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Maziyar PANAHI

@MaziyarPanahi • 17,593 subscribers

Building @OpenMed_AI · 3,500+ open-source medical models · #1 on HuggingFace Daily Papers · Shipping OpenMed Agent today: Terminal-native AI for Healthcare

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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.

593,183 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?

367,946 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.

158,615 просмотров

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?

121,675 просмотров

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?

177,050 просмотров

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!

202,305 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.

62,969 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.

25,364 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

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

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

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?

30,457 просмотров

OpenAI's privacy-filter, retrained on NVIDIA's Nemotron data. PII Masking leaderboard: → openai/privacy-filter: #10 → privacy-filter-nemotron: #4 → OpenMed-PII-SuperClinical: #1, #2 Six places gained from retraining. Demo on web + iPhone:

OpenAI's privacy-filter, retrained on NVIDIA's Nemotron data. PII Masking leaderboard: → openai/privacy-filter: #10 → privacy-filter-nemotron: #4 → OpenMed-PII-SuperClinical: #1, #2 Six places gained from retraining. Demo on web + iPhone:

18,113 просмотров

Jets. Helicopters. Wildfire. Traffic. Crowds. Falcon Perception. 0.6B parameters. "Find every person." It finds every person. "Find the fire." It finds the fire. +21.9 over SAM 3 on spatial understanding. Running locally via MLX. No cloud. What should I throw at it next?

Jets. Helicopters. Wildfire. Traffic. Crowds. Falcon Perception. 0.6B parameters. "Find every person." It finds every person. "Find the fire." It finds the fire. +21.9 over SAM 3 on spatial understanding. Running locally via MLX. No cloud. What should I throw at it next?

18,179 просмотров

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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. 👇

262,977 просмотров • 14 дней 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. 👇

67,617 просмотров • 16 дней 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.

95,504 просмотров • 1 месяц 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 • 1 месяц назад

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