January AI
Metabolic & nutrition infrastructure for developers
What it does
Add meal scanning, restaurant search, device-free glucose predictions, and access to millions of verified foods to your health product. Built with the precision health demands and the simplicity modern developers expect. Sign up, create an API key, and start building for free. Available via APIs and SDKs.
Build better health experiences without building the health intelligence underneath them. Meal Scan, device-free glucose prediction, and the Health Context Engine, through APIs or complete experiences.
Predict a person's glucose response to a meal without requiring a CGM. Photo food search, logging, and personalized healthy food alternatives. Health context layer for AI with personalized knowledge graph, built on medical ontologies and FHIR integration. Surface nutrition and metabolic insights within clinician workflows and EHRs. Published in npj Digital Medicine, a Nature Portfolio journal, alongside additional studies and presentations. Meal Scan outperforms frontier LLMs . January's Health Context Engine scores 88% on endocrine board exams with a 0.8% hallucination rate. Clinician Nutrition Monitor is qualified on Mayo Clinic Platform and runs inside the Epic patient chart. Security,…from january.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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