Brain Byte
Turn anything you study into a learning journey.
What it does
BrainByte turns anything you study —PDFs, notes, and videos — into a learning journey: concepts, challenges, and targeted review, engineered for retention. Students spend less time searching, passively watching, and rewatching, and more time understanding, practicing, and revising Badges
Brain Byte transforms PDFs, notes and videos into concepts, challenges and targeted review. An adaptive learning journey that feels like a game and works like a tutor.
Transform PDFs, notes and videos into concepts, challenges and targeted review. A network partition hits. Your cluster continues to serve write requests. Which CAP property did it prioritize? Drop a PDF, paste notes, or link a YouTube video. Brain Byte does the rest. Brain Byte turns passive material into an active path: read less, recall more, and always know what to do next. Drop in PDFs, lecture notes, slides or YouTube videos. Brain Byte reads them and drafts a structured journey of bite-sized concepts. Ideas are linked by dependency, so you always learn foundations before the things built on them. Every concept ends in a challenge that tests understanding — not recognition. Weak spots…from brain-byte-one.vercel.app
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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…
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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
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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