NVIDIA's PiD is a new pixel diffusion decoder for... high-res image models. It skips decode-then-upscale stop, making sharper outputs faster. > Directly generates 4K images > Up to 5.9x faster than SeedVR2 > Free & open weightsshow more

> be fal > release new video model >... generates 15 sec video > takes 3.5 sec > video literally takes longer to watch than generate > somehow faster than image models > user is now the latencyshow more

computer use on mac just got a lot faster.... i built arc-driver - a lean & powerful computer-use driver for macos. >faster than cua-driver on most tasks. up to 8x on some. >plug it into your coding agents with a single command. open source. link below.show more

P-Image-Upscale is the fastest and cheapest image upscaler in... the world: supporting outputs up to 128 MP under 1 seconds. A couple of weeks ago we released p-image-upscale, and it now works better than ever. It’s the fastest image upscaler in the world, supporting outputs up to 128 MP while keeping pricing simple and predictable: - $0.005/image for 1–4 MP - $0.01/image for 4–8 MP - $0.02/image for 8–16 MP - $0.04/image for 16–32 MP - $0.06/image for 32–64 MP - $0.12/image for 64–128 MP That means you can go from low-res input to production-ready output with extreme speed, while preserving detail and keeping costs easy to understand. Available on: - Pruna AI | - each::labs | - inference shell | - Replicate | - Runware | - Segmind | - WaveSpeedAI | - Wiro AI — Ship AI Faster | If you’re building workflows where image quality, price, latency, and scale all matter, p-image-upscale is built for you.show more

Bytedance just dropped realtime voice translation 3x faster than... before, with only a ~3s lag! Seed LiveInterp 2 is a full duplex speech-to-speech model with >70% correctness. When this makes it to video calls, it'll open up previously impossible connections.show more

🌟 New mini feature today on Photo AI It... now auto recognizes your model's attributes when you upload photos of yourself Like gender, age, ethnicity, eye color, etc This uses xAI's Grok because it's the least restricted and other models don't let you do any identity type stuff due to safety (???) This saves new users a few more seconds when creating new models: training a model takes only about 30 seconds now, and then it also auto generates free sample pics for you in about 5-10 seconds So in under a minute of signing up and uploading a few selfies you got your first AI photo shoot pics I learnt the faster I get new users to the classic "WOW" moment the happier they get and the less churn I get 😊show 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

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

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!show more

BREAKING: SpaceXAI has released a major new update for... Grok Build (v1.0.13). The update improves reliability with automatic retries and recovery from truncated responses, inference failures, oversized images and session-saving issues. It also adds smarter hooks, better Windows support, faster MCP and session startup, improved scheduled tasks and quicker compressed CLI downloads. Features • Length-truncated responses now continue automatically instead of failing the turn. • Hooks can now ask the user to confirm a tool call instead of always allowing or denying. • Hooks can now request deferral or add context shown to the model after a tool runs. • Session close now records detailed timing data for performance analysis. • Credit limit upsell now offers a Try Again button to retry the last prompt. • Pasted images now show a live pixel preview in the prompt box on iTerm2. Bug Fixes • Transient inference failures (stalls, drops, 5xx) now retry automatically instead of ending the turn. • Windows users can now correctly open ~/.grok and worktree sessions. • Session data is now more reliably saved after prompts and on power loss. • Compaction failures now show the actual error instead of a generic message. • Truncation error messages now show the right guidance instead of suggesting an unhelpful retry. • Truncated tool calls are now executed instead of failing the turn when arguments are complete. • Images larger than 2000px no longer brick sessions on many-image requests. • Wrapped hyperlinks in the pager now remain fully clickable on Windows Terminal instead of only the first line. • Recurring scheduled tasks now include a reminder to stop the monitor when work finishes. • Scheduled task IDs are now full UUID strings, preventing collisions when tasks are created in the same millisecond. Performance • Subagent spawning is faster when connections drop during bursts. • Session startup with MCP servers is now much faster when auth is already configured. • MCP server startup no longer stalls behind a fixed batch size. • CLI downloads are now compressed, making fresh installs and updates substantially faster. Download Grok Build: Update to the latest Alpha release: grok update --alpha Update to the latest Stable release: grok updateshow more

