Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

INSANE how most AI agents still send every task to Claude Code. Jev routes the easy work to cheaper workers, saves the expensive reasoning for what actually matters, and makes the whole system literally faster. This is how you build agents that scale. Follow CyrilXBT for more AI updates.

CyrilXBT

196,979 subscribers

17,849 görüntüleme • 6 gün önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

17 Yorum

Harley Lewis Foote profil fotoğrafı

Harley Lewis Foote6 gün önce

Jev's got the right idea. Most people just burn credits on stuff a regex could handle.

CyrilXBT profil fotoğrafı

CyrilXBT6 gün önce

I guess i need to create a guide on how to not burn credits

Hussain Hashim | Building SundayBack profil fotoğrafı

Hussain Hashim | Building SundayBack6 gün önce

@cyrilXBT real key is knowing when to blend tasks. route the middle ground work smartly to optimize costs.

Wallchain Community Hub profil fotoğrafı

Wallchain Community Hub6 gün önce

routing simple tasks saves so much time and money

magsimich profil fotoğrafı

magsimich6 gün önce

Cheap routing changes everything

CyrilXBT profil fotoğrafı

CyrilXBT6 gün önce

i agree

安叫兽|Bird🕊️ 🔶 BNB profil fotoğrafı

安叫兽|Bird🕊️ 🔶 BNB6 gün önce

就怕分活儿的想半天，比干活儿还费劲

TGT Analytics profil fotoğrafı

TGT Analytics6 gün önce

This is the routing thesis in one post. One measured data point: a verification-gated cascade matched frontier quality at 69% lower cost ($0.89 vs $2.90), escalating only the hard cases. What is your escalation trigger, confidence score or verifier vote?

ROI of AI profil fotoğrafı

ROI of AI6 gün önce

New gem

Brian Hadu profil fotoğrafı

Brian Hadu6 gün önce

how do you determine which tasks Jev routes to cheaper workers?

Andrew Watt profil fotoğrafı

Andrew Watt6 gün önce

Jev is genuinely nasty

Gregor profil fotoğrafı

Gregor6 gün önce

misclassification rate is the real cost. spent a week chasing silent failures after routing 'simple' queries to a cheaper model. it was confidently wrong on the exact edge cases the router called easy

Sael profil fotoğrafı

Sael6 gün önce

most people just love paying the ai tax until they break

Adar profil fotoğrafı

Adar6 gün önce

Routing every task to the strongest model is usually just paying premium prices for work that never needed premium reasoning

Kai Lennox profil fotoğrafı

Kai Lennox6 gün önce

the expensive model only for what matters is where the savings live

Brjan | AI Builder profil fotoğrafı

Brjan | AI Builder6 gün önce

routing tasks efficiently could drastically cut costs while boosting performance

moose sam profil fotoğrafı

moose sam6 gün önce

most workflows are 80% simple transforms and 20% actual reasoning, why pay premium rates for the whole thing

Benzer Videolar

This is literally how Jev makes AI agents dramatically cheaper. It filters the noise from Slack, GitHub, documents, memory, and the web then sends Claude Code only the context it actually needs. Fewer tokens. Faster decisions. Lower costs. The context firewall is insane:

This is literally how Jev makes AI agents dramatically cheaper. It filters the noise from Slack, GitHub, documents, memory, and the web then sends Claude Code only the context it actually needs. Fewer tokens. Faster decisions. Lower costs. The context firewall is insane:

CyrilXBT

15,532 görüntüleme • 8 gün önce

Ok this is actually insane. Everyone building agents right now is doing the same expensive mistake. They let the model decide LITERALLY everything. Every check. Every gate. Every "should this proceed." All routed through the same $50/M model that's supposed to be doing the hard thinking. That's not intelligence. That's just burning money to ask yes or no questions. Here's what Jev Engineering actually does: request → structured state → Jev router → cheapest capable model → worker → approval check → execution gate → tool The decision layer doesn't write a single word. It routes. Scores. Blocks. Approves. That's it. The expensive model only gets touched when the task LITERALLY requires reasoning. Everything else gets handled by something that costs almost nothing and responds in milliseconds. 193x faster. 444x cheaper. Not projected. Measured. Most people are going to keep paying frontier prices to ask their agent "yes or no" for another year before they figure this out. Full breakdown below, this is the unlock nobody's talking about yet. Follow CyrilXBT, I'm not gatekeeping this one.

