Qurify
Internal AI Search, Reimagined
What it does
Qurify is a revolutionary AI-powered internal search engine designed to transform user experience on content-heavy websites. Traditional keyword-based site search often frustrates users with irrelevant results. Qurify solves this by reading and understanding every article, guide, and page on your platform.
Qurify reads and understands every page of your website, delivering instant, context-aware answers instead of keyword matches.
Qurify reads and understands every article, guide, and page on your website — then answers your visitors' questions directly, like a brilliant assistant who has memorized your entire site. No more keyword guessing. No more dead-end results. Qurify analyzes user intent, continuously syncs with your published content, and transforms site search into actionable audience intelligence. Qurify tracks queries, answers delivered, and content gaps in real time — turning your site search into a live map of audience demand. Qurify continuously reads and indexes every article, guide, and page you publish. Under the hood, Qurify combines full-site AI comprehension with lightning-fast retrieval — so…from qurify.io
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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.
AI · 17d ago · simedw.com