Machina
Open-source AI dev tools that learn your workflow
What it does
Free, open-source tools that close the gap between "I see the bug" and "the AI fixes it." 🐛 BugCapture — screen + audio recording → AI-readymd with screenshots + Whisper transcript. Drop into Claude or Copilot; the AI understands the bug immediately. 🧠 LearnBoard — persistent AI memory via LEARNING.md. Sessions get smarter over time. 🎨 PromptBoard — drag-and-drop canvas for building AI prompts visually. Try in-browser at machina.chat. MIT licensed, runs locally.
Does the same job
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- AMAux Machina – AI photo generator without complex prompting2024 · auxmachina.com · ▲6
Hey HN, We’ve been on a wild journey building Aux Machina, and think it’s fun to share a bit of the ride with you. It all starts with a “what if.” What if we make AI-powered visuals without needing a PhD in prompting? Sounds straightforward, right? Turns out, it’s more like a series of sleepless nights and way more Python than we bargain for . Picture this: We met at Harrison.ai in 2019, knee-deep in CNNs in a pre-ChatGPT world. We were building healthcare AI that saves lives — serious stuff. But between all the daily stand-ups and coffee refills , we realised, “Hey, we actually like working…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, June 2026
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Today, I’m proud to announce Homebrew 6.0.0. The most significant changes since 5.1.0 are a new tap trust security mechanism, the new faster, smaller, default internal Homebrew JSON API, sandboxing on Linux, better defaults informed by our user survey, many brew bundle improvements, improved performance and initial support for macOS 27 (Golden Gate). Happy to discuss any questions here!
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