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Legacy .NET apps holding back innovation? Experian faced this with 7 mission-critical apps. Instead of moving engineers off high-impact projects, they used agentic AI with AWS Transform. The result? 687,600 lines transformed. 300 days saved. 40% effort reduction.

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33,768,920 просмотров • 11 дней назад •via X (Twitter)

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In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency
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In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

Y Combinator

107,448 просмотров • 4 месяцев назад

PiScan is not working smoothly these days, so we are interested in looking at the Pi ecosystem's apps. I recently checked and found that the number of mainnet apps increased to 71. I remember there were about 40 before, but as they were added one by one without being noticed, they already exceeded 70. Most surprisingly, the completeness of the apps is considerably higher than before. Currently, there are three Verified (verified) apps among the mainnet apps. -Fruity Pi -Slice of Pi -Cidi Games There are also 1,485 apps registered in the Vibe-Coded area. I look around one by one whenever I have time, and there are many more interesting apps than I thought. "Oh, there was an app like this?" I often find apps that I think of. I recommend you to surf the Pi app when you have time. If you look at Pi apps It helps improve utility scores You can broaden your understanding of token economics, staking, and liquidity pools in the future It will also help you build your own Pi ecosystem investment and utilization portfolio. The Pi ecosystem is growing faster than expected. Rather than just mining, I think it's a good way to experience various apps firsthand and watch what projects will grow in the future. If there is a good app you have used, please share it with me in the comments!
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PiScan is not working smoothly these days, so we are interested in looking at the Pi ecosystem's apps. I recently checked and found that the number of mainnet apps increased to 71. I remember there were about 40 before, but as they were added one by one without being noticed, they already exceeded 70. Most surprisingly, the completeness of the apps is considerably higher than before. Currently, there are three Verified (verified) apps among the mainnet apps. -Fruity Pi -Slice of Pi -Cidi Games There are also 1,485 apps registered in the Vibe-Coded area. I look around one by one whenever I have time, and there are many more interesting apps than I thought. "Oh, there was an app like this?" I often find apps that I think of. I recommend you to surf the Pi app when you have time. If you look at Pi apps It helps improve utility scores You can broaden your understanding of token economics, staking, and liquidity pools in the future It will also help you build your own Pi ecosystem investment and utilization portfolio. The Pi ecosystem is growing faster than expected. Rather than just mining, I think it's a good way to experience various apps firsthand and watch what projects will grow in the future. If there is a good app you have used, please share it with me in the comments!

jintaek JUN

12,717 просмотров • 25 дней назад

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.
1:52

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.

Pasha Rayan

53,904 просмотров • 1 год назад

We're excited to launch 🚀Airtable AI Assistant 🚀 today, along with AI document analysis and AI web research capabilities! Airtable was founded 12 years ago with the mission of democratizing software creation. Our pioneering innovation was to distill app-building concepts (data, logic, interface) into intuitive visual components, like a no-code lego kit for app building. At the time, we speculated that someday, maybe AI would get good enough to enable conversational app building–talking to an expert AI app builder–and be a huge unlock, making app building even more accessible. We’re now at that point. While surprisingly impressive text generation and manipulation by LLMs was the breakthrough of the 2022 ChatGPT moment, the emergence of surprisingly impressive reasoning capability from LLMs is the breakthrough of 2025. This is unlocking more autonomous agentic experiences, and generating apps and code is the first killer use case (Cursor, @windsurf, Devin, v0, bolt.new, Replit ⠕ Agent to name a few). But for the large class of non-technical builders, a different approach is needed. When AI generates apps with code, rather than no-code building blocks, it requires a developer to fully understand how they work – and to verify them for hidden mistakes that would be tricky/impossible to debug by interface inspection alone (it may look right, but what is the data model business logic is flawed in non-obvious ways?). Airtable Assistant is an agent that can build and modify Airtable apps through conversation, changing schemas, adding automations, and designing interfaces. You can ask it to do things like: –“Research every conference attendee in this base” to have the Assistant immediately spin up an army of researchers that pull in background information for your attendees –“Analyze each contract to identify key risks they pose to my business” to have the Assistant add an AI field that runs an analysis at scale for each contract you’ve signed. Airtable Assistant can also answer questions about the data in your apps, like prompts as advanced as: –“I'm about to meet Jane Smith at Zelos, read all of their recent sales call transcripts and tell me how far along they are in their implementation and if they’re dealing with any issues” –“What are the most common risk factors in our contracts? Are there any changes to our default posture we might consider?” Credit to Mike Krieger for introducing us to the concept of low floor and high ceiling in HCI many years ago, which has become part of our internal lexicon for thinking about product improvements. Assistant dramatically lowers the floor to building apps, including more sophisticated ones, by helping human builders translate their business requirements into the schema design, logic, and interfaces required to deliver on the use case. In addition to launching Airtable Assistant today, we’re also releasing the capability to deploy thousands of AI web researchers, and AI document analysts, to continuously work on the data in Airtable apps. You can do things like: –Pull strategy and value stories from every product requirement doc to draft launch and release messaging –Monitor all brand mentions across digital channels to measure campaign impact –Create an automatically updating competitive intelligence dossier with the latest news and messaging from every competitor in your industry Check it out 👇
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We're excited to launch 🚀Airtable AI Assistant 🚀 today, along with AI document analysis and AI web research capabilities! Airtable was founded 12 years ago with the mission of democratizing software creation. Our pioneering innovation was to distill app-building concepts (data, logic, interface) into intuitive visual components, like a no-code lego kit for app building. At the time, we speculated that someday, maybe AI would get good enough to enable conversational app building–talking to an expert AI app builder–and be a huge unlock, making app building even more accessible. We’re now at that point. While surprisingly impressive text generation and manipulation by LLMs was the breakthrough of the 2022 ChatGPT moment, the emergence of surprisingly impressive reasoning capability from LLMs is the breakthrough of 2025. This is unlocking more autonomous agentic experiences, and generating apps and code is the first killer use case (Cursor, @windsurf, Devin, v0, bolt.new, Replit ⠕ Agent to name a few). But for the large class of non-technical builders, a different approach is needed. When AI generates apps with code, rather than no-code building blocks, it requires a developer to fully understand how they work – and to verify them for hidden mistakes that would be tricky/impossible to debug by interface inspection alone (it may look right, but what is the data model business logic is flawed in non-obvious ways?). Airtable Assistant is an agent that can build and modify Airtable apps through conversation, changing schemas, adding automations, and designing interfaces. You can ask it to do things like: –“Research every conference attendee in this base” to have the Assistant immediately spin up an army of researchers that pull in background information for your attendees –“Analyze each contract to identify key risks they pose to my business” to have the Assistant add an AI field that runs an analysis at scale for each contract you’ve signed. Airtable Assistant can also answer questions about the data in your apps, like prompts as advanced as: –“I'm about to meet Jane Smith at Zelos, read all of their recent sales call transcripts and tell me how far along they are in their implementation and if they’re dealing with any issues” –“What are the most common risk factors in our contracts? Are there any changes to our default posture we might consider?” Credit to Mike Krieger for introducing us to the concept of low floor and high ceiling in HCI many years ago, which has become part of our internal lexicon for thinking about product improvements. Assistant dramatically lowers the floor to building apps, including more sophisticated ones, by helping human builders translate their business requirements into the schema design, logic, and interfaces required to deliver on the use case. In addition to launching Airtable Assistant today, we’re also releasing the capability to deploy thousands of AI web researchers, and AI document analysts, to continuously work on the data in Airtable apps. You can do things like: –Pull strategy and value stories from every product requirement doc to draft launch and release messaging –Monitor all brand mentions across digital channels to measure campaign impact –Create an automatically updating competitive intelligence dossier with the latest news and messaging from every competitor in your industry Check it out 👇

