Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

There has been a huge debate recently about the best approach for image background removal. Here's my attempt: - In-browser inference w/ 🤗 Transformers.js - WebGPU accelerated (fast!) - Costs $0 (no image hosting or server processing) - No data leaves your device (privacy!)

Xenova

18,711 subscribers

419,020 görüntüleme • 2 yıl önce •via X (Twitter)

Bilim & Teknoloji Eğitim

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

I've always been kind of annoyed I wasn't better at Rubik's cube solving. So a Gemma 4 design session while on a plane w/ no internet + a Claude Code ~one shot yielded this Rubik's solver/trainer. Create your cube or paste a flat-net image of your scramble → it reads the colors, finds a 22-or-fewer-move solution, and animates the 3D cube through every turn. No backend. 100% in the browser. Run it: Code:

I've always been kind of annoyed I wasn't better at Rubik's cube solving. So a Gemma 4 design session while on a plane w/ no internet + a Claude Code ~one shot yielded this Rubik's solver/trainer. Create your cube or paste a flat-net image of your scramble → it reads the colors, finds a 22-or-fewer-move solution, and animates the 3D cube through every turn. No backend. 100% in the browser. Run it: Code:

Jeff Huber 🇺🇸

269,652 görüntüleme • 4 ay önce

🚨 JUST IN: THIS FREE TOOL JUST REPLACED FOUR AI IMAGE AND VIDEO SUBSCRIPTIONS AT ONCE. Midjourney. Krea. Higgsfield. Openart. One repo. 200+ models. Zero dollars a month. Here is what it actually does. It is a full image and video studio that runs in your browser or as a desktop app. Text to image, image to image, text to video, image to video, lip sync, cinema mode with real camera controls. All of it. 4,500 people already starred this. What you get for free: → 50+ image models including Flux, Midjourney v7, Ideogram, GPT-4o, Seedream → 60+ video models including Kling, Sora, Veo, Runway, Wan, Hailuo → lip sync studio with 9 dedicated models. upload a portrait and audio and it talks → cinema studio with real camera controls. lens, focal length, aperture, film stock → feed up to 14 reference images into one generation → self-hosted. your data never leaves your machine The crazy part is there is also a hosted version that needs zero setup. Just open the link and start generating. Now the math. Midjourney Standard: $30/month Krea AI Pro: $30/month Higgsfield Plus: $49/month Openart AI: $15/month That is $124 a month. $1,488 a year. This repo does everything all four do. With more models than any of them. For free. Forever. No subscription. No vendor lock-in. MIT licensed. Download it in one click on Mac or Windows. Someone should have told me about this sooner. I feel like an idiot. ( save this )

🚨 JUST IN: THIS FREE TOOL JUST REPLACED FOUR AI IMAGE AND VIDEO SUBSCRIPTIONS AT ONCE. Midjourney. Krea. Higgsfield. Openart. One repo. 200+ models. Zero dollars a month. Here is what it actually does. It is a full image and video studio that runs in your browser or as a desktop app. Text to image, image to image, text to video, image to video, lip sync, cinema mode with real camera controls. All of it. 4,500 people already starred this. What you get for free: → 50+ image models including Flux, Midjourney v7, Ideogram, GPT-4o, Seedream → 60+ video models including Kling, Sora, Veo, Runway, Wan, Hailuo → lip sync studio with 9 dedicated models. upload a portrait and audio and it talks → cinema studio with real camera controls. lens, focal length, aperture, film stock → feed up to 14 reference images into one generation → self-hosted. your data never leaves your machine The crazy part is there is also a hosted version that needs zero setup. Just open the link and start generating. Now the math. Midjourney Standard: $30/month Krea AI Pro: $30/month Higgsfield Plus: $49/month Openart AI: $15/month That is $124 a month. $1,488 a year. This repo does everything all four do. With more models than any of them. For free. Forever. No subscription. No vendor lock-in. MIT licensed. Download it in one click on Mac or Windows. Someone should have told me about this sooner. I feel like an idiot. ( save this )

