Grok 4.7 just built a GTA-style open-world game from a single prompt in Grok Build You can walk around, drive cars, enter vehicles and explore an entire city It looks insane, and I had way too much fun playing it 😂

🚨 Researchers gave 293 writers the same AI and the results were shocking. 600 readers rated the AI-assisted stories as more creative, better written, and more enjoyable. Then the researchers measured how similar the stories were to each other. The AI-assisted ones clustered together. The writers had anchored on the AI's idea. Better output. Less of you in it. Most people are still asking: Which model is the smartest? Which one writes best? The real question is different: Whose voice is the model learning? Right now the answer is everyone's. One model, trained on the whole internet, serving millions of people at once. Of course the output converges. That is why I think Individual AI is becoming a real category. On Uare ai, every person gets their own AI. You own the model. It learns from you and no one else. It writes like you, answers like you, and works through a problem like you. Publish it and people reach your expertise directly. You set the price and keep 70 percent. I built mine. It is live now, trained on my own work and the way I answer these questions. Go ask it something: utm.genai.works/r/h6zhnak

Introducing Claude Opus 5.5, the first model in our new Claude 5.5 family. It performs at the level of Claude Fable 5.1 for most tasks, and costs 40% less to run than Opus 5.

I think we'll see 5x the number of people become hardware and robotics founders over the next 18 months Why? Because you can rent every step, from design to manufacturing: DESIGN - Astra to drive Blender, FreeCAD, and KiCad the way a person would, so you get editable geometry instead of a dead render. Zoo dot dev's text to CAD API if you want it programmatic. - Or have Claude write build123d code, which is Python parametric CAD, and there's a Claude Code plugin that runs it and exports the STL for you. PROTOTYPE - Bambu A1 mini is $299, P1S around $399 on sale. Or skip owning one, JLC3DP prints and mails it for about $20. PCBWay does resin, SLS, and CNC if plastic won't cut it. ELECTRONICS - KiCad for the board, Astra can drive it. JLCPCB fabricates and assembles, often under $100 for a small run. ESP32 for wifi, Raspberry Pi if it needs a brain. ANYTHING THAT MOVES - Unitree sells a Go2 with a full SDK for about $2,500. - Hugging Face put out a $399 open-source biped. - LeRobot gives you the whole train in sim, deploy to real pipeline, and Physical Intelligence open-sourced π0 so you're fine-tuning instead of starting from zero. - Prototype the policy in MuJoCo or Isaac Lab first. MANUFACTURING - Alibaba RFQ for the first 100 units. Check 1688 to see what the factory actually charges domestically, then negotiate. Pietra or Sourcify if you want someone to handle it. FULFILLMENT AND SELLING - ShipBob or Amazon FBA. Shopify for the store, TikTok Shop for distribution, Kickstarter if you want the money before you build it. How to think about starting your own robotics or hardware company: 1. Pick a niche that's already buying weird gear. Cyclists, tabletop gamers, beekeepers, home baristas, dog people with mobility issues. These groups spend money on specific objects and complain in public about what doesn't exist. Or grab ideas off Ideabrowser.com 2. Go read the complaints. Reddit, IG etc Search "I wish someone made," "does anyone make," and "modified my." That last one is the best signal, because someone already hacked the product together and you're just manufacturing what they built by hand. Also check Etsy!! If 3 sellers are doing a janky 3D printed version with 400 reviews each, the market is validated. 3. Make one. Describe it to Astra or Claude, get the CAD, print it in ugly gray PLA, use it, fix it. 4. Only go to Alibaba or similar once you've sold a few. Message 10 suppliers through RFQ, take the third cheapest, always pay the $50 for a sample before the real order. 5. Film everything from day 1. The first ugly print, the failed version, the box of 100 arriving. That's your entire marketing budget, and hardware is one of the few categories where people actually want to watch the thing get made. 6. Raise the price. Almost everyone here anchors on what the plastic cost. Your customer is comparing you to nothing, because the alternative is the product doesn't exist. Start at 5x COGS and go up. THIS GOAL OF THIS POST IS JUST HERE TO GET YOUR CREATIVE JUICES FLOWING. Of course, you can build robotics/hardware in a bunch of different ways. One thing I've learned is data couldn't be more important when you're building a hardware/robotics startups. These models learn from first person video of a human doing the task, and that footage doesn't exist for almost any job. So basically you pick one repetitive job people quit over, film someone doing it for 2 weeks, fine-tune π0 on that footage, use organic to sell the first few units and figure out scaling, 5 years ago you needed a factory, a supply chain, and a $1M just to find out if anyone wanted the thing. Now, anyone can become a hardware/robitics founder. And I suspect a lot of people will become one! Vibe manufacturers.

We’ve had an AI breakthrough at Figure and will be showcasing this tomorrow

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Introducing the Notion Skills API. Edit your team’s skills in Notion. Use them in ChatGPT, Claude, and all of your agents. Here’s what’s new:

Lovable in Sweden Higgsfield in Kazakhstan Viktor in Poland Big ARR numbers. Fast. The rest of the world has something figured out. Fryd is recording that video in a self-driving car by the way. How you do one thing, is how you do everything. @viktor_com as an AI employee seems promising, I wonder if he's driving the car too. Anyone try it? Try it for growth? Paid Partnership

Exciting update! We've hit $50M in annualized revenue. After just 7 months! BUT we're not even 0.002% into our mission. We're just getting started.

big AI news Google just demonstrated a recursive self improvement loop for AI discovery Google/DeepMind researchers introduced Dream-RSI, a system where an AI agent improves how it explores problems by replaying its past discovery attempts, testing thousands of alternative strategies cheaply, then deploying the better strategy in the next round. Across algorithm design, mathematical optimization, and GPU kernel engineering, it matched or improved discovery quality while cutting search costs dramatically, in one setting reducing agent calls by up to 162x. 👀 Importantly, it improves the exploration policy, not the underlying model weights.