Vinay Rao

Now we know robots can take blood samples safely, but can you imagine a robot performing an ultrasound exam? A study just proved that it is possible. The authors developed an autonomous lightweight ultrasound robot comprising an AI agent that integrates multimodal perception with memory attention. "Our robot can autonomously acquire expert-level standard liver ultrasound planes and detect pathology in patients... Our system performs effectively on rapid-motion individuals and in wilderness environments. This work represents the first demonstration of autonomous sonography across multiple challenging scenarios, potentially transforming access to expert-level diagnostics in underserved regions." It is designed for now for regions with limited medical resources. So your next ultrasound sonographer will not fly into the room as a robot anytime soon. The study: nature.com/articles/s41467-0…

🚨 WHAT IF YOUR AI AGENT COULD DESIGN REAL 3D PARTS? Meet SolidCraft. An open-source parametric CAD app built entirely in Rust. And yes, AI agents can use it. Here’s what makes it interesting: → Sketch and build 3D solids. → Change dimensions and rebuild the entire model. → Create holes, threads, fillets, patterns, and assemblies. → Design sheet-metal parts and plastic enclosures. → Export formats like STEP, STL, and 3MF. → Let AI agents create models through a built-in MCP server. The wild part? You can use it on your desktop, in your browser, or through a command-line interface. So an AI agent can do more than generate a pretty 3D image. It can work with editable CAD geometry and a parametric feature timeline. The project reports that 75 of 76 public reference parts match their expected geometric measurements. It’s still early, but this is a fascinating step toward AI-assisted engineering. Explore SolidCraft 👇 github.com/tangency-labs/sol… #AI #CAD #OpenSource #3DModeling

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”Just make it look like the 3D model.” Famous last words. 😅 For sheet metal parts, unclear dimensions can turn manufacturing into a guessing game. Need a hole in the right spot after bending? Make sure your drawing clearly defines the critical dimensions, tolerances, and reference datums. Your CAD model shows the shape. Your engineering drawing makes the critical requirements explicit. Less guessing. Fewer surprises. Better-fitting parts. #SheetMetalDesign #EngineeringDrawings #DFM #MechanicalEngineering

Deepinder Goyal’s Temple brain-monitoring wearable is reportedly priced at ₹85,000, including a lifetime subscription, with pre-orders opening on November 1, 2026. The device tracks changes in cerebral blood flow and generates an Entropy score to indicate physiological strain. livemint.com/technology/tech…

We’re starting smoothie blenders! Perfect product for us, plastic with AC motor, can easily Indianise 100% Need your help in coming up with some design ideas. Which of these look best, and what’s the direction to go? Reply 1, 2, 3 or 4 and give your inputs. If you’ve seen any other great designs please share. We want to get this right from the get go, and there is no better audience than you to help us do that.

Meta Muse will probably create 1000+ millionaires This is the most slept on arbitrage I’ve seen in years and it’s not even close. Here’s how i’d build a 1 person business powered by AI/Muse with with $0 and 0 social following

Why is China better than India in manufactuing? Because they are ready to fail. They try 5 things, fail at 4, and move on. We’re still discussing whether the first one is worth trying. In China, opening a new mould is almost routine. They already have the motors, components, and supply chains. Change the outer shell, and they have a new product. No 6-month planning cycle. They know most bets won’t work. One winner can pay for all the failures. At Topsail, we’ve learned this the hard way too. By the time our mould is ready, Chinese manufacturers have moved on. Buyers tell us: “Your model is outdated.” So we’ve changed how we approach tooling. Unless it’s a timeless classic, we’d rather invest in a product we believe will sell tomorrow than spend months building what everyone wants today. Will some bets fail? Absolutely. But we’d rather risk building a product nobody wants than build a product everyone wanted six months ago. That’s how we plan to close the gap with China. Not by playing it safe - by moving faster.

Tim Cook's take on why competitors can't copy Apple is sharper than most people realize. They start by copying the product. Then they realize the product isn't the hard part. Cook said it directly: people who try to copy Apple first try to copy what they can see. The hardware. The design. The packaging. And they can get close on all of it. But the thing that made the product — the organizational culture, the decision-making, the obsessive prioritization — that's invisible from the outside. You can't find it in a teardown. This is why Apple's moat has held for decades while better-funded competitors kept losing. Samsung had more SKUs. Google had more engineers. Amazon had more distribution. None of it translated into the same output because none of it replicated the same input. The lesson for founders is uncomfortable: if your competitive advantage is visible in your product, it's already copyable. The real moat is the culture that makes the product — and that takes decades to build, not quarters. Reverse-engineering the iPhone is an engineering problem. Reverse-engineering how Apple decides what to build is a different problem entirely.

The Bar Tab A few weeks ago I wrote that the music was playing and everyone in venture capital felt obliged to dance. A few weeks after that I wrote that history doesn't repeat but it rhymes, and walked through the last time a band this loud played a room this full, which was 2000, A lot of you wrote in to ask what comes next. I didn't know. So I did what any responsible investor does when he doesn't know something: I got a napkin and started doing arithmetic I wasn't qualified to do. Fair warning. Every time I tried to make the numbers kinder, they got worse. This is a known property of napkins. Start with the size of the band. Microsoft, Alphabet, Amazon, Meta and Oracle spent about $230 billion on AI capex in 2024, about $400 billion in 2025, and have guided to around $800 billion this year. Goldman has $1.2 trillion next year and $1.4 trillion the year after. Add it up through 2029 and $4 trillion is the floor. Consensus is closer to $5.5 trillion. I'm using $4 trillion because I'm an optimist and because I only had one napkin. Now, a bar tab is a thing that eventually gets handed to somebody. What does $4 trillion have to earn? Say 12%, which is about what a sane person wants for setting that much money on fire in West Texas. That's $480 billion a year. Call it $500 billion of free cash flow by 2029. Then I remembered a GPU is not a building. Amazon depreciates servers over five years, and Michael Burry thinks that's charitable. If you want 12% on $4 trillion of equipment that'll be scrap before my youngest finishes college, you need about $1.1 trillion a year. I'm going to keep using $500 billion anyway. When the story is bad enough, you don't need to make it worse to get people's attention. Five hundred billion of free cash flow at a 50% gross margin means $1 trillion of revenue. Somebody has to write checks to the hyperscalers for a trillion dollars a year of AI compute by 2029. Who? Some of it is the hyperscalers buying themselves a round: Meta's capex buys Meta's ads. But the biggest outside checks come from the frontier labs, the handful of companies that train the models the rest of us build on. Full disclosure: Menlo is an investor in this category, so adjust for the fact that I'm a man with a drink in his hand telling you the bar is overpriced. Say the labs are half of it. That's $500 billion of revenue, as a group, by 2029. Their combined run-rate today is north of $100 billion. Three years ago it was $2 billion. That is the fastest revenue ramp in the history of business, it isn't close, and it still isn't enough. To hit $500 billion the category has to grow fivefold in three years, 70% a year, every year, from a base the size of Coca-Cola. There's no precedent for that. There was also no precedent for the last three years, which is the whole trouble with this moment. The people who've been right so far have been right by ignoring precedent, and a man who got rich ignoring precedent is the last man alive who'll start consulting it. So there are two forks in the road, and both of them are toll roads. Fork one: the labs don't get there, and the $4 trillion doesn't earn its keep. People say "debt crisis." Careful. Here's where the rhyme matters. In 2001 the trillion dollars of telecom debt was junk paper issued by CLECs with no revenue, and the bondholders got sixteen cents. Today the four big spenders fund about 70% of their capex out of operating cash flow and carry AA ratings. The debt lives at Oracle, CoreWeave, xAI, Meta's off-balance-sheet vehicles and in about $350 billion of private credit. That's not 2008. It's 2001 with better credit: write-downs, capex cuts, and a few bodies in the second tier. The band doesn't die. It just stops taking requests. And here's the thing nobody says out loud. About 40% of AI capex is GPUs, and Nvidia's gross margin is 75%. So roughly 30% of the $4 trillion has already been earned. By Nvidia. The return on this capex has, in a sense, already happened. It went to one company in Santa Clara, which is like finding out the band got paid up front and the tab is yours. Fork two: the labs do get there. Then $500 billion a year has to come from somewhere, because money is not conjured, whatever the deck says, and there are only three somewheres. It comes out of wages. US compensation of employees is $15.7 trillion. Five hundred billion is 3.2% of it. At $100,000 per fully loaded worker, that's five million jobs, which takes unemployment from 4.3% to about 7%. "Productivity" is what we call this at conferences. Or it comes from growth. Five hundred billion is 1.6% of US GDP. If AI makes the economy 1.6% bigger than it would otherwise have been, nobody loses, every deck was right, and I owe a great many people an apology, which I'll deliver cheerfully and from a boat. I'd note that AI is already adding about a point and a half to GDP growth, but that's the capex itself, not the output. So far the bar tab is being counted as revenue, which bartenders have been doing since the invention of bartenders. Or it comes out of someone else's budget. The world spends $1.44 trillion a year on software. Five hundred billion is 35% of every software dollar on earth, so software can't be the whole answer, but it's the first place the labs will look, and the IT services and outsourcing budgets behind it are another $1.7 trillion. In this version the thing that goes to zero isn't the hyperscalers. It's SaaS at ten times revenue. If you're long SaaS at ten times revenue, read that sentence twice. The honest answer is a blend, and the whole game is which blend, because every blend has a different loser. In fork one it's the lenders. In the wage version it's people with jobs. In the budget version it's everyone long software. In the growth version nobody loses, which is exactly why I don't trust it. In thirty years I have never seen a version where nobody loses. I have seen a great many decks that promised one. Reflexivity ends when the story and the cash get too far apart. The story is $4 trillion. The cash is $100 billion, growing faster than anything ever has. That gap closes one of two ways: the cash catches up to the story, or the story comes down to the cash. The music's still playing, the floor's still full, and I'm out there with everybody else, because that's the job. But I've started keeping one eye on the bar. In 2000 the bartender came around in about ninety days. He didn't ask who'd had the most fun. He just asked whose card was open.