Kanika

14,769 görüntüleme • 4 ay önce

We wanted to get video overviews in your hands as soon as we possibly could. While we've been LOVING all the excitement, we know there are still some crucial features we need to add. For example, currently the best way to add images to your video overviews is through Docs, Slides, or image-only PDFs. While support for pulling images from all PDFs is our TOP priority, here's a "super easy" and "clear to understand" hack on how to make it work in the meantime ❤️❤️❤️

We wanted to get video overviews in your hands as soon as we possibly could. While we've been LOVING all the excitement, we know there are still some crucial features we need to add. For example, currently the best way to add images to your video overviews is through Docs, Slides, or image-only PDFs. While support for pulling images from all PDFs is our TOP priority, here's a "super easy" and "clear to understand" hack on how to make it work in the meantime ❤️❤️❤️

Gemini Notebook

56,732 görüntüleme • 1 yıl önce

Update 1: Visit to hacked by peniche Service Centre for Camera Image Shift issue in #GalaxyS24Ultra 1,3 - Faulty device 2,4 - Showroom device Upon first encountering the issue and providing video evidence, they adamantly denied its existence. Despite demonstrating the issue in person, they insisted it was within normal functioning for the #GalaxyS24Ultra 🤯 I questioned the training provided to Samsung India's service center staff. 🤔 The representative asked for 30 minutes and left the service center. Upon returning, he claimed to have tested another device in the showroom and declared the issue common, refusing a return. Their attempt to normalize the issue was frustrating. 😒 Taking matters into my own hands, I visited the Samsung Mobile Showroom where I purchased my device and tested the demo units. They all functioned flawlessly, with smooth lens transitions from 1x to 10x. I recorded a video for comparison, highlighting the discrepancy with my own device. Furthermore, the photos from my device appeared washed out. I captured photos and videos in front of showroom staff, providing undeniable evidence. Returning to the service center, I presented all evidence. Their response? They insisted on flashing my device, despite my previous factory reset. Their treatment resembled that of a post-14-day return policy scenario. Reluctantly, I agreed. They flashed my device with the same AWM9 build. 😑 Upon testing the camera again, the same image shift issue persisted. Was a return initiated? No. 👎🏻 Despite live proof, they required approval from the Area Manager. 🙄 After forwarding all evidence via WhatsApp, there was no response. Calls to the manager during a supposed 30-minute wait yielded no results. Arriving at the service center at 11 am, I waited until 5 pm for the return process, spending six hours in the center to no avail. They claimed they could only process the return with the manager's approval, yet his silence persisted. They assured me of approval by the end of the day, but as it's now 9:30 pm, there have been no updates or returned calls. What's the standard for Premium Service on Premium Devices, Samsung Electronics Samsung India Samsung Mobile? 🤔 Faced issue with #GalaxyS22Ultra earlier. Still Samsung India Service hasn't changed a bit. Check this out too. Please amplify Ranjit Varun Krishnan Ershad Kaleebullah Pratik Rai Parth Monish Kohli Mukul Sharma Rohit Arora A2D Channel Siva Bharani Udaya Selvan Ice Universe W CID Alpaca Tech😎

