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Built a tiny custom app that lets me yell at my computer and turns it into coherent writing—instantly in my clipboard. Personal software is already here. Mass-market is next. The middle is death. AI differentiation will come down to: - Regulatory leverage - Real network effects - Proprietary data... show more

Carl Rivera

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18,928 просмотров • 6 месяцев назад •via X (Twitter)

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The new bar for software: "can I vibecode this?" We’re heading into a world of personal software — built for one person and their needs — and premium software, designed with such depth that you’re buying a solution, not a tool. Most software today sits in the middle. And the middle is death. As the market catches up to our new reality, it won’t just be the ai bubble popping — it’ll be a lot of traditional SaaS getting wiped out (From my recent Kinference talk.)

The new bar for software: "can I vibecode this?" We’re heading into a world of personal software — built for one person and their needs — and premium software, designed with such depth that you’re buying a solution, not a tool. Most software today sits in the middle. And the middle is death. As the market catches up to our new reality, it won’t just be the ai bubble popping — it’ll be a lot of traditional SaaS getting wiped out (From my recent Kinference talk.)

Carl Rivera

98,551 просмотров • 9 месяцев назад

This is the highest-leverage thing I've done with AI in months. I just built a personal AI OS that runs my ENTIRE life. Watch this, and you'll instantly want to build your own. Full walkthrough:

This is the highest-leverage thing I've done with AI in months. I just built a personal AI OS that runs my ENTIRE life. Watch this, and you'll instantly want to build your own. Full walkthrough:

Miles Deutscher

14,259 просмотров • 2 месяцев назад

1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.

1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.

CherryAI Robotics

90,786 просмотров • 11 месяцев назад

Our CEO Roland Busch explained at #CES2026 that many companies already have #DigitalTwins and software – but that alone doesn’t make them AI-ready. 🔑 The key is an end-to-end #IndustrialAI stack that turns fragmented data into real-world impact.

Our CEO Roland Busch explained at #CES2026 that many companies already have #DigitalTwins and software – but that alone doesn’t make them AI-ready. 🔑 The key is an end-to-end #IndustrialAI stack that turns fragmented data into real-world impact.

Siemens

11,617 просмотров • 7 месяцев назад

$SPCX spending $60B on Cursor validates the idea that real AI value accrues to the application layer that owns the workflow, proprietary data loop and enterprise distribution. $NOW is building exactly that as the orchestration layer for enterprise AI agents across business workflows. The Cursor deal is a reminder that the market will pay massive premiums for software that owns where work actually happens.

$SPCX spending $60B on Cursor validates the idea that real AI value accrues to the application layer that owns the workflow, proprietary data loop and enterprise distribution. $NOW is building exactly that as the orchestration layer for enterprise AI agents across business workflows. The Cursor deal is a reminder that the market will pay massive premiums for software that owns where work actually happens.

Shay Boloor

82,503 просмотров • 1 месяц назад

Wix at 1x ARR is not a SaaS bloodbath. It is a reckoning for businesses that were always just sales and marketing operations dressed up as software. 1. The client-server to SaaS shift was more disruptive than anything AI is doing to software right now. This is incremental. The market is finally pricing it that way. 2. SaaS was never that hard to spin up. When the moat is a sales team and a logo, the multiple compresses. That is not a surprise, that is gravity. 3. The companies that survive this are the ones with complex customer relationships and data sets that cannot be recreated. Wix does not have that. Base44 might not either. 4. Vibe coding tools doing $170M ARR getting priced at $2.1BN is the same mistake in reverse. Getting real workflow change adopted at scale takes longer than the ARR curve suggests. 5. The best software companies right now are not pure software companies. The ones with physical world integration and proprietary data loops are harder to replicate and harder to compress. 6. Everyone who raised at 20x ARR on a sales-led motion took on leverage they did not call leverage. It cuts just as hard on the way down. The bloodbath is not random. It is selection pressure doing exactly what it is supposed to do. Will Quist

Slow Ventures

10,068 просмотров • 29 дней назад

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 просмотров • 1 месяц назад

🚨 ELON MUSK: "AI will outperform humans at most jobs." “I do think it’s going to be a bumpy road, because AI will be able to do any job better than any person can do that job. That will be true very quickly for jobs that are digital, or really anything that involves a person at a computer or on a phone. AI will be able to do all of that very soon. For software engineering, we already have a situation where AI is better than at least 90% of humans at writing software, including 90% of professional software engineers. It really is getting to the point where it will be better than 99%, and then it will get to the point where there’s just no way to compete. It will get to what I call Stockfish level. Stockfish is a chess program that is so good at chess that it will easily beat Magnus Carlsen, or anyone, even while running on a small computer. I suspect you could probably run Stockfish on your phone and beat Magnus Carlsen. That’s how good it is at chess, and that’s how good AI will be at writing software. And that applies to everything.”

🚨 ELON MUSK: "AI will outperform humans at most jobs." “I do think it’s going to be a bumpy road, because AI will be able to do any job better than any person can do that job. That will be true very quickly for jobs that are digital, or really anything that involves a person at a computer or on a phone. AI will be able to do all of that very soon. For software engineering, we already have a situation where AI is better than at least 90% of humans at writing software, including 90% of professional software engineers. It really is getting to the point where it will be better than 99%, and then it will get to the point where there’s just no way to compete. It will get to what I call Stockfish level. Stockfish is a chess program that is so good at chess that it will easily beat Magnus Carlsen, or anyone, even while running on a small computer. I suspect you could probably run Stockfish on your phone and beat Magnus Carlsen. That’s how good it is at chess, and that’s how good AI will be at writing software. And that applies to everything.”

DogeDesigner

61,112 просмотров • 21 дней назад

Manetho is digitizing the museum experience with AI. Using computer vision and NLP, it translates hieroglyphs in real time and turns artifacts into interactive learning tools. Built on Huawei Cloud, ModelArts, and MindSpore, it supports deeper access to Egyptian history.

Manetho is digitizing the museum experience with AI. Using computer vision and NLP, it translates hieroglyphs in real time and turns artifacts into interactive learning tools. Built on Huawei Cloud, ModelArts, and MindSpore, it supports deeper access to Egyptian history.

Huawei

1,552,492 просмотров • 2 месяцев назад

AI is leaving the data center. NVIDIA is betting on local, personal compute. Edge-agent research is showing why that matters: the closer the agent is to the user, the more usable it becomes. That is the OptimAI Claw thesis. Search brings the signal. Claw turns it into action on your device. The network keeps intelligence distributed. This is what agent-native infrastructure looks like when it is built for the edge.

AI is leaving the data center. NVIDIA is betting on local, personal compute. Edge-agent research is showing why that matters: the closer the agent is to the user, the more usable it becomes. That is the OptimAI Claw thesis. Search brings the signal. Claw turns it into action on your device. The network keeps intelligence distributed. This is what agent-native infrastructure looks like when it is built for the edge.

OptimAI Network

27,234 просмотров • 2 месяцев назад

One week ago, we launched Typeahead. The product is already meaningfully better. We’ve shipped new features, Typeahead 2.0 is almost ready, and we’re already planning 3.0. 3.0 is going to be big. That pace is part of the point. Sam Asante and I started a new company together because we wanted to see how far we could push local AI software. Typeahead is the first thing we shipped. It is a local AI writing app for Mac. You type, suggestions appear inline, and it learns how you actually write. It works offline. You pay once. $79 and you own it. The product is intentionally simple. The thesis behind it is bigger. We think local models are going to create a new class of software. Fast, private and offline. By default. Personal without being creepy. Useful without turning everything into a subscription. Most AI products today assume the model lives in the cloud. That will not be the only path. The machines we already own are getting powerful enough. The models are getting small enough. And the experience can start to feel less like chatting with a remote service and more like using software that belongs on your computer. We have already built a few fully local experiments together. Typeahead felt right because the value is immediate. A few people have asked me how this fits with Crazy Egg. Crazy Egg is still my main work. This is a focused company with Sam, built around a thesis we both believe in. I’ve learned that the people I work with shape the work more than almost anything else. Sam was already on my short list of people I wanted to build with. Typeahead is the first public proof of the thesis. Watch the video. Get it here:

