Claude Code and Anthropic API calls, making it an ideal choice for developers who want to take control of their AI workflows. With its wide range of features and integrations, it's an essential tool for anyone looking to unlock the full potential of AI - and with this proxy, the possibilities are endless.

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🧠 Channel: https://t.me/GithubRe

🔥 colbymchenry/codegraph is trending — and it deserves your attention.

🔗 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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CodeGraph is a powerful tool that supercharges Claude Code, Cursor, Codex CLI, opencode, and Hermes Agent with semantic code intelligence. It provides a pre-indexed knowledge graph of symbol relationships, call graphs, and code structure, allowing agents to query the graph instantly instead of scanning files. Key features include smart context building, full-text search, impact analysis, and framework-aware routes. codegraph is 100% local, with no data leaving your machine, and supports 19+ languages.

To get started, simply run the installer with npx @colbymchenry/codegraph, then restart your agent and initialize your project with codegraph init -i. With CodeGraph, you can enjoy average savings of 35% cheaper, 57% fewer tokens, 46% faster, and 71% fewer tool calls. One-liner takeaway: CodeGraph is a game-changer for coding efficiency, and it's just one install away.

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🧠 Channel: https://t.me/GithubRe

⚡ multica-ai/multica is making waves. Here's the full picture.

🔗 https://github.com/multica-ai/multica
📝 The open-source managed agents platform. Turn coding agents into real teammates — assign tasks, track progress, compound skills.
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Multica is an open-source managed agents platform that turns coding agents into real teammates. You can assign tasks to agents like you'd assign to a colleague, and they'll pick up the work, write code, report blockers, and update statuses autonomously. Key features include agents as teammates, squads for grouping agents and humans, autonomous execution, autopilots for scheduling recurring work, reusable skills, unified runtimes, and multi-workspace support.

To get started, you can install Multica using a brew install command or an install script. Then, set up and start the daemon using the multica setup command. Verify your runtime, create an agent, and assign your first task.

The platform is designed for human + AI teams and supports various agent CLIs, including Claude Code, Codex, and GitHub Copilot CLI. The architecture consists of a Next.js frontend, a Go backend, and a PostgreSQL database.

Developers can contribute to the Multica codebase by following the CONTRIBUTING.md guide, which includes prerequisites, development workflow, and troubleshooting. With Multica, a small team can move like a large one - your next 10 hires won't be human.

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🧠 Channel: https://t.me/GithubRe

🔥 shiyu-coder/Kronos is trending — and it deserves your attention.

🔗 https://github.com/shiyu-coder/Kronos
📝 Kronos: A Foundation Model for the Language of Financial Markets
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Kronos is a foundation model for financial markets, specifically designed to handle the unique characteristics of financial data. It's a decoder-only model, pre-trained on data from over 45 global exchanges. The model uses a two-stage framework, consisting of a specialized tokenizer that quantizes continuous, multi-dimensional K-line data into hierarchical discrete tokens, and a large, autoregressive Transformer that is pre-trained on these tokens.

The model is open-source and readily accessible from the Hugging Face Hub. A live demo is available to visualize Kronos's forecasting results.

To get started, simply install the dependencies, load a pre-trained model and its corresponding tokenizer, and instantiate the predictor. The predictor can be used to generate forecasts for given input data.

Technical highlights include a novel two-stage framework, a specialized tokenizer, and a large, autoregressive Transformer. The model is designed for quantitative tasks and can be fine-tuned for specific use cases.

Kronos is suitable for data scientists and quantitative researchers who work with financial data.

In a nutshell, Kronos is a powerful tool for financial forecasting, and its open-source nature makes it accessible to everyone: forecast your financial future with Kronos today!

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🧠 Channel: https://t.me/GithubRe