Update 1: Visit to hacked by peniche Service Centre for Camera Image Shift issue in #GalaxyS24Ultra 1,3 - Faulty device 2,4 - Showroom device Upon first encountering the issue and providing video evidence, they adamantly denied its existence. Despite demonstrating the issue in person, they insisted it was within normal functioning for the #GalaxyS24Ultra 🤯 I questioned the training provided to Samsung India's service center staff. 🤔 The representative asked for 30 minutes and left the service center. Upon returning, he claimed to have tested another device in the showroom and declared the issue common, refusing a return. Their attempt to normalize the issue was frustrating. 😒 Taking matters into my own hands, I visited the Samsung Mobile Showroom where I purchased my device and tested the demo units. They all functioned flawlessly, with smooth lens transitions from 1x to 10x. I recorded a video for comparison, highlighting the discrepancy with my own device. Furthermore, the photos from my device appeared washed out. I captured photos and videos in front of showroom staff, providing undeniable evidence. Returning to the service center, I presented all evidence. Their response? They insisted on flashing my device, despite my previous factory reset. Their treatment resembled that of a post-14-day return policy scenario. Reluctantly, I agreed. They flashed my device with the same AWM9 build. 😑 Upon testing the camera again, the same image shift issue persisted. Was a return initiated? No. 👎🏻 Despite live proof, they required approval from the Area Manager. 🙄 After forwarding all evidence via WhatsApp, there was no response. Calls to the manager during a supposed 30-minute wait yielded no results. Arriving at the service center at 11 am, I waited until 5 pm for the return process, spending six hours in the center to no avail. They claimed they could only process the return with the manager's approval, yet his silence persisted. They assured me of approval by the end of the day, but as it's now 9:30 pm, there have been no updates or returned calls. What's the standard for Premium Service on Premium Devices, Samsung Electronics Samsung India Samsung Mobile? 🤔 Faced issue with #GalaxyS22Ultra earlier. Still Samsung India Service hasn't changed a bit. Check this out too. Please amplify Ranjit Varun Krishnan Ershad Kaleebullah Pratik Rai Parth Monish Kohli Mukul Sharma Rohit Arora A2D Channel Siva Bharani Udaya Selvan Ice Universe W CID Alpaca Tech😎

S M A Sithick

158,362 görüntüleme • 2 yıl önce

Along with @levelsio, we’re looking for the best games you make with AI. More in his tweet below. Cursor 3 has a built-in browser, image generation, and cloud agents that can playtest, while Composer 2 is fast with Three.js. Great for building games! Even developers at Rockstar are using AI now. Games can be built faster, made more beautiful and complex, and created by more people. It's surprisingly easy if you have no experience. The games shown below were already made with Cursor by Startracker 🔺, Danny Limanseta, David Bui, and Chong-U. Show us what you can build! Can't wait to see your submissions.

Along with @levelsio, we’re looking for the best games you make with AI. More in his tweet below. Cursor 3 has a built-in browser, image generation, and cloud agents that can playtest, while Composer 2 is fast with Three.js. Great for building games! Even developers at Rockstar are using AI now. Games can be built faster, made more beautiful and complex, and created by more people. It's surprisingly easy if you have no experience. The games shown below were already made with Cursor by Startracker 🔺, Danny Limanseta, David Bui, and Chong-U. Show us what you can build! Can't wait to see your submissions.

edwin

78,395 görüntüleme • 4 ay önce

Claude + GPT Image 2 + Seedance + Meta Ads MCP Just fired my $10k/month performance marketing agency. Here's the exact stack (and how it runs): Step 1: Research Hand Claude your product page, your competitors' pages, and your best ad angles. It spits out a full brand brief + competitor breakdown in minutes. No strategist. No $200/hour consultant. Step 2: Static ads in seconds Claude crafts image prompts from your brief. Drop those into GPT Image 2. You get scroll-stopping creative. Batched. On-brand. No Figma needed. Step 3: Video ads that convert Claude writes video prompts. Feed them into Seedance 2.0. UGC-style clips. AI actor formats. Product showcases. All generated. Zero filming. (Both GPT Image 2 + Seedance are live on HeyOz right now.) Step 4: Publish + optimize on autopilot Hook Claude into Meta Ads MCP. It pushes your creatives live, tracks performance, and keeps iterating. Your agency was billing you hourly for this exact thing. I documented the full workflow in a guide: prompts, setup, and the exact MCP config. Why this matters: Most brands are still bleeding money on slow, overpriced creative production. The ones who crack this in the next 90 days will own an unfair advantage. Don't be the brand that figures it out too late. Comment "REPLACE" and I'll DM it to you.

