🚀 Exciting news! The #GeoAI Python package now lets... you train land cover classification models with just one line of code. Leverage any PyTorch segmentation model from — with hundreds of image encoders & pretrained weights available. 📍 GitHub: 📓 Notebook: #GeoAI #Geospatial #DeepLearningshow more

🚀 A sneak peek of a new feature in... the #GeoAI Python package! Now you can train an image segmentation model for extracting features (e.g., buildings) from satellite or aerial imagery—all with just a few lines of code. 🛠️ Check out the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python #DeepLearningshow more

🚀 The #GeoAI Python package now supports feature segmentation... from high-resolution satellite and aerial imagery using text prompts, such as trees, buildings, etc. It efficiently processes large datasets with automatic tiling and can save results as a single image. Stay tuned for more features coming soon! 📓 Access the notebook: 🛠️ Explore the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python #DeepLearningshow more

🚀 I spent the entire day training image segmentation... models from scratch! I've created several pretrained models to detect features like buildings, cars, ships, solar panels, and wetlands. Video tutorials are coming soon! 🤗 Check out the pretrained models on Hugging Face: 🛠️ Check out the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist: #GeoAI #geospatial #AI #Python #DeepLearningshow more

A sneak peek of a new feature in the... #GeoAI Python package! 🎉 Now you can detect cars from georeferenced aerial imagery using deep learning—all with just a few lines of code. Stay tuned for an in-depth video tutorial coming soon! 🛠️ Explore the GitHub repository: 📚 Dive into the documentation: 📺 Check out the entire YouTube playlist:show more

Create Stunning Time-Series Satellite Images in Seconds! The GEE... Data Catalogs Plugin v0.5 for QGIS is now available and it's a powerful upgrade. You can now create time-series satellite imagery with just a few clicks using a simple interface. The new version also supports direct downloads to your computer, making the workflow faster and more efficient. Key Features: - Access over 80 petabytes of satellite and geospatial datasets from Google Earth Engine - Generate animated time-series imagery effortlessly - Export results directly from QGIS to your local machine Useful Links: QGIS Plugin Page: GitHub Repository: Video Tutorial: #QGIS #geospatial #EarthEngine #Python #datascience #satelliteshow more

As announced in partnership with NVIDIA at CES, we’re... excited to introduce Stable Point Aware 3D (SPAR3D), setting a new standard in 3D generation. Ideal for running on NVIDIA RTX AI PCs, SPAR3D enables real-time editing and complete structure generation of 3D objects from a single image in under a second. You can download the weights on Hugging Face and code on GitHub, or access the model through the Stability AI API. Learn more here: (1/3)show more

🚀 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 #AIResearchshow more

Throughout my journey in developing multimodal models, I’ve always... wanted a framework that lets me plug & play modality encoders/decoders on top of an auto-regressive LLM. I want to prototype fast, try new architectures, and have my demo files scale effortlessly — with full support for parallelism and optimization. Not just to hack⚙️, but also to scale🚀. So finally we built it for ourselves. LMMs-Engine: a lean, efficient framework built to train unified multimodal model at scale. From Qwen LLM, VLM, LLaVA-OV, and WanVideo, to unified models like Qwen-Omni and BAGEL — plus Linear-Attn GDN and research prototypes like RAE and SiT - all under one modular system that seamlessly integrates diverse datasets and optimization strategies. Powered by FSDP2 multi-dim parallelism, Ulysses sequence parallel, Flash-Attention, Liger Kernels, and Native Sparse Attention (also with bonus support for the Muon optimizer for all models).show more

this might be the E2B killer for AI agent... sandboxes. forkd is an open-source microVM sandbox runtime built on Firecracker, made for AI agent fan-out, code interpreters, eval harnesses, anything that spins up a lot of short-lived sandboxes. before: every sandbox cold-boots its own VM and re-imports the whole runtime from scratch, numpy, torch, JIT compilation, model weights, all of it. now: forkd boots one parent VM once, warms it with your runtime already imported, then forks children from that snapshot using copy-on-write memory. the repo's own benchmark: spawning 100 sandboxes takes 101ms with forkd, versus 759ms for a raw Firecracker cold-boot, and well over a minute for Docker or gVisor. the SDK is a literal drop-in for E2B's Python client, so if you're already running code-interpreter agents on it, swapping the import line gets you a self-hosted runtime with the same isolation model at a fraction of the per-sandbox cost. pre-built recipes ship for e2b-style code interpreters, Jupyter kernels, SWE-bench coding agents, and Playwright browser fan-outshow more

img2threejs v1.3 is now available. 🎋 One photo →... procedural Three.js code. No meshes. No manual modeling. GitHub: v1.3 brings major improvements to geometry reconstruction, material generation, validation, and overall output quality. For this demo, I used the reference image of a CS2 ★ M9 Bayonet | Doppler Phase 3: If you’re new to img2threejs, now is a great time to try it. If you’ve used it before, I’d love for you to compare the results with previous versions. Feedback, bug reports, and PRs are always welcome.show more

