📦 oceanbase/powercontext

Powercontext: The Memory Layer for AI Agents

Powercontext is the memory layer that gives AI agents a full story, not just isolated facts. It solves the problem of context loss by capturing experiences, handoffs, and profiles into a unified system. Think of it as a long-term brain for your code assistants. When you pass a task to another agent, Powercontext ensures they remember the history and decisions, not just the current prompt. This makes your AI workflow feel continuous and aware. If you are building agents that need to learn and adapt over time, this is the infrastructure you need. It turns temporary chat sessions into persistent, evolving knowledge. Your agents stop forgetting. They start remembering.

📰 https://news.ycombinator.com/item?id=49707246

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📦 drandyhaas/kicadroutingtools

This AI Routes Your Circuit Boards

KiCad Routing Tools uses an AI assistant to automatically trace and route your entire circuit board, saving you from manually drawing every single wire. It analyzes your design, spots tricky connections like differential pairs, and even generates a step-by-step plan for how to route everything. You can watch the AI work in real time, adjust its plan, or just let it go and handle the busywork for you. It turns a tedious, error-prone task into a simple, automated workflow. This is a massive time-saver for any hobbyist or professional who wants to focus on designing rather than wiring.

📰 https://news.ycombinator.com/item?id=49707571

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📦 pku-yuangroup/openai4s

OpenAI4S: Run AI Science for Under Ten Yuan

A single API call costs nine yuan and ninety cents to run a full scientific agent that feels like a million-dollar lab in your pocket. OpenAI4S is an open-source platform that connects standard language models to real scientific workflows, allowing you to run complex tasks like protein design and genomic analysis without expensive proprietary subscriptions. It solves the problem of high entry costs for AI-assisted research by using a budget-friendly interface that mimics premium capabilities. This makes advanced computational science accessible to individual researchers who cannot afford enterprise pricing. The project proves that powerful scientific automation does not have to be locked behind a paywall.

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📦 amirivojdan/neyshekar

Neyshekar: The Persian Speech Dataset

Neyshekar is the open Persian speech dataset that finally gives researchers a clean, massive foundation for voice technology. It is not just a folder of random sounds, but a carefully curated collection of ninety-nine hours of audio recorded by native speakers through a web platform. This solves the critical problem of finding reliable data for speech recognition and text-to-speech systems in Persian. The dataset includes specific train, validation, and test splits, ensuring that experiments are reproducible and fair. By keeping the data open and free, it removes the biggest barrier to innovation for this language. If you work with speech tech, this is your new baseline for accuracy.

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📦 khansimon/field_converter

Field Converter: Soccer Player 3D Pose Estimation

Field Converter places 3D soccer players on the field with about ten centimeters of error. It takes a standard TV broadcast, finds the player’s position in 3D space, and anchors that skeleton to the actual pitch. It does not guess where the player is from scratch; it starts with a solid geometric estimate from the camera and the ground, then uses a short video clip to correct small mistakes. This means it works on real, messy broadcast footage, not just clean studio shots. It is a strong step toward tracking players for tactics, training, and video analysis from ordinary TV.

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📦 tanguymagne/syntheticdoc

SyntheticDoc: A Dataset for Perfect Document Scans

SyntheticDoc is the massive dataset that finally teaches computers to flatten wrinkled paper. When you photograph a document on a desk, the page bends, creating shadows and curves that make text hard to read. This project solves that by simulating real physics to warp virtual papers in every possible way, from gentle curves to deep folds. It then renders these scenes with photorealistic lighting, creating over one million perfect examples of messy photos paired with their ideal flat versions. This allows image models to learn exactly how to remove the warping and correct the lighting.

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📦 zjuscl/awesome-robot-verifier

The Truth About Robot AI Judges

Awesome Robot Verifier is the curated reading list that finally maps out who is actually grading robot actions. It tackles the real headache of knowing if a robot is truly safe or just acting confident. The team at Zhejiang University organized one hundred fifty three papers into a single clear framework. They ask one simple question for every tool: who is providing the score? A human, a math rule, a trained model, or the robot itself? This list shows you exactly how reliable each judge is. You get a searchable catalog that cuts through the noise and shows where the weak spots are.

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📦 agencyenterprise/glossogen

GlossoGen: The Lab for AI Teams

Put a team of artificial intelligence agents into a digital sandbox and watch them collaborate. GlossoGen is a platform that runs controlled experiments on these multi-agent groups, letting researchers simulate complex scenarios like coordinating a satellite repair or managing a hospital ward. The most fascinating feature is the ability to swap one agent for another mid-run, or fork the simulation, to see exactly how the team adapts. Every interaction is recorded in a detailed log, allowing you to rewind time and analyze how specific changes affect the group’s communication and success.

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📦 laravel/vet

Vet: The AI Security Guard for PHP

Vet is the dependency auditor that makes your codebase actually secure. Every time you update your packages, it shows you the exact code changes before they land on your machine. The real magic is that it hands those updates to the coding agent you already have, like Claude or Gemini, and lets it read through the files for you. If the agent finds a security risk, it flags it immediately. If the code looks safe, it passes. You just look at the failures and trust the rest, saving hours of manual checking.

📰 https://news.ycombinator.com/item?id=49701618

🆔 @hackernewsgithubprojects

📦 frankweb33/flybrain-robot-bridge

FlyBrain Robot Bridge: Turning Video Into Motion

A simple webcam can now act as a bridge to control a physical robot without any human input. This project, called FlyBrain Robot Bridge, uses optical flow to track movement in video and feeds that data into a mock neural backend. The system processes these visual signals and sends simple motor commands over a local network to drive servos. It is designed as a proof of concept to test how a digital brain can interface with real-world hardware. The code is open for anyone to experiment with. If you want to see how software can drive motion, this is a fascinating place to start.

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📦 tau-intelligence/mujoco-drones-gym

MuJoCo Drones Gym

Fly a swarm of quadcopters inside a physics simulator that actually models the wind pushing against them. This project, called MuJoCo Drones Gym, lets you train artificial brains to keep small drones stable and flying in tight formations, even when gusts of air try to knock them over. The really cool part is that it runs on your graphics card, so you can simulate hundreds of these flights at once to learn faster. Instead of guessing how to balance, the computer figures out exactly how to fight the wind just by trying thousands of scenarios in a row.

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📦 yzhao062/catchbench

CatchBench: The Audit for AI Agent Failures

CatchBench is the benchmark that finally audits agent failures across their entire lifecycle. It solves a specific mystery by asking what an auditor can actually catch at three distinct moments: the declared plan before a run starts, a growing stream of data while it executes, and the finished trace after it ends. By pinning down exactly when evidence is available, it reveals where our current safety nets break down. It is a clever way to test if we can spot a bad agent before, during, or after it messes up.

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📦 brig-sh/brig

Brig: The Sandboxed Coding Agent

Brig lets you run an AI coding agent inside a tiny virtual machine on your own computer, ensuring it cannot touch anything you did not explicitly give it. The tool creates a completely isolated environment where the agent only sees the specific project folder you point it at. It cannot reach your personal files, your password manager, or any other part of your system. If the AI makes a mess or runs a bad command, the damage stays locked inside that single folder. When you are finished, you simply throw the whole sandbox away and start fresh.

📰 https://news.ycombinator.com/item?id=49709131

🆔 @hackernewsgithubprojects

📦 anuj0456/openarch

OpenArch: LLMs Written for Humans

Every major AI lab hides the secrets of their models in code optimized for speed, making the architecture nearly impossible to read. OpenArch fixes this by rewriting the internal structure of giants like Llama and DeepSeek from scratch into a single, readable file per model. The goal is clarity, not competition. You can finally see exactly how attention mechanisms and expert routing actually work without wading through production optimizations. If you want to truly understand how these models think, this is the clearest map available.

📰 https://news.ycombinator.com/item?id=49693384

🆔 @hackernewsgithubprojects

📦 jub0t/concat

Cut videos locally with Concat

Cut, trim, and caption your videos entirely on your own machine with Concat. This open-source editor gives you the full power of professional video tools without the monthly subscription or the nagging watermarks. The coolest part is that the AI features, like automatic subtitles and text-to-speech, run locally right on your computer. Nothing ever leaves your device, so your private clips stay private while you still get those slick automated edits. It is a native app that feels fast and smooth, giving you complete control over your creative process.

🆔 @hackernewsgithubprojects

📦 bashebr/ai-native-sdlc

AI-Native SDLC: The Agent That Needs Your Permission

Your coding agent can now build your entire app in an afternoon, but it still has to ask you before it touches production. AI-Native SDLC is a ready-to-use skill that turns this idea into a working system. It walks your agent through the full journey from a vague idea to a live deployment, pausing at every critical step to get your explicit approval. It treats the process like a map where every decision is logged and checked. The agent does the heavy lifting, but you hold the keys to the car. It is not magic, it is just a very disciplined workflow.

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📦 mekion/the-bimo-project

Bimo: The 3D Printable Walking Robot

The Bimo Project is a two-legged robot you can print yourself that actually walks. It is not a toy. It is a real research platform that lets you train a brain in a computer simulation and then plug that exact same logic into the physical robot without any extra tweaking. Usually, moving from digital to real life is a nightmare of adjustments, but here, the robot just gets up and starts walking straight away. You can control it with simple Python scripts or let it run on its own tiny computer board.

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📦 yesianrohn/water

Water: The 90% Text Recognition Breakthrough

Water reads the wildest, most chaotic text styles found in real-world signs, stickers, and logos with a precision that used to be impossible. This research project, accepted for a major computer vision conference, stops relying on messy, inconsistent real photos to teach computers how to see. Instead, it builds its own massive, synthetic world of artistic text using over eleven thousand unique fonts and tricky layouts like curved paths and warped perspectives. By feeding this clean, controlled data into a specialized model, it finally breaks through that stubborn ninety percent accuracy barrier on the hardest text benchmarks.

🆔 @hackernewsgithubprojects

📦 google/mantis

Mantis: The AI Security Audit Tool

Point an AI agent at your code and let it hunt for bugs. Mantis is a new toolkit from Google that turns standard coding assistants into autonomous security auditors. It does not just guess at problems; it builds a map of your software, finds potential weaknesses, and then actively tries to break its own code to prove the bug is real. Once it confirms the issue, it writes the fix and tests that fix against new attacks. This closed loop means you get verified patches, not just warnings. It is a powerful way to catch security gaps before they reach production.

🆔 @hackernewsgithubprojects

📦 noizefield/audio-plugin-coder

Audio Plugin Coder: Describe, Don't Code

Audio Plugin Coder lets you build professional audio plugins just by describing them in plain English. The surprising part is that it handles the entire creation process for you, from the initial idea all the way to a finished file you can actually use in your music software. It works by guiding AI coding tools through every step, like designing the interface and writing the sound processing logic, so you do not have to learn complex programming languages first. You simply tell the system what you want, and it builds it on your computer.

📰 https://news.ycombinator.com/item?id=49710997

🆔 @hackernewsgithubprojects