Ahad Shams

18,670 görüntüleme • 3 ay önce

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Anshu

19,931 görüntüleme • 2 ay önce

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

Guri Singh

2,180,357 görüntüleme • 5 ay önce

Google Translate is cooked after this. A developer built a local AI translation engine that runs 40 languages entirely on your own laptop. It's called LibreTranslate. No API key. No usage limits. No sending your documents to Google's servers. You install it once. It runs forever. Here's what it handles: → Paste text. Translated instantly. → Drop in a file. Outputs the translated version. → Point it at a URL. Returns the page in your language. → Build it into your own app via its local REST API. The speed is not the story. The privacy is. Google Translate reads every sentence you paste into it. Legal contracts. Medical records. Internal emails. Client documents. Every word goes to their servers and stays there. LibreTranslate runs entirely offline. Nothing leaves your machine. Ever. The numbers: → 40 languages supported → Runs on CPU -- no GPU needed → Self-hosted in under 5 minutes → REST API built in for developers → 10K+ stars on GitHub 100% open source. MIT licensed. Price: $0. Google charges nothing for Translate either but it charges you something else. GitHub:

Google Translate is cooked after this. A developer built a local AI translation engine that runs 40 languages entirely on your own laptop. It's called LibreTranslate. No API key. No usage limits. No sending your documents to Google's servers. You install it once. It runs forever. Here's what it handles: → Paste text. Translated instantly. → Drop in a file. Outputs the translated version. → Point it at a URL. Returns the page in your language. → Build it into your own app via its local REST API. The speed is not the story. The privacy is. Google Translate reads every sentence you paste into it. Legal contracts. Medical records. Internal emails. Client documents. Every word goes to their servers and stays there. LibreTranslate runs entirely offline. Nothing leaves your machine. Ever. The numbers: → 40 languages supported → Runs on CPU -- no GPU needed → Self-hosted in under 5 minutes → REST API built in for developers → 10K+ stars on GitHub 100% open source. MIT licensed. Price: $0. Google charges nothing for Translate either but it charges you something else. GitHub:

Rimsha Bhardwaj

89,515 görüntüleme • 2 ay önce

Real or AI? AI stadium broadcast trend 💛 💙 • Create the video here: 🔗[ ] - How it works? 1. Upload your photo to ChatGPT with this prompt: [PHOTO PROMPT] Realistic sports broadcast screenshot-style documentary photo set in the spectator stands of a [WRITE YOUR TEAM HERE] football match. Analyze the uploaded image and show the person sitting in the stadium seats. The person has delicate facial features and a surprised yet focused expression while looking toward the field. The person is wearing a [WRITE YOUR TEAM HERE] jersey. OUTPUT: ratio: 16:9 broadcast frame, realistic TV capture quality. 2. Open the link above → select “Text to Video” → upload the generated image + use this prompt: [VIDEO PROMPT] Dimage = character identity reference only (face, hairstyle, proportions).Preserve exact face, hairstyle, skin texture, and identity. Do NOT stylize or beautify.Output: single continuous live sports broadcast shot, 4-5s, 16:9, 1080p, no cuts. SUBJECT:A young woman based on Image, sitting in a [WRITE YOUR TEAM HERE] football stadium audience.Hands resting naturally on her lap or lightly placed on the seat.Neutral, slightly distant expression.Natural breathing, minimal movement. ENVIRONMENT: [WRITE YOUR TEAM HERE] stadium crowd during live match.Plastic seats, fans around her wearing [WRITE YOUR TEAM HERE] jerseys. Background slightly out of focus.Realistic stadium lighting - day or night.Slight haze from broadcast compression. MOOD:Unstaged, candid, real broadcast moment No cinematic drama. Pure live TV capture. CAMERA:Telephoto broadcast lens (120-150mm).Long-distance zoom from upper stands camera.Strong compression, shallow depth of field.Eye-level, very slight upward tilt.Subtle micro-shake from broadcast stabilization. ACTION (4-5s):[0-2s] She sits still, blinks once. Hands resting naturally.[2-4s] Subtle weight shift, naturally adjusting posture. Minimal body movement.[4-5s] Small hand reposition on lap or seat. Slight head turn toward the field._ DETAILS:No posing. No eye contact with camera. Skin texture realistic, no smoothing or beautification. Slight motion blur on background crowd.Faint broadcast scoreboard UI visible in corner.

