OSINT Framework
- osint-framework
- osint-resources
- osint
- intelligence-gathering
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OSINT Framework
Grunt: The JavaScript Task Runner
Spectacular Test Runner for JavaScript
Turn your Notion workspace into a fast, customizable website. Built with Next.js + Notion API, with multi-platform deployment and no self-hosted server required.
Program for determining types of files for Windows, Linux and MacOS.
Connect APIs, remarkably fast. Free for developers.
🤖 Multi-platform IM AI Agent for Telegram, WhatsApp, Lark, and WeChat. Connects ChatGPT / Claude / Kimi / DeepSeek / Ollama / Pi for auto-replies, community analysis, contact management, and inactive-friend detection.
A JavaScript engine optimized for running React Native.
TranslatedBackend service for xiaozhi-esp32, helps you quickly build an ESP32 device control server.
Spug is a lightweight agent-free automatic operation and maintenance platform designed for small and medium-sized enterprises. It integrates host management, host batch execution, host online terminal, file management, application release and deployment, pipelines, online task planning, configuration center, monitoring, alarm and so on.
RegExr is a HTML/JS based tool for creating, testing, and learning about Regular Expressions.
A simple, beautiful, and embeddable JavaScript Markdown editor. Delightful editing for beginners and experts alike. Features built-in autosaving and spell checking.
Elegant terminal spinner
🗺 MapSCII is a Braille & ASCII world map renderer for your console - enter => telnet mapscii.me <= on Mac (brew install telnet) and Linux, connect with PuTTY on Windows
A bit like Solr, but much smaller and not as bright
Learning Convolutional Neural Networks with Interactive Visualization.
📡 Simple WebRTC video, voice, and data channels
A polyfill for ES6-style Promises
Automate running Lighthouse for every commit, viewing the changes, and preventing regressions
GraphicsMagick for node
An unobtrusive and user-friendly desktop application for IPFS on Windows, Mac and Linux.
Countly is a privacy-first, AI-powered analytics and engagement platform for understanding and optimizing customer journeys across digital applications, from desktop and mobile to IoT and connected environments.
A simple, distributed task scheduler and runner with a web based UI.
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.