Cue
Every AI ships the same hero. Cue ships the one that lands.
What it does
Every AI tool ships the same-looking hero. Cue is the taste layer that fixes that — hand-picked prompts for Bolt, v0, Cursor, and Framer that land you at the version worth shipping to a client. 50+ prompts curated from Awwwards-tier sites. Not generated to fill the grid — each one replaced when a better reference exists. New drops every week, forever. Founding lifetime: $99 for the first 50 members. After that, $249. One-time. No subscription.
Awwwards-tier UI components with AI prompts + React source. Hand-picked, taste-driven. $99 lifetime for the founding 50.
Cue is an Awwwards-tier component library for anyone building to stand out — the best-in-class components from across the internet, curated by hand. Not just for AI builders: designers, developers, agencies, solo makers, and product teams who refuse to ship generic-looking work. Each entry is a component reference sourced from an Awwwards Site of the Day, a Behance-featured interaction, or a production output the founder considered best-of-class. Today Cue ships AI prompts for every component (drop into Bolt, v0, Cursor, Framer AI, ChatGPT, Claude); React source code and an MCP server (native access from Cursor / Claude Desktop / any MCP-aware AI tool) are actively rolling out. Cue does not…from cuedesign.space
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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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Launched alongside, August 2026
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Life & fun · 10d ago · louisabraham.github.io
- SA
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 · 16d ago · simedw.com