Real or AI? AI stadium broadcast trend 💛 💙 • Create the video here: 🔗[ ] - How it works? 1. Upload your photo to ChatGPT with this prompt: [PHOTO PROMPT] Realistic sports broadcast screenshot-style documentary photo set in the spectator stands of a [WRITE YOUR TEAM HERE] football match. Analyze the uploaded image and show the person sitting in the stadium seats. The person has delicate facial features and a surprised yet focused expression while looking toward the field. The person is wearing a [WRITE YOUR TEAM HERE] jersey. OUTPUT: ratio: 16:9 broadcast frame, realistic TV capture quality. 2. Open the link above → select “Text to Video” → upload the generated image + use this prompt: [VIDEO PROMPT] Dimage = character identity reference only (face, hairstyle, proportions).Preserve exact face, hairstyle, skin texture, and identity. Do NOT stylize or beautify.Output: single continuous live sports broadcast shot, 4-5s, 16:9, 1080p, no cuts. SUBJECT:A young woman based on Image, sitting in a [WRITE YOUR TEAM HERE] football stadium audience.Hands resting naturally on her lap or lightly placed on the seat.Neutral, slightly distant expression.Natural breathing, minimal movement. ENVIRONMENT: [WRITE YOUR TEAM HERE] stadium crowd during live match.Plastic seats, fans around her wearing [WRITE YOUR TEAM HERE] jerseys. Background slightly out of focus.Realistic stadium lighting - day or night.Slight haze from broadcast compression. MOOD:Unstaged, candid, real broadcast moment No cinematic drama. Pure live TV capture. CAMERA:Telephoto broadcast lens (120-150mm).Long-distance zoom from upper stands camera.Strong compression, shallow depth of field.Eye-level, very slight upward tilt.Subtle micro-shake from broadcast stabilization. ACTION (4-5s):[0-2s] She sits still, blinks once. Hands resting naturally.[2-4s] Subtle weight shift, naturally adjusting posture. Minimal body movement.[4-5s] Small hand reposition on lap or seat. Slight head turn toward the field._ DETAILS:No posing. No eye contact with camera. Skin texture realistic, no smoothing or beautification. Slight motion blur on background crowd.Faint broadcast scoreboard UI visible in corner.

Zaylee

26,925 görüntüleme • 3 ay önce

ADMIN POST. Huge update. Today we've spoken to Tommy and the latest information is as follows; He's currently safe in the segregated wing he served his last sentence in, however the Governor of Belmarsh high security prison has told him he must move "for monetary reasons" ASAP. Either into the main prison system, which comes at a huge security risk, as the prison is filled with the worst jihadis the country has. Or go "down the block", which is a small cell with no bed, tv or anything, which also leaves him at risk, psychologically and physically, as the block has inmate cleaners etc. The same prison is holding the Southport child killer, does this monster deserve better privileges than Tommy, who is in there for mere civil offences, not criminal?! He's also been told any visits he has, even with family, must be closed, ie behind a screen, treating him like Hannibal lector, for showing a film!

