Maziyar PANAHI
@MaziyarPanahi
the on-device clinical AI guy | building @OpenMed_AI | making open models @arcee_ai | AI infra & research @CNRS | follow for local clinical AI demos
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70,111 CT scans trained this model. one NVIDIA GPU turned a full chest CT into a radiology report in under 30 seconds 🔥
top finding: a spiculated nodule, right upper lobe. the expert's tumor marking it never saw? right lung.
open weights from @nvidia on @huggingface 🤗
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i'm releasing 180,000 Jev-format tool-calling decisions, typed and labeled, so a tiny model can make them instead of your LLM 🔥
which tool, whether to call one, are the args complete. clean splits. built on NVIDIA's open agent data.
train your own router on it, then race Jev.
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okay this is too cute 😭 i saw Microduck and immediately asked Qwen3.8-27B to build a tiny browser gym where i could teach it a treat-delivery trick. 250 episodes later, the little guy learned it in 9 steps.
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wtf, i asked Qwen3.8 27B to build me an FPS and now i'm in a Chinese courtyard shooting porcelain drones 😭
mouse lock, sprint, reload, hold Z for a tactical scope. fully local in the browser.
Qwen wrote the game pass. ChatGPT Image made the world + assets. ridiculous.
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locally on the Mac, nothing leaves anyway. but if you don't have the hardware, you can run GLM-5.2 via HF Inference Providers instead, and OpenMed still de-identifies every name on-device FIRST. Live redaction. Open models:
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Got GLM-5.2 running on my Mac Studio via llama.cpp, the reasoning behind all my medical agentic workflows.
It orchestrates a swarm of tiny on-device OpenMed experts: oncology, meds, labs.
No cloud, no rate limits, nobody can take it away.
AI must be owned, not rented.
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