As always everyone is blind staring at the progress... of LLMs for coding and chat But meanwhile the new SOTA video model Seedance 2.5 has been slowly rolling out and it's really quite exceptional It's made by ByteDance (TikTok) who of course have lots of training data With just a few reference pics, it can get quite close to how you look IRL and you can do quite professional video shots with just a prompt I'd say it's the first video model that's now at the level of image models with the level of character likeness, cracking that in image models also took about 3 years (2022-2025) Generating 15 seconds takes about 4 minutes I put it live now on Photo AI, you can use it under [ Make video ] from the sidebar with just a prompt and your model selected! So you don't need to take an AI photo first and then turn that into a video! Saves lots of time :D It's more expensive than but I kept the credits the same (30 for 1 video) It also works inside the new video editor and you can make changes in your video with [ Magic edit ] in both the main app and the video editor Also a message for my server guy Daniel Lockyer (it can do voice too and you can even submit a voice sample of yourself, but I didn't here)show more

MOST PEOPLE WHO BUILD WEBSITES USE CLAUDE CODE THE... WRONG WAY A few people train it properly and get the bottom one. The difference is not the model. It is the skills you feed it. Regular Claude without web-design systems produces clean but average product pages. The same Claude, loaded with the right skills, starts delivering sites that look like they cost $5,000. Those skills are free right now. They live on GitHub. The exact names sit in the article below. One set of prompts. One trained workflow. Suddenly every luxury brand page you build stops looking like a template and starts looking like money.show more

🚀 f(x) v2.0 is LIVE! After extensive development and... testing, we're revolutionizing leverage trading and yield generation in DeFi. ​ ​ Here's why you shouldn't look anywhere else for yield on stables or leverage trading from now on: ​ Fixed, Zero-Stress Leverage on ETH No funding fees. No liquidations. No forced margin calls. Just pure, fixed leverage that lets you sleep at night while your position works for you. ​ Earn Triple Yields Our Stability Pool rewards you with trading fees, ETH staking yields, and FXN emissions - all in one place. ​ Built for Reliability This launch is the culmination of extensive research, rigorous testing, refining, and perfecting. We’ve worked tirelessly to make f(x) v2.0 your go-to protocol for a safer, smarter trading experience. ​ Thoroughly Audited Every line of code has been analyzed by SECBIT in an extensive process that took over 6 weeks. You can read the audit report: 💰 Start Earning Have you seen the yield provided in the Stability Pool? Hint: it's FOUR digits 🤯 Earn now: ​ ❗ Initially, only users holding xstETH or xfrxETH on V1 who migrate to V2 can open xPOSITIONs. Once migration reaches 80%, this priority period ends, and xPOSITIONs become available to everyone. Once a position is migrated, the leverage won't be adjustable until migration reaches 80%. ​ Migration details can be found here: ​ ❗ ❗ During the initial bootstrapping phase, the maximum leverage will be 7X. Once the system has sufficiently stabilized, we will increase it to 10X.show more

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft paper... page: Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective for creating instruction-following sequential decision-making agents. STEVE-1 is trained in two steps: adapting the pretrained VPT model to follow commands in MineCLIP's latent space, then training a prior to predict latent codes from text. This allows us to finetune VPT through self-supervised behavioral cloning and hindsight relabeling, bypassing the need for costly human text annotations. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. We provide experimental evidence highlighting key factors for downstream performance, including pretraining, classifier-free guidance, and data scaling. All resources, including our model weights, training scripts, and evaluation tools are made available for further research.show more