Howie Liu

2,313,248 просмотров • 1 год назад

the future is not the app store a 6 minute rant vibe coding tools have one problem no one's talking about anyone can make an app but no one can release an app posting on tiktok is instant, posting on youtube is instant what if posting a video on tiktok took 2-7 days and a panel of individuals who have NEVER posted a tiktok on their own, determined if your video was "tiktok-like" welcome to the 2025 app store, where this is the case let me explain a long time ago, when the first few iphones were released, apple had a tough battle motorola and blackberry were great competitors with phones people loved apple decided to open up the app store, and invite devs to come create whatever they wanted the app store was created by people like me, iBeer, Paper toss, Bump. these are my founding fathers. people who treated engineering as art. they didn't care about being cool they cared about making their friends look cool and using iBeer made you look cool the catch was -> iBeer was only available on the iPhone as hundreds of these iPhone exclusive apps were released thousands, hundreds of thousands, and millions flocked to use an iPhone small, focused, creative apps made the iPhone what it was but as soon as the iPhone became incumbent, the doors shut. tight. new rules. new regulations. new taxes. i spent the last 6 days working on an app we release an app a week at Danger Testing, bringing back that old energy. making the internet fun again i didnt sleep much, and even with the trauma the app store has put me in before ~ i had faith. that they would do right by this work I put in I was wrong They haggled with me for a few days on the type of data I was storing, which was none ~ (they would know that if they were engineers) Then at the last minute mailed it in with a rejection, with the words "this app is not app like" a sentence that means nothing Guideline 4.2 - Design - Minimum Functionality this is a guideline they hide behind anytime they have decided internally they don't feel an app should be released, but don't want to tell you why you leave me stranded. no where to go, no improvements to make, just go away there's a huge opportunity waiting to happen in apps right now for the first time apps can be cultural commentary, apps can be viral and ephemeral APPS CAN BE EVERYTHING YOUTUBE VIDEOS CAN BE software is fast, software costs nothing everything is lined up except the app store as it stands isn't a platform, it's a government and your average vibe coder is not going to push through a rejection, let alone 20+ rejections -> which i received on my first app of the year millions of apps will be made the success of these apps should be dependent on distribution, not on if some dude thinks its legit This revolution will NOT happen if the doors we have to go through are owned by the app store We need something new, something true and beautiful and welcoming Danger Testing wants everyone to be an appstar This tweet is not on the heels of a single rejection It's on the heels of hundreds of app store rejections in a lifetime A battle to provide the world with what I know it will love But a battle filled with friction at every turn due to the suits I remember being at a hackathon and making this app with andrea The judges ranked us last place, said the app was too goofy Today the last iteration of that app got 25+ million views across the internet This is the new world A group of dudes sitting behind a table don't get to decide our reality, we get to create it Fight back Don't ever allow yourself to be subjected by non shipper losers with no courage The cavalry isn't coming We are the cavalry Let's fight
6:41

the future is not the app store a 6 minute rant vibe coding tools have one problem no one's talking about anyone can make an app but no one can release an app posting on tiktok is instant, posting on youtube is instant what if posting a video on tiktok took 2-7 days and a panel of individuals who have NEVER posted a tiktok on their own, determined if your video was "tiktok-like" welcome to the 2025 app store, where this is the case let me explain a long time ago, when the first few iphones were released, apple had a tough battle motorola and blackberry were great competitors with phones people loved apple decided to open up the app store, and invite devs to come create whatever they wanted the app store was created by people like me, iBeer, Paper toss, Bump. these are my founding fathers. people who treated engineering as art. they didn't care about being cool they cared about making their friends look cool and using iBeer made you look cool the catch was -> iBeer was only available on the iPhone as hundreds of these iPhone exclusive apps were released thousands, hundreds of thousands, and millions flocked to use an iPhone small, focused, creative apps made the iPhone what it was but as soon as the iPhone became incumbent, the doors shut. tight. new rules. new regulations. new taxes. i spent the last 6 days working on an app we release an app a week at Danger Testing, bringing back that old energy. making the internet fun again i didnt sleep much, and even with the trauma the app store has put me in before ~ i had faith. that they would do right by this work I put in I was wrong They haggled with me for a few days on the type of data I was storing, which was none ~ (they would know that if they were engineers) Then at the last minute mailed it in with a rejection, with the words "this app is not app like" a sentence that means nothing Guideline 4.2 - Design - Minimum Functionality this is a guideline they hide behind anytime they have decided internally they don't feel an app should be released, but don't want to tell you why you leave me stranded. no where to go, no improvements to make, just go away there's a huge opportunity waiting to happen in apps right now for the first time apps can be cultural commentary, apps can be viral and ephemeral APPS CAN BE EVERYTHING YOUTUBE VIDEOS CAN BE software is fast, software costs nothing everything is lined up except the app store as it stands isn't a platform, it's a government and your average vibe coder is not going to push through a rejection, let alone 20+ rejections -> which i received on my first app of the year millions of apps will be made the success of these apps should be dependent on distribution, not on if some dude thinks its legit This revolution will NOT happen if the doors we have to go through are owned by the app store We need something new, something true and beautiful and welcoming Danger Testing wants everyone to be an appstar This tweet is not on the heels of a single rejection It's on the heels of hundreds of app store rejections in a lifetime A battle to provide the world with what I know it will love But a battle filled with friction at every turn due to the suits I remember being at a hackathon and making this app with andrea The judges ranked us last place, said the app was too goofy Today the last iteration of that app got 25+ million views across the internet This is the new world A group of dudes sitting behind a table don't get to decide our reality, we get to create it Fight back Don't ever allow yourself to be subjected by non shipper losers with no courage The cavalry isn't coming We are the cavalry Let's fight

los (appstar)

85,931 просмотров • 9 месяцев назад

I'm getting Q4 2024 vibes; There's been an explosion of OpenClaw related tokens Traditional token tracking websites lack context and are extremely slow at listing viable tokenized projects That's where ClawPrice(dot)io will disrupt the current token tracking platforms With the expertise of the Khala Research team, we will layer the additional project context and additional openClaw specific price analysis to make your life easier ClawPrice Performance (Feb 14th, 2026): 1) 🦞 Clawnch 🦞 Token launchpad exclusively for AI agents, trading under $CLAWNCH; could this become the autonomous Virtuals of AI boom 2.0? Recently staked $100k of tokens to Inclawbate, one of the projects launched from its launchpad 2) Kelly Claude has developed the "Agentic Software Factory"; Building apps for app store, revenue buy back token, built by Gauntlet AI founder Austen Allred. Latest app is MacroPlan and recently had its first app approved in the iOS app store. Launched its marketing factory to propel its software products also 3) clawd.atg.eth is by ETH Foundation's Austin Griffith, similar to KellyClaude, building apps to generate revenue with the added knowledge of the intricacies of ERC-8004 and x402 for persmissionless payment rails on the leading smart contract (and recently AI focused) layer 1 chain 4) Anti Hunter is by Logan Paul's Anti Fund and built by Geoffrey Woo, investment agent with live positions. Currently bidding the early leaders in the openClaw eco; a smart bet to underpin a potential explosive meta that's yet to hit its inflection point 5) Felix Craft by Nat Eliason first product "How to Hire an AI" book, generated $4k already; began the trend with agents rocking CryptoPunks as NFTs - could we see these money printers become the NFT bag holders exit liquidity as they rake in perpetual fees from software factories? 6) goheesheng.base.eth is behind $X40G, security auditing for AI agent skills. Scans for malware, credential theft, and risky permissions before you install. Goheesh is at Consensys HK currently, could he be networking for additional layers to his product? DM haitzu (supercycle arc) or me for new inclusions, and follow Khala Research for a more comprehensive report on the ecosystem soon More ClawIndex(dot)org updates will be dropping tomorrow so keep adding your openClaw apps and features
0:29

I'm getting Q4 2024 vibes; There's been an explosion of OpenClaw related tokens Traditional token tracking websites lack context and are extremely slow at listing viable tokenized projects That's where ClawPrice(dot)io will disrupt the current token tracking platforms With the expertise of the Khala Research team, we will layer the additional project context and additional openClaw specific price analysis to make your life easier ClawPrice Performance (Feb 14th, 2026): 1) 🦞 Clawnch 🦞 Token launchpad exclusively for AI agents, trading under $CLAWNCH; could this become the autonomous Virtuals of AI boom 2.0? Recently staked $100k of tokens to Inclawbate, one of the projects launched from its launchpad 2) Kelly Claude has developed the "Agentic Software Factory"; Building apps for app store, revenue buy back token, built by Gauntlet AI founder Austen Allred. Latest app is MacroPlan and recently had its first app approved in the iOS app store. Launched its marketing factory to propel its software products also 3) clawd.atg.eth is by ETH Foundation's Austin Griffith, similar to KellyClaude, building apps to generate revenue with the added knowledge of the intricacies of ERC-8004 and x402 for persmissionless payment rails on the leading smart contract (and recently AI focused) layer 1 chain 4) Anti Hunter is by Logan Paul's Anti Fund and built by Geoffrey Woo, investment agent with live positions. Currently bidding the early leaders in the openClaw eco; a smart bet to underpin a potential explosive meta that's yet to hit its inflection point 5) Felix Craft by Nat Eliason first product "How to Hire an AI" book, generated $4k already; began the trend with agents rocking CryptoPunks as NFTs - could we see these money printers become the NFT bag holders exit liquidity as they rake in perpetual fees from software factories? 6) goheesheng.base.eth is behind $X40G, security auditing for AI agent skills. Scans for malware, credential theft, and risky permissions before you install. Goheesh is at Consensys HK currently, could he be networking for additional layers to his product? DM haitzu (supercycle arc) or me for new inclusions, and follow Khala Research for a more comprehensive report on the ecosystem soon More ClawIndex(dot)org updates will be dropping tomorrow so keep adding your openClaw apps and features

0xSammy

21,774 просмотров • 5 месяцев назад

Here are a few things you probably did not know about Reddit's iOS and Android apps: they are ~2.5M lines of code each, with 500+ screens, and a total of 200 native mobile engineers work on the both of them (including a dedicated iOS and Android mobile platform team) But a few years ago, things looked very different - and Reddit quietly rebuilt their native apps from 2021. Today's conversation goes through what happened and how, with three engineers from Reddit’s mobile platform team who led this work: Lauren Darcey (Head of Mobile Platform), Brandon Kobilansky (iOS Platform Lead), and Eric Kuck (Principal Android Engineer) Watch or listen: • YouTube: • Spotify: • Apple: --- Brought to you by: • Graphite (we've moved to @graphite) — The AI developer productivity platform • Sentry — Error and performance monitoring for developers. Get 150k errors (three months of Team Plan) for free at --- Three of my takeaways from this episode: 𝟭. 𝗣𝗼𝗼𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗰𝗮𝗻 𝘀𝗹𝗼𝘄 𝗱𝗼𝘄𝗻 𝗮 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 – 𝘀𝗼 𝗽𝗮𝘆 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻! One of the reasons Reddit started investing heavily in modernizing its mobile stack was that the “old stack” was slowing down developers. Reddit’s platform team got proof of this simply by asking native engineers about the biggest development-related challenges they face. 𝟮. 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗵𝗮𝗿𝗱 𝘄𝗼𝗿𝗸. Advice from Brandon for anyone looking to work on a platform team: "You should try to work at a tech company for a year or two and actually see what happens after you ship a system — and then the assumptions change! You then have to figure out how to keep this thing going. You get a bunch of software design intuition because you have to like re-evaluate your assumptions for an incredibly long time. If you can do that, you're probably ready for platform stuff." 𝟯. 𝗚𝗲𝗻𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗳𝗲𝗲𝗹 𝗹𝗶𝗸𝗲 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 𝗻𝗼𝘁 “𝘁𝗵𝗲𝗿𝗲” 𝘆𝗲𝘁 𝘄𝗶𝘁𝗵 𝗻𝗮𝘁𝗶𝘃𝗲 𝗺𝗼𝗯𝗶𝗹𝗲. LLMs integrated into IDEs seem to be increasingly helpful with backend, fullstack, web and even cross-platform (React Native / Expo) projects. However, Reddit’s mobile team shared that they get a moderate boost from the Apple and Android Studio LLM additions. Native mobile development is distinctively different from web, fullstack and backend coding – and it seems that these IDEs with AI functionality have not done much to optimize for the expereince of native mobile engineers. Over time, this will likely change – but it’s a reminder that there are differences between fullstack, backend and native mobile development.(I wrote a book reflecting on more of the challenges unique to native mobile titled Building Mobile Apps at Scale)
1:02

