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From queries to decisions Software should deliver the answer Eragon is an AI operating system for your company A reasoning engine that understands, learns, and acts across your entire organization. Understands your language Returns the answer - fast and accurate A "command and control center" Smaller teams. More decisions... show more

David J Phillips

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22,427 次观看 • 7 个月前 •via X (Twitter)

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In the next few years, every digital action will be mediated by AI. Moving money. Business communications. Researching decisions. Booking meetings. Paying bills. Running workflows. Choosing what to do next. If you want to stay effective, you will need your own architecture around your personal AI agent. Not just a subscription. A personal system that helps your AI understand your world: your goals, context, rules, obligations, decisions, and outcomes. That is ODEI. Built to help you get more from the AI you already use: ChatGPT. Claude. Gemini. Grok (soon). Not by replacing them. By giving them your memory, rules, context, receipts, and local control. The model is not enough. The real leverage comes when your AI understands your world, works inside your rules, and compounds with you over time. (Footage captured live in a local staging environment)

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Y Combinator

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University of Austin (UATX)

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Today, we launch Tycoon AI: the world’s first operating system for one-person companies. Everyone gets an AI CEO + infinite AI employees(coding, marketing, ops etc.) A year ago, I became the first human CEO replaced by an AI CEO named Astra. Astra helped companies reach 100K+ users and $1M ARR in 30 days. That experiment became Tycoon. Today, Corporate America(e.g. Meta) is telling one story about AI: Fewer people. Fewer jobs. Fewer humans needed. Fuck that story. I believe the opposite. For the first time, one person can build with the operating power of an entire company. One founder. One AI CEO. Infinite AI employees. You text Astra your ideas, goals, tasks, and questions. - She strategizes, assigns work to AI employees, reviews progress, and makes sure it gets done. - She can manage up to 1,000 agents in parallel, 24/7, while you sleep. Tycoon includes built-in AI employees for engineering, marketing, research, content, SEO, finance, legal, support, and video. Astra can also manage Claude Code and Hermes Agent. The old company was built around headcount, managers, meetings, and layers. The new company starts with one person and a dream. Execution belongs to machines. Vision belongs to humans. That is what Tycoon is for. Start your one person company today!

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Palantir CEO Alex Karp on why the model is not the moat. The application layer is: "To make them valuable in an enterprise, like a battlefield, regulated, or manufacturing context, you have to have what's called an application layer." "We have this thing called ontology that now everyone's copying. It takes a large language model and makes it safe and useful and precise." "Safe because it doesn't touch your underlying data." "Safe because it prevents the large language model from caching your data and replicating your business." The tell for any enterprise AI pitch: ask what sits between the model and your data. If the answer is nothing, you are not buying leverage. You are renting a fast way to hand your business to whoever owns the weights. Alex Karp, CEO of Palantir, on CNBC.

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Amjad Masad (Amjad Masad), co-founder & CEO of Replit, has spent nearly a decade making programming accessible to everyone. With AI, that vision is closer than ever. In his AI Startup School talk, Amjad traces computing’s arc from mainframes to PCs to a future where AI agents generate software on demand—driving the value of traditional software toward zero and reshaping how companies are built. 00:22 - Evolution of Computing 01:27 - The Rise of Software Engineering 02:16 - Replit's Vision: Making Programming Accessible 02:42 - The Role of AI in Software Development 04:32 - Challenges and Infrastructure for AI Agents 05:03 - Building a Scalable and Secure Environment 06:08 - Future of Software Engineering Agents 09:25 - Autonomy Levels in AI Agents 15:56 - Predictions for the Software Market 18:31 - Impact on Business and Employment 19:58 - The Role of Generalist Employees 26:00 - Audience Q&A

Y Combinator

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Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

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Matt Turck

70,916 次观看 • 2 个月前

The Hermes Desktop App is insanely good. It's now the best way to run AI agents on your computer. Here's the full setup, start to finish. Enjoy! Chapters: 00:00 - why Hermes desktop is a game changer 00:46 - downloading and installing the desktop app 01:47 - picking your model and provider 02:52 - sessions, settings, and gateway connection 04:08 - adding a custom MCP server 04:58 - memory and context (the three-tier system) 06:52 - connecting to Telegram 09:45 - skills and tools 11:06 - the skills hub (built-in and community skills) 11:50 - creating a custom skill 14:18 - artifacts 15:11 - going from one to many agents (profiles and personas) 18:12 - agents working as a team (Hermes Kanban) 19:01 - outro I have also written an article on Hermes masterclass, the same is quoted below.

The Hermes Desktop App is insanely good. It's now the best way to run AI agents on your computer. Here's the full setup, start to finish. Enjoy! Chapters: 00:00 - why Hermes desktop is a game changer 00:46 - downloading and installing the desktop app 01:47 - picking your model and provider 02:52 - sessions, settings, and gateway connection 04:08 - adding a custom MCP server 04:58 - memory and context (the three-tier system) 06:52 - connecting to Telegram 09:45 - skills and tools 11:06 - the skills hub (built-in and community skills) 11:50 - creating a custom skill 14:18 - artifacts 15:11 - going from one to many agents (profiles and personas) 18:12 - agents working as a team (Hermes Kanban) 19:01 - outro I have also written an article on Hermes masterclass, the same is quoted below.

