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Vector databases are a scam. Not technically, they do exactly what they say. Return the most cosine-similar string to your query. The scam is the entire industry pretending that's the same thing as relevance. It isn't. Search "Apple." You get the fruit, the company, the watch, and a recipe...

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Julia Fedorin

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186,958 просмотров • 5 месяцев назад •via X (Twitter)

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Your AI Agents deserve something More Powerful Than Vector DB Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't. HydraDB is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.
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Your AI Agents deserve something More Powerful Than Vector DB Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't. HydraDB is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.

Harnoor Singh

13,367 просмотров • 7 месяцев назад

Is AI being designed to fail? Everyone talks about reasoning. But when given a task, the AI isn't reasoning the way you might expect. It looks at your input, finds the closest match it's seen before, and predicts the most likely next action. That process is called vector similarity search. It's genuinely powerful. It's also not the same thing as understanding what you actually meant. Think of a plumber who hears the word "leak" and starts pulling up floorboards before you've finished the sentence. He's not being careless. He's pattern-matching - that's exactly how he was trained. Your AI agent is doing the same thing. Context is the one thing that gets deprioritized when teams are racing to ship. But without it, you don't have an intelligent agent. You have a very fast guesser. Similarity ≠ relevance. How? Find out with the link in the comments ⬇️
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Is AI being designed to fail? Everyone talks about reasoning. But when given a task, the AI isn't reasoning the way you might expect. It looks at your input, finds the closest match it's seen before, and predicts the most likely next action. That process is called vector similarity search. It's genuinely powerful. It's also not the same thing as understanding what you actually meant. Think of a plumber who hears the word "leak" and starts pulling up floorboards before you've finished the sentence. He's not being careless. He's pattern-matching - that's exactly how he was trained. Your AI agent is doing the same thing. Context is the one thing that gets deprioritized when teams are racing to ship. But without it, you don't have an intelligent agent. You have a very fast guesser. Similarity ≠ relevance. How? Find out with the link in the comments ⬇️

Nishkarsh

823,237 просмотров • 7 месяцев назад

AI agents are failing in production...not a surprise. As you scale your knowledge base, embeddings start creating noise. It’s called ‘semantic collapse’ - when conversations run too long, you have hundreds of PDFs, millions of data points to give to your AI. Your AI can’t flag it because it doesn’t know it’s hallucinating. Similarity gets passed off as relevance. Fix your context. Make your agents work. Build intelligent AI. If your AI is plateauing at 50% accuracy and hallucinations are still a problem, let's talk. Book a 20 minute demo with the link in the next thread. We'll dig into your setup and find out how we can help.
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AI agents are failing in production...not a surprise. As you scale your knowledge base, embeddings start creating noise. It’s called ‘semantic collapse’ - when conversations run too long, you have hundreds of PDFs, millions of data points to give to your AI. Your AI can’t flag it because it doesn’t know it’s hallucinating. Similarity gets passed off as relevance. Fix your context. Make your agents work. Build intelligent AI. If your AI is plateauing at 50% accuracy and hallucinations are still a problem, let's talk. Book a 20 minute demo with the link in the next thread. We'll dig into your setup and find out how we can help.

Nishkarsh

1,298,331 просмотров • 7 месяцев назад

MOST AI STACKS ARE A COMPLETE MESS 🚨 MongoDB just dropped 3 Free AI Skill Badges covering the AI foundations that ACTUALLY matter: 1/ a unified semantic search experience 2/ a scalable vector database 3/ a persistent memory state for agents Because production AI doesn't need a dozen disconnected tools. Yet most teams still fall into the trap of building exactly that: > a specialized vector database here > a separate memory layer there > an orchestration framework on top! and suddenly... your architecture is like a bowl of spaghetti. What you should do is much simpler. These 3 free modules are hands-on lessons for building production-ready AI apps on the unified database you already know. Here's what each one covers 🧵↓
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MOST AI STACKS ARE A COMPLETE MESS 🚨 MongoDB just dropped 3 Free AI Skill Badges covering the AI foundations that ACTUALLY matter: 1/ a unified semantic search experience 2/ a scalable vector database 3/ a persistent memory state for agents Because production AI doesn't need a dozen disconnected tools. Yet most teams still fall into the trap of building exactly that: > a specialized vector database here > a separate memory layer there > an orchestration framework on top! and suddenly... your architecture is like a bowl of spaghetti. What you should do is much simpler. These 3 free modules are hands-on lessons for building production-ready AI apps on the unified database you already know. Here's what each one covers 🧵↓

