Github Top Repositories

🎯 ayghri/i-have-adhd landed on trending. Worth a proper look. 🔗 https://github.com/ayghri/i-have-adhd 📝 A skill for your coding agent to stop it from burying the answer. ADHD-friendly output. ────────────────────────────── The ayghri/i-have-adhd GitHub repository is designed to provide ADHD-friendly outputs for coding assistants, making it easier for users to focus on the task at hand. The key feature of this repository is its ability to stop burying the answer and instead provide action-first, step-by-step instructions. To use this repository, you can install it as a plugin in your preferred coding assistant, such as Claude Code or Codex, using the provided

claude plugin install

or

codex plugin add

commands. The repository follows 10 rules to ensure that the output is concise and easy to follow, including leading with the next action, numbering multi-step tasks, and suppressing tangents. This repository is suitable for anyone who wants to improve their productivity and focus while working with coding assistants. In short, ayghri/i-have-adhd helps you get straight to the point and start coding - no more scrolling past unnecessary text! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🚀 Meet ruvnet/RuView: a gem from today's GitHub trending list. 🔗 https://github.com/ruvnet/RuView 📝 π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video. ────────────────────────────── RuView is a WiFi sensing platform that transforms radio signals into spatial intelligence. It detects people, tracks movement, and monitors rooms through walls, in the dark, with no cameras or wearables. Key features include presence and occupancy detection, vital sign measurement, activity recognition, environment mapping, and sleep quality monitoring. Technical highlights include the use of Channel State Information (CSI) from low-cost ESP32 sensors, spiking neural networks for local learning, and cryptographic attestation via an Ed25519 witness chain. The system integrates with major smart-home ecosystems like Home Assistant, Apple Home, Google Home, and Alexa. Usage options range from a simple docker pull and docker run for simulated data to live sensing with ESP32-S3 hardware or a full system with Cognitum Seed. The platform is designed for low-power edge applications and provides a range of edge modules for various use cases. The target audience includes developers, researchers, and users interested in spatial intelligence, WiFi sensing, and smart home automation. With RuView, you can turn ordinary WiFi into a contactless sensor and unlock new possibilities for spatial awareness and automation. One line takeaway: RuView revolutionizes spatial intelligence by transforming WiFi signals into actionable data, enabling a wide range of applications, from smart homes to healthcare, without the need for cameras or wearables. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔥 koala73/worldmonitor is trending — and it deserves your attention. 🔗 https://github.com/koala73/worldmonitor 📝 Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface ────────────────────────────── The World Monitor is a real-time global intelligence dashboard that provides AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking. Its key features include 500+ curated news feeds, a dual map engine, and cross-stream correlation for convergence of military, economic, disaster, and escalation signals. To use World Monitor, you can git clone the repository, npm install, and npm run dev to start the app. The project has a tech stack that includes Vanilla TypeScript, Vite, globe.gl, and deck.gl for the frontend, and Tauri 2 with Node.js sidecar for the desktop app. World Monitor is designed for programmatic access, with a MCP server, REST API, and CLI tools. It also has SDKs for Python, Ruby, and Go. The project aggregates data from 65+ external providers and APIs, and its data sources catalog is available for reference. The World Monitor community welcomes contributions, and the project is licensed under AGPL-3.0-only. Overall, World Monitor is a powerful tool for geopolitical monitoring and analysis, and its open-source nature makes it a great resource for researchers, analysts, and developers. The World Monitor is a game-changer for anyone looking to stay on top of global events - track the world, in real-time. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 KnockOutEZ/wigolo just hit the trending charts — here's why it matters. 🔗 https://github.com/KnockOutEZ/wigolo 📝 The go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta. ────────────────────────────── wigolo is a local-first web intelligence platform designed for AI agents, providing a single surface for web-related tasks like search, fetch, crawl, extract, cache, and research. It runs on the user's machine, eliminating the need for API keys, cloud services, and metered bills. Key features include multi-engine web search, fetch and structured extraction, whole-site crawl and mapping, and verbatim excerpts with byte-pinned source spans. Usage is straightforward: initialize the platform with npx wigolo init, and then use various tools like wigolo search, wigolo fetch, and wigolo extract to perform tasks. The platform also supports interactive setup with --interactive or --wizard flags. Technical highlights include a tiered router for fetching pages, a headless browser engine for handling anti-bot challenges, and a caching mechanism for storing query results. The platform also supports various LLM providers like Gemini, Anthropic, and OpenAI, allowing users to choose their preferred language model. The target audience for wigolo includes developers, researchers, and users of AI agents, such as Claude Code, Cursor, and Codex. The platform is designed to be private by default, with all data stored locally under ~/.wigolo/. In summary, wigolo is a powerful, local-first web intelligence platform that provides a single surface for AI agents to perform various web-related tasks, all while maintaining user privacy and eliminating the need for API keys and cloud services. Take control of your AI agent's web interactions with wigolo! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ dottxt-ai/outlines is making waves. Here's the full picture. 🔗 https://github.com/dottxt-ai/outlines 📝 Structured Outputs ────────────────────────────── Introducing Outlines, a revolutionary tool that guarantees structured outputs for Large Language Models (LLMs). With Outlines, you can ensure that your LLMs produce predictable, high-quality outputs, eliminating the need for post-generation parsing or regex. This tool is designed to work seamlessly with any LLM, including OpenAI, Ollama, and vLLM, and provides simple integration through a single line of code: model(prompt, output_type). Outlines is built around a philosophy of simplicity, using Python's type system to define the desired output structure. You can use Literal types for simple classification, int for numerical values, or define complex objects using Pydantic models. The tool also supports union types for handling incomplete data or uncertain outputs. To get started with Outlines, simply pip install outlines and connect to your preferred LLM using from_transformers. You can then use the model function to generate structured outputs, such as sentiment analysis, product categorization, or event parsing. Outlines is trusted by top companies like NVIDIA, Cohere, and HuggingFace, and is designed for a wide range of applications, from customer support triage to e-commerce product categorization. Whether you're a developer, researcher, or business leader, Outlines is the perfect tool for unlocking the full potential of LLMs. In short, Outlines is the key to unleashing the power of LLMs - try it today and discover a world of structured, predictable, and high-quality outputs! Outlines: where LLMs meet structure. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 microsoft/Ontology-Playground caught my eye on GitHub Trending today. 🔗 https://github.com/microsoft/Ontology-Playground 📝 Free, open-source web app for learning about ontologies and Microsoft Fabric IQ. Explore a catalogue of pre-built ontologies, design your own visually, export as RDF/XML, and share interactive diagrams. Zero backend, fully static. ────────────────────────────── The Ontology Playground is a free, open-source web application for learning about ontologies and Microsoft Fabric IQ. This platform allows users to explore pre-built ontologies, design their own in a visual editor, export as RDF/XML, and share interactive diagrams. The key features include an interactive graph exploration, a curated ontology catalogue, a visual ontology designer, and RDF import & export capabilities. Developers can use the Ontology Playground to create and edit ontologies, and then deploy them to various environments, including Azure Static Web Apps and GitHub Pages. The project structure is well-organized, with separate directories for React components, data, lib, and styles. The target audience includes developers, data scientists, and anyone interested in learning about ontologies and knowledge graphs. With its user-friendly interface and extensive documentation, the Ontology Playground is an ideal platform for both beginners and experienced professionals. One-liner takeaway: The Ontology Playground is a powerful tool for creating, editing, and deploying ontologies, making it an essential resource for anyone working with knowledge graphs and Microsoft Fabric IQ. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 schollz/croc landed on trending. Worth a proper look. 🔗 https://github.com/schollz/croc 📝 Easily and securely send things from one computer to another 🐊 📦 ────────────────────────────── croc is a free, open-source tool that lets you easily and securely transfer files between two computers. It's unique because it allows any two computers to transfer data using a relay, provides end-to-end encryption, and enables easy cross-platform transfers. You can use it on Windows, Linux, or Mac, and it supports multiple file transfers, resuming interrupted transfers, and more. To get started, you can curl https://getcroc.schollz.com | bash to install, or download the latest release for your system. Once installed, you can send a file with croc send [file] and receive it on another computer with croc [code-phrase]. Some technical highlights include its use of password-authenticated key agreement (PAKE) for encryption, IPv6-first with IPv4 fallback, and the ability to use a proxy like Tor. croc is perfect for developers, sysadmins, and anyone looking for a secure way to transfer files between computers. In short: say goodbye to tedious file transfers and hello to simplicity and security with croc - the ultimate file transfer tool! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

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🔍 Deep-diving into agegr/pi-web — fresh off the trending list. 🔗 https://github.com/agegr/pi-web 📝 Web UI for the pi coding agent ────────────────────────────── Pi Web is a local web UI for the pi coding agent, providing a browser workspace for session browsing, real-time chat, model configuration, skill management, and project file preview. Key features include browsing previous conversations, trying different directions safely, working across branches, and configuring models from the web UI. To get started, simply run npx @agegr/pi-web@latest or install globally with npm install -g @agegr/pi-web and access it at http://localhost:30141. Technical highlights include a customizable port, hostname, and environment variables for configuration. This project is suitable for developers who want a more intuitive interface for their pi coding agent. In short, Pi Web streamlines your pi coding experience - so why not give it a try and level up your coding game? ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 diegosouzapw/OmniRoute caught my eye on GitHub Trending today. 🔗 https://github.com/diegosouzapw/OmniRoute 📝 Never stop coding. Free MIT AI gateway: one endpoint, 268+ providers (50+ free), 500+ models — Claude, GPT, Gemini, Kimi K3, GLM, DeepSeek. Works with Claude Code, Codex, Cursor, Cline & Copilot. Quota-aware auto-fallback, RTK+Caveman compression saves 15-95% tokens, MCP/A2A, multimodal, Desktop/PWA. Built by 500+ contributors. ────────────────────────────── The OmniRoute GitHub repository offers a free AI gateway that connects to 250 providers, with 90+ free options, through a single endpoint. Key features include auto-fallback across providers, RTK + Caveman compression to save up to 95% of tokens, and a cost-optimized routing system. To use OmniRoute, simply point your CLI tools or coding agents to the /v1 endpoint. The repository provides a dashboard to track free tiers, quota, and token usage. Technical highlights include circuit breakers, TLS stealth, and 21,000+ tests for production-grade reliability. OmniRoute is suitable for developers, researchers, and businesses looking to streamline their AI workflows. In a nutshell, OmniRoute helps you never hit limits and save up to 95% tokens while providing a unified endpoint for all your AI tools - code without limits! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: langchain-ai/open_deep_research — let's break it down. 🔗 https://github.com/langchain-ai/open_deep_research 📝 No description. ────────────────────────────── Open Deep Research is a fully open-source, configurable, and high-performance deep research agent that works with multiple model providers, search tools, and MCP servers. It's designed to simplify complex research tasks and has achieved a #6 ranking on the Deep Research Bench Leaderboard. The agent can be easily set up and customized using the configuration.py file, where you can select different LLM providers, search APIs, and other settings. For example, you can choose from various LLM providers like OpenAI, Anthropic, or local models via Ollama, and configure the search API to use Tavily or other compatible tools. To get started, follow the quickstart guide: git clone https://github.com/langchain-ai/open_deep_research.git cd open_deep_research uv venv source .venv/bin/activate uv sync uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.11 langgraph dev --allow-blocking You can then launch the agent with the LangGraph server locally and access the LangGraph Studio UI in your browser. The agent is also deployable to the LangGraph Platform or Open Agent Platform for non-technical users. With its high performance, flexibility, and ease of use, Open Deep Research is a powerful tool for anyone looking to streamline their research workflow - and with great customization options, you can research smarter, not harder. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe