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Maziyar PANAHI

@MaziyarPanahi • 18,614 subscribers

Building @OpenMed_AI · 3,500+ open-source models · Apache 2.0, runs on-device · #1 org in HF Daily Papers

Shorts

I finally got an open model to do structural biology by itself 🔥 GLM-5.2 drives the Mol* viewer, judges its own render through Qwen3-VL, and refines until the drug pops in its pocket. Then I spun it in 3D. All open, on Hugging Face. What should it build next?

I finally got an open model to do structural biology by itself 🔥 GLM-5.2 drives the Mol* viewer, judges its own render through Qwen3-VL, and refines until the drug pops in its pocket. Then I spun it in 3D. All open, on Hugging Face. What should it build next?

60,802 просмотров

Gemma 4 looks at a parking lot. Decides what to ask. Calls SAM 3.1. "Segment all vehicles." 64 found. "Now just the white ones." 23 found. One model reasoning and orchestrating. One model executing. Both running locally on a MacBook. MLX. No cloud. No API.

Gemma 4 looks at a parking lot. Decides what to ask. Calls SAM 3.1. "Segment all vehicles." 64 found. "Now just the white ones." 23 found. One model reasoning and orchestrating. One model executing. Both running locally on a MacBook. MLX. No cloud. No API.

593,790 просмотров

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.

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.

87,461 просмотров

Gemma 4 watches raw video. Understands the scene. Then prompts SAM 3 to segment and RF-DETR to track. One AI directing two others. Fighter jets. Crowds. Aerial defense footage. All three models running locally on a MacBook. No cloud. What scene should I point this at next?

Gemma 4 watches raw video. Understands the scene. Then prompts SAM 3 to segment and RF-DETR to track. One AI directing two others. Fighter jets. Crowds. Aerial defense footage. All three models running locally on a MacBook. No cloud. What scene should I point this at next?

369,034 просмотров

I finally managed to use Unlimited-OCR and Gemma 4 together in OpenMed. 🔥 A real patient chart: read, de-identified, and mapped to FHIR on one laptop. No cloud, no API key, nothing leaves the machine. All via llama.cpp, all free on Hugging Face. 🤗 What do we do next?

I finally managed to use Unlimited-OCR and Gemma 4 together in OpenMed. 🔥 A real patient chart: read, de-identified, and mapped to FHIR on one laptop. No cloud, no API key, nothing leaves the machine. All via llama.cpp, all free on Hugging Face. 🤗 What do we do next?

41,819 просмотров

🚨 BREAKING: Apple just acquired Meta's SAM team! On-device segmentation now ships natively in macOS 26.x This is what it looks like when you run SAM3 on MLX locally. Every car. Every fire. Real-time. M2 laptop. No cloud.

🚨 BREAKING: Apple just acquired Meta's SAM team! On-device segmentation now ships natively in macOS 26.x This is what it looks like when you run SAM3 on MLX locally. Every car. Every fire. Real-time. M2 laptop. No cloud.

329,986 просмотров

Gemma 4 just dropped. I had it captioning video in real-time within an hour. Running locally on a MacBook. No cloud. No API. Real-time scene understanding. Oh and SAM3 is segmenting every object in the same frame. Same laptop.

Gemma 4 just dropped. I had it captioning video in real-time within an hour. Running locally on a MacBook. No cloud. No API. Real-time scene understanding. Oh and SAM3 is segmenting every object in the same frame. Same laptop.

196,846 просмотров

Gemma 4 analyzes the video. Generates key questions. Calls Falcon Perception. "Find all the people." 156 found. "Detect only white cars." 8 found. A 26B model is running agentic multi-QA vision orchestration. The models are running locally on a MacBook with MLX. No API.

Gemma 4 analyzes the video. Generates key questions. Calls Falcon Perception. "Find all the people." 156 found. "Detect only white cars." 8 found. A 26B model is running agentic multi-QA vision orchestration. The models are running locally on a MacBook with MLX. No API.

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Wow! This is amazing! Segmented every car locally in real time with Meta's SAM3 converted to MLX. Just on-device (M2 laptop) vision getting absurdly good. Local AI is moving faster than most people realize! What other models should we test? what kind of videos?

Wow! This is amazing! Segmented every car locally in real time with Meta's SAM3 converted to MLX. Just on-device (M2 laptop) vision getting absurdly good. Local AI is moving faster than most people realize! What other models should we test? what kind of videos?

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Same text. Two privacy filters. OpenAI's model catches 8 categories. OpenMed catches 55+: medical record numbers, blood type, API keys, financial codes, demographics. Trained on Nemotron data by Nvidia. All on-device. All open-source. Coming soon! What's missing?

Same text. Two privacy filters. OpenAI's model catches 8 categories. OpenMed catches 55+: medical record numbers, blood type, API keys, financial codes, demographics. Trained on Nemotron data by Nvidia. All on-device. All open-source. Coming soon! What's missing?

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it's crazy what a 1.5B model can do these days! "VibeThinker-1.5B is a 1.5-billion parameter dense language model. With a total training cost of only $7,800 USD, it achieves reasoning performance comparable to larger models like GPT OSS-20B Medium." runs perfectly on device!

it's crazy what a 1.5B model can do these days! "VibeThinker-1.5B is a 1.5-billion parameter dense language model. With a total training cost of only $7,800 USD, it achieves reasoning performance comparable to larger models like GPT OSS-20B Medium." runs perfectly on device!

202,350 просмотров

I showed you SAM 3 all week. This is a 0.6B model that outperforms it. Falcon Perception. Type "detect the plane" and it segments every plane in the frame. Pixel-accurate masks from natural language. Fighter jets. Fire. Crowds. All on a MacBook via MLX. No cloud.

I showed you SAM 3 all week. This is a 0.6B model that outperforms it. Falcon Perception. Type "detect the plane" and it segments every plane in the frame. Pixel-accurate masks from natural language. Fighter jets. Fire. Crowds. All on a MacBook via MLX. No cloud.

63,029 просмотров

Opus 4.8 just did the most important thing in clinical AI: it said no. Asked to reconcile 3 guideline bodies on aspirin, OpenMed Agent searched, found the guidelines weren't in its sources, and labeled 8 gaps instead of inventing them. Refusing to fabricate is the feature.

Opus 4.8 just did the most important thing in clinical AI: it said no. Asked to reconcile 3 guideline bodies on aspirin, OpenMed Agent searched, found the guidelines weren't in its sources, and labeled 8 gaps instead of inventing them. Refusing to fabricate is the feature.

27,947 просмотров

From parked cars to an Airbus A321 at cruising altitude. Same laptop. MLX & Torch. SAM3 segments every vehicle on the ground. Yes, just cars. RF-DETR spots the Austrian Airlines jet overhead. Real-time detection. Two open-source models running locally. No cloud. No API.

From parked cars to an Airbus A321 at cruising altitude. Same laptop. MLX & Torch. SAM3 segments every vehicle on the ground. Yes, just cars. RF-DETR spots the Austrian Airlines jet overhead. Real-time detection. Two open-source models running locally. No cloud. No API.

46,612 просмотров

1 week, 4 open-source medical AI shipments: → 35 PII models for Portuguese → openmed==1.1.0 (Brazilian + EU coverage) → OpenMedKit on iPhone: GLiNER + MLX → OpenAI's privacy-filter ported to MLX (24-33x faster) All Apache 2.0. All on-device. 15 seconds recap:

1 week, 4 open-source medical AI shipments: → 35 PII models for Portuguese → openmed==1.1.0 (Brazilian + EU coverage) → OpenMedKit on iPhone: GLiNER + MLX → OpenAI's privacy-filter ported to MLX (24-33x faster) All Apache 2.0. All on-device. 15 seconds recap:

35,507 просмотров

GPT-5.5 + OpenMed Agent planning a 64-step clinical workflow. Watch plans, sub-plans, and tool calls materialize, every step visible, every finalization gated. Medical intelligence on Hugging Face. The loop is the product.

GPT-5.5 + OpenMed Agent planning a 64-step clinical workflow. Watch plans, sub-plans, and tool calls materialize, every step visible, every finalization gated. Medical intelligence on Hugging Face. The loop is the product.

25,417 просмотров

SAM 3D Body on a gymnast. One RGB frame in. Full 3D body mesh out. 18,439 vertices. 36,874 faces. Rotating 360° around a real human. Locally on a 3-year old MacBook. What subject should I mesh next?

SAM 3D Body on a gymnast. One RGB frame in. Full 3D body mesh out. 18,439 vertices. 36,874 faces. Rotating 360° around a real human. Locally on a 3-year old MacBook. What subject should I mesh next?

30,238 просмотров

Gemma 4 sees a kid and three dogs. Decides what matters. Calls SAM 3.1 Mask and bounding box. Spotlight on subjects. Background blur. Background pixelation. Four effects. Fully agentic. Two models talking to each other on a MacBook. No App. No cloud. What would you edit?

Gemma 4 sees a kid and three dogs. Decides what matters. Calls SAM 3.1 Mask and bounding box. Spotlight on subjects. Background blur. Background pixelation. Four effects. Fully agentic. Two models talking to each other on a MacBook. No App. No cloud. What would you edit?

30,513 просмотров

Arabic. Japanese. Turkish. Redacting clinical discharge summaries in real-time. 30+ new open-source PII models shipped today on Hugging Face. 30+ MLX variants as native Swift packages for macOS and iOS. OpenMed PII family: 1M+ downloads in 20 days. Apache 2.0.

Arabic. Japanese. Turkish. Redacting clinical discharge summaries in real-time. 30+ new open-source PII models shipped today on Hugging Face. 30+ MLX variants as native Swift packages for macOS and iOS. OpenMed PII family: 1M+ downloads in 20 days. Apache 2.0.