you are making video games all wrong for decades... almost all game art time went into reverse-engineering the end result you start with concept art of what you WANT it to look like. then for YEARS you walk the computer backwards into something it can process: a rendering engine, 3d assets that fit that engine, and a gamer’s local hardware that has to turn all of that back into a high fidelity image incredibly slow. incredibly expensive. but it was the best we had ai makes that pipeline faster as everyone is figuring out today. but they’re missing the real unlock: YOU DON’T NEED THAT PIPELINE AT ALL if you have a reference for the end look, you don’t model it in 3d. you don’t reverse-engineer into an engine you keep a very basic greybox model of the world as the source of truth, and track every entity in real time. it doesn’t need to look good at all… the player never sees it! a fast video model turns that into the “game” in real time. it doesn’t have to look like a game. it can look like a 2d cartoon or cinematic live action fal’s h3 max is very fast. astra 6 low-effort too. neither is fully realtime yet but together they can generate continuous, coherent, player-controlled scenes, faster than playback early demo:show more

Today's recap: - Initial prototype of Divine's face was... printed but it had human assistance. - Files are generated from stable diffusion prompt -> NeRF by divine and were based on community sentiment from early sketches she made. - Having divine redesign the 3D file with different Hugging Face models to get better quality. Have not found a great model like our video generator. - Ordered new table for divine's print arm. The table her arm is on is too flimsy. Since Divine's vision system is still clearing customs, if she is not perfectly positioned she can be prone to hit things, like the fume box the printer is in. ETA: 1-2 days for table. 1 week for vision system. - Another part of Divine's coming stream will be attempting to surpass the skills of this AI. - Stacking more content for when the stream goes live, a lot of people were expecting a 24/7 stream, we said this would be a test stream to print the face. The test was a failure. We will try and try again until we are 24/7. If anyone can please try and beat us to doing this, it will help me get it done faster. - TikTok account for divine is growing at 500 follows per day, it is now growing faster than our X account. - Got replies functioning in high quality testing in Discord. Fine tuning based on community feedback today. Will soon deploy to Twitter/Telegram/X - Lots of good partnership calls, interviews and hires. We now have over 10 team members around the world working on divine. Expect a lot of my shortcomings to be caught up. - OF made? - Surprises.show more

CRAZY 🚨 I built a scanner that shows you... world's sentiment on social media in real-time Right now, it is pointed at Netanyahu's speech at the United Nations Imagine reading all posts on X and Instagram at the speed of light Meet How does it work? 1. It picks up posts from a set of keywords, topics, and accounts from X and Instagram 2. then uses the Sage decision model to filter if it's relevant to the UN speech, whether it's supportive or critical for Benjamin Netanyahu - בנימין נתניהו, and the post's narrative topic (ie., is the post about the Speech, Israel, or Iran?) 3. The image and text are classified separately, providing different weights to the sentiment and narrative rankings. This all produces short, mid, and long-term sentiment measurements. 1. Ben reacts every 5 seconds. 2. Narrative weights are updated every hour. 3. The 24h dial shows conglomerate sentiment measurement. I built this yesterday to test out Sage's new reasoning and image capabilities. It's the only AI model we used to build it. This is the power of decision models. Sage reasons when things are complicated and can interpret images, making this app possible. Disclaimer: This tool is politically neutral. It's difficult to touch anything related to geopolitics and be fairly neutral. We did our best on filter selection - if you think the method could have been better, please drop a comment below. I will use World Signal for future trends. If you'd like me to point it at a narrative next week, send me a comment below.show more

Before the week ends, let's acknowledge one of the... most INSANE week ever for open AI, with 25+ notable open-weight drops across every modality: 🧠 LLMs → NVIDIA Nemotron 3 Ultra: 550B hybrid Mamba-MoE, only 55B active, 1M context, MMLU 89.1. NVFP4 variant claims ~5x throughput on Blackwell. First openly-weighted 550B hybrid Mamba-Transformer, closing the gap with frontier closed models. → Google Gemma 4 12B: fully open dense any-to-any (text/image/audio/video), 256k context, encoder-free, 140+ languages, AIME 2026 at 77.5. Shipped with a 23-checkpoint QAT wave (mobile ONNX + MLX). Most deployable model of the week. → StepFun Step-3.7-Flash: 198B sparse MoE VLM, ~11B active, SWE-Bench PRO 56.3. Apache 2.0. → Liquid AI LFM2.5-8B-A1B: edge MoE, just 1.5B active, 128k ctx, MATH500 88.8, MLX-ready. Best on-device option this week. → JetBrains Mellum2-12B-A2.5B-Thinking: their first open MoE, near-Qwen3-14B coding at 2.5B active. Apache 2.0. 🎨 Image gen (the surprise of the week) → Ideogram 4: their FIRST-EVER open weights. 9.3B flow-matching DiT trained from scratch. #2 overall behind GPT Image 2, top open-weight model on Design Arena + LMArena. Strongest open checkpoint for text-rich images, full stop. It has taste. Still can't believe this is open weights. 🔊 Audio & Speech (a breakout week for open TTS, 4 labs shipped) → Boson Higgs Audio v3 4B: 102 languages, 21 emotions, singing/whispering/shouting, sub-second TTFA. → RedNote dots.tts: the only fully continuous (no codec) open TTS pipeline, Apache 2.0. → Google Magenta RealTime 2: real-time music gen, <200ms latency, text+audio+MIDI. multimodalart ported it to PyTorch within hours with live ZeroGPU demos. → NVIDIA Nemotron-3.5 ASR: 600M streaming, 17x more concurrent streams vs Parakeet RNNT 1.1B. 👁️ Vision & VLMs → PaddleOCR-VL-1.6: SOTA document parsing at 1B params, Apache 2.0. → Baidu NAVA: 6.3B joint audio-video gen, best-in-class A/V sync, Apache 2.0. 🎬 Video, 3D & World Models → NVIDIA Cosmos3-Super: 64B omnimodal world model coupling action trajectories with video+audio gen, for Physical AI. → JD JoyAI-Echo: up to 5-min multi-shot text-to-video on LTX-2.3. → ByteDance Bernini-R + VAST TripoSplat (single-image-to-3D Gaussian splats, MIT).show more

💬 We get asked Should I pause my active... strategies when the market suddenly becomes highly volatile? ❕ Answer from a GT App Trader: Usually, high volatility is where manual traders lose the most due to panic. For automated strategies, however, sharp price swings are the best environment to stack gains. 🔸 Capitalizing on Swings When the price aggressively bounces up and down, the system executes trades much faster than any human. It snaps up assets on sudden drops and sells them instantly on the rebounds, turning chaos into systematic profit. 🔸 Volatility Protection Through the Strategy Builder, setups use dynamic grid spacing and safety orders. This means the strategy doesn't deploy all capital at once; it scales into positions safely as the market moves, lowering the average entry price. 🔸 No Emotional Mistakes The system sticks strictly to the set mathematical rules. It won't freeze, FOMO into a bad trade, or close a position early out of fear. It just executes. Don't fear the volatility, let the algorithms trade it.show more

