Egamus
Your Online Ceo
What it does
EGAMUS is an AI-powered business command center for founders, startups and growing teams. It brings product, growth, revenue and operational signals into one place, then uses an AI CEO to explain what is happening, what needs attention and what to do next. Track product health, users, retention, errors, goals and more through one intelligent workspace. Analytics tells you what happened. EGAMUS helps you decide what happens next.
Turn verified product data into decisions, campaigns, and founder-approved action from one intelligent command center.
Create a real workspace, connect a live signal, and validate EGAMUS before choosing a paid plan. EGAMUS turns product signals into priorities, strategy, content, and clear answers—in the language your team speaks. This workspace is using clearly labelled sample data. Connect analytics, revenue, and reliability sources so the AI CEO can advise NinthcoreAI from verified evidence. Connect the production metrics endpoint before scaling traffic. Measure whether multilingual voice users return after sevenfrom egamus.ninthcoreai.in
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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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