Someone is vibe coding a live 3D news globe... with Claude Code that pins real world events on a spinning Earth as they happen. Instead of doomscrolling a feed, you open a tab with a 3D Earth and watch the world light up in real time. Breaking news, conflicts, disasters and humanitarian alerts get pinned to the exact spot on the map, alongside live flights and upcoming rocket launches. Click any event and it pulls up a short brief with its actual sources, so you can see where it came from. People are already saying it's the only way they want to read news now, the kind of thing you keep open in a tab and just glance at all day.show more

MLP in PyTorch by hand ✍️ ~ 7 steps... walkthrough below Goal: fill in every blank in the PyTorch code to build a multi-layer perceptron. 1. Given Let us start with a code template on the left and the network it is supposed to build on the right. Every blank in the code can be worked out from the picture. 2. Linear layer We count: 3 features in, 4 features out. So the weight matrix is 4 by 3. There is an extra column for the biases, which means bias = T. 3. ReLU Let us apply the activation. ReLU crosses out the negatives, so -1 becomes 0. 4. Linear layer The input size is 4, because that is what the previous layer put out. The output size is 2. A 2 by 4 weight matrix, and this time no extra column, so bias = F. 5. ReLU We cross out the negatives again. 6. Linear layer Two features in, five out. A 5 by 2 weight matrix, with a bias column, so bias = T. 7. Sigmoid Let us finish. Sigmoid squashes the raw scores (3, 0, -2, 5, -5) into probabilities between 0 and 1. You have just implemented a three-layer deep neural network by hand. ✍️ == Story == Three years ago I gave this exercise to my students, to connect the code to the math. They found it odd. Every other AI course they were taking lived inside a Jupyter notebook, and here I was handing out paper. Three years later, my colleagues are the ones rushing to move their materials to paper. The exercise has not changed. Paper still asks the one thing a notebook lets you skip: do you actually understand what the code is doing? If you can tell me why the weight matrix is 4 by 3, and why bias is F on the second layer, you understand nn.Linear better than someone who has been copy-pasting it for a year. 💾 Save this post! #AIbyHand #PyTorch #DeepLearningshow more

🚨 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 )show more

BREAKING: LLMs just learned to COMPUTE for real, it's... mean NO MORE GUESSING math. Chinese college kid Guo Hanjiang vibe-coded MiroFish in 10 days (23k+ GitHub stars, $4.1M from Shanda in 24h) - the AI swarm simulator that’s already printing. ByteDance (VolcEngine) dropped the nuclear upgrade: OpenViking - structured viking:// filesystem memory (L0 ultra-summary -> L2 full details) - agents now run 100+ steps with zero amnesia or hallucinations, 11.6k stars and climbing. Now this just dropped and the entire AI timeline is shaking. Startup Percepta embedded a full WASM virtual machine directly into Transformer weights. No more external Python sandboxes. No more hallucinations in exact tasks. The model streams raw machine code at 30,000+ tokens/sec on CPU, executes millions of steps, and solves the world’s hardest Sudoku via real backtracking + constraint propagation - 100% accurate, zero bullshit. They killed the Attention Bottleneck with Exponentially Fast Attention (HullKVCache + 2D heads + convex hull queries in log time). What used to die at 1k steps now flies. This is the bridge: System 1 intuition (normal LLMs) + System 2 deterministic logic (native code execution) in ONE brain. Agents won’t need tools anymore. Heavy simulations will run inside the weights. Check out: Now put it all together: MiroFish swarms + OpenViking infinite memory + Percepta native flawless compute = agents that can hardcore simulate millions of future scenarios, run perfect logic loops for days, and predict events/markets/reality with god-tier accuracy. No drift. No bullshit. Just pure foresight. This combo will change everything, imo. The era of predictive super-agents that actually print the future is here. We’re watching this one closely. Save this combo.show more

Black Forest Labs just announced FLUX 3: a unified... multimodal model for image, video, audio and action prediction. Founded in Freiburg, Germany, one of the few globally significant European companies in the AI ​​sector. The current release is gated early access, not open source. But if FLUX 3 Dev actually ships with usable open weights, this could become one of the most important open-model releases in generative video yet. Especially after the Chinese Minimax H3 release. BFL claims, based on preliminary internal comparisons, that FLUX 3 outperformed models such as Runway Gen-4.5, Luma Ray 3.2, Kling 3 Pro, and Seedance 2.0. However, these figures stem from the early-access phase and do not constitute independent validation. Be that as it may, it's great to see the quality that today's video models can produce at affordable prices. Something that would have been unthinkable a year ago. Really cool release!show more