avg $1,230/day on Polymarket. it comes from changes in... the weather forecast, which is free and open to everyone. the setup guide is in this post the market already prices the forecast itself pretty accurately. the money shows up the moment the forecast changes and the price hasn't yet. how it works in practice: when a new forecast for the next day comes out and the temperature in it moved by at least 0.5°C, you buy the option one degree closer to the new forecast. say the morning forecast promised 16.8°C in London, so the 17°C option is the priciest. in the afternoon a new forecast comes out with 15.8°C. your job is to buy 16°C while it's still cheap. the buy range where the EDGE holds: 5¢ to 60¢. the faster you do it, the higher your ROI how to set it up: 1 - get forecasts for free on Open-Meteo. it has the american GFS model and the european ECMWF. use the airport coordinates, that's where Polymarket measures the temperature. 2 - the bot should check on a timer whether a new forecast is out, and if it is, compare it to the previous one. 3 - if tomorrow's high moved by 0.5°C or more, the bot buys the option one degree closer to the new forecast. 4 - keep each trade under $25. cheap sellers on these markets are thin, and more size will eat the edge. one bot trades these forecast shifts across 50 cities every hour. in september that's avg $1,230/day here's the profile:show more

We’re partnering with Paul Jankura to launch the biggest... Protein Design Competition in the world, challenging people around the world to use AI to design new potential drug candidates for diseases that affect millions of lives. The competition will feature five challenges, each focused on a specific disease or biological mechanism. Compared to previous competitions, it will be a big step-up in complexity and scale to push the boundaries of AI-driven protein design. Together with Anthropic, we’re sponsoring over $1 million in experimental validation, making it possible to test more than 5,000 protein designs in our automated lab at no cost to participants. Anthropic is providing an additional $1 million in Claude credits. All experimental results will be published openly on Proteinbase, including designs that didn’t work, so anyone can access the data and build on what we learn. The competition is open to everyone and free to enter. It will feature 3 tracks: - Track 1 is aimed at expert protein designers, with up to 20 teams to be selected. - Track 2 is targeting life science academics and industry researchers. - Track 3 is open to everyone from tech enthusiasts to high-school students. By combining Anthropic’s models with access to our automated lab, we want to make it possible for anyone with a laptop and an internet connection to join the global effort to advance human health with AI. A big thanks to Modal for contributing compute for protein design and to Twist Bioscience for contributing the DNA for the experimental validation! Sign up link below -show more

🚨 THE RACE TO 6G JUST ACCELERATED. Northrop Grumman... has developed a W-band GaN chip operating at up to 110 GHz and took it from concept to market-ready hardware in less than six months. The new gallium nitride chip operates in the W-band (75–110 GHz), a frequency range that delivers massive bandwidth, extremely high data rates, and much lower latency than current systems. What makes this impressive is the speed: the chip went from concept to market-ready hardware in less than six months through a U.S. government-backed microelectronics program. That’s unusually fast for advanced defense-grade semiconductors. The chip acts as a high-power signal amplifier that can strengthen wireless links while shrinking the size and power consumption of the hardware. It’s designed for military radar, secure satellite communications, and the coming wave of 6G networks. Why this matters: • W-band offers far more spectrum than current 5G bands, enabling much faster data transmission and higher-resolution sensing • Gallium nitride can handle significantly higher power and frequencies than silicon, making it ideal for these demanding applications • The rapid development cycle shows how public-private collaboration can accelerate critical semiconductor technologies • The same tech that strengthens military radar and satellite links will directly feed into future commercial 6G infrastructure The deeper implication: We’re watching the foundation of next-generation wireless and sensing systems being laid in real time. High-frequency GaN chips like this won’t just improve existing radar and satellite systems they’re likely to become core building blocks for 6G, autonomous systems, and advanced defense platforms. The fact that this moved from lab to market in under six months suggests the pace of high-frequency electronics is accelerating dramatically. The future of wireless isn’t just faster. It’s operating at frequencies most people have never heard of and it’s being built right now. How soon do you think W-band and GaN technology will start appearing in everyday 6G devices? Follow for more frontier semiconductors, defense tech, and next-generation wireless systems.show more

Google dropped a new AI paper called LUMIERE. It's... remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:show more

