Brainberyl
Where Every Assessment Drives Success
What it does
BrainBeryl is an AI-powered operating system for coaching institutes that replaces multiple disconnected tools with one unified platform. It automates answer sheet evaluation, fee management, attendance, tests, analytics, and parent communication using AI. Unlike traditional ERPs that only store data, BrainBeryl actively reduces teachers' workload, provides actionable insights, and helps institutes operate smarter while improving student outcomes.
Create quizzes in 2 minutes. Grade 100 students in 30 seconds.
Create quizzes in simple natural language. Grade 100 copies in 30 seconds. Zero App Downloads. Everything on WhatsApp. It starts with a few quizzes. Then it becomes a mountain of paper. Your weekends vanish. Students wait days for results. It's an outdated cycle. We stripped away the complexity of traditional LMS to give you something pure, fast, and remarkably effective. Create comprehensive quizzes in minutes, not hours. Reclaim your Sundays. Grading happens automatically. Insights are delivered instantly via WhatsApp. Students love the instant feedback loop. Participation rates skyrocket. Type a topic or upload a PDF. Our AI crafts the perfect quiz in seconds. Get a magic link. Send it…from brainberyl.com
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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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