Here are a few things you probably did not know about Reddit's iOS and Android apps: they are ~2.5M lines of code each, with 500+ screens, and a total of 200 native mobile engineers work on the both of them (including a dedicated iOS and Android mobile platform team) But a few years ago, things looked very different - and Reddit quietly rebuilt their native apps from 2021. Today's conversation goes through what happened and how, with three engineers from Reddit’s mobile platform team who led this work: Lauren Darcey (Head of Mobile Platform), Brandon Kobilansky (iOS Platform Lead), and Eric Kuck (Principal Android Engineer) Watch or listen: • YouTube: • Spotify: • Apple: --- Brought to you by: • Graphite (we've moved to @graphite) — The AI developer productivity platform • Sentry — Error and performance monitoring for developers. Get 150k errors (three months of Team Plan) for free at --- Three of my takeaways from this episode: 𝟭. 𝗣𝗼𝗼𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗰𝗮𝗻 𝘀𝗹𝗼𝘄 𝗱𝗼𝘄𝗻 𝗮 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 – 𝘀𝗼 𝗽𝗮𝘆 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻! One of the reasons Reddit started investing heavily in modernizing its mobile stack was that the “old stack” was slowing down developers. Reddit’s platform team got proof of this simply by asking native engineers about the biggest development-related challenges they face. 𝟮. 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗵𝗮𝗿𝗱 𝘄𝗼𝗿𝗸. Advice from Brandon for anyone looking to work on a platform team: "You should try to work at a tech company for a year or two and actually see what happens after you ship a system — and then the assumptions change! You then have to figure out how to keep this thing going. You get a bunch of software design intuition because you have to like re-evaluate your assumptions for an incredibly long time. If you can do that, you're probably ready for platform stuff." 𝟯. 𝗚𝗲𝗻𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗳𝗲𝗲𝗹 𝗹𝗶𝗸𝗲 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 𝗻𝗼𝘁 “𝘁𝗵𝗲𝗿𝗲” 𝘆𝗲𝘁 𝘄𝗶𝘁𝗵 𝗻𝗮𝘁𝗶𝘃𝗲 𝗺𝗼𝗯𝗶𝗹𝗲. LLMs integrated into IDEs seem to be increasingly helpful with backend, fullstack, web and even cross-platform (React Native / Expo) projects. However, Reddit’s mobile team shared that they get a moderate boost from the Apple and Android Studio LLM additions. Native mobile development is distinctively different from web, fullstack and backend coding – and it seems that these IDEs with AI functionality have not done much to optimize for the expereince of native mobile engineers. Over time, this will likely change – but it’s a reminder that there are differences between fullstack, backend and native mobile development.(I wrote a book reflecting on more of the challenges unique to native mobile titled Building Mobile Apps at Scale)

Gergely Orosz

67,225 просмотров • 1 год назад

$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.
0:49

$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.

Mike

511,082 просмотров • 9 месяцев назад

OpenClaw - the agentic software spreading like wildfire - was built on top of Pi, a minimalist, self-modifying agent. I sat down with Pi's creator, Mario Zechner and longtime Pi user (+ the creator of Flask) Armin Ronacher ⇌ to talk Pi, and their (very grounded!) takes on building with AI. Timestamps: 00:00 Intro 07:30 How Mario, Armin, and Peter Steinberger met 15:15 How 30 dev teams use AI agents: learnings 21:50 The importance of judgment 24:26 Challenges when non-engineers write code 28:30 Downsides of over-automation 32:18 Pi 48:09 OpenClaw + Pi 50:54 “Clankers” 57:32 Open source and AI 1:00:22 Complexity as the enemy 1:02:50 Building an AI-native startup 1:11:52 “Slow the F down” 1:16:40 MCPs vs. CLI 1:25:03 Predictions and staying up to date • YouTube: • Spotify: • Apple: Brought to you by: • Statsig – ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar — The makers of SonarQube, the industry standard for code verification and automated code review. Try it out for yourself. • WorkOS – WorkOS gives you APIs to ship enterprise features – SSO, directory sync, RBAC, audit logs – in days, not months. Visit learn more. --- Three parts I found especially interesting in this discussion: 1. New trend: AI makes it harder for senior engineers to reject pointless complexity. Historically, senior engineers kept software complexity at bay simply by saying “no” a lot. But Armin observes that these days, more junior engineers and product managers deploy agent-scripted counterarguments when a senior colleague kicks an idea to the curb. This makes decision-making exhausting, and more bad ideas make it into production as a result. 2. It should be MUCH easier to build specialized tools for specific tasks. Different projects need different harness types because, as Mario points out, the same hammer is not ideal for every single construction job. As such, Pi is built with the goal of allowing the creation of specialized harnesses. It can modify itself so that a user can create the bespoke harness needed for any task. Mario believes it’s a preview of how self-modifiable software might look in the future. 3. Automation bias is one of the biggest risks of working with AI agents. Once devs confirm that an AI agent can produce acceptable code, they start to review its output less often, even though agents can – and do! – produce slop. Mario advises being far more sceptical with agents, and cautions that the quality of their output isn’t guaranteed, however well they performed previously.
1:33:55