$ASI watch out! We've got more projects popping up... on Bittensor and $TAO and just starting, Akash $AKT, Shell $SHELL, Einstein-AIT $AIT, Sturdy $STRDY, Stratos $STOS and Comtensor $COMAI and more. Each pushing what's possible with AI and crypto. MyShell: $SHELL First up, MyShell is doing something amazing work. They're all about making AI chat like humans. They're using this TTS Subnet on Bittensor, powered by thier tokens, to make it happen. AI doesn't have to be complicated thing only some can use. They want everyone in on the action, making AI smarter in the process. Einstein-AIT: $AIT Then there's Einstein-AIT. Imagine this as the network's brain but on a turbocharge. It's all about math, logic, and crunching numbers. This subnet makes the whole Bittensor network sharper. They've got NumPAL, and it's like giving the AI a smarter way to think about time and dates automatically. Sturdy: $STRDY Jumping into DeFi, we've got Sturdy. These guys are on a mission to make lending and borrowing way less of a headache. They've got isolated lending pools, you can pick and choose how to manage your risks and money. And they're using some serious tech to keep your investments growing without you needing to babysit them. It's like having a smart financial assistant. Comtensor: $COMAI Comtensor is where it gets interesting. Think of it as a bridge between CommuneAI and Bittensor, kind of like the best of both worlds. It's about making sure all these AI modules and subnets can talk to each other smoothly. The goal? To boost decentralized AI by making everything more connected and smart. Stratos: $STOS Teaming up with τensorage to supercharge the $TAO ecosystem on Bittensor. Stratos is all about solid, decentralized storage - think of it as the bedrock for making sure data's not just stored but also used right . Then there’s τensorage, making sure everything AI needs is stored safe. Together, they’re making sure Bittensor's AI brainpower gets a boost, making everything faster, safer, and smoother. Akash: $AKT Compute Subnet 27 linked up with Akash. All about keeping things open-source. With Akash as a decentralized GPU provider into the mix. Giving access to top-notch AI processing power on top of Neural Internets compute-composable subnet, integrating various cloud platforms. This partnership is all about breaking away from those giants and opening the doors wide for smaller teams and startups who need this tech to innovate. Whats this all about? Making decentralized AI a something that everyone can get behind and into. Whether it's chatting with AI, boosting the network's IQ, DeFi, compute or making sure all these projects work together, it's about pushing forward. Each project has its own way of making things better, faster, and smarter for all of us. Inviting everyone to join in, contribute, and be a part of community effort to make AI not just for the few but for everyone. Credit to Mr.Franc Q for the awesome video production!show more

it got worse? Nano Banana 2.1 is here so... had to put it up against NB2 for the good old continuity stress test. the strange thing is 2.0 kept my likeness a lot more all the way through. I'm still very different from the original, but you can kinda recognise me? On 2.1 it just regenerates a new man multiple times. I guess they've made changes to the Synth ID watermarking they do on the images and the noise causes the model to go blind? it's funny how both models seem to become conscious of the fact they've made a complete mess with the red distortion, and then revert back to a clean generation with no noise at all. but when 2.1 does it, it completely forgets what I look like because the distortion is heavier on 2.1. Looking at the positives though 2.1 is it's much better at knowing what needs to be removed even with a super simple prompt. In 2.0, after the flashlight, it kept the light beams in the generations after that. then holding the teacup, it brought in another hand to hold the bottom of the cup and that hand didn't disappear in the generations afterwards. Whereas on 2.1, my position and posture never changed. From one generation to the next, the entire object disappeared and the new one was placed exactly where the previous one was. No drift in that sense. so 2.1 is the better editor. 2.0 is better at remembering what I look like. I think watermarking is a great, but it needs to go unnoticed for at least 10 iterations on the same image. It's too quick to notice in NB2.1. Still 1 or 2 iterations better than GPT Image 2.5 though.show more