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Maziyar PANAHI

@MaziyarPanahi • 17,532 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.

592,818 次观看

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?

367,117 次观看

🚨 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 次观看

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 次观看

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?

120,723 次观看

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.

158,435 次观看

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?

176,929 次观看

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!

202,224 次观看

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.

62,921 次观看

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.

25,030 次观看

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.

20,247 次观看

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:

35,095 次观看

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 次观看

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?

29,085 次观看

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 次观看

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 次观看

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?

18,156 次观看

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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. 👇

201,263 次观看 • 1 天前

🚨 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 次观看 • 23 天前

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 次观看 • 1 个月前

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