OpenClaw - the agentic software spreading like wildfire - was built on top of Pi, a minimalist, self-modifying agent. I sat down with Pi's creator, Mario Zechner and longtime Pi user (+ the creator of Flask) Armin Ronacher ⇌ to talk Pi, and their (very grounded!) takes on building with AI. Timestamps: 00:00 Intro 07:30 How Mario, Armin, and Peter Steinberger met 15:15 How 30 dev teams use AI agents: learnings 21:50 The importance of judgment 24:26 Challenges when non-engineers write code 28:30 Downsides of over-automation 32:18 Pi 48:09 OpenClaw + Pi 50:54 “Clankers” 57:32 Open source and AI 1:00:22 Complexity as the enemy 1:02:50 Building an AI-native startup 1:11:52 “Slow the F down” 1:16:40 MCPs vs. CLI 1:25:03 Predictions and staying up to date • YouTube: • Spotify: • Apple: Brought to you by: • Statsig – ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar — The makers of SonarQube, the industry standard for code verification and automated code review. Try it out for yourself. • WorkOS – WorkOS gives you APIs to ship enterprise features – SSO, directory sync, RBAC, audit logs – in days, not months. Visit learn more. --- Three parts I found especially interesting in this discussion: 1. New trend: AI makes it harder for senior engineers to reject pointless complexity. Historically, senior engineers kept software complexity at bay simply by saying “no” a lot. But Armin observes that these days, more junior engineers and product managers deploy agent-scripted counterarguments when a senior colleague kicks an idea to the curb. This makes decision-making exhausting, and more bad ideas make it into production as a result. 2. It should be MUCH easier to build specialized tools for specific tasks. Different projects need different harness types because, as Mario points out, the same hammer is not ideal for every single construction job. As such, Pi is built with the goal of allowing the creation of specialized harnesses. It can modify itself so that a user can create the bespoke harness needed for any task. Mario believes it’s a preview of how self-modifiable software might look in the future. 3. Automation bias is one of the biggest risks of working with AI agents. Once devs confirm that an AI agent can produce acceptable code, they start to review its output less often, even though agents can – and do! – produce slop. Mario advises being far more sceptical with agents, and cautions that the quality of their output isn’t guaranteed, however well they performed previously.

Gergely Orosz

172,263 просмотров • 2 месяцев назад

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman
2:52

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman

Varun

158,712 просмотров • 1 год назад

It's finally here - the Official Lean AI Company Playbook 1000+ founders, investors and execs have been flooding my DMs about. Ever since I created the Official Lean AI Leaderboard after exiting my $150M annual revenue startup, founders from across the globe have asked me this one question: "How are these lean AI companies generating millions with tiny teams?" So I spent the last 3 months obsessively tracking and collaborating with 30+ lean AI-native founders, researching the answer. Here's what I discovered: They've built operational systems that multiply human capability. Instead of scaling headcount, they've created AI-powered processes that let small teams operate at unprecedented scale. And their secret goes far beyond the specific AI tools they use. It lies in redesigning their entire company operations around a fundamentally different approach to growth and execution. After countless conversations, interviews, and behind-the-scenes exclusive access, I finally documented the exact playbook with complete systems. This comprehensive playbook includes: • A complete breakdown of the tech stack and operational workflows • The organizational design principles that enable tiny teams to do massive work • Critical inflection points where things break (and how to navigate them) • A detailed 6-month implementation plan for starting your own lean AI company (with weekly actions) • Implementation best practices from dozens of successful lean AI-native companies Want the ultimate Lean AI Playbook or help transforming your Lean AI operations? 👇 • Like and Share this post • Comment "Lean AI Playbook" • Follow me (so I can DM you) --------------- PS: Separately, I'm opening a limited number of high-impact advising slots for serious founders and operators who want to work directly with me—beyond just consuming my content. If you're ready to transform how your company operates using AI, DM me. (Please note: these are paid spots due to limited availability and time atm)
0:48

It's finally here - the Official Lean AI Company Playbook 1000+ founders, investors and execs have been flooding my DMs about. Ever since I created the Official Lean AI Leaderboard after exiting my $150M annual revenue startup, founders from across the globe have asked me this one question: "How are these lean AI companies generating millions with tiny teams?" So I spent the last 3 months obsessively tracking and collaborating with 30+ lean AI-native founders, researching the answer. Here's what I discovered: They've built operational systems that multiply human capability. Instead of scaling headcount, they've created AI-powered processes that let small teams operate at unprecedented scale. And their secret goes far beyond the specific AI tools they use. It lies in redesigning their entire company operations around a fundamentally different approach to growth and execution. After countless conversations, interviews, and behind-the-scenes exclusive access, I finally documented the exact playbook with complete systems. This comprehensive playbook includes: • A complete breakdown of the tech stack and operational workflows • The organizational design principles that enable tiny teams to do massive work • Critical inflection points where things break (and how to navigate them) • A detailed 6-month implementation plan for starting your own lean AI company (with weekly actions) • Implementation best practices from dozens of successful lean AI-native companies Want the ultimate Lean AI Playbook or help transforming your Lean AI operations? 👇 • Like and Share this post • Comment "Lean AI Playbook" • Follow me (so I can DM you) --------------- PS: Separately, I'm opening a limited number of high-impact advising slots for serious founders and operators who want to work directly with me—beyond just consuming my content. If you're ready to transform how your company operates using AI, DM me. (Please note: these are paid spots due to limited availability and time atm)

Henry Shi

121,562 просмотров • 1 год назад

ADAPTING. INNOVATING. EXCELLING — KAMENG HIMALAYAS @ 16,000 FT Operations in the unforgiving expanse of the Kameng Himalayas, where at 16000 feet, sheer baren cliffs, unpredictable weather, and extreme altitude routinely sever vital supply lines, #GajrajCorps has delivered an innovative milestone in high-altitude logistics solution. In an environment where forward posts are often cut off for days under the snow and traditional means of transport struggle against the rugged terrain, ensuring timely delivery of essential stores has long been a formidable challenge. Rising to this operational necessity, the Gajraj Corps has successfully conceptualised, engineered, and deployed an indigenous High Altitude Mono Rail System, fully operational, validated, and already transforming logistics at 16,000 ft. This innovative solution—capable of transporting 300+ kg of load in a single run- is proving to be a lifeline to remote posts that have no alternative means of communication or supply links. It enables reliable movement of mission-critical stores, ammunition, rations, fuel, engineering equipment, and other heavy or awkward loads that are otherwise difficult to ferry across steep gradients and unstable surfaces. This transportation facility is fully operation worthy to be used day or night, with or without escort, during hail or storm irrespective of the weather conditions Beyond logistics, the system has also emerged as a potential enabler for rapid casualty evacuation, offering a safe and efficient means to move injured personnel across inhospitable stretches where helicopters may not always operate and foot evacuation is slow and risky. A testament to the ingenuity, adaptability, and relentless spirit of #GajrajCorps, this in-house innovation ensures enhanced operational readiness, strengthens sustainability in isolated high-altitude posts, and showcases the Army’s commitment to solving complex challenges with practical, mission-focused solutions.
0:16

