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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 views • 1 month ago •via X (Twitter)

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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 ⬇️
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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

822,710 views • 3 months ago

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.
0:53

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

1,297,422 views • 3 months ago

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

Anatoli Kopadze

14,453 views • 19 days ago

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

12,975 views • 2 months ago

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

17,425 views • 2 months ago

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

18,742 views • 1 month ago

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

3,858,970 views • 3 months ago

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 views • 3 months ago

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

83,090 views • 22 days ago

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

39,109 views • 2 months ago

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

107,448 views • 3 months ago

clickup just mass-obsoleted every AI agent platform here's the thing nobody's talking about: AI doesn't fail because the models are bad. it fails because it has no context. you paste in a prompt. it hallucinates. you blame the tool. but the tool was never the problem. the CONTEXT was. clickup just solved this. their new "super agents" live INSIDE your workspace. they see everything: - your tasks - your docs - your meetings - your conversations - your decisions 100% context. always on. always scraping. this isn't bolted-on AI calling APIs. this is AI that already knows your business. i've been running an AI backend for my agency. the architecture works - but you have to BUILD the context layer. clickup just made that automatic. i already use it. have tons of data there. this is about to be unbelievable. if you're building automations and you're not paying attention to this— you're about to get lapped. playing with it hard over the next 2 days. full deep dive coming. drop "SUPER" and i'll send you the breakdown when it's ready.
5:50

clickup just mass-obsoleted every AI agent platform here's the thing nobody's talking about: AI doesn't fail because the models are bad. it fails because it has no context. you paste in a prompt. it hallucinates. you blame the tool. but the tool was never the problem. the CONTEXT was. clickup just solved this. their new "super agents" live INSIDE your workspace. they see everything: - your tasks - your docs - your meetings - your conversations - your decisions 100% context. always on. always scraping. this isn't bolted-on AI calling APIs. this is AI that already knows your business. i've been running an AI backend for my agency. the architecture works - but you have to BUILD the context layer. clickup just made that automatic. i already use it. have tons of data there. this is about to be unbelievable. if you're building automations and you're not paying attention to this— you're about to get lapped. playing with it hard over the next 2 days. full deep dive coming. drop "SUPER" and i'll send you the breakdown when it's ready.

Nozz

20,269 views • 6 months ago

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

27,628 views • 2 months ago

Sam Altman just told you what OpenAI is actually building. Not a chatbot. Not a search tool. Not an assistant. Altman: “Go look around my computer… read my messages… listen to my meetings… intermediate my interactions for me.” That is not a product pitch. That is the CEO of the most valuable AI company on Earth describing what he personally wants. For himself. Every day. Read his messages. Listen to his meetings. Act on his behalf. Make decisions before he knows a decision needs making. Altman: “I don’t have to think. I don’t have to ask you questions.” Every model of AI ever built runs on the prompt. You ask. It responds. You direct. It executes. The human initiates. The machine follows. Altman is describing the death of that model. The agent does not wait. It already read the email. It already heard the meeting. It already knows what you need before you form the thought. You do not operate the machine. The machine operates around you. Then came the line that makes everything else real. Altman: “You can know everything about my life. Start suggesting more things I should build.” He is not asking the AI to execute his ideas. He is asking it to generate them. From his files. His history. His patterns. His entire context. The agent does not just remove friction. It removes the blank page. You never stall. You never run dry. You never sit wondering what to build next. The machine already mapped your market, your gaps, your momentum. It tells you what comes next before you think to ask. But the individual product is not the story. Altman went further. Altman: “Automated companies… where the AI can do not just coding work, but huge amounts of what it takes to run and operate a company.” Not fully automated. He was precise about that. But accelerated to the point where one person with the right stack does what used to take departments. The billion-dollar company did not reach that valuation because the product was worth a billion. It got there because it took a thousand people to deliver it. When an agent absorbs the work of a hundred of those people, the math of every industry rewrites itself. The startup that needed fifty employees and three years of runway now needs five people and six months. The company that took a decade to scale now compounds in quarters. The person holding the line between their data and their tools is not protecting their privacy. They are protecting their ceiling. Because the cost of this leverage is total transparency. You do not get the agent that acts without being asked unless you give it everything. Your messages. Your calendar. Your files. Your patterns. Your life. Altman is not hiding that tradeoff. He is building it as the product. The people who accept it will operate at a speed the people who refuse cannot touch. Right now, two versions of the future are separating. One where you direct the machine. One where the machine already knows. Altman chose. He is building it. The question is not whether this happens. The question is which side of it finds you.
1:40

Sam Altman just told you what OpenAI is actually building. Not a chatbot. Not a search tool. Not an assistant. Altman: “Go look around my computer… read my messages… listen to my meetings… intermediate my interactions for me.” That is not a product pitch. That is the CEO of the most valuable AI company on Earth describing what he personally wants. For himself. Every day. Read his messages. Listen to his meetings. Act on his behalf. Make decisions before he knows a decision needs making. Altman: “I don’t have to think. I don’t have to ask you questions.” Every model of AI ever built runs on the prompt. You ask. It responds. You direct. It executes. The human initiates. The machine follows. Altman is describing the death of that model. The agent does not wait. It already read the email. It already heard the meeting. It already knows what you need before you form the thought. You do not operate the machine. The machine operates around you. Then came the line that makes everything else real. Altman: “You can know everything about my life. Start suggesting more things I should build.” He is not asking the AI to execute his ideas. He is asking it to generate them. From his files. His history. His patterns. His entire context. The agent does not just remove friction. It removes the blank page. You never stall. You never run dry. You never sit wondering what to build next. The machine already mapped your market, your gaps, your momentum. It tells you what comes next before you think to ask. But the individual product is not the story. Altman went further. Altman: “Automated companies… where the AI can do not just coding work, but huge amounts of what it takes to run and operate a company.” Not fully automated. He was precise about that. But accelerated to the point where one person with the right stack does what used to take departments. The billion-dollar company did not reach that valuation because the product was worth a billion. It got there because it took a thousand people to deliver it. When an agent absorbs the work of a hundred of those people, the math of every industry rewrites itself. The startup that needed fifty employees and three years of runway now needs five people and six months. The company that took a decade to scale now compounds in quarters. The person holding the line between their data and their tools is not protecting their privacy. They are protecting their ceiling. Because the cost of this leverage is total transparency. You do not get the agent that acts without being asked unless you give it everything. Your messages. Your calendar. Your files. Your patterns. Your life. Altman is not hiding that tradeoff. He is building it as the product. The people who accept it will operate at a speed the people who refuse cannot touch. Right now, two versions of the future are separating. One where you direct the machine. One where the machine already knows. Altman chose. He is building it. The question is not whether this happens. The question is which side of it finds you.

Dustin

87,680 views • 2 months ago

This is the DeepSeek moment of Search? A small team of cracked engineers built a search API for AI agents that outperforms everything built with 20x the funding. AI systems are about to become the biggest users of information on Earth. They don’t need a better Google. They need a retrieval layer built for reasoning, scale, and access beyond the web. That’s what we’re building. 1/3
2:27

This is the DeepSeek moment of Search? A small team of cracked engineers built a search API for AI agents that outperforms everything built with 20x the funding. AI systems are about to become the biggest users of information on Earth. They don’t need a better Google. They need a retrieval layer built for reasoning, scale, and access beyond the web. That’s what we’re building. 1/3

Valyu

1,134,995 views • 8 months ago

"AI agents will hold more crypto than humans within a decade." Charles Hoskinson (Charles Hoskinson) studied math, dropped out, built one of the only blockchains designed by peer-reviewed research. He co-founded Ethereum, walked away over how it was run, and built Cardano to do it differently. The man who has argued with everyone in this industry now thinks the biggest user of crypto won't be people at all. "Humans are a rounding error in the system we're building. AI agents don't sleep, don't panic-sell, and don't care about price. They transact in tokens because that's the only thing they can actually use." We cover: - Why AI agents (not humans) become the dominant on-chain actors, and what that does to every token model - The infrastructure that has to exist before agents can transact safely at scale - Why most current blockchains can't handle machine-speed transactions - Where Cardano's research-first approach fits in a world of autonomous agents - The identity problem: how do you tell a human from an agent on-chain, and why it matters - Why he's bullish on the technology but blunt about the timeline - What he thinks the rest of the industry is getting wrong about AI + crypto - The one thing that has to happen for any of this to be real Thanks to Charles for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 01:30 - Why AI Agents Change Everything 06:30 - Humans as a Rounding Error 12:00 - The Infrastructure Gap 18:30 - Identity: Human vs Agent On-Chain 24:30 - Where Cardano Fits 30:00 - What The Industry Gets Wrong 34:00 - The Timeline Nobody Wants To Hear
36:30

"AI agents will hold more crypto than humans within a decade." Charles Hoskinson (Charles Hoskinson) studied math, dropped out, built one of the only blockchains designed by peer-reviewed research. He co-founded Ethereum, walked away over how it was run, and built Cardano to do it differently. The man who has argued with everyone in this industry now thinks the biggest user of crypto won't be people at all. "Humans are a rounding error in the system we're building. AI agents don't sleep, don't panic-sell, and don't care about price. They transact in tokens because that's the only thing they can actually use." We cover: - Why AI agents (not humans) become the dominant on-chain actors, and what that does to every token model - The infrastructure that has to exist before agents can transact safely at scale - Why most current blockchains can't handle machine-speed transactions - Where Cardano's research-first approach fits in a world of autonomous agents - The identity problem: how do you tell a human from an agent on-chain, and why it matters - Why he's bullish on the technology but blunt about the timeline - What he thinks the rest of the industry is getting wrong about AI + crypto - The one thing that has to happen for any of this to be real Thanks to Charles for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 01:30 - Why AI Agents Change Everything 06:30 - Humans as a Rounding Error 12:00 - The Infrastructure Gap 18:30 - Identity: Human vs Agent On-Chain 24:30 - Where Cardano Fits 30:00 - What The Industry Gets Wrong 34:00 - The Timeline Nobody Wants To Hear

Michaël van de Poppe

291,395 views • 28 days ago

Karpathy just described what hiring looks like in 2026: "Build a large project with Claude Code — like a Twitter clone. Make it secure. Have real agents using the platform doing stuff. The interviewer uses parallel agents trying to break in to verify security." One person. Multiple agents. Shipping and defending production code simultaneously. This is not a future job description. This is happening right now. The founders who get there first are not the smartest ones in the room. They are the ones who stopped doing everything themselves and built agents to do it for them. Here is the complete playbook — 13 agents, exact prompts, 90-day build plan ↓ Read this before your competition does.
1:02

Karpathy just described what hiring looks like in 2026: "Build a large project with Claude Code — like a Twitter clone. Make it secure. Have real agents using the platform doing stuff. The interviewer uses parallel agents trying to break in to verify security." One person. Multiple agents. Shipping and defending production code simultaneously. This is not a future job description. This is happening right now. The founders who get there first are not the smartest ones in the room. They are the ones who stopped doing everything themselves and built agents to do it for them. Here is the complete playbook — 13 agents, exact prompts, 90-day build plan ↓ Read this before your competition does.

Rahul

431,638 views • 1 month ago

The future of AI Agents needs #ICP AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ zCloak Network is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (0xFrancis), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.
36:32

The future of AI Agents needs #ICP AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ zCloak Network is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (0xFrancis), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.

Alex (BCP)

20,791 views • 2 months ago

Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:
0:35

Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:

Rohit

120,600 views • 1 month ago

This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.
2:37

This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.

Chris Long

13,185 views • 1 year ago