20,541 просмотров

10x speedup: OpenAI's privacy-filter on MLX vs CPU, side by side in your browser. 1,818 tok/s vs 179 tok/s. Real-time PII redaction across English, German, French. On-device. No cloud. Privacy-first. Which model should we port next?

10x speedup: OpenAI's privacy-filter on MLX vs CPU, side by side in your browser. 1,818 tok/s vs 179 tok/s. Real-time PII redaction across English, German, French. On-device. No cloud. Privacy-first. Which model should we port next?

25,329 просмотров

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We got 755 tokens per second! 🔥 That's OpenMed privacy-filter v2 (nemotron, MLX 8-bit) reading a 13,000-token clinical file and redacting every identifier as it streams by. 1,152 caught across 22 PII categories, on a Mac. Nothing left the machine.

We got 755 tokens per second! 🔥 That's OpenMed privacy-filter v2 (nemotron, MLX 8-bit) reading a 13,000-token clinical file and redacting every identifier as it streams by. 1,152 caught across 22 PII categories, on a Mac. Nothing left the machine.

146,885 просмотров • 16 дней назад

Frontier medical reasoning on your iPhone, with GPT-5.5, and zero identifiers ever leaving the device. OpenMed Agent reads your Apple Health, wearable trends, and any medical report you scan, redacts on-device, then asks GPT-5.5 via your own ChatGPT account. App Store next. 👇

Frontier medical reasoning on your iPhone, with GPT-5.5, and zero identifiers ever leaving the device. OpenMed Agent reads your Apple Health, wearable trends, and any medical report you scan, redacts on-device, then asks GPT-5.5 via your own ChatGPT account. App Store next. 👇

263,597 просмотров • 1 месяц назад

🚨 Today: OpenMed Agent ships in preview. Built on Hugging Face: → HF endpoints power clinical extraction + terminology → MCP for your own services → Every tool call, every plan, fully visible 1,000+ OpenMed medical models on HF. Preview today.

🚨 Today: OpenMed Agent ships in preview. Built on Hugging Face: → HF endpoints power clinical extraction + terminology → MCP for your own services → Every tool call, every plan, fully visible 1,000+ OpenMed medical models on HF. Preview today.

99,723 просмотров • 2 месяцев назад

🚨 Welna is live on the App Store today. Frontier AI that reasons over your Apple Health, your labs, your Apple Watch, and never learns your name. GPT-5.5 reasons. OpenMed's on-device filter strips every identifier first. The understanding without the exposure. 👇

🚨 Welna is live on the App Store today. Frontier AI that reasons over your Apple Health, your labs, your Apple Watch, and never learns your name. GPT-5.5 reasons. OpenMed's on-device filter strips every identifier first. The understanding without the exposure. 👇

51,356 просмотров • 1 месяц назад

I printed a patient's discharge summary, packed with names, IDs, phone, email, and address, then scanned it with my iPhone. 31 pieces of PHI masked on-device. Then it pulled the diagnoses, meds, and doses out as clean data. No server. No Python. Nothing leaves the phone. 👇

I printed a patient's discharge summary, packed with names, IDs, phone, email, and address, then scanned it with my iPhone. 31 pieces of PHI masked on-device. Then it pulled the diagnoses, meds, and doses out as clean data. No server. No Python. Nothing leaves the phone. 👇

71,390 просмотров • 1 месяц назад

> RTX Pro 6000 96GB Prime Intellect > Nanonets-OCR Nanonets > vLLM vLLM > 2000 images from cifar10 Hugging Face one word, damn!

> RTX Pro 6000 96GB Prime Intellect > Nanonets-OCR Nanonets > vLLM vLLM > 2000 images from cifar10 Hugging Face one word, damn!

229,897 просмотров • 9 месяцев назад

OpenMed Agent + Claude Opus 4.7 just ran a 14-step special-pathogen ED workup on a synthetic VHF case. Live CDC + WHO + PubMed retrieval. Evidence-weighted differential. Clinician signature required before any artifacts finalized. The loop is the product.

OpenMed Agent + Claude Opus 4.7 just ran a 14-step special-pathogen ED workup on a synthetic VHF case. Live CDC + WHO + PubMed retrieval. Evidence-weighted differential. Clinician signature required before any artifacts finalized. The loop is the product.

18,695 просмотров • 2 месяцев назад

Turned real footage into a Call of Duty HUD. 3 scenes. 1 open-vocab stack. 0 training runs. Type the thing, the HUD finds the thing, green containers, cargo near the water, incoming aircraft. No GPU. Just a MacBook. What game should the HUD cosplay next?

Turned real footage into a Call of Duty HUD. 3 scenes. 1 open-vocab stack. 0 training runs. Type the thing, the HUD finds the thing, green containers, cargo near the water, incoming aircraft. No GPU. Just a MacBook. What game should the HUD cosplay next?

11,541 просмотров • 3 месяцев назад

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