🌟 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.
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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.

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🧠 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
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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!

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🧠 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.
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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.

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🧠 Channel: https://t.me/GithubRe

🚀 Meet jamwithai/production-agentic-rag-course: a gem from today's GitHub trending list.

🔗 https://github.com/jamwithai/production-agentic-rag-course
📝 No description.
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The jamwithai/production-agentic-rag-course is a hands-on project where you'll build a complete research assistant system that automatically fetches academic papers, understands their content, and answers your research questions using advanced RAG techniques.

This course is designed for learners who want to master AI engineering skills, particularly in building production-grade RAG systems. The system, called The arXiv Paper Curator, uses a foundation-first approach, starting with keyword search foundations and then enhancing with vector search for hybrid retrieval.

Key features include:
- Automated data pipeline fetching and parsing academic papers from arXiv
- Production BM25 keyword search with filtering and relevance scoring
- Intelligent chunking and hybrid search combining keywords with semantic understanding
- Complete RAG pipeline with local LLM, streaming responses, and Gradio interface
- Production monitoring with Langfuse tracing and Redis caching for optimized performance
- Agentic RAG with LangGraph and Telegram Bot for mobile access

Technical highlights include:

Docker, FastAPI, PostgreSQL, OpenSearch, and Airflow

The course is structured into 7 weeks, each focusing on a different aspect of building a production RAG system.

In summary, this course is perfect for those who want to build modern AI systems from the ground up and master in-demand AI engineering skills.
Takeaway: Building a production RAG system is not just about AI, it's about creating a robust search foundation first.

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🧠 Channel: https://t.me/GithubRe