Marginal – See which customers and features drive your AI API costs
Demo account (shared, please be nice): https://marginalhq.com/login - email [email protected], password marginal-demo-2026. It's a seeded workspace for a fictional company; poke around Overview/Insights/Explorer. Or sign up and send real events.
What it does
Track every LLM call's cost and slice it by customer, feature, model — any field you define.
Marginal tracks the cost of every LLM call and slices it by the fields you care about — customer, feature, model. marginal.track({ provider : "openai" , model : response.model, usage : response.usage, fields : { customer : "acme-corp" , feature : "summarize" , }, }); One call per LLM request. Cost is computed server-side — usage in, dollars out. Slice spend by what matters in your product, see every dimension charted automatically, and check exactly what your integration sent. Insights charts spend for every dimension automatically — each model, provider, and registered field gets its own chart. No setup, no query builder. Every API request is logged with its outcome — accepted counts,…from marginalhq.com
Does the same job
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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 · 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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