Github Top Repositories

🚀 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 is a treasure trove of resources for anyone looking to apply machine learning to trading. The repo is based on a book that aims to provide a practical and comprehensive guide to using machine learning in algorithmic trading. With over 150 notebooks, the repository offers a wealth of examples and code to help readers implement the concepts and techniques discussed in the book. The repository covers a wide range of topics, including data sourcing, financial feature engineering, and portfolio management. It also explores the use of supervised and unsupervised machine learning algorithms for trading, as well as deep learning models like CNN and RNN. The notebooks provide numerous examples of how to work with and extract signals from market, fundamental, and alternative text and image data. To get the most out of the repository, readers are encouraged to review the notebooks while reading the book. The notebooks are usually in an executed state and often contain additional information not included in the book due to space constraints. The repository also includes installation instructions and configuration files for setting up various conda environments and installing the packages used in the notebooks. The target audience for this repository includes traders, data scientists, and developers interested in applying machine learning to trading. Whether you're a beginner or an experienced practitioner, the repository has something to offer. So why not join the ML4T Community and start exploring the world of machine learning for trading? In short, this repository is a must-visit for anyone looking to leverage machine learning for trading strategies - learn by doing, and trade with code. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔥 OpenBMB/VoxCPM is trending — and it deserves your attention. 🔗 https://github.com/OpenBMB/VoxCPM 📝 VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning ────────────────────────────── VoxCPM2 is a tokenizer-free Text-to-Speech system that generates continuous speech representations via an end-to-end diffusion autoregressive architecture. It supports 30 languages, voice design, controllable voice cloning, and 48kHz studio-quality audio output. Key features include: - Multilingual support: Input text in any of the 30 supported languages and synthesize directly, no language tag needed - Voice design: Create a brand-new voice from a natural-language description alone - Controllable cloning: Clone any voice from a short reference clip, with optional style guidance - 48kHz high-quality audio: Directly outputs 48kHz studio-quality audio via AudioVAE V2's asymmetric encode/decode design To get started, you can install VoxCPM using pip install voxcpm and use the Python API to generate speech. There's also a CLI for voice design, controllable voice cloning, and ultimate cloning. The project is fully open-source & commercial-ready under the Apache-2.0 license. For high-throughput serving, consider using Nano-vLLM-VoxCPM or vLLM-Omni for production multi-tenant deployments. In short, VoxCPM2 is a game-changer for multilingual speech synthesis, offering unparalleled naturalness and expressiveness - give it a try and hear the difference for yourself! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 reconurge/flowsint caught my eye on GitHub Trending today. 🔗 https://github.com/reconurge/flowsint 📝 A modern platform for visual, flexible, and extensible graph-based investigations. For cybersecurity analysts and investigators. ────────────────────────────── Introduction to Flowsint: Flowsint is an open-source OSINT graph exploration tool designed for ethical investigation, transparency, and verification. The tool allows users to explore relationships between entities through a visual graph interface and automated enrichers. Key Features: - Graph-based investigation - Visual graph interface - Automated enrichers for domains, IPs, social media, and more - Support for multiple data types, including domains, IPs, ASNs, and more Usage: To get started with Flowsint, users need to install the required prerequisites, including Docker and Make. The tool can be installed by running the command:

git clone https://github.com/reconurge/flowsint.git
cd flowsint
make prod

Then, users can access the tool at http://localhost:5173/register and create an account. Technical Highlights: - Modular structure with separate modules for core utilities, enrichers, API, and frontend application - Support for multiple databases, including PostgreSQL and Neo4j - Authentication and authorization mechanisms - Real-time event streaming Audience: Flowsint is designed for cybersecurity researchers and analysts, journalists and OSINT investigators, law enforcement or fraud investigation teams, and organizations conducting internal threat intelligence or digital risk analysis. Remember: Flowsint must be used strictly for lawful, ethical investigation and research purposes. Any misuse of this software is strictly prohibited. Here's the punchy one-liner takeaway: Flowsint is a game-changing OSINT tool that helps investigators uncover hidden relationships and stay one step ahead of threats. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

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🔍 Deep-diving into nesquena/hermes-webui — fresh off the trending list. 🔗 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 interface for the Hermes Agent, a sophisticated autonomous agent that lives on your server. This web UI provides a convenient way to access the agent's features, including chat, sessions, workspace file browsing, and more, all from a web browser. Key features include a three-panel layout, model and profile controls, a circular context ring for token usage, and a Hermes Control Center for settings and session tools. The web UI also supports light mode, customizable settings, and password configuration. From a technical standpoint, Hermes Web UI is built using Python and vanilla JavaScript, with no build step, framework, or bundler required. The application is designed to be self-hosted, with support for SSH tunneling for secure access. The target audience for Hermes Web UI appears to be developers and power users who want a convenient and intuitive way to interact with the Hermes Agent from a web browser. With its robust feature set and flexible configuration options, Hermes Web UI is an attractive choice for anyone looking to get the most out of their Hermes Agent setup. In short, Hermes Web UI is a powerful tool that puts the full capabilities of the Hermes Agent at your fingertips, from anywhere, at any time - revolutionizing the way you interact with AI. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

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🎯 D4Vinci/Scrapling landed on trending. Worth a proper look. 🔗 https://github.com/D4Vinci/Scrapling 📝 🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl! ────────────────────────────── Scrapling is an adaptive web scraping framework that streamlines the process of extracting data from websites. Its key features include an intelligent parser that learns from website changes, fetchers that bypass anti-bot systems, and a spider framework for concurrent, multi-session crawls. Key highlights of Scrapling include: - Selection methods for precise data extraction - Fetchers for bypassing anti-bot systems like Cloudflare Turnstile - Spiders for scalable, concurrent crawls - Proxy Rotation for automatic rotation of proxies Technical highlights include: - Blazing fast crawls with real-time stats and streaming - StealthyFetcher for fetching websites under the radar - DynamicFetcher for handling dynamic content Usage examples include:

from scrapling.fetchers import Fetcher, AsyncFetcher, StealthyFetcher, DynamicFetcher
StealthyFetcher.adaptive = True
p = StealthyFetcher.fetch('https://example.com', headless=True, network_idle=True)  
products = p.css('.product', auto_save=True)                                       
products = p.css('.product', adaptive=True)                                         

Audience: Web scrapers, data extraction professionals, and anyone looking to extract data from websites. Scrapling handles everything from single requests to full-scale crawls, making it an essential tool for anyone looking to extract data from the web. Scrapling in a nutshell: Scrape smarter, not harder. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: affaan-m/ECC — let's break it down. 🔗 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. ────────────────────────────── The ECC project is a harness-native operator system designed for agentic work, built from real-world multi-harness engineering workflows. It's not just about configurations, but a complete system that includes skills, instincts, memory optimization, continuous learning, security scanning, and research-first development. With 182K+ stars, 28K+ forks, and 170+ contributors, ECC supports 12+ language ecosystems and enables cross-harness agent workflows. The system is production-ready and works across various AI agent harnesses, including Codex, Claude Code, Cursor, OpenCode, Gemini, Zed, and GitHub Copilot. ECC provides a range of features, including token optimization, memory persistence, continuous learning, and security scanning. To get started, users can follow the Shorthand Guide, Longform Guide, or Security Guide, which cover setup, foundations, philosophy, and security best practices. The project also offers a dashboard GUI and a range of operator workflows, including brand-voice, social-graph-ranker, and customer-billing-ops. The ECC community is active, with discussions, sponsorship, and pro subscriptions available. The project is MIT-licensed and will remain free and open-source forever. In short, ECC is a powerful tool for agentic work that's constantly evolving to meet the needs of its users. Join the community and start building with ECC today - the future of agentic work is here! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 microsoft/markitdown just hit the trending charts — here's why it matters. 🔗 https://github.com/microsoft/markitdown 📝 Python tool for converting files and office documents to Markdown. ────────────────────────────── MarkItDown is a Python utility for converting various files to Markdown, ideal for text analysis pipelines and large language models. It supports a wide range of file formats, including PDF, PowerPoint, Word, Excel, Images, Audio, and more. Key features include: - File format conversion to Markdown - Preservation of important document structure and content - Support for large language models and text analysis tools - Optional dependencies for specific file formats - Plugin support for extending functionality Usage is straightforward, with both command-line and Python API interfaces available. For example, you can use the command-line interface like this: markitdown path-to-file.pdf > document.md Technical highlights include the use of Azure Content Understanding for higher-quality conversion and structured field extraction, as well as support for Azure Document Intelligence for document conversion. The target audience for MarkItDown includes developers and data scientists working with text analysis pipelines and large language models. One-liner takeaway: MarkItDown simplifies file format conversion to Markdown, making it easier to work with text analysis tools and large language models. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 chopratejas/headroom just hit the trending charts — here's why it matters. 🔗 https://github.com/chopratejas/headroom 📝 Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server. ────────────────────────────── Headroom is a context compression layer for AI agents that reduces the number of tokens by 60-95%, making it a game-changer for those who run AI coding agents daily. This library, proxy, and MCP server offers local-first and reversible compression, ensuring that originals are never deleted and can be retrieved on demand. With headroom, you can compress everything your AI agent reads, including tool outputs, logs, RAG chunks, files, and conversation history, without changing your code. The technical highlights of headroom include its ability to work with multiple agents, providing shared memory and cross-agent compatibility. It also features a ContentRouter that detects content type and selects the right compressor, as well as SmartCrusher, CodeCompressor, and Kompress-base for compressing JSON, AST, and prose. To get started with headroom, you can install it using pip install "headroom-ai[all]" or npm install headroom-ai, then pick your mode by wrapping an agent, using the proxy, or importing the library. The target audience for headroom includes developers who work with AI coding agents and want to reduce costs without compromising performance. One-liner takeaway: With headroom, you can significantly reduce the number of tokens your AI agent processes, resulting in massive savings without sacrificing accuracy, and that's a total game-changer. ────────────────────────────── 🧠 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 comprehensive collection of guides for building various technologies from scratch. The purpose of this repository is to provide a hands-on learning experience for developers, helping them understand complex systems by recreating them. Key features include step-by-step tutorials for building a wide range of technologies, such as 3D renderers, AI models, augmented reality systems, blockchains, bots, command-line tools, databases, and more. The repository covers various programming languages, including C, C++, Java, Python, JavaScript, and many others. To get started, users can browse the repository's table of contents and choose a technology to build. Each guide provides a detailed, step-by-step approach to building the technology, often accompanied by code examples and explanations. From a technical standpoint, the guides cover various aspects of building these technologies, such as architecture, algorithms, data structures, and implementation details. The repository is suitable for developers of all levels, from beginners looking to learn new concepts to experienced developers seeking to deepen their understanding of complex systems. In conclusion, the codecrafters-io/build-your-own-x repository is an invaluable resource for anyone looking to learn by doing. By building technologies from scratch, developers can gain a deeper understanding of how they work and develop practical skills to apply in their own projects. So, get building and take your skills to the next level! ────────────────────────────── 🧠 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 ────────────────────────────── fff is a file search toolkit designed for both humans and AI agents, offering really fast search capabilities. Its key features include typo-resistant path and content search, frecency-ranked file access, a background watcher, and a lightweight in-memory content index. The toolkit is way faster than traditional CLIs like ripgrep and fzf, especially in long-running processes that search more than once. Initially started as a Neovim plugin, fff has evolved into a library that provides accurate and fast file search capabilities for various applications, including AI harnesses and code editors. fff offers several components, including an MCP server and a Pi agent extension, each with its own set of features and installation instructions. The MCP server works with various AI clients, reducing the number of grep roundtrips and providing faster answers. The Pi extension, on the other hand, swaps the native tools for fff implementations and feeds the interactive editor's autocomplete from the frecency-ranked index. For Neovim users, fff.nvim provides a public API with functions like find_files, live_grep, and scan_files, allowing for programmatic search and integration with other plugins. The plugin also offers customizable configuration options, commands, and keymaps. Whether you're a developer, AI researcher, or simply a power user, fff is an incredibly powerful tool that can supercharge your file search capabilities. With its flexibility, customizability, and blazing-fast performance, fff is an essential addition to any workflow: search smarter, not harder, with fff. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe