TinyFish
The web operating layer for AI agents
What it does
TinyFish is a unified web operations platform built for AI agents and AI applications. It enables developers to search the live web, extract clean structured content, browse dynamic websites, and automate authenticated workflows through a single platform. By providing reliable access to current web data and web interactions, TinyFish helps AI systems deliver more accurate answers, reduce operational complexity, and scale production workloads.
TinyFish provides enterprise infrastructure for AI web agents. Run hundreds of websites simultaneously to navigate, authenticate, extract data, and automate web workflows with a fully serverless architecture.
Search the web, fetch clean content from any URL, run a real browser, and deploy agents. All from one API . Search returns fresh URLs and Fetch turns pages into clean content. Both are free at any Wallet balance and never draw from it. Real browser-rendered search that returns structured JSON from dynamic pages and sources that change too fast for traditional search. Free API access for agents. Built for pricing shifts, earnings monitoring, and breaking news. Renders the page in a real browser and returns clean markdown, JSON, or HTML. Your models and pipelines get the page as it actually appears, stripped and token-efficient. Free API access for agents. Built for research pipelines,…from tinyfish.ai
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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 · 9d ago · louisabraham.github.io
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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 · 16d ago · simedw.com