ADAPTING. INNOVATING. EXCELLING — KAMENG HIMALAYAS @ 16,000 FT Operations in the unforgiving expanse of the Kameng Himalayas, where at 16000 feet, sheer baren cliffs, unpredictable weather, and extreme altitude routinely sever vital supply lines, #GajrajCorps has delivered an innovative milestone in high-altitude logistics solution. In an environment where forward posts are often cut off for days under the snow and traditional means of transport struggle against the rugged terrain, ensuring timely delivery of essential stores has long been a formidable challenge. Rising to this operational necessity, the Gajraj Corps has successfully conceptualised, engineered, and deployed an indigenous High Altitude Mono Rail System, fully operational, validated, and already transforming logistics at 16,000 ft. This innovative solution—capable of transporting 300+ kg of load in a single run- is proving to be a lifeline to remote posts that have no alternative means of communication or supply links. It enables reliable movement of mission-critical stores, ammunition, rations, fuel, engineering equipment, and other heavy or awkward loads that are otherwise difficult to ferry across steep gradients and unstable surfaces. This transportation facility is fully operation worthy to be used day or night, with or without escort, during hail or storm irrespective of the weather conditions Beyond logistics, the system has also emerged as a potential enabler for rapid casualty evacuation, offering a safe and efficient means to move injured personnel across inhospitable stretches where helicopters may not always operate and foot evacuation is slow and risky. A testament to the ingenuity, adaptability, and relentless spirit of #GajrajCorps, this in-house innovation ensures enhanced operational readiness, strengthens sustainability in isolated high-altitude posts, and showcases the Army’s commitment to solving complex challenges with practical, mission-focused solutions.

Aditya Raj Kaul

13,338 просмотров • 8 месяцев назад

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

263,023 просмотров • 2 месяцев назад

In the latest episode of Professionally Curious 🧠, Joanna Zeng @ SOON (🔴,💊)📍ETHDenver🇺🇸 (🧧Arc) sits down with Nemil Dalal, one of the most recognizable minds shaping modern crypto — from his early Y Combinator days to leading product innovation at Coinbase, and now pushing forward the next frontier with X402. Nemil opens up about his unconventional crypto origin story, the internal cultural evolution at Coinbase, and what it took for the company to shift from “just an exchange” into a force driving open standards, infrastructure, and developer ecosystems. He talks candidly about product velocity, philosophical disagreements, and why crypto needed a reset toward programmability and open protocols. Joanna and Nemil break down why X402 exists, what it unlocks for AI-native finance, and how integrating payments directly into the internet changes everything — from commerce to capital markets to how users interact with apps. They explore how stablecoins were only the first chapter, and why the world is inevitably moving toward millions of tokens, programmable markets, and agent-driven transaction layers. Think ahead: AI agents executing trades, routing payments, rebalancing portfolios, settling invoices, and powering prediction markets — all through a universal open standard. A world where every app can pay, transact, and coordinate without friction. This episode dives into the architecture behind it 👇 SOON takeaway: Our work with X402 isn’t theoretical. Joanna discusses how SOON’s high-performance SVM infra and our on-chain prediction experiments surfaced exactly the type of bottlenecks Nemil describes — blockspace saturation, facilitator constraints, and UX limitations. It’s reaffirmed why open coordination layers like X402 matter, and why building in public with community feedback is the only way to get this right. Key themes: • Why Coinbase’s culture needed to evolve • Programmability as the next era of finance • Stablecoins as the gateway to millions of assets • AI agents reshaping how users interact with crypto • X402 as the payment layer of the internet • Open standards vs walled-garden platforms • The growing demand for real use cases • Hackathons as catalysts for new ecosystems • Bridging Web2 familiarity with Web3 autonomy • Community-led innovation across protocols ⏱️ Timestamps 00:00 – Performance metrics & scalability questions 00:31 – Coinbase’s cultural pivot toward innovation 03:38 – Nemil’s personal journey into crypto 06:32 – The future of crypto founders’ ecosystems 09:35 – Philosophical foundations of the X402 protocol 15:29 – Open standards & the role of community 21:37 – AI + commerce: new use cases emerging 23:09 – Hackathons as engines of innovation 24:14 – Practical bridges from Web2 → Web3 25:07 – AI capital markets & internet-native payments 26:16 – Tokenization, blockchains & market structure 27:44 – Prediction markets + AI integration 31:10 – Designing user-friendly crypto UX 33:10 – High-performance infra & dev tooling 35:37 – Collaboration across the ecosystem 40:21 – Spicy takes about crypto’s future
48:10

In the latest episode of Professionally Curious 🧠, Joanna Zeng @ SOON (🔴,💊)📍ETHDenver🇺🇸 (🧧Arc) sits down with Nemil Dalal, one of the most recognizable minds shaping modern crypto — from his early Y Combinator days to leading product innovation at Coinbase, and now pushing forward the next frontier with X402. Nemil opens up about his unconventional crypto origin story, the internal cultural evolution at Coinbase, and what it took for the company to shift from “just an exchange” into a force driving open standards, infrastructure, and developer ecosystems. He talks candidly about product velocity, philosophical disagreements, and why crypto needed a reset toward programmability and open protocols. Joanna and Nemil break down why X402 exists, what it unlocks for AI-native finance, and how integrating payments directly into the internet changes everything — from commerce to capital markets to how users interact with apps. They explore how stablecoins were only the first chapter, and why the world is inevitably moving toward millions of tokens, programmable markets, and agent-driven transaction layers. Think ahead: AI agents executing trades, routing payments, rebalancing portfolios, settling invoices, and powering prediction markets — all through a universal open standard. A world where every app can pay, transact, and coordinate without friction. This episode dives into the architecture behind it 👇 SOON takeaway: Our work with X402 isn’t theoretical. Joanna discusses how SOON’s high-performance SVM infra and our on-chain prediction experiments surfaced exactly the type of bottlenecks Nemil describes — blockspace saturation, facilitator constraints, and UX limitations. It’s reaffirmed why open coordination layers like X402 matter, and why building in public with community feedback is the only way to get this right. Key themes: • Why Coinbase’s culture needed to evolve • Programmability as the next era of finance • Stablecoins as the gateway to millions of assets • AI agents reshaping how users interact with crypto • X402 as the payment layer of the internet • Open standards vs walled-garden platforms • The growing demand for real use cases • Hackathons as catalysts for new ecosystems • Bridging Web2 familiarity with Web3 autonomy • Community-led innovation across protocols ⏱️ Timestamps 00:00 – Performance metrics & scalability questions 00:31 – Coinbase’s cultural pivot toward innovation 03:38 – Nemil’s personal journey into crypto 06:32 – The future of crypto founders’ ecosystems 09:35 – Philosophical foundations of the X402 protocol 15:29 – Open standards & the role of community 21:37 – AI + commerce: new use cases emerging 23:09 – Hackathons as engines of innovation 24:14 – Practical bridges from Web2 → Web3 25:07 – AI capital markets & internet-native payments 26:16 – Tokenization, blockchains & market structure 27:44 – Prediction markets + AI integration 31:10 – Designing user-friendly crypto UX 33:10 – High-performance infra & dev tooling 35:37 – Collaboration across the ecosystem 40:21 – Spicy takes about crypto’s future