ADMIN POST. Huge update. Today we've spoken to Tommy and the latest information is as follows; He's currently safe in the segregated wing he served his last sentence in, however the Governor of Belmarsh high security prison has told him he must move "for monetary reasons" ASAP. Either into the main prison system, which comes at a huge security risk, as the prison is filled with the worst jihadis the country has. Or go "down the block", which is a small cell with no bed, tv or anything, which also leaves him at risk, psychologically and physically, as the block has inmate cleaners etc. The same prison is holding the Southport child killer, does this monster deserve better privileges than Tommy, who is in there for mere civil offences, not criminal?! He's also been told any visits he has, even with family, must be closed, ie behind a screen, treating him like Hannibal lector, for showing a film!

Tommy Robinson 🇬🇧

3,025,501 görüntüleme • 1 yıl önce

Ezra Olubi, OON. A disgrace to the nation, or just a reflection of how shallow our moral standards have become? Should we now say there was little or no scrutiny before he was conferred a national honor? Or do we pretend not to know what kind of image this paints for the country? If the Presidency had known earlier, would he still have been honoured? Or do we separate personal conduct from professional brilliance when it comes to celebrating national achievers? And now that these revelations are out, will the Presidency quietly distance itself knowing that a holder of the one of the country’s second-highest order of merit is found wanting in character? These questions have been running through my mind.

Ezra Olubi, OON. A disgrace to the nation, or just a reflection of how shallow our moral standards have become? Should we now say there was little or no scrutiny before he was conferred a national honor? Or do we pretend not to know what kind of image this paints for the country? If the Presidency had known earlier, would he still have been honoured? Or do we separate personal conduct from professional brilliance when it comes to celebrating national achievers? And now that these revelations are out, will the Presidency quietly distance itself knowing that a holder of the one of the country’s second-highest order of merit is found wanting in character? These questions have been running through my mind.

𝐌𝐔𝐁𝐀𝐑𝐀𝐊🗽

108,355 görüntüleme • 9 ay önce

I can now slowly make micro series about these Javanese Kebaya assassins 😎 I really love Kling 3.0 Multi-cam Kling AI All I did was use one base image (which is the very first frame of the video) and divide the prompt into six shots. That’s it!! Yes, you can use Nano Banana to generate multiple frames, but i'm too lazy to animate it one by one LMAO 😆😆😆 The one-frame approach has been my favorite way for the past few months. People always ask, “How do you storyboard your AI video?” No, I don’t hahahah 🤣 Trust me, my workflow is just chaotic. But again, it depends on what type of project you’re making. I personally just want to create very ultra-short videos. (I’m one of those AI slop bros who’s not interested in making long-format AI movies LMAO 😂😂😂 Because i just wanna do this for fun) So this approach of using only one source image works perfectly for me.

I can now slowly make micro series about these Javanese Kebaya assassins 😎 I really love Kling 3.0 Multi-cam Kling AI All I did was use one base image (which is the very first frame of the video) and divide the prompt into six shots. That’s it!! Yes, you can use Nano Banana to generate multiple frames, but i'm too lazy to animate it one by one LMAO 😆😆😆 The one-frame approach has been my favorite way for the past few months. People always ask, “How do you storyboard your AI video?” No, I don’t hahahah 🤣 Trust me, my workflow is just chaotic. But again, it depends on what type of project you’re making. I personally just want to create very ultra-short videos. (I’m one of those AI slop bros who’s not interested in making long-format AI movies LMAO 😂😂😂 Because i just wanna do this for fun) So this approach of using only one source image works perfectly for me.