Charly Wargnier ☄️

29,522 просмотров • 4 месяцев назад

You do not need a CS degree to become an AI engineer. You need the map. Tokens. APIs. Python. Git. Context engineering. Structured outputs. RAG. Vector databases. Agents. Tool use. Evals. Cost control. Security. Shipping. That is the real curriculum. Not conference words. Not ML theory cosplay. Not pretending you need a PhD before building anything. The people getting hired are not the ones who memorised all 36 concepts. They are the ones who learned enough to ship 3 real projects and explain the tradeoffs. AI engineering in 2026 is not about knowing everything. It is about building systems that survive outside the demo. Bookmark this before you spend another year waiting for permission to start.
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You do not need a CS degree to become an AI engineer. You need the map. Tokens. APIs. Python. Git. Context engineering. Structured outputs. RAG. Vector databases. Agents. Tool use. Evals. Cost control. Security. Shipping. That is the real curriculum. Not conference words. Not ML theory cosplay. Not pretending you need a PhD before building anything. The people getting hired are not the ones who memorised all 36 concepts. They are the ones who learned enough to ship 3 real projects and explain the tradeoffs. AI engineering in 2026 is not about knowing everything. It is about building systems that survive outside the demo. Bookmark this before you spend another year waiting for permission to start.

Fluixo

14,539 просмотров • 4 месяцев назад

CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below
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CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below

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14,783 просмотров • 4 месяцев назад

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

OptimAI Network

17,506 просмотров • 6 месяцев назад

We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.
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We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.

Anni Wang

13,028 просмотров • 6 месяцев назад

Agent search is easy to demo and hard to evaluate. Exa is building the index, retrieval systems, and eval discipline agents need to rely on the web in production. We at Felicis are true believers in Will Bryk, Jeffrey Wang, and the Exa team. 🎧 cc: Peter Deng
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Agent search is easy to demo and hard to evaluate. Exa is building the index, retrieval systems, and eval discipline agents need to rely on the web in production. We at Felicis are true believers in Will Bryk, Jeffrey Wang, and the Exa team. 🎧 cc: Peter Deng

Tobi Coker

22,824 просмотров • 26 дней назад

One click away, and a new world starts to unfold.💫 What looks like a small extension is actually the starting point for a personal AI agent that can search, think, and act with you. That is what we are building at OptimAI: live context, autonomous execution, and agents that feel native to the way people actually work. Not a demo. Not a gimmick. A new interface for the agent-native internet. Build your own personal AI agent:
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One click away, and a new world starts to unfold.💫 What looks like a small extension is actually the starting point for a personal AI agent that can search, think, and act with you. That is what we are building at OptimAI: live context, autonomous execution, and agents that feel native to the way people actually work. Not a demo. Not a gimmick. A new interface for the agent-native internet. Build your own personal AI agent:

OptimAI Network

21,303 просмотров • 5 месяцев назад

Ex-Google engineer just released a free 3-hour course on building and monetizing AI agents. How to go from one agent to a full system that finds leads and makes money: 00:00 - Design an AI agent system 07:38 - Add human handoffs 19:27 - Understand RAG and vector databases 52:38 - Deploy agents to Google Cloud 1:25:57 - Turn agents into a paid WhatsApp business 1:33:00 - Convert conversations into leads 2:24:01 - Learn loops vs graphs 2:33:08 - Build a multi-tool agent graph Most people are still building AI demos. This course shows the full stack: Agents → RAG → Deployment → Leads → Revenue Building agents is the old workflow. Monetizing agent systems is the new one. This 3-hour watch covers more than most $500 paid courses. Save it before everyone starts selling the same thing.
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Ex-Google engineer just released a free 3-hour course on building and monetizing AI agents. How to go from one agent to a full system that finds leads and makes money: 00:00 - Design an AI agent system 07:38 - Add human handoffs 19:27 - Understand RAG and vector databases 52:38 - Deploy agents to Google Cloud 1:25:57 - Turn agents into a paid WhatsApp business 1:33:00 - Convert conversations into leads 2:24:01 - Learn loops vs graphs 2:33:08 - Build a multi-tool agent graph Most people are still building AI demos. This course shows the full stack: Agents → RAG → Deployment → Leads → Revenue Building agents is the old workflow. Monetizing agent systems is the new one. This 3-hour watch covers more than most $500 paid courses. Save it before everyone starts selling the same thing.

Lunar

355,233 просмотров • 2 месяцев назад

Aravind Srinivas just described a future most founders are pretending they are ready for. One person. One machine. A company that runs itself. Srinivas: “Buy a Mac mini, set up a Perplexity personal computer, and run their business on that.” Not a side project. Not a pitch deck. A real business with real revenue while the founder is not in the building. AI runs the ads. Handles SEO. Integrates Stripe. Ships features. Answers customers. All of it executing without a single employee. Srinivas: “Have this all working while you can be sipping wine in Napa.” But before he sold the dream he killed the one most people are already chasing. Srinivas: “Everybody talks about this one-person one-billion-dollar company. It’s not truly moving the GDP by one billion. It’s not truly creating new value.” One researcher collecting a billion in equity does not grow an economy. It rearranges numbers between balance sheets. Nothing gets built. No customer gets served. That is not value creation. That is valuation creation. Srinivas wants no part of it. What he described is the opposite. The person driving Uber between shifts who has the idea but not the payroll. Not the engineering. Not the marketing. Not the support staff. That person gets a machine that replaces all of it. Hundreds of thousands in revenue. Millions. Generated by autonomous systems doing the work that used to require ten employees and a burn rate. Not paper wealth. Not valuation theater. Output that moves through an economy and touches real customers. That is what moves GDP. Not one person worth a billion dollars. A million people each building something worth a million. That math rewrites a country. Then Srinivas said the part that separates him from every hype merchant in the room. Srinivas: “Everybody thinks AI is already there. It’s not there yet. Someone has to do that hard work.” The vision is real. The infrastructure is not. The agents are not autonomous. The integrations are not seamless. The plumbing is not finished. Someone has to wire the APIs. Connect the billing. Build the bridge between what a founder wants and what a machine can deliver. That work is not a keynote. It is not a tweet thread. It is engineering that nobody wants to do and everybody will depend on. Whoever finishes it first does not just build a product. They hand every ambitious person on Earth a company they can run alone. The corporations that need five hundred people to do what one founder with the right infrastructure could do are not efficient. They are exposed. And the person building the thing that exposes them just told you exactly what it looks like. He also told you it is not going to build itself.
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Aravind Srinivas just described a future most founders are pretending they are ready for. One person. One machine. A company that runs itself. Srinivas: “Buy a Mac mini, set up a Perplexity personal computer, and run their business on that.” Not a side project. Not a pitch deck. A real business with real revenue while the founder is not in the building. AI runs the ads. Handles SEO. Integrates Stripe. Ships features. Answers customers. All of it executing without a single employee. Srinivas: “Have this all working while you can be sipping wine in Napa.” But before he sold the dream he killed the one most people are already chasing. Srinivas: “Everybody talks about this one-person one-billion-dollar company. It’s not truly moving the GDP by one billion. It’s not truly creating new value.” One researcher collecting a billion in equity does not grow an economy. It rearranges numbers between balance sheets. Nothing gets built. No customer gets served. That is not value creation. That is valuation creation. Srinivas wants no part of it. What he described is the opposite. The person driving Uber between shifts who has the idea but not the payroll. Not the engineering. Not the marketing. Not the support staff. That person gets a machine that replaces all of it. Hundreds of thousands in revenue. Millions. Generated by autonomous systems doing the work that used to require ten employees and a burn rate. Not paper wealth. Not valuation theater. Output that moves through an economy and touches real customers. That is what moves GDP. Not one person worth a billion dollars. A million people each building something worth a million. That math rewrites a country. Then Srinivas said the part that separates him from every hype merchant in the room. Srinivas: “Everybody thinks AI is already there. It’s not there yet. Someone has to do that hard work.” The vision is real. The infrastructure is not. The agents are not autonomous. The integrations are not seamless. The plumbing is not finished. Someone has to wire the APIs. Connect the billing. Build the bridge between what a founder wants and what a machine can deliver. That work is not a keynote. It is not a tweet thread. It is engineering that nobody wants to do and everybody will depend on. Whoever finishes it first does not just build a product. They hand every ambitious person on Earth a company they can run alone. The corporations that need five hundred people to do what one founder with the right infrastructure could do are not efficient. They are exposed. And the person building the thing that exposes them just told you exactly what it looks like. He also told you it is not going to build itself.

Dustin

64,593 просмотров • 7 месяцев назад

Andrej Karpathy just said what most AI builders refuse to admit "The demo is near, but the product is far" One output can look impressive A real product has to work reliably at scale Most AI companies are still showing capabilities, not building complete products 00:00 - Why Transformers may outperform the human brain 05:09 - AI as your exocortex and who controls it 10:04 - Building AI to empower people, not replace them 15:16 - Why the demo is near but the real product is far In this 23-minute conversation, Karpathy explains where AI is actually heading Worth more than most $300 AI courses Bookmark and watch it later
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Andrej Karpathy just said what most AI builders refuse to admit "The demo is near, but the product is far" One output can look impressive A real product has to work reliably at scale Most AI companies are still showing capabilities, not building complete products 00:00 - Why Transformers may outperform the human brain 05:09 - AI as your exocortex and who controls it 10:04 - Building AI to empower people, not replace them 15:16 - Why the demo is near but the real product is far In this 23-minute conversation, Karpathy explains where AI is actually heading Worth more than most $300 AI courses Bookmark and watch it later

rari

37,193 просмотров • 3 месяцев назад

We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built HydraDB for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️
1:08

We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built HydraDB for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️

Nishkarsh

3,884,456 просмотров • 7 месяцев назад

Don't waste 2 years building AI agents the wrong way. An Anthropic engineer dropped a 1 hour course on the exact way to build AI agents from scratch. • 00:00 - building a Claude AI agent live • 34:01 - taking AI agents to production • 52:20 - AI agent harness Prompting is the old job. Building AI agentic loops is the new one. This one video replaces 10 paid courses on vibe coding. Bookmark & watch it now. Then build your own AI agent with the guide below.
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Don't waste 2 years building AI agents the wrong way. An Anthropic engineer dropped a 1 hour course on the exact way to build AI agents from scratch. • 00:00 - building a Claude AI agent live • 34:01 - taking AI agents to production • 52:20 - AI agent harness Prompting is the old job. Building AI agentic loops is the new one. This one video replaces 10 paid courses on vibe coding. Bookmark & watch it now. Then build your own AI agent with the guide below.

Roan

15,118 просмотров • 2 месяцев назад

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.
2:28

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.

Dustin

39,652 просмотров • 6 месяцев назад

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
56:43

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

85,083 просмотров • 4 месяцев назад

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
39:32

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

107,806 просмотров • 7 месяцев назад

Ed Zitron said on Bloomberg that every dollar you spend on an AI subscription burns between three and thirteen dollars behind the scenes. The price you pay has almost nothing to do with what the product costs. Your twenty dollar plan feels cheap because it is supposed to. The real cost of running the model on your prompts is several times what you hand over. The gap is covered by venture capital, not by the business working. That gap cannot stay open forever. At some point the price has to move toward the real cost. When it does, the product you built your day around gets more expensive or gets worse. Probably both. We have already seen the preview. When Anthropic raised its real token rates last year, the startups built on top of it had to scramble. Cursor, one of the fastest growing coding tools in the world, was forced to tear up its pricing overnight because the underlying cost it had been shielded from suddenly landed on its books. That is the whole industry in miniature. Everyone downstream is building on a number that is not real yet. Millions of people are wiring AI into how they work, how they write, how they code. They are doing it at a price that exists to hook them, not to last. The subsidy is not generosity. It is a customer acquisition cost with an expiration date. The bill is coming. Most people building on top of it have no idea what their workflow actually costs. Watch the interview on Bloomberg Podcasts
0:22

Ed Zitron said on Bloomberg that every dollar you spend on an AI subscription burns between three and thirteen dollars behind the scenes. The price you pay has almost nothing to do with what the product costs. Your twenty dollar plan feels cheap because it is supposed to. The real cost of running the model on your prompts is several times what you hand over. The gap is covered by venture capital, not by the business working. That gap cannot stay open forever. At some point the price has to move toward the real cost. When it does, the product you built your day around gets more expensive or gets worse. Probably both. We have already seen the preview. When Anthropic raised its real token rates last year, the startups built on top of it had to scramble. Cursor, one of the fastest growing coding tools in the world, was forced to tear up its pricing overnight because the underlying cost it had been shielded from suddenly landed on its books. That is the whole industry in miniature. Everyone downstream is building on a number that is not real yet. Millions of people are wiring AI into how they work, how they write, how they code. They are doing it at a price that exists to hook them, not to last. The subsidy is not generosity. It is a customer acquisition cost with an expiration date. The bill is coming. Most people building on top of it have no idea what their workflow actually costs. Watch the interview on Bloomberg Podcasts

Ihtesham Ali

22,640 просмотров • 4 месяцев назад

An entire empire was overthrown over a two percent tax on a breakfast beverage. Look at what you tolerate now. You are taxed when you earn it. Taxed when you spend it. Taxed when you save it. Taxed when you invest it. And when you die, they tax whatever is left. That is not a system. That is a harvest. You commute in a car you paid sales tax to buy. You drive it on roads you were already taxed to build. You fill it with gas taxed by the gallon. When you sell that car, the next buyer pays sales tax on it again. The same car. Taxed every time it changes hands. You arrive at a job where your salary is cut before it ever touches your hands. If you work for yourself, you pay both sides. Two people on paper. Neither one keeps what they earned. Then you go home. Every bill you open has a government standing behind it with its hand out. You buy a house with money they already took their share of. Then they charge you property tax on it every year for the rest of your life. You want to renovate your own kitchen. You need a permit. You want to build a deck on your own land. You need a permit. You pay for the property. Then you pay for permission to use it. Stop paying property tax and they seize your home. Not because you missed a mortgage payment. Because you missed a payment to the government for the privilege of keeping what is already yours. You do not own your home. You rent it from the state. If you leave something behind for your children, they are taxed on what you were already taxed to earn. The same wealth. Taxed at every stage of your life. Then taxed one final time because you had the audacity to die. They found a way to monetize your absence. We are told this is the price of civilization. It is not. It is architecture. The most effective prison ever built is the one where the inmates believe they are free. They did not take your freedom. They priced you out of it. If you kept the full value of your labor, you would be free within years. Not decades. Years. The system cannot allow that. A machine built on consumption needs a consumer that never stops. You did not sign a social contract. You were assigned one. Now pay attention. They spent decades perfecting the extraction of your productivity. Now they are building the technology to replace you. AI is not coming for your job because corporations are greedy. It is coming because a system that already takes half your output just realized it can take all of it. Without needing you in the equation. You were never the point of this arrangement. You were the input. And the moment they engineer a cheaper one, you become a rounding error on a quarterly earnings call. They did not build AI to free you. They built it to finish what the tax code started. It was never about the tea. It was about the precedent. Today we hand over half our waking lives and thank them for the potholes. You do not live in a free economy. You live in a subscription you never signed up for. And the penalty for canceling is everything you have.
0:57

An entire empire was overthrown over a two percent tax on a breakfast beverage. Look at what you tolerate now. You are taxed when you earn it. Taxed when you spend it. Taxed when you save it. Taxed when you invest it. And when you die, they tax whatever is left. That is not a system. That is a harvest. You commute in a car you paid sales tax to buy. You drive it on roads you were already taxed to build. You fill it with gas taxed by the gallon. When you sell that car, the next buyer pays sales tax on it again. The same car. Taxed every time it changes hands. You arrive at a job where your salary is cut before it ever touches your hands. If you work for yourself, you pay both sides. Two people on paper. Neither one keeps what they earned. Then you go home. Every bill you open has a government standing behind it with its hand out. You buy a house with money they already took their share of. Then they charge you property tax on it every year for the rest of your life. You want to renovate your own kitchen. You need a permit. You want to build a deck on your own land. You need a permit. You pay for the property. Then you pay for permission to use it. Stop paying property tax and they seize your home. Not because you missed a mortgage payment. Because you missed a payment to the government for the privilege of keeping what is already yours. You do not own your home. You rent it from the state. If you leave something behind for your children, they are taxed on what you were already taxed to earn. The same wealth. Taxed at every stage of your life. Then taxed one final time because you had the audacity to die. They found a way to monetize your absence. We are told this is the price of civilization. It is not. It is architecture. The most effective prison ever built is the one where the inmates believe they are free. They did not take your freedom. They priced you out of it. If you kept the full value of your labor, you would be free within years. Not decades. Years. The system cannot allow that. A machine built on consumption needs a consumer that never stops. You did not sign a social contract. You were assigned one. Now pay attention. They spent decades perfecting the extraction of your productivity. Now they are building the technology to replace you. AI is not coming for your job because corporations are greedy. It is coming because a system that already takes half your output just realized it can take all of it. Without needing you in the equation. You were never the point of this arrangement. You were the input. And the moment they engineer a cheaper one, you become a rounding error on a quarterly earnings call. They did not build AI to free you. They built it to finish what the tax code started. It was never about the tea. It was about the precedent. Today we hand over half our waking lives and thank them for the potholes. You do not live in a free economy. You live in a subscription you never signed up for. And the penalty for canceling is everything you have.

Dustin

27,945 просмотров • 6 месяцев назад