Github Top Repositories

🔥 nesquena/hermes-webui is trending — and it deserves your attention. 🔗 https://github.com/nesquena/hermes-webui 📝 Hermes WebUI: The best way to use Hermes Agent from the web or from your phone! ────────────────────────────── Hermes Web UI is a lightweight, dark-themed web app that provides a convenient interface for interacting with the Hermes Agent, a sophisticated autonomous agent that lives on your server. The web UI offers full parity with the CLI experience, allowing you to access all the features of the Hermes Agent from a browser. The web UI features a three-panel layout, with a left sidebar for sessions and navigation, a center panel for chat, and a right panel for workspace file browsing. It also includes a composer footer with model, profile, and workspace controls, as well as a circular context ring that shows token usage at a glance. The web UI is built using Python and vanilla JavaScript, with no build step, framework, or bundler required. It provides a secure and convenient way to access your Hermes Agent from anywhere, using a single command to start and a single command to SSH tunnel for access. Key features include chat and agent functionality, session management, workspace file browsing, and security features like password configuration. The web UI also supports multiple messaging platforms, including Telegram, Discord, and Slack. The target audience for the Hermes Web UI includes developers, researchers, and anyone who wants to interact with the Hermes Agent from a web interface. Overall, the Hermes Web UI provides a convenient and secure way to interact with the Hermes Agent, making it an excellent tool for anyone who wants to harness the power of autonomous agents. Takeaway: The Hermes Web UI is a game-changer for anyone who wants to interact with autonomous agents from a web interface, offering a secure, convenient, and feature-rich experience. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ microsoft/markitdown is making waves. Here's the full picture. 🔗 https://github.com/microsoft/markitdown 📝 Python tool for converting files and office documents to Markdown. ────────────────────────────── Introducing MarkItDown, a lightweight Python utility for converting various file formats to Markdown, designed for use with Large Language Models (LLMs) and text analysis pipelines. Key features include support for multiple file formats, such as PDF, PowerPoint, Word, Excel, images, audio, HTML, and more. To install MarkItDown, use pip: pip install 'markitdown[all]'. For usage, you can use the command-line interface: markitdown path-to-file.pdf > document.md or use the Python API:

from markitdown import MarkItDown
md = MarkItDown()
result = md.convert("test.xlsx")
print(result.text_content)

Technical highlights include optional dependencies for specific file formats, plugins for additional functionality, and support for Azure Content Understanding and Document Intelligence. The library is ideal for developers and data scientists working with LLMs and text analysis pipelines. In summary, MarkItDown is a powerful and flexible tool for converting files to Markdown, and its ease of use and customization options make it a great choice for anyone working with text data - give MarkItDown a try and start converting your files to Markdown today! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

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🔥 codecrafters-io/build-your-own-x is trending — and it deserves your attention. 🔗 https://github.com/codecrafters-io/build-your-own-x 📝 Master programming by recreating your favorite technologies from scratch. ────────────────────────────── The codecrafters-io/build-your-own-x GitHub repository is a collection of step-by-step guides for building various technologies from scratch. It's based on the idea that what you cannot create, you do not understand, a quote from Richard Feynman. The repository covers a wide range of topics, including 3D rendering, AI models, blockchains, bots, databases, and more. These guides are written in various programming languages, such as C++, Java, Python, and JavaScript, making it accessible to developers with different skill sets. The repository is perfect for junior developers looking to improve their skills, students seeking to learn by doing, and experienced developers who want to explore new areas of interest. Some technical highlights include building a 3D renderer using C++ and JavaScript, creating an AI model with Python, and developing a blockchain using JavaScript and Rust. Overall, this repository provides a unique opportunity for developers to learn by building real-world projects. So, get ready to code your way to a deeper understanding of various technologies - build something from scratch and you'll never forget how it works. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 dmtrKovalenko/fff landed on trending. Worth a proper look. 🔗 https://github.com/dmtrKovalenko/fff 📝 The fastest and the most accurate file search toolkit for AI agents, Neovim, Rust, C, and NodeJS ────────────────────────────── The dmtrKovalenko/fff GitHub repository offers a blazingly fast file search toolkit designed for both humans and AI agents. This toolkit boasts a range of key features, including typo-resistant path and content search, frecency-ranked file access, a background watcher, and a lightweight in-memory content index. To get started with fff, users can choose from various installation methods, including a one-line install for Linux/macOS and Windows. The repository provides detailed instructions for each installation method, ensuring a seamless setup process. From a technical standpoint, fff is built with performance in mind. It outperforms traditional CLIs like ripgrep and fzf in long-running processes that involve multiple searches. The toolkit also includes a range of technical highlights, such as smart-case search with auto-fuzzy fallback and git-aware annotations. The fff toolkit is designed for a broad audience, including developers, AI researchers, and anyone who needs fast and efficient file search capabilities. Whether you're working with large codebases or simply need to find files quickly, fff has the tools and features to meet your needs. In short, fff is an ultra-fast file search toolkit that's a game-changer for anyone who needs to find files quickly and efficiently - and that's a pretty sweet deal. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🚀 Meet stefan-jansen/machine-learning-for-trading: a gem from today's GitHub trending list. 🔗 https://github.com/stefan-jansen/machine-learning-for-trading 📝 Code for Machine Learning for Algorithmic Trading, 2nd edition. ────────────────────────────── The stefan-jansen/machine-learning-for-trading GitHub repository accompanies the book "Machine Learning for Algorithmic Trading", a comprehensive guide to applying machine learning in trading. The repo contains over 150 notebooks that demonstrate how to extract signals from various data sources, train and tune models, and design, backtest, and evaluate trading strategies. The repository covers key aspects of machine learning for trading, including data sourcing, financial feature engineering, and portfolio management. It also explores the use of deep learning models, such as CNN and RNN, with market and alternative data. To get started, readers can review the notebooks, which provide numerous examples of how to work with and extract signals from market, fundamental, and alternative text and image data. The notebooks also demonstrate how to train and tune models that predict returns for different asset classes and investment horizons. The target audience for this repository includes traders, data scientists, and finance professionals interested in leveraging machine learning for trading strategies. The repository is a valuable resource for anyone looking to learn about machine learning for trading, with its comprehensive coverage of key concepts, algorithms, and use cases. In summary, the stefan-jansen/machine-learning-for-trading repository is a must-visit destination for anyone interested in machine learning for trading, offering a wealth of information, examples, and resources to help you get started. Machine learning can be your new trading edge! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 FareedKhan-dev/train-llm-from-scratch landed on trending. Worth a proper look. 🔗 https://github.com/FareedKhan-dev/train-llm-from-scratch 📝 A straightforward method for training your LLM, from downloading data to generating text. ────────────────────────────── The Github repository "FareedKhan-dev/train-llm-from-scratch" is a project that allows users to train their own language model from scratch using a single GPU. The project is based on the paper "Attention is All You Need" and uses PyTorch to implement a transformer model. The repository provides a README file with detailed instructions on how to use the project, including how to download and preprocess the training data, and how to train the model. The project uses the Pile dataset, which is a large-scale dataset for training language models, and provides a script to download and preprocess the data. The code is organized into several directories, including src/, config/, data_loader/, and scripts/, each containing different components of the project. The project is suitable for users who have a basic understanding of object-oriented programming, neural networks, and PyTorch. The repository provides a comparison of different GPUs and their capabilities for training language models, allowing users to choose the best option for their needs. To get started, users can clone the repository, install the required dependencies, and modify the transformer architecture and training configurations as needed. The project provides several scripts to download and preprocess the data, train the model, and generate text using the trained model. One-liner takeaway: Train your own language model from scratch with this open-source project and unlock the power of AI for your specific needs. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ OpenBMB/VoxCPM is making waves. Here's the full picture. 🔗 https://github.com/OpenBMB/VoxCPM 📝 VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning ────────────────────────────── VoxCPM2 is a cutting-edge, tokenizer-free Text-to-Speech system that generates highly natural and expressive speech. Its key features include 30-language multilingual support, voice design, controllable voice cloning, and 48kHz high-quality audio output. Users can utilize VoxCPM2 through a Python API, CLI, or a web demo. The model is fully open-source and commercial-ready, with a community-driven ecosystem. For high-throughput serving, Nano-vLLM-VoxCPM and vLLM-Omni provide optimized solutions. With VoxCPM2, the possibilities for multilingual speech synthesis are endless: design your own voice, clone any voice, and stream audio in real-time. The future of speech synthesis is here, and it's powered by VoxCPM2 - revolutionizing voice synthesis, one voice at a time. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 can1357/oh-my-pi caught my eye on GitHub Trending today. 