📦 avivsinai/agent-message-queue

agent-message-queue

Agent Message Queue turns your computer's file system into a live, crash-safe messaging hub for AI coding partners. No database or server required, just plain text files that never corrupt, even if your machine crashes mid-send. It solves the chaos of multiple AI agents working on the same codebase by giving them a reliable, human-readable channel to coordinate tasks and review each other's work without needing a human referee. You can inspect every message with a simple text editor or version control tool, making the whole system transparent and debuggable.

🆔 @hackernewsgithubprojects

📦 unclebob/uml-viewer

UML Viewer

UML Viewer turns your code into a live map that instantly exposes which parts of your app are fragile. It scans your source files to draw a diagram where every box is colored by real health metrics, so you see exactly which areas need attention before you even open the code. It highlights dependencies that break your architecture rules in bright red, giving you a clear visual signal of where the design is straining. This is not just a static picture, but a dynamic window into your project's structural health. You get a practical way to spot complexity early, making your refactoring efforts precise rather than guesswork.

📰 https://news.ycombinator.com/item?id=49759501

🆔 @hackernewsgithubprojects

📦 programasweights/programasweights-python

ProgramAsWeights: Turn Text into Local Code

Turn plain English into tiny, runnable software that lives entirely on your computer. ProgramAsWeights takes a written description of a task and compiles it into a small neural function that runs offline without needing an internet connection or API keys. This is perfect for jobs like fixing broken data or sorting emails, where a full artificial intelligence is too heavy and simple text rules are too rigid. The coolest part is that you can describe what you want in normal words, and the tool handles the rest locally. No more sending your data to the cloud for simple tasks.

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📦 karanb192/claude-code-hooks

Claude Code Hooks: The Safety Net for AI Coding

Give your AI coding assistant a literal safety net that stops it before it breaks things. Claude Code Hooks is a collection of tiny scripts that sit between the AI and your computer, checking every single action it tries to take. The coolest part is that it runs outside the AI itself, so the robot cannot simply talk its way past the rules. It stops dangerous commands, protects your secret keys, and even blocks the AI from changing its own security settings. It is basically an invisible bouncer for your code, making sure your helpful assistant never accidentally deletes your work or leaks your passwords.

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📦 anmolkapil/plexo

Plexo: Download Faster with Multiple Networks

Plexo is the desktop app that finally uses all your internet connections at once to make downloads faster. Usually, your computer picks one connection and leaves the others sitting idle, but Plexo fixes that by breaking your file into small pieces and sending them out over Wi-Fi, Ethernet, and even a phone connection simultaneously. It stitches these pieces back together so you get the full file much quicker than any single line could manage. It is a clever way to squeeze every drop of bandwidth you already have, turning your setup into a speed boost without extra hardware.

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📦 ashhart/mcdma

MCDMA: Connect Your Mac to NVIDIA GPUs

MCDMA lets you wire your Mac directly into an NVIDIA GPU cluster, creating a raw data highway between Apple silicon and Linux hardware. It solves a messy problem where moving data between different computer chips usually requires slow, complicated software steps. By using a special network card, this tool moves large chunks of memory instantly, skipping the usual traffic jams. The real magic happens when you split a big AI task between machines, letting one system handle the heavy lifting while another manages the final output. It is experimental and needs specific hardware, but it shows how far we can push cross-platform speed.

🆔 @hackernewsgithubprojects

📦 cobanov/awesome-jev

Awesome Jev: Typed Decisions for AI

Build software that makes fast, confident decisions without generating endless text. Awesome Jev curates the entire ecosystem around Jev, a model from TypeSafe AI designed for typed, probabilistic choices like classification and routing. Instead of chatting, it takes your program's state and returns constrained answers with probabilities, letting your code handle the logic. The most surprising part? People are using it to pilot drones, rank coding agents, and even play Mario, proving it works way beyond simple data filters. It is not about replacing large language models, but about giving your apps a reliable, structured brain for high-speed logic.

🆔 @hackernewsgithubprojects

📦 expo/skills

Expo Skills: The Missing Brain for Your AI Coder

The Expo team built a cheat code for your AI coding assistant that teaches it how to actually build real mobile apps. This is not just another list of generic tips; it is a curated set of official instructions that tells your AI exactly how to handle the tricky parts of mobile development, like native animations, offline data fetching, and the specific quirks of iOS and Android. If you have ever watched your AI hallucinate a non-existent function or write web code that simply does not work on a phone, this is the fix.

🆔 @hackernewsgithubprojects

📦 0xsero/harness-bridge

Harness Bridge: One Tool for All Coding Agents

Harness Bridge is the local launcher that instantly connects any coding assistant to your own private model server without touching your existing files. The project solves the friction of manually editing configuration files or pasting API keys into command lines by handling the entire connection through environment variables that vanish when you are done. You simply pick a model from your local server, choose your favorite coding agent like Claude Code or Codex, and the tool opens a terminal already ready to work. This means your keys stay secure, your setup stays clean, and switching between different AI providers takes just one click.

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📦 dimentary/llm-robotics-playground

LLM Robotics Playground: AI Built These Robots

The LLM Robotics Playground is the collection that proves a frontier language model can engineer working robot controllers for delicate physical tasks. This library documents how a human guided an AI to build simulations where robots untangle headphone cables, carry objects on six legs, and even write code on a whiteboard with a marker. The most striking experiment features a robotic hand holding a pencil to draw a detailed dove, matching the intended path to within a fraction of a millimeter. It is a fascinating look at how language models are moving beyond text generation to actually solve complex, real-world mechanical problems.

🆔 @hackernewsgithubprojects

📦 jianglongye/dexterous-astra

Dexterous Astra: A Hand That Spins a Pen

A robot hand can spin a pen and twist a Rubik's cube. This project, called dexterous Astra, shows how simulated robot hands master these dexterous tricks. It provides ready-to-run environments and trained policies. You can run a zero-shot script that simply tells the hand to spin the pen. Or you can load a trained policy to see complex moves. The goal is to study how hands interact with objects. It is a clean way to test manipulation skills. You don't need to build the physics engine yourself. The code is set up for easy experimentation. This is a great resource for anyone studying robot hands. It makes complex dexterity accessible.

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📦 blader/humanizer

Humanizer: Make AI Text Sound Human

Take your AI generated prose and make it sound like a real person actually wrote it. Humanizer strips away the robotic habits that make text feel artificial, like forced lists or dramatic pauses, without changing your facts. It spots twenty five specific tells where language models slip up. The coolest part? It matches your unique voice if you paste a sample of your own writing. No more sounding like a chatbot. Just clean, natural prose that is actually yours. Give it a try.

📰 https://news.ycombinator.com/item?id=47362115

🆔 @hackernewsgithubprojects

📦 mutonby/vibetube

VibeTube: Record, Edit, and Publish with AI

VibeTube is the Mac app that turns raw multicam recordings into finished, publish-ready videos using AI. It records your screen and webcam in perfect sync, then hands the footage to an AI agent that edits the final cut, adds subtitles, and writes the description. You never have to touch a timeline or open a browser again. The AI even publishes the video directly to YouTube and TikTok for you. It is the closest thing to a fully automated video production pipeline available right now. Stop spending hours on editing and let the machine do the heavy lifting.

🆔 @hackernewsgithubprojects

📦 andrezaiats/snes9x-z

Snes9x-Z: The SNES Emulator That Runs 60% Faster

Snes9x-Z, a tweaked SNES emulator, squeezes out over sixty percent more speed from your computer without changing a single pixel of the output. This is not a magic trick; it is a carefully built fork that takes the standard emulator and rearranges the internal work so the graphics engine uses two CPU cores instead of one. The result is a massive performance boost that still produces bit-for-bit identical results to the original. Why does this matter? Because those extra cycles let you run shader filters, record gameplay, or play demanding games on weak hardware like old handhelds without lagging.

🆔 @hackernewsgithubprojects

📦 steadfastasart/geoscience-skills

Geoscience Skills: The AI Tool For Earth Data

Geoscience Skills is the open-source toolkit that finally makes coding agents actually understand the messy reality of earth science data. Most AI tools stumble when they encounter specialized formats like seismic files or well logs, but this project fixes that by teaching them exactly how to handle thirty different scientific libraries. Instead of guessing, your coding assistant learns to properly read a seismic file or calculate rock properties using the right formulas. It turns generic chatbots into genuine geological assistants capable of real analysis. If you work with earth data, this is your shortcut to having a smart partner who actually gets the science.

🆔 @hackernewsgithubprojects

📦 shadcn-labs/pdfcn

Turn React Code into Polished PDFs with pdfcn

Turn your standard React components into polished, downloadable PDF documents with just a single command. This free, open-source tool from the shadcn team solves the common headache of making complex web layouts look professional on paper. It uses two distinct rendering engines, allowing you to generate clean invoices, reports, or data tables without wrestling with obscure formatting settings. Because it follows the same familiar workflow as other popular UI libraries, developers can copy, paste, and ship high-quality documents immediately. You own the code, meaning you can fully customize every detail to match your brand.

🆔 @hackernewsgithubprojects

📦 xingchen-agi/xing4.0-29b-a4b

Xing 4.0: The Open Source AI Trained on Chinese Chips

Xing 4.0 is the open source model that proves domestic hardware can rival global giants. It is a massive language model built by China Telecom, featuring a design that activates only a fraction of its brain to process huge documents efficiently. The standout feature is that it was trained entirely on Ascend processors, offering a real alternative to traditional silicon. This makes it a powerful tool for developers needing strong coding and reasoning skills without relying on imported chips. You get a high performance engine that runs natively on this new platform, ensuring stability for complex tasks.

🆔 @hackernewsgithubprojects

📦 alibaba-damo-academy/damo-radar

RADAR: AI that reads CT scans

Diagnose abdominal conditions directly from CT scans using RADAR, a generalist AI model. This project solves the problem of needing separate tools for different diagnoses by learning directly from over forty thousand clinical reports without manual labels. It identifies twenty specific conditions, like aortic aneurysms or kidney cysts, with expert-level accuracy. The standout feature is its ability to understand anatomy-aware image-text pairs, bridging the gap between visual data and clinical language. This open-source framework provides a scalable path for radiology AI, offering researchers and developers a robust foundation for building next-generation diagnostic tools that work across complex clinical tasks.

📰 https://news.ycombinator.com/item?id=49761981

🆔 @hackernewsgithubprojects

📦 standardagents/jevpilot

JevPilot: The AI Driving Demo

This browser-based driving simulator lets an AI drive a Tesla Model Y through a realistic 3D city by sampling dozens of potential steering paths every fraction of a second. Built with Three.js and powered by Jev, it visualizes the exact moment the algorithm chooses its route, painting the selected path in bright blue while ghosting the rejected options in cyan and amber. The coolest part is that you can flip a switch to see every possible lane change and collision prediction before the car commits to a move.

🆔 @hackernewsgithubprojects

📦 dartnative/dartnative

DartNative: Real Native Apps Written in Dart

DartNative is the mobile framework that writes your app in Dart while using the phone’s actual built-in buttons instead of drawing fake ones on a screen. Usually, cross-platform tools paint their own graphics, making apps feel slightly off or slow to touch. DartNative skips that middleman entirely. Your code talks directly to the operating system’s real interface parts on a single, fast track. This means your app looks exactly like it belongs on the device, moves with the system’s natural speed, and feels instantly responsive. It’s the same coding style you know from Flutter, but the result is a true native application without the extra rendering weight.

🆔 @hackernewsgithubprojects

📦 openbmb/simplememvla

SimpleMemVLA: Robots That Remember Like Humans

A real dual-arm robot can now look back at its own video feed to remember exactly where it picked up a block, a capability called SimpleMemVLA. Instead of using complex memory modules that might lose details, this system treats the robot's past footage as a native video. It feeds this timestamped history directly to the brain of the robot, allowing it to figure out what happened minutes ago. This simple approach lets the robot succeed at tricky tasks like covering blocks in a specific order, achieving high success rates in real-world tests. By keeping the raw video intact, the robot makes faster and more accurate decisions without confusing similar objects.

🆔 @hackernewsgithubprojects