One week ago, we launched Typeahead. The product is already meaningfully better. We’ve shipped new features, Typeahead 2.0 is almost ready, and we’re already planning 3.0. 3.0 is going to be big. That pace is part of the point. Sam Asante and I started a new company together because we wanted to see how far we could push local AI software. Typeahead is the first thing we shipped. It is a local AI writing app for Mac. You type, suggestions appear inline, and it learns how you actually write. It works offline. You pay once. $79 and you own it. The product is intentionally simple. The thesis behind it is bigger. We think local models are going to create a new class of software. Fast, private and offline. By default. Personal without being creepy. Useful without turning everything into a subscription. Most AI products today assume the model lives in the cloud. That will not be the only path. The machines we already own are getting powerful enough. The models are getting small enough. And the experience can start to feel less like chatting with a remote service and more like using software that belongs on your computer. We have already built a few fully local experiments together. Typeahead felt right because the value is immediate. A few people have asked me how this fits with Crazy Egg. Crazy Egg is still my main work. This is a focused company with Sam, built around a thesis we both believe in. I’ve learned that the people I work with shape the work more than almost anything else. Sam was already on my short list of people I wanted to build with. Typeahead is the first public proof of the thesis. Watch the video. Get it here:

Hiten Shah

18,627 просмотров • 2 месяцев назад

This is how it works in the real RLM warehouse. Rich evans actually built this in real life and I asked him and he gave me his ppersonal architectural files. I said “thanks dad” (he is my biological father) it is my personal belief that they keep the black void down there

This is how it works in the real RLM warehouse. Rich evans actually built this in real life and I asked him and he gave me his ppersonal architectural files. I said “thanks dad” (he is my biological father) it is my personal belief that they keep the black void down there

jon | super vhs

54,657 просмотров • 1 месяц назад

Today Sam Asante and I are releasing Typeahead 2.0 on Product Hunt. Sam and I started this company because we think local AI software is going to become a much bigger part of how people use their computers. Typeahead is the first product from that thesis. The first version made the idea clear. AI autocomplete should work in the apps where writing already happens. Since then, we have been shipping toward the product that idea deserves. The app feels faster and more natural. Suggestions are easier to control. The experience is more native to the Mac. It's getting better at staying out of the way until the exact moment it can help. That matters because writing is personal. The best AI writing help keeps you in the sentence. You stay in flow and keep your voice. The machine helps without taking over. Typeahead works across your Mac, runs locally, works offline, and is built to feel like software you own. We’re live on Product Hunt today.

Today Sam Asante and I are releasing Typeahead 2.0 on Product Hunt. Sam and I started this company because we think local AI software is going to become a much bigger part of how people use their computers. Typeahead is the first product from that thesis. The first version made the idea clear. AI autocomplete should work in the apps where writing already happens. Since then, we have been shipping toward the product that idea deserves. The app feels faster and more natural. Suggestions are easier to control. The experience is more native to the Mac. It's getting better at staying out of the way until the exact moment it can help. That matters because writing is personal. The best AI writing help keeps you in the sentence. You stay in flow and keep your voice. The machine helps without taking over. Typeahead works across your Mac, runs locally, works offline, and is built to feel like software you own. We’re live on Product Hunt today.

Hiten Shah

28,014 просмотров • 1 месяц назад

Sebastian Siemiatkowski, the co-founder and CEO of Klarna: SaaS is Dead. "The cost of creating software is going down to zero. And that means everyone will be able to generate software at any point in time. " But the real threat to SaaS will shows up next when AI makes data migration one-click—no more hostage data models, no more painful switching costs. --- From '20VC with Harry Stebbings' YT channel ( link in comment)

Sebastian Siemiatkowski, the co-founder and CEO of Klarna: SaaS is Dead. "The cost of creating software is going down to zero. And that means everyone will be able to generate software at any point in time. " But the real threat to SaaS will shows up next when AI makes data migration one-click—no more hostage data models, no more painful switching costs. --- From '20VC with Harry Stebbings' YT channel ( link in comment)

