🔍 Deep-diving into HKUDS/Vibe-Trading — fresh off the trending list.

🔗 https://github.com/HKUDS/Vibe-Trading
📝 "Vibe-Trading: Your Personal Trading Agent"
──────────────────────────────

Vibe-Trading is a personal trading agent that can be empowered with comprehensive trading capabilities using just one command. The platform is built using Python 3.11+, FastAPI for the backend, and React 19 for the frontend.

Key features include a Shadow Account for rule extraction and code generation, support for multiple message adapters, and a Web UI for easy management. The platform also has a strong focus on security, with features like OAuth and API keys for secure authentication.

To get started, users can simply run pip install vibe-trading-ai and follow the Quick Start guide. The platform is suitable for both beginners and experienced traders, with a wide range of features and tools available.

One of the most interesting aspects of Vibe-Trading is its ability to attach the same agent session runtime to 16 built-in message adapters, allowing for seamless communication and integration with various platforms.

Vibe-Trading is an open-source project, with a community-driven approach to development and a strong focus on collaboration.

Overall, Vibe-Trading is a powerful and flexible trading platform that can help users take their trading to the next level.

With Vibe-Trading, you can trade smarter, not harder!

──────────────────────────────
🧠 Channel: https://xn--r1a.website/GithubRe

🔍 Deep-diving into hasaneyldrm/exercises-dataset — fresh off the trending list.

🔗 https://github.com/hasaneyldrm/exercises-dataset
📝 A comprehensive dataset of 433 fitness exercises. Each entry includes name, category, target muscle group, equipment, instructions, thumbnail image, and animation video.
──────────────────────────────

The hasaneyldrm/exercises-dataset repository is a treasure trove for fitness enthusiasts and developers alike, offering a structured, multilingual exercise dataset that's perfect for building workout planning applications, machine learning projects, or health and wellness research. This dataset boasts 1,324 exercises with detailed metadata, including category, body part, equipment, target muscle, and step-by-step instructions in six languages: English, Spanish, Italian, Turkish, Russian, and Chinese.

The repository includes an interactive browser for exploring exercises and a developer setup guide to help integrate the dataset into your own application, complete with SQL scripts for various databases and code examples in multiple programming languages. The dataset itself is formatted in JSON and covers a wide range of muscle groups, equipment types, and exercise categories.

Whether you're a fitness enthusiast looking for new exercises, a developer seeking to build a workout app, or a researcher interested in exercise recognition or recommendation, this dataset has something for everyone. So why wait? Dive into the world of fitness and start exploring the hasaneyldrm/exercises-dataset today - your next great workout or app idea is just a git clone away!

──────────────────────────────
🧠 Channel: https://xn--r1a.website/GithubRe

🚀 Meet facebook/astryx: a gem from today's GitHub trending list.

🔗 https://github.com/facebook/astryx
📝 An open source design system that's fully customizable and agent ready
──────────────────────────────

Astryx is an open-source design system built on React and StyleX, providing 150+ accessible components, brand-level theming, and a CLI. It's customizable, with open internals and no styling lock-in, allowing developers to override styles using className with Tailwind, CSS modules, or plain CSS.

To get started, install Astryx and a theme using

npm install @astryxdesign/core @astryxdesign/theme-neutral

, then import pre-built CSS and use typed React components. The project includes a CLI tool for component documentation, templates, and themes.

Technical highlights include a modular architecture, strong conventions, and a focus on accessibility. The project is designed for both humans and AI assistants, with a consistent API, documentation, and CLI.

Astryx is suitable for developers and designers looking for a customizable design system. One-liner takeaway: Astryx empowers you to build consistent, accessible interfaces with ease, and customize them to fit your unique needs.

──────────────────────────────
🧠 Channel: https://xn--r1a.website/GithubRe

🔍 Deep-diving into diegosouzapw/OmniRoute — fresh off the trending list.

🔗 https://github.com/diegosouzapw/OmniRoute
📝 Never stop coding. Free AI gateway: one endpoint, 231+ providers (50+ free), connect Claude Code, Codex, Cursor, Cline & Copilot to FREE Claude/GPT/Gemini. RTK+Caveman stacked compression saves 15-95% tokens, smart auto-fallback, MCP/A2A, multimodal APIs, Desktop/PWA.
──────────────────────────────

Imagine a world where you can access 236 AI providers through one endpoint, never hitting limits or breaking the bank. OmniRoute makes this a reality, offering ~1.6B free tokens/month and the ability to save up to 95% tokens with RTK + Caveman compression.

This production-grade solution supports 16+ coding agents, including Claude Code, Codex, and Copilot, and features 17 routing strategies, circuit breakers, and TLS stealth. With $0 to start and 50+ providers with a free tier, OmniRoute is perfect for developers who want to build without limits.

Join the community today and experience the power of OmniRoute for yourself. One endpoint, endless possibilities: OmniRoute is the ultimate game-changer for AI development.

──────────────────────────────
🧠 Channel: https://xn--r1a.website/GithubRe

🔥 allenai/olmocr is trending — and it deserves your attention.