DAN · MXVDXN

16,386 görüntüleme • 6 ay önce

iMessage search is still broken in 2025. So I fixed it myself. It doesn't even work most of the time. When it does, it's slow, misses messages constantly, and only matches exact words. Search "dinner" and you won't find "let's grab food." iMessage Intelligence fixes all of this while also being insanely fast. Here's how it works: → Full text search index on your messages for keyword matching → On-device ML (MiniLM via CoreML) generates semantic embeddings. Your Mac generates these embeddings in under a minute, then syncs to your phone → sqlite-vec stores vectors locally for similarity search → Hybrid results: keyword hits appear in <10ms, semantic matches merge in <60ms It also has advanced filters iMessage doesn't have: sort options, date ranges, conversation filters, sent vs received, groups vs 1-on-1. Search runs 100% on-device. No cloud processing. Sign up for the waitlist:

iMessage search is still broken in 2025. So I fixed it myself. It doesn't even work most of the time. When it does, it's slow, misses messages constantly, and only matches exact words. Search "dinner" and you won't find "let's grab food." iMessage Intelligence fixes all of this while also being insanely fast. Here's how it works: → Full text search index on your messages for keyword matching → On-device ML (MiniLM via CoreML) generates semantic embeddings. Your Mac generates these embeddings in under a minute, then syncs to your phone → sqlite-vec stores vectors locally for similarity search → Hybrid results: keyword hits appear in <10ms, semantic matches merge in <60ms It also has advanced filters iMessage doesn't have: sort options, date ranges, conversation filters, sent vs received, groups vs 1-on-1. Search runs 100% on-device. No cloud processing. Sign up for the waitlist:

Seif Abdelaziz

97,261 görüntüleme • 9 ay önce

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: **Highlights:** 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: Highlights: 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

Bo Wang

178,579 görüntüleme • 2 yıl önce

"Look at the dust on the road to your temple. Drive there next Saturday. Walk the last mile if you can. Approach it the way the early Saints approached Kirtland. On foot, in the open air, with time to reflect on what you are about to do. The modern temple experience has been engineered for convenience. The parking lot is right there. The locker room is right there. The session begins at a posted time and ends at a posted time and you are out and on with your day. This convenience is mercy. The Brethren have made the temple accessible to a busy people, and the accessibility is itself a gift. But the convenience has cost us something. The early Saints walked. They walked from Far West to Kirtland for the dedication. They walked from Nauvoo to the unfinished temple on the hill. They walked from their wagon trains to the Endowment House in the valley. The walking was preparation. By the time they crossed the threshold, they had already left the world behind in their bodies, in their feet, in their breath. They arrived primed for the encounter. You will not walk a thousand miles. You can walk a mile. You can fast that morning. You can sit in the car for fifteen minutes before going in, with no phone, doing nothing but settling your spirit. You can treat the approach to the temple as part of the temple, the way they did. The temple has not been diminished by our convenience, but our reception of it has been. Restore the approach. Make the journey, however short, into something that prepares you. The temple will then meet you with a fullness it cannot give to someone who walks in distracted."

"Look at the dust on the road to your temple. Drive there next Saturday. Walk the last mile if you can. Approach it the way the early Saints approached Kirtland. On foot, in the open air, with time to reflect on what you are about to do. The modern temple experience has been engineered for convenience. The parking lot is right there. The locker room is right there. The session begins at a posted time and ends at a posted time and you are out and on with your day. This convenience is mercy. The Brethren have made the temple accessible to a busy people, and the accessibility is itself a gift. But the convenience has cost us something. The early Saints walked. They walked from Far West to Kirtland for the dedication. They walked from Nauvoo to the unfinished temple on the hill. They walked from their wagon trains to the Endowment House in the valley. The walking was preparation. By the time they crossed the threshold, they had already left the world behind in their bodies, in their feet, in their breath. They arrived primed for the encounter. You will not walk a thousand miles. You can walk a mile. You can fast that morning. You can sit in the car for fifteen minutes before going in, with no phone, doing nothing but settling your spirit. You can treat the approach to the temple as part of the temple, the way they did. The temple has not been diminished by our convenience, but our reception of it has been. Restore the approach. Make the journey, however short, into something that prepares you. The temple will then meet you with a fullness it cannot give to someone who walks in distracted."

Kirk Rollins

21,874 görüntüleme • 1 ay önce