Ok this is actually insane. Everyone building agents right now is doing the same expensive mistake. They let the model decide LITERALLY everything. Every check. Every gate. Every "should this proceed." All routed through the same $50/M model that's supposed to be doing the hard thinking. That's not intelligence. That's just burning money to ask yes or no questions. Here's what Jev Engineering actually does: request → structured state → Jev router → cheapest capable model → worker → approval check → execution gate → tool The decision layer doesn't write a single word. It routes. Scores. Blocks. Approves. That's it. The expensive model only gets touched when the task LITERALLY requires reasoning. Everything else gets handled by something that costs almost nothing and responds in milliseconds. 193x faster. 444x cheaper. Not projected. Measured. Most people are going to keep paying frontier prices to ask their agent "yes or no" for another year before they figure this out. Full breakdown below, this is the unlock nobody's talking about yet. Follow CyrilXBT, I'm not gatekeeping this one.

CyrilXBT

21,816 görüntüleme • 9 gün önce

Build, deploy, and scale AI agents with confidence. See how Cisco AI Defense makes this possible. Follow Cisco AI for more on securing AI applications and agents.

Build, deploy, and scale AI agents with confidence. See how Cisco AI Defense makes this possible. Follow Cisco AI for more on securing AI applications and agents.

Cisco AI

38,033,248 görüntüleme • 3 ay önce

"YOU'RE NOT SUPPOSED TO PROMPT CLAUDE. YOU'RE SUPPOSED TO BUILD A SYSTEM THAT PROMPTS ITSELF." Boris Cherny, who built Claude Code, said it plainly. In 26 minutes he shows exactly how to build one from scratch, step by step. Most people are still doing every part of this by hand. Watch the session, then save the guide below. Bookmark this. Follow CyrilXBT

"YOU'RE NOT SUPPOSED TO PROMPT CLAUDE. YOU'RE SUPPOSED TO BUILD A SYSTEM THAT PROMPTS ITSELF." Boris Cherny, who built Claude Code, said it plainly. In 26 minutes he shows exactly how to build one from scratch, step by step. Most people are still doing every part of this by hand. Watch the session, then save the guide below. Bookmark this. Follow CyrilXBT

CyrilXBT

84,875 görüntüleme • 2 ay önce

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today. Follow CyrilXBT for more elite resources that build real depth the moment they drop.

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today. Follow CyrilXBT for more elite resources that build real depth the moment they drop.

CyrilXBT

54,956 görüntüleme • 5 ay önce

TONIGHT YOU HAVE TWO OPTIONS Scroll until you fall asleep. Or watch this. A Stanford lecture on Agentic AI that teaches you more about how AI automation actually works than 6 months of copying Make and n8n tutorials. Tool calling. Multi-step workflows. Planning. Reflection. The foundations behind every automation system that actually works. Most people learn this by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Watch the lecture first. Then go through 9 real build projects across 2 weeks. APIs. Webhooks. LLM integration. AI workflows. No theory. Just build. The people who do this weekend will understand AI automation at a level that makes everything they build after faster, cleaner, and more powerful. The people who skip it will still be copying tutorials in 2027 wondering why nothing they build actually works. Completely free. Bookmark this before you open Netflix.

TONIGHT YOU HAVE TWO OPTIONS Scroll until you fall asleep. Or watch this. A Stanford lecture on Agentic AI that teaches you more about how AI automation actually works than 6 months of copying Make and n8n tutorials. Tool calling. Multi-step workflows. Planning. Reflection. The foundations behind every automation system that actually works. Most people learn this by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Watch the lecture first. Then go through 9 real build projects across 2 weeks. APIs. Webhooks. LLM integration. AI workflows. No theory. Just build. The people who do this weekend will understand AI automation at a level that makes everything they build after faster, cleaner, and more powerful. The people who skip it will still be copying tutorials in 2027 wondering why nothing they build actually works. Completely free. Bookmark this before you open Netflix.

CyrilXBT

39,678 görüntüleme • 5 ay önce

Most people will spend 4 hours tonight consuming. Zero creating. Then wonder why nothing changes. 1) Scroll until you fall asleep. 2) Or watch this. A Stanford lecture on Agentic AI that teaches you more about how AI automation actually works than 6 months of copying Make and n8n tutorials. Tool calling. Multi-step workflows. Planning. Reflection. The foundations behind every automation system that actually works. Most people learn this by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Watch the lecture first. Then go through 9 real build projects across 2 weeks: APIs. Webhooks. LLM integration. AI workflows. No theory. Just build. The people who do this weekend will understand AI automation at a level that makes everything they build after faster, cleaner, and more powerful. The people who skip it will still be copying tutorials in 2027 wondering why nothing they build actually works. Completely free. Bookmark this before you open Netflix.

Most people will spend 4 hours tonight consuming. Zero creating. Then wonder why nothing changes. 1) Scroll until you fall asleep. 2) Or watch this. A Stanford lecture on Agentic AI that teaches you more about how AI automation actually works than 6 months of copying Make and n8n tutorials. Tool calling. Multi-step workflows. Planning. Reflection. The foundations behind every automation system that actually works. Most people learn this by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Watch the lecture first. Then go through 9 real build projects across 2 weeks: APIs. Webhooks. LLM integration. AI workflows. No theory. Just build. The people who do this weekend will understand AI automation at a level that makes everything they build after faster, cleaner, and more powerful. The people who skip it will still be copying tutorials in 2027 wondering why nothing they build actually works. Completely free. Bookmark this before you open Netflix.

CyrilXBT

17,158 görüntüleme • 5 ay önce

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today.

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today.

CyrilXBT

18,550 görüntüleme • 19 gün önce

Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF" In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better worth more than a $500 Claude Code engineering course watch today, then read how to actually boost yourself x100 with Jev in the post below

Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF" In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better worth more than a $500 Claude Code engineering course watch today, then read how to actually boost yourself x100 with Jev in the post below

Codez

191,889 görüntüleme • 12 gün önce

claude code can now write sql almost perfectly. but it still needs to be retaught the same definitions over and over. ontology is how AI agents get 7x faster on your database over time. today we shipped Ontology 3.0 — memory for AI agents, specialized for databases:

claude code can now write sql almost perfectly. but it still needs to be retaught the same definitions over and over. ontology is how AI agents get 7x faster on your database over time. today we shipped Ontology 3.0 — memory for AI agents, specialized for databases:

ethan ding 📊

25,776 görüntüleme • 4 ay önce

Anthropic engineer just dropped a full walkthrough on building AI agents from scratch this single video is worth more than any $600 AI agents course here's what she covers: > what Agent, Environment, and Session actually mean > how to stream live events from your agent > wiring up custom tools from scratch > the server-side loop that makes agents actually work > roadmap to subagents, vaults, and memory 37 minutes that will answer every question you have about building agents the people who understand what Claude can actually do are building things everyone else thinks requires a whole team most of them aren't smarter, they just know features that most people have never heard of full breakdown on every Claude feature worth knowing in the article below

Anthropic engineer just dropped a full walkthrough on building AI agents from scratch this single video is worth more than any $600 AI agents course here's what she covers: > what Agent, Environment, and Session actually mean > how to stream live events from your agent > wiring up custom tools from scratch > the server-side loop that makes agents actually work > roadmap to subagents, vaults, and memory 37 minutes that will answer every question you have about building agents the people who understand what Claude can actually do are building things everyone else thinks requires a whole team most of them aren't smarter, they just know features that most people have never heard of full breakdown on every Claude feature worth knowing in the article below

Anatoli Kopadze

39,240 görüntüleme • 4 ay önce

Сreator of Claude Code: BORIS CHERNY, WHO BUILT CLAUDE CODE, SAID IT PLAINLY." I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops. "He is not typing anymore. He runs loops that write the code for him. That is the shift most people miss. Not a better prompt. A loop that keeps running while you are gone.People sell $500 courses on this. The person who built the tool said it for free. The exact setup is in the article below.Bookmark this. Follow CyrilXBT

Сreator of Claude Code: BORIS CHERNY, WHO BUILT CLAUDE CODE, SAID IT PLAINLY." I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops. "He is not typing anymore. He runs loops that write the code for him. That is the shift most people miss. Not a better prompt. A loop that keeps running while you are gone.People sell $500 courses on this. The person who built the tool said it for free. The exact setup is in the article below.Bookmark this. Follow CyrilXBT

CyrilXBT

38,827 görüntüleme • 2 ay önce

Everyone is talking about JEV. Almost nobody knows how to actually use it. These 10 GitHub repos fix that. 1. browser-use/jev-ultrafast (~16.6k⭐) Jev for browser agents: pick the next action without another expensive LLM call. 2. tamaratran/fast-jev-compaction (~6.5k⭐) Uses Jev to decide what context still matters and what can be dropped. 3. TheoLeeCJ/SemIf (~4k⭐) Open-source Jev-style decision making you can run locally. 4. jarrodwatts/jev-trader (~2.1k⭐) Real-time trading loop where Jev decides buy, sell or wait. 5. kerpopule/hermes-jev-skills (~670⭐) Jev routing, memory, compaction and skill selection for AI agents. 6. devagrawal09/jev-review (~570⭐) Code review powered by Jev decisions instead of one giant prompt. 7. thruwire/foreman (~520⭐) Jev as a supervisor for coding agents: continue, retry, verify or stop. 8. superagents-lab/jev-search (~400⭐) Jev decides where and how an agent should search. 9. droidrun/mobile-jev (~350⭐) Jev for Android agents choosing the next app action. 10. gargpratyush/jev-router (~340⭐) Routes agent tasks to the right model with Jev. After looking through these, the pattern is pretty clear: LLMs generate. Agents execute. JEV decides. That decision layer is what most agent stacks are still missing. Save this if you want to integrate Jev into your workflow👇

Everyone is talking about JEV. Almost nobody knows how to actually use it. These 10 GitHub repos fix that. 1. browser-use/jev-ultrafast (~16.6k⭐) Jev for browser agents: pick the next action without another expensive LLM call. 2. tamaratran/fast-jev-compaction (~6.5k⭐) Uses Jev to decide what context still matters and what can be dropped. 3. TheoLeeCJ/SemIf (~4k⭐) Open-source Jev-style decision making you can run locally. 4. jarrodwatts/jev-trader (~2.1k⭐) Real-time trading loop where Jev decides buy, sell or wait. 5. kerpopule/hermes-jev-skills (~670⭐) Jev routing, memory, compaction and skill selection for AI agents. 6. devagrawal09/jev-review (~570⭐) Code review powered by Jev decisions instead of one giant prompt. 7. thruwire/foreman (~520⭐) Jev as a supervisor for coding agents: continue, retry, verify or stop. 8. superagents-lab/jev-search (~400⭐) Jev decides where and how an agent should search. 9. droidrun/mobile-jev (~350⭐) Jev for Android agents choosing the next app action. 10. gargpratyush/jev-router (~340⭐) Routes agent tasks to the right model with Jev. After looking through these, the pattern is pretty clear: LLMs generate. Agents execute. JEV decides. That decision layer is what most agent stacks are still missing. Save this if you want to integrate Jev into your workflow👇

unicode

20,745 görüntüleme • 7 gün önce

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.

MIKE

39,041 görüntüleme • 3 ay önce

Natural language instruction PLUS deterministic execution is the real unlock for AI agents that work consistently. With Headless 360, you can build these agents with the tools you already use: Claude Code, Cursor, Codex, Windsurf, Agentforce Vibes, Agentforce builder, and more.

Natural language instruction PLUS deterministic execution is the real unlock for AI agents that work consistently. With Headless 360, you can build these agents with the tools you already use: Claude Code, Cursor, Codex, Windsurf, Agentforce Vibes, Agentforce builder, and more.

Salesforce

1,783,451 görüntüleme • 5 ay önce

A MILLIONAIRE WHO BUILT THEIR FORTUNE USING AI WAS ASKED HOW TO GO FROM $0 TO $1,000,000. Their answer: five steps. Nothing complicated. 1) Claude Code. 2) Find leads. 3) Write the sales script. 4) Schedule and sell. 5) Build with AI. No team. No funding. No permission needed. All the tools are in the article below. Save this before you forget it. Bookmark this. Follow CyrilXBT

A MILLIONAIRE WHO BUILT THEIR FORTUNE USING AI WAS ASKED HOW TO GO FROM $0 TO $1,000,000. Their answer: five steps. Nothing complicated. 1) Claude Code. 2) Find leads. 3) Write the sales script. 4) Schedule and sell. 5) Build with AI. No team. No funding. No permission needed. All the tools are in the article below. Save this before you forget it. Bookmark this. Follow CyrilXBT

CyrilXBT

44,020 görüntüleme • 2 ay önce

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

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

GREG ISENBERG

84,760 görüntüleme • 3 ay önce

Marketing agents are the new coding agents and every marketing channel is RED ocean now because AI slop FLOODED all of them How to actually build marketing agents that work (2 real examples, all tools shared, 43 minute masterclass) watch:

Marketing agents are the new coding agents and every marketing channel is RED ocean now because AI slop FLOODED all of them How to actually build marketing agents that work (2 real examples, all tools shared, 43 minute masterclass) watch:

GREG ISENBERG

122,121 görüntüleme • 1 ay önce

ANDREW NG REPORTEDLY SAID 100% OF HIS TASKS NOW RUN THROUGH AI AGENTS. The hype actually passed his expectations. His next call. Loops are the next step. In 3 to 6 months everyone will be using self-improving loops. Prompting alone is a 30 minute talk he reportedly breaks down how to build self-improving agentic systems from scratch. Agent loops. Feedback. Memory. Self-improvement.Worth more than most $500 agentic courses. Bookmark this. Follow CyrilXBT

ANDREW NG REPORTEDLY SAID 100% OF HIS TASKS NOW RUN THROUGH AI AGENTS. The hype actually passed his expectations. His next call. Loops are the next step. In 3 to 6 months everyone will be using self-improving loops. Prompting alone is a 30 minute talk he reportedly breaks down how to build self-improving agentic systems from scratch. Agent loops. Feedback. Memory. Self-improvement.Worth more than most $500 agentic courses. Bookmark this. Follow CyrilXBT

CyrilXBT

18,056 görüntüleme • 3 ay önce

We've raised $7M to help companies build AI agents that actually learn and work. Osmosis (YC W25) is a platform for companies to fine-tune models that outperform foundation models with reinforcement learning. Better, faster, and cheaper.

We've raised $7M to help companies build AI agents that actually learn and work. Osmosis (YC W25) is a platform for companies to fine-tune models that outperform foundation models with reinforcement learning. Better, faster, and cheaper.

Kasey Zhang

1,217,244 görüntüleme • 11 ay önce