Rohan Paul

93,441 просмотров • 5 месяцев назад

ATOMS is effectively making the opposite bet to Sequoia. Sequoia’s thesis is that AI companies will move beyond selling software seats and start selling completed work, capturing the much larger services and labor budget. In that model, AI becomes the worker: the company produces the legal analysis, customer-support resolution, accounting work, or marketing output. Kalanick believes the bigger opportunity is not replacing knowledge workers through AI services. It is using AI, robotics, sensors, manufacturing, real estate, and transportation to digitize entire physical industries. ATOMS wants to build what he calls “atoms-based computers,” where manufacturing is the CPU, real estate is storage, and transportation is the network. That difference matters because an AI services company can be commoditized when foundation models learn to perform its task directly. ATOMS intends to own the physical infrastructure, deployments, operational data, supply chains, and regulatory relationships required to produce and move real things. Better AI should therefore make ATOMS more efficient rather than eliminate the company. So Kalanick is trying to prove that the next enormous AI companies will not simply sell work instead of software. They will use software to control the physical economy. Sequoia says services are the new software. Kalanick is betting that the physical world is.

ATOMS is effectively making the opposite bet to Sequoia. Sequoia’s thesis is that AI companies will move beyond selling software seats and start selling completed work, capturing the much larger services and labor budget. In that model, AI becomes the worker: the company produces the legal analysis, customer-support resolution, accounting work, or marketing output. Kalanick believes the bigger opportunity is not replacing knowledge workers through AI services. It is using AI, robotics, sensors, manufacturing, real estate, and transportation to digitize entire physical industries. ATOMS wants to build what he calls “atoms-based computers,” where manufacturing is the CPU, real estate is storage, and transportation is the network. That difference matters because an AI services company can be commoditized when foundation models learn to perform its task directly. ATOMS intends to own the physical infrastructure, deployments, operational data, supply chains, and regulatory relationships required to produce and move real things. Better AI should therefore make ATOMS more efficient rather than eliminate the company. So Kalanick is trying to prove that the next enormous AI companies will not simply sell work instead of software. They will use software to control the physical economy. Sequoia says services are the new software. Kalanick is betting that the physical world is.

Guillermo Flor

41,170 просмотров • 14 дней назад

Saw a post yesterday about "Duolingo for Cooking." So I vibe coded it. Left my AI agent running overnight. Woke up and checked the logs. It finished the whole thing in 6 hours. It cost me $250 in compute credits. AI is simply incredible. Software Engineers are absolutely cooked. This is a million dollar app. [Built w/ Cursor + Perplexity Computer]

Saw a post yesterday about "Duolingo for Cooking." So I vibe coded it. Left my AI agent running overnight. Woke up and checked the logs. It finished the whole thing in 6 hours. It cost me $250 in compute credits. AI is simply incredible. Software Engineers are absolutely cooked. This is a million dollar app. [Built w/ Cursor + Perplexity Computer]

Zoomer 🧢

78,223 просмотров • 5 месяцев назад

AI is redefining cinema. A single filmmaker using AI has the leverage of a Hollywood studio and what used to cost millions. In the 70s, Steve Jobs and Woz built the personal computer revolution in a garage. In the 2000s, Zuckerberg built a software empire from his dorm room. Today, filmmakers are building the next era of cinema from their bedrooms. Not because AI replaces vision, taste, or storytelling. It doesn’t. Without the fundamentals, it's just slop. But with them, AI becomes a multiplier. This is just getting started.

AI is redefining cinema. A single filmmaker using AI has the leverage of a Hollywood studio and what used to cost millions. In the 70s, Steve Jobs and Woz built the personal computer revolution in a garage. In the 2000s, Zuckerberg built a software empire from his dorm room. Today, filmmakers are building the next era of cinema from their bedrooms. Not because AI replaces vision, taste, or storytelling. It doesn’t. Without the fundamentals, it's just slop. But with them, AI becomes a multiplier. This is just getting started.