🔗 https://github.com/allenai/olmocr
📝 Toolkit for linearizing PDFs for LLM datasets/training
──────────────────────────────

The allenai/olmocr GitHub repository provides a toolkit for converting PDFs and image-based documents into clean, readable plain text format. This toolkit supports various features such as converting PDF, PNG, and JPEG documents into Markdown, handling equations, tables, handwriting, and complex formatting, and automatically removing headers and footers.

The olmOCR toolkit is efficient, with a cost of less than $200 USD per million pages converted, and it requires a GPU for operation. The toolkit has a benchmark suite that covers over 7,000 test cases across 1,400 documents to measure the performance of OCR systems.

To install the toolkit, you can use pip install with various options, including gpu for local GPU inference, beaker for Beaker cluster execution, and bench for running the benchmark suite.

The toolkit can be used to convert single or multiple PDFs, and it supports remote inference servers. You can view the results as Markdown files inside the workspace folder.

The olmOCR toolkit is suitable for developers, researchers, and anyone looking to convert large volumes of documents into readable text format.

Here's a simple usage example:

olmocr ./localworkspace --markdown --pdfs olmocr-sample.pdf

In summary, allenai/olmocr is a powerful toolkit for document conversion, and its efficiency and features make it an ideal choice for large-scale document processing - Convert your documents in a snap with olmOCR!

──────────────────────────────
🧠 Channel: https://xn--r1a.website/GithubRe

🔍 Deep-diving into logto-io/logto — fresh off the trending list.

🔗 https://github.com/logto-io/logto
📝 🧑‍🚀 Authentication and authorization infrastructure for SaaS and AI apps, built on OIDC and OAuth 2.1 with multi-tenancy, SSO, and RBAC.
──────────────────────────────

Logto is an open-source auth infrastructure designed for SaaS and AI apps, simplifying OIDC and OAuth 2.1 implementation. It offers multi-tenancy, enterprise SSO, and RBAC out-of-the-box, along with pre-built sign-in flows, customizable UIs, and SDKs for over 30 frameworks.

Key features include:
- Full support for OIDC, OAuth 2.1, and SAML
- Pre-built sign-in flows and customizable UIs
- SDKs for 30+ frameworks

To get started, you can use Logto Cloud for a fully managed experience, launch it in GitPod for a quick start, or opt for local development using Docker Compose or Node.js.

Logto is perfect for developers and teams looking to simplify authentication without the usual headaches. With its flexible integration options and industry-standard protocols, it's suitable for a wide range of applications, from SPAs and web apps to mobile apps, APIs, and M2M services.

One-liner takeaway: Logto revolutionizes authentication by providing a modern, open-source auth infrastructure that's easy to use and scales with your SaaS or AI app.

──────────────────────────────
🧠 Channel: https://xn--r1a.website/GithubRe

🚀 Meet togatoga/karukan: a gem from today's GitHub trending list.

🔗 https://github.com/togatoga/karukan
📝 Japanese Input Method System for Linux, macOS, Neural Kana-Kanji Conversion Engine
──────────────────────────────

Karukan is a Japanese input system for Linux and macOS, featuring a neural kana-kanji conversion engine. The system consists of several components, including karukan-fcitx5 for Linux, karukan-macos for macOS, karukan-im for shared IME engine, karukan-engine for core library, and karukan-cli for CLI tools and server.

Key features include ニューラルかな漢字変換 (neural kana-kanji conversion) using GPT-2 based models, live conversion, context-aware conversion, and conversion learning. The system also supports システム辞書 (system dictionary) construction from SudachiDict data and 候補リライター (candidate relighter) for generating related candidates.

To get started, users can refer to the installation instructions for Linux (fcitx5) and macOS. The project is licensed under MIT OR Apache-2.0.

The code is well-organized, with each component having its own repository. For example, the

karukan-engine

directory contains the core library for neural kana-kanji conversion.

Karukan is perfect for users looking for a highly customizable and efficient Japanese input system - it's time to type in Japanese like never before!

──────────────────────────────
🧠 Channel: https://xn--r1a.website/GithubRe

🔥 Mebus/cupp is trending — and it deserves your attention.

🔗 https://github.com/Mebus/cupp
📝 Common User Passwords Profiler (CUPP)
──────────────────────────────

The CUPP tool, or Common User Passwords Profiler, is a password profiling tool designed to help users create stronger passwords and to assist in legal penetration tests and forensic crime investigations. It works by asking interactive questions to create a profile of the user's password, which can then be used to generate a list of possible passwords.

The key features of CUPP include:
- Interactive questions for user password profiling
- Using existing dictionaries to generate possible passwords
- Downloading huge wordlists from the repository
- Parsing default usernames and passwords from the Alecto DB

To use CUPP, you need to have Python 3 installed. You can start by running python3 cupp.py -h to see the available options.

From a technical standpoint, CUPP is a Python-based tool that uses a configuration file called cupp.cfg to store its settings.

The target audience for CUPP includes security professionals and law enforcement agencies who need to perform password profiling and penetration testing.

In short, CUPP is a powerful tool for creating stronger passwords and assisting in cybersecurity investigations - and with it, you can crack the code to making your passwords unbreakable!

──────────────────────────────
🧠 Channel: https://xn--r1a.website/GithubRe