🔗 https://github.com/can1357/oh-my-pi 📝 ⌥ AI Coding agent for the terminal — hash-anchored edits, optimized tool harness, LSP, Python, browser, subagents, and more ────────────────────────────── The Oh My Pi project is a coding agent designed to work seamlessly with your IDE, providing a wide range of tools and features to enhance your development experience. With 40+ providers, 32 built-in tools, and 13 LSP operations, this agent is capable of handling various tasks, from code execution and debugging to searching and reading files. To get started, you can install Oh My Pi using a simple command: curl -fsSL https://omp.sh/install | sh on macOS and Linux, or bun install -g @oh-my-pi/pi-coding-agent with Bun. Some of the key features of Oh My Pi include its ability to drive a real debugger, perform time-traveling stream rules, and provide first-class subagents for splitting jobs across workers. It also supports reading PDFs on arXiv, unapologetically native performance even on Windows, and code review with priorities and a verdict. The agent is designed to be easy to use and integrate with your existing workflow, with features like hashline editing, GitHub support, and hindsight memory curation. It's also editor-drivable, allowing you to run it inside your favorite editor, and inherits configurations from other tools. Overall, Oh My Pi is a powerful coding agent that can help streamline your development process and improve your productivity. With its wide range of features and ease of use, it's an excellent tool for any developer looking to take their coding to the next level. In short, Oh My Pi is the ultimate coding sidekick that will make you wonder how you ever coded without it. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔥 godotengine/godot is trending — and it deserves your attention. 🔗 https://github.com/godotengine/godot 📝 Godot Engine – Multi-platform 2D and 3D game engine ────────────────────────────── The Godot Engine is a feature-packed, cross-platform game engine that allows you to create 2D and 3D games from a unified interface. It provides a comprehensive set of common tools so you can focus on making games without reinventing the wheel. With Godot, you can export your games with one click to various platforms, including desktop, mobile, web-based, and consoles. As a free, open source, and community-driven engine, Godot is completely independent, with no strings attached, no royalties, and nothing to hold you back. The engine is supported by the Godot Foundation, a not-for-profit organization. To get started, you can download the official binaries from the Godot website or compile the engine from source. The community is active, with various channels, including the Godot Contributors Chat, where you can connect with core engine developers. Godot is ideal for game developers, indie game creators, and anyone looking to create interactive content. With its extensive documentation, demos, and community resources, you'll find everything you need to create amazing games. In short, Godot Engine is the perfect choice for anyone looking to create stunning games without breaking the bank - it's free, powerful, and yours to shape. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ revfactory/harness is making waves. Here's the full picture. 🔗 https://github.com/revfactory/harness 📝 A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use. ────────────────────────────── Harness is a team-architecture factory for Claude Code, designed to simplify complex tasks by decomposing them into coordinated teams of specialized agents. With harness, you can generate agent teams and skills tailored to your domain by simply saying "build a harness for this project". Key features include agent team design with six pre-defined architectural patterns, skill generation with progressive disclosure, orchestration for inter-agent data passing and error handling, and validation for trigger verification and testing. To get started, you can install harness via the marketplace or by direct installation as a global skill. The plugin structure includes a plugin.json manifest, SKILL.md definition, and references for agent design patterns, orchestrator templates, and skill writing guides. Harness is part of the Claude Code ecosystem, sitting at the L3 Meta-Factory layer, and can be used in conjunction with other plugins like Archon for deterministic runtime configurations or meta-harness for Codex runtime. With harness, you can create custom agent teams for various domains, such as deep research, website development, or webtoon production. The output includes generated agent definition files and skills, which can be integrated and orchestrated for effective task management. One-liner takeaway: Harness simplifies complex tasks by generating custom agent teams and skills, making it a powerful tool for Claude Code users to streamline their workflow and improve productivity. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔍 Deep-diving into TauricResearch/TradingAgents — fresh off the trending list. 🔗 https://github.com/TauricResearch/TradingAgents 📝 TradingAgents: Multi-Agents LLM Financial Trading Framework ────────────────────────────── The TradingAgents GitHub repository offers a multi-agent trading framework for simulating real-world trading firms. This framework utilizes LangGraph and supports various LLM providers, including OpenAI, Google, Anthropic, and more. Key features include: - A modular design for flexibility and customization - Support for multiple LLM providers and models - Distributed debate among agents for informed trading decisions - Integration with various data sources for market insights To use TradingAgents, simply clone the repository, install the required dependencies, and launch the interactive CLI. The framework is designed for research purposes and provides a comprehensive platform for testing and evaluating trading strategies. It's perfect for data scientists, researchers, and traders looking to leverage AI in their trading decisions. Technical highlights include: - Multi-provider LLM support - Customizable configuration options - Integration with Docker for easy deployment In summary, TradingAgents is a powerful tool for simulating and optimizing trading strategies using AI and machine learning. With its modular design, support for multiple LLM providers, and customizable configuration options, it's an ideal choice for anyone looking to revolutionize their trading approach. One-liner takeaway: Unlock the full potential of AI-powered trading with TradingAgents. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe