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Show HN: Browse 61 3D Printable Robots Robotics is advancing really fast lately, with AI inference, different controllers, software, and parts always changing. I wanted a place that supports many device types, Raspberry Pi, NVDA Jetson, Arduino, ESP32, hardware sources, and maximizes for printability. Instructables, Github, and Thingiverse are currently popular but aren't really focused on robotics, So I built orobot.io to try and make printing robots as standardized and accessible as possible. It uses a lot of Agent built content custom to each project, and every project is designed to be used by humans or your agent. Features: - Photos and Estimated Prices for all projects - Links back to source GitHub projects - LLMs write descriptions and tips on how to build - View + Download 3d printable STL files in browser - BOM purchase links are kept up to date with LLMs checking Amazon link health - LLMs write Javascript install and controller wrappers custom to each project so a single...

Show HN: Nibble An attempt at a single pass LLVM frontend in ~3000 lines of C without external dependencies, malloc, or an AST. Included are some graphical examples. The IR isn't perfect, and the README touches on one particular downfall https://ift.tt/IAQme6U May 14, 2026 at 07:16AM

Show HN: Petri – Drop-in Postgres image that forks a DB per test Rolling it out at work to parallelize 4,257 tests across 5 services. It fixes our tests running in band and DB mocking in API tests. It's a drop-in Postgres image, with a Golang proxy. :5432 is passthrough, :5433 forks the DB per conn (CREATE DATABASE … TEMPLATE …, dropped on disconnect). If you use it, let me know what you like or don't like, so I can make it better. Cheers! https://ift.tt/yF0BtEz May 14, 2026 at 05:02AM

Show HN: Micromort Risk Visualizer https://boxed.github.io/micromort/ May 14, 2026 at 12:09AM

Show HN: Neural window manager, neural network moving windows from mouse actions I'd been mulling over this crazy idea for a while. Can programs be generated? Inspired by recent advances in world models, I wondered if we could do away with source code and generate pixels directly and interactively. As an experiment to answer this, I set out to create a neural window manager, training a neural network to predict what the screen would look like next. Basically, the idea was to generate the next frame based on the last two frames and the mouse position. That's it: moving windows without programming an event system, just a simple convolutional neural network guessing pixels. To implement the experiment, I used Pygame to simulate a turquoise desktop background, a gray window with a navy blue title bar, a white cursor, and four colors in total. Then, a bot randomly dragged the window, and I recorded everything, processing the frames as color index matrices (not RGB, to avoid complica...

Show HN: Statewright – Visual state machines that make AI agents reliable Agentic problem solving in its current state is very brittle. I fell in love with it, but it creates as many problems as it solves. I'm Ben Cochran, I spent 20+ years in the trenches with full-stack Engineering, DevOps, high performance computing & ML with stints at NVIDIA, AMD and various other organizations most recently as a Distinguished Engineer. For agents to work reliably you either need massive parameter counts or massive context windows to keep the solution spaces workable. Most people are brute forcing reliability with bigger models and longer prompts. What if I made the problem smaller instead of making the model bigger? I took a different approach by using smaller models: models in the 13-20B parameter range and set them to task solving real SWE-bench problems. I constrained the tool and solution spaces using formal state machines. Each state in the machine defines which tools the model can a...