
OmniRouter
Access multiple AI models through one simple API
What it does
OmniRouter provides a single API for accessing multiple AI models from different providers. Instead of managing separate APIs, accounts, and billing for each provider, developers can use one interface and balance while switching between models easily. OmniRouter focuses on simple integration, competitive pricing, and convenient access to multiple models through one API.
Build with leading AI models through one reliable, OpenAI-compatible API. Transparent prepaid pricing and automatic provider failover.
Build with Claude, GPT, Gemini, Grok, DeepSeek, and more through a single reliable endpoint. Pay only for what you use, with no subscription and no provider juggling. A focused API surface for shipping, monitoring, and paying for AI workloads without stitching together vendor accounts. Switch between leading models without replacing your SDK or rebuilding the integration. Requests route through ranked providers and fail over automatically when one slows down. See tokens, latency, model, and the exact charge for every request in one clear ledger. API keys are stored as secure hashes. Provider credentials never enter your application. Add credit when you need it. No subscriptions, seat…from omnirouter.li
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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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