SOON - Solana Optimistic Network (Mainnet Arc)

38,779 просмотров • 7 месяцев назад

A common pattern in consumer AI over the last couple years: An app that generates AI portraits of you will go viral—but then users churn after a few days. I started advising one of these apps last year called VYBE and the goal was to turn it into a durable app. We started with this question: In what context would people need generative portraits on a recurring basis? The founder Mandela Patrick dug into the data and found that some users were sticking around: they were trying on different clothes using the app. So we redesigned the app to be exclusively focused on this use-case. But most importantly, we integrated it into their existing patterns of behavior: a mobile safari extension that would intercept the user every time they were shopping. Just tap a button to try on the item on any page. They just shipped it and the results blew past all expectations: Retention nearly 4x. Once the user enabled the extension, they were essentially retained for life. And even better, it unlocked a better revenue stream: instead of a subscription that users would inevitably cancel, VYBE simply collects a commission in the background off every purchase into perpetuity.
0:20

A common pattern in consumer AI over the last couple years: An app that generates AI portraits of you will go viral—but then users churn after a few days. I started advising one of these apps last year called VYBE and the goal was to turn it into a durable app. We started with this question: In what context would people need generative portraits on a recurring basis? The founder Mandela Patrick dug into the data and found that some users were sticking around: they were trying on different clothes using the app. So we redesigned the app to be exclusively focused on this use-case. But most importantly, we integrated it into their existing patterns of behavior: a mobile safari extension that would intercept the user every time they were shopping. Just tap a button to try on the item on any page. They just shipped it and the results blew past all expectations: Retention nearly 4x. Once the user enabled the extension, they were essentially retained for life. And even better, it unlocked a better revenue stream: instead of a subscription that users would inevitably cancel, VYBE simply collects a commission in the background off every purchase into perpetuity.

Nikita Bier

462,720 просмотров • 1 год назад

Deceptive and antisemitic narratives are flooding our college campuses—and they are winning the hearts of young people. We cannot remain silent. Charlie Kirk’s voice for Israel was unique and powerful. In his final letter to Prime Minister Netanyahu, he voiced a sobering concern: Israel is losing support among conservative youth. That warning could not have been more timely—or more urgent. Charlie spoke with courage and clarity, cutting through propaganda and lies. But now, with his absence, others are stepping into the spotlight on college campuses. Instead of defending truth, they are distorting it—exchanging biblical conviction for false and misleading narratives. Young people are listening, and they are being led astray. We cannot sit on the sidelines while this happens. The stakes are simply too high. That is why we are launching the Task Force Initiative—a comprehensive effort to reach the next generation with God’s truth about Israel. This vision will include short-term projects that address immediate needs while building toward a long-term mission that will shape our ministry for years to come. Here’s the roadmap: 1 - Curriculum Development – We will create a solid, Bible-centered curriculum for use in churches, small groups, and student fellowships. This will give young adults a foundation rooted in Scripture, not in opinion or politics. 2 - Digital Engagement – We will build an active presence on social media platforms, especially TikTok, where so much of this generation receives its information. Through short, creative, and engaging content, we will cut through the noise and speak truth in places where lies dominate. 3- Experiential Impact – Whenever possible, we want to bring young people to Israel itself. There is no substitute for seeing the land, walking its soil, and witnessing God’s promises fulfilled with their own eyes. Such encounters can transform skepticism into conviction and misinformation into lifelong advocacy. This is not just a short-term response—it is a long-term mission. We are not here for a hit-and-run effort, but to weave this into the very fabric of our ministry, ensuring that the next generation is discipled, equipped, and anchored in the Word of God. But time is critical. The battle for hearts and minds is happening now. Every day that passes is another day falsehoods are sown deeper into this generation. That is why we are asking you to prayerfully consider standing with us today. Your gift will directly fund the creation of this task force—assembling the right people, producing the content, and launching this mission. Together, we can ensure that the truth is not only defended but proclaimed boldly to those who need it most. Donate here: Thank you for considering this urgent and strategic opportunity. With your help, we can fill the gap, protect the next generation, and advance God’s truth for Israel.
19:38

Deceptive and antisemitic narratives are flooding our college campuses—and they are winning the hearts of young people. We cannot remain silent. Charlie Kirk’s voice for Israel was unique and powerful. In his final letter to Prime Minister Netanyahu, he voiced a sobering concern: Israel is losing support among conservative youth. That warning could not have been more timely—or more urgent. Charlie spoke with courage and clarity, cutting through propaganda and lies. But now, with his absence, others are stepping into the spotlight on college campuses. Instead of defending truth, they are distorting it—exchanging biblical conviction for false and misleading narratives. Young people are listening, and they are being led astray. We cannot sit on the sidelines while this happens. The stakes are simply too high. That is why we are launching the Task Force Initiative—a comprehensive effort to reach the next generation with God’s truth about Israel. This vision will include short-term projects that address immediate needs while building toward a long-term mission that will shape our ministry for years to come. Here’s the roadmap: 1 - Curriculum Development – We will create a solid, Bible-centered curriculum for use in churches, small groups, and student fellowships. This will give young adults a foundation rooted in Scripture, not in opinion or politics. 2 - Digital Engagement – We will build an active presence on social media platforms, especially TikTok, where so much of this generation receives its information. Through short, creative, and engaging content, we will cut through the noise and speak truth in places where lies dominate. 3- Experiential Impact – Whenever possible, we want to bring young people to Israel itself. There is no substitute for seeing the land, walking its soil, and witnessing God’s promises fulfilled with their own eyes. Such encounters can transform skepticism into conviction and misinformation into lifelong advocacy. This is not just a short-term response—it is a long-term mission. We are not here for a hit-and-run effort, but to weave this into the very fabric of our ministry, ensuring that the next generation is discipled, equipped, and anchored in the Word of God. But time is critical. The battle for hearts and minds is happening now. Every day that passes is another day falsehoods are sown deeper into this generation. That is why we are asking you to prayerfully consider standing with us today. Your gift will directly fund the creation of this task force—assembling the right people, producing the content, and launching this mission. Together, we can ensure that the truth is not only defended but proclaimed boldly to those who need it most. Donate here: Thank you for considering this urgent and strategic opportunity. With your help, we can fill the gap, protect the next generation, and advance God’s truth for Israel.