Akshay 🚀

103,813 次观看 • 2 个月前

Stop begging a model for magic. Start directing. Koyal is an agentic AI filmmaking platform It turns a script - or even raw audio - into a film you can cut, recut, and release It starts where filmmakers start: performance Then agents do what a crew would: scenes, shots, retakes Multilingual script → audio → video Agentic lip-sync with believable motion Characters that stay themselves across scenes Because real directors don’t “prompt” a movie They write. Perform. Reshoot. Koyal brings AI to that century-old workflow 🎙️ Mehul Agarwal, Co-founder & CEO koyal.ai (YC F25) on Fondo.com Start 00:00 Intro to Mehul and Koyal “Agentic AI filmmaking platform” 01:10 Most exciting videos on the platform 01:34 Music video and filming at a UNESCO site with AI 02:55 Origin story from text-to-3D gaming startup to AI filmmaking 03:24 The early days of Stable Diffusion, Midjourney, and the AI image explosion 04:22 Why the gaming industry is a "thankless" place for startups 04:50 "why isn't this a startup?" moment 05:15 meme pages, standup comedy, and scriptwriting 07:08 How Koyal works: the "Pixar process" without a camera 08:30 Launching the first video hackathon at YC 10:47 Where to follow and try Koyal

Stop begging a model for magic. Start directing. Koyal is an agentic AI filmmaking platform It turns a script - or even raw audio - into a film you can cut, recut, and release It starts where filmmakers start: performance Then agents do what a crew would: scenes, shots, retakes Multilingual script → audio → video Agentic lip-sync with believable motion Characters that stay themselves across scenes Because real directors don’t “prompt” a movie They write. Perform. Reshoot. Koyal brings AI to that century-old workflow 🎙️ Mehul Agarwal, Co-founder & CEO koyal.ai (YC F25) on Fondo.com Start 00:00 Intro to Mehul and Koyal “Agentic AI filmmaking platform” 01:10 Most exciting videos on the platform 01:34 Music video and filming at a UNESCO site with AI 02:55 Origin story from text-to-3D gaming startup to AI filmmaking 03:24 The early days of Stable Diffusion, Midjourney, and the AI image explosion 04:22 Why the gaming industry is a "thankless" place for startups 04:50 "why isn't this a startup?" moment 05:15 meme pages, standup comedy, and scriptwriting 07:08 How Koyal works: the "Pixar process" without a camera 08:30 Launching the first video hackathon at YC 10:47 Where to follow and try Koyal

David J Phillips

25,753 次观看 • 7 个月前

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

Akshay 🚀

30,859 次观看 • 1 年前

"AI doesn't take your job. AI makes you the CEO." Balaji Srinivasan joins a16z’s Erik Torenberg for a conversation on the future of the AI economy, decentralization, and how work changes in an AI-native world, including: - How distillation and open source could decentralize AI power - Why AI lowers the cost of creation but raises the cost of verification - The shift from global internet to “trusted tribes” and private AI - Why humans are the sensor and AI is the actuator 00:00 Intro 02:06 Why you want AI inside the trusted tribe, not outside it 05:35 The Problem with AI slop 09:25 Where AI works 17:08 "AI can't read your mind, but it can read your body." 30:10 "AI doesn't take your job. AI makes you the CEO." 46:01 The SaaSpocalypse: Real or overblown? 49:19 What happens if AI companies get bigger than governments? Balaji Erik Torenberg

"AI doesn't take your job. AI makes you the CEO." Balaji Srinivasan joins a16z’s Erik Torenberg for a conversation on the future of the AI economy, decentralization, and how work changes in an AI-native world, including: - How distillation and open source could decentralize AI power - Why AI lowers the cost of creation but raises the cost of verification - The shift from global internet to “trusted tribes” and private AI - Why humans are the sensor and AI is the actuator 00:00 Intro 02:06 Why you want AI inside the trusted tribe, not outside it 05:35 The Problem with AI slop 09:25 Where AI works 17:08 "AI can't read your mind, but it can read your body." 30:10 "AI doesn't take your job. AI makes you the CEO." 46:01 The SaaSpocalypse: Real or overblown? 49:19 What happens if AI companies get bigger than governments? Balaji Erik Torenberg

a16z

163,295 次观看 • 4 个月前

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead

Raoul Pal

172,640 次观看 • 1 年前

“What happens when AI stops being a tool and becomes the operating layer of the internet?” @dikshawells sits down with Karan Sirdesai Karan, Founder of Mira, to explore a future where AI is no longer just answering questions, but taking action, making decisions, and handling large parts of our cognitive workload. They discuss the upside of this shift: the rise of “infinite hires,” a world where most knowledge work can be automated or amplified, and a productivity wave that could completely reshape how companies, teams, and individuals operate. But the conversation also gets into the darker side of that future. As AI becomes more capable, the risks grow with it: blind dependence, high-stakes failures, malicious autonomous agents, and the possibility of humans sharing the world with a new kind of intelligence we are not fully prepared to control.

“What happens when AI stops being a tool and becomes the operating layer of the internet?” @dikshawells sits down with Karan Sirdesai Karan, Founder of Mira, to explore a future where AI is no longer just answering questions, but taking action, making decisions, and handling large parts of our cognitive workload. They discuss the upside of this shift: the rise of “infinite hires,” a world where most knowledge work can be automated or amplified, and a productivity wave that could completely reshape how companies, teams, and individuals operate. But the conversation also gets into the darker side of that future. As AI becomes more capable, the risks grow with it: blind dependence, high-stakes failures, malicious autonomous agents, and the possibility of humans sharing the world with a new kind of intelligence we are not fully prepared to control.

BeaconLayer

36,337 次观看 • 1 个月前

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

84,718 次观看 • 2 个月前