Reid Hannaford

20,792 просмотров • 1 месяц назад

Infrastructure defines what scales. Electricity enabled industry. The internet enabled software. Now AI is moving toward systems that run continuously, across data, execution, and feedback. 💡At OptimAI, we’re building that infrastructure. A decentralized search layer for real-time data. A network of nodes powering continuous retrieval and validation. Agents that execute tasks directly on your device. This is not theoretical - we’re already seeing it in production, with millions of nodes and growing usage across the network. We’re putting together a short documentary to show how this shift is happening, and where it leads next. If you’re building, exploring, or just getting started - this is the layer to pay attention to. Welcome to OptimAI.

Infrastructure defines what scales. Electricity enabled industry. The internet enabled software. Now AI is moving toward systems that run continuously, across data, execution, and feedback. 💡At OptimAI, we’re building that infrastructure. A decentralized search layer for real-time data. A network of nodes powering continuous retrieval and validation. Agents that execute tasks directly on your device. This is not theoretical - we’re already seeing it in production, with millions of nodes and growing usage across the network. We’re putting together a short documentary to show how this shift is happening, and where it leads next. If you’re building, exploring, or just getting started - this is the layer to pay attention to. Welcome to OptimAI.

OptimAI Network

17,472 просмотров • 4 месяцев назад

The reason $META is a winner in the AI economy is that it can embed AI directly into products already used by billions of consumers and millions of businesses. This lets them turn model improvements into better ads, WhatsApp agents, AI glasses, recommendation systems and creative tools almost immediately. Those products may look separate but they all run on same infrastructure, models, proprietary data and distribution creating a shared AI stack that can compound across multiple revenue streams.

The reason $META is a winner in the AI economy is that it can embed AI directly into products already used by billions of consumers and millions of businesses. This lets them turn model improvements into better ads, WhatsApp agents, AI glasses, recommendation systems and creative tools almost immediately. Those products may look separate but they all run on same infrastructure, models, proprietary data and distribution creating a shared AI stack that can compound across multiple revenue streams.

Shay Boloor

71,172 просмотров • 1 месяц назад

The Tesla Megapod trademark might be the most important filing nobody is talking about (Save this). Tesla wants to build a product that is basically a self contained AI computer in a box. You truck it somewhere, plug it into power, and it runs AI workloads. The reason this matters is that the AI industry has a problem because you can't build data centers fast enough. Getting land approved, getting power from the grid, getting zoning cleared, that process takes 3–5 years, and demand is moving much faster than that. US data center construction actually fell in 2025 for the first time in years, because developers kept hitting bureaucratic walls. Tesla's answer to that problem is simple. They already own thousands of Supercharger stations across the country and those stations already have land, power, and grid connections, all the hard stuff is done. The idea is to drop a Megapod at those sites and turn idle charging infrastructure into AI compute, almost overnight. What Jason, Chamath, and Friedberg were all circling around is that this drop a box on a concrete pad approach is already how serious companies are deploying AI compute right now. Vertiv and DUG Technology both ship GPU-packed containers that can go live in 90 days and Tesla would just be bringing that idea to a network it already owns. The opportunity is very real whether Tesla is the one to actually execute on it is still an open question.

The Tesla Megapod trademark might be the most important filing nobody is talking about (Save this). Tesla wants to build a product that is basically a self contained AI computer in a box. You truck it somewhere, plug it into power, and it runs AI workloads. The reason this matters is that the AI industry has a problem because you can't build data centers fast enough. Getting land approved, getting power from the grid, getting zoning cleared, that process takes 3–5 years, and demand is moving much faster than that. US data center construction actually fell in 2025 for the first time in years, because developers kept hitting bureaucratic walls. Tesla's answer to that problem is simple. They already own thousands of Supercharger stations across the country and those stations already have land, power, and grid connections, all the hard stuff is done. The idea is to drop a Megapod at those sites and turn idle charging infrastructure into AI compute, almost overnight. What Jason, Chamath, and Friedberg were all circling around is that this drop a box on a concrete pad approach is already how serious companies are deploying AI compute right now. Vertiv and DUG Technology both ship GPU-packed containers that can go live in 90 days and Tesla would just be bringing that idea to a network it already owns. The opportunity is very real whether Tesla is the one to actually execute on it is still an open question.

Milk Road AI

16,630 просмотров • 1 месяц назад