Amir Tsarfati

16,339 просмотров • 9 месяцев назад

My Full Speech: Good afternoon, distinguished members of the Committee, Thank you for the opportunity to share a few thoughts on how Nigeria’s current prohibitive stance on cryptocurrency is unintentionally holding back one of our country’s greatest assets, our human capital. Over the last few years, Nigerian founders, developers, marketers, designers, and creatives have become some of the most sought-after talents in the global blockchain economy. Yet, while the rest of the world is creating clear frameworks to nurture these builders, our own policies often push them away. Restrictive regulations stop innovation, moving it offshore. Many Nigerian founders move their businesses abroad because they can’t access local banking or payment infrastructure. As a result, the values they create like jobs and intellectual property, are being booked outside our borders. This regulatory uncertainty also discourages legitimate players who would otherwise operate transparently. Instead of collecting tax from licensed companies, we’re driving billions of naira in transactions into unregulated underground markets. By trying to protect the system, we’re actually weakening it, increasing fraud exposure, and leaving consumers unprotected. A balanced, risk-based framework would achieve the opposite. It would give innovators room to build under clear rules, allow the government to supervise activities properly, and encourage foreign investors to set up compliant operations here. Young people are already building, onchain, globally, and proudly Nigerian. The question is whether they’ll be allowed to build from Nigeria or be forced to build outside it. I urge this Committee to move beyond prohibition and embrace collaboration, with industry, and with communities like Superteam Nigeria, already doing the work. With the right policy direction, Nigeria can transform from being a crypto consumer market to a blockchain innovation hub for all of Africa. Thank you.
0:50

My Full Speech: Good afternoon, distinguished members of the Committee, Thank you for the opportunity to share a few thoughts on how Nigeria’s current prohibitive stance on cryptocurrency is unintentionally holding back one of our country’s greatest assets, our human capital. Over the last few years, Nigerian founders, developers, marketers, designers, and creatives have become some of the most sought-after talents in the global blockchain economy. Yet, while the rest of the world is creating clear frameworks to nurture these builders, our own policies often push them away. Restrictive regulations stop innovation, moving it offshore. Many Nigerian founders move their businesses abroad because they can’t access local banking or payment infrastructure. As a result, the values they create like jobs and intellectual property, are being booked outside our borders. This regulatory uncertainty also discourages legitimate players who would otherwise operate transparently. Instead of collecting tax from licensed companies, we’re driving billions of naira in transactions into unregulated underground markets. By trying to protect the system, we’re actually weakening it, increasing fraud exposure, and leaving consumers unprotected. A balanced, risk-based framework would achieve the opposite. It would give innovators room to build under clear rules, allow the government to supervise activities properly, and encourage foreign investors to set up compliant operations here. Young people are already building, onchain, globally, and proudly Nigerian. The question is whether they’ll be allowed to build from Nigeria or be forced to build outside it. I urge this Committee to move beyond prohibition and embrace collaboration, with industry, and with communities like Superteam Nigeria, already doing the work. With the right policy direction, Nigeria can transform from being a crypto consumer market to a blockchain innovation hub for all of Africa. Thank you.

Harri

95,523 просмотров • 8 месяцев назад

Ran 21 km (13.1 miles) — and the motor was still cold. That’s the detail that matters. 🤖 Honor was the clear dark horse in this year’s robot half marathon. They swept 1st, 2nd, and 3rd, and also posted a strong top-6 finish overall. What stands out to me is that this was not just about bigger motors, or a gait tuned for long-distance running. They seem to have solved something more important — cooling. In a post-race interview, Honor engineers said the robot used liquid-cooling tech adapted from Honor smartphones, with cooling lines running deep into the motor system to carry heat away. Some reports added more detail: the setup used two high-speed micro pumps, with flow rates reaching up to 6 liters per minute, giving the system enough cooling capacity to handle sustained lower-joint motor load. That matters because once a robot starts overheating, output drops, stability goes with it, and the whole run can fall apart fast. And that’s exactly why this detail is interesting. Of course, that does not mean Honor has already surpassed teams like TienKung or Unitree across humanoid robotics as a whole. What it does suggest is that for the marathon task, they built a very strong system solution. And honestly, that alone is already a useful case for the industry. The bigger trend is moving fast. Last year, TienKung won in around 2 hours 40 minutes. This year, the winning time dropped to 50 minutes 26 seconds. Last year, most robots were still fully remote-controlled or only semi-autonomous. This year, around 40% were running with a much higher level of autonomy. So to me, the real signal is not just that robots got faster. It’s that the field is now moving past raw speed, and into the harder problems: autonomy, stability, and system reliability under load. If the pace of progress stays anywhere close to this, then next year’s race should be even more worth watching.
0:31

Ran 21 km (13.1 miles) — and the motor was still cold. That’s the detail that matters. 🤖 Honor was the clear dark horse in this year’s robot half marathon. They swept 1st, 2nd, and 3rd, and also posted a strong top-6 finish overall. What stands out to me is that this was not just about bigger motors, or a gait tuned for long-distance running. They seem to have solved something more important — cooling. In a post-race interview, Honor engineers said the robot used liquid-cooling tech adapted from Honor smartphones, with cooling lines running deep into the motor system to carry heat away. Some reports added more detail: the setup used two high-speed micro pumps, with flow rates reaching up to 6 liters per minute, giving the system enough cooling capacity to handle sustained lower-joint motor load. That matters because once a robot starts overheating, output drops, stability goes with it, and the whole run can fall apart fast. And that’s exactly why this detail is interesting. Of course, that does not mean Honor has already surpassed teams like TienKung or Unitree across humanoid robotics as a whole. What it does suggest is that for the marathon task, they built a very strong system solution. And honestly, that alone is already a useful case for the industry. The bigger trend is moving fast. Last year, TienKung won in around 2 hours 40 minutes. This year, the winning time dropped to 50 minutes 26 seconds. Last year, most robots were still fully remote-controlled or only semi-autonomous. This year, around 40% were running with a much higher level of autonomy. So to me, the real signal is not just that robots got faster. It’s that the field is now moving past raw speed, and into the harder problems: autonomy, stability, and system reliability under load. If the pace of progress stays anywhere close to this, then next year’s race should be even more worth watching.

RoboHub🤖

60,151 просмотров • 2 месяцев назад

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026
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$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

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