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I got tired of explaining myself to AI every single day. Same context. Same workflow. Same instructions. Copy-pasted into every new session like it’s 2022. So I tried Panofy. Uploaded my own scripts and documents to train an agent — then gave it a single instruction. 🔥 It ran...

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Aryan Rakib

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33,551 görüntüleme • 3 ay önce •via X (Twitter)

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You’re not going to lose your job to AI. You might lose it to somebody who knows how to use AI. It’s made me a smarter, more thoughtful writer and producer. A lot of what I talk about I research through AI first, but the key is asking it the deepest, most penetrating questions you can think of. That’s when it actually delivers. The real promise here isn’t just productivity. It could give us a four or even three day workweek at the same living standards. Work 20 to 40 percent less and maintain the same lifestyle. Learn the tools. Ask better questions. Get your life back.
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You’re not going to lose your job to AI. You might lose it to somebody who knows how to use AI. It’s made me a smarter, more thoughtful writer and producer. A lot of what I talk about I research through AI first, but the key is asking it the deepest, most penetrating questions you can think of. That’s when it actually delivers. The real promise here isn’t just productivity. It could give us a four or even three day workweek at the same living standards. Work 20 to 40 percent less and maintain the same lifestyle. Learn the tools. Ask better questions. Get your life back.

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Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!
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Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

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Many “AI helpers” feel like early-stage demos. Then I tested Sintra AI—and it’s the first one that genuinely delivered value for me. Instead of spending hours on complex automations, I turned my knowledge and creator workflow into an AI helper in minutes. Simple build. No coding. No technical setup. Just plug in your expertise. I built one focused on: • AI creator growth • viral content strategy • campaign ideas • outreach + positioning And honestly… It feels like a mini version of me working around the clock. The best part? This is now accessible far beyond developers. Creators. Consultants. Educators. Founders. Anyone with valuable knowledge can package it into an AI helper people can interact with anytime. That’s the shift many people are overlooking. AI won’t only streamline tasks. It will scale expertise. And Sintra makes onboarding refreshingly simple compared to most tools I’ve tried. It feels less like “another chatbot.” More like building a digital extension of your brain. If you’ve been thinking about creating your own AI helper, this is one of the easiest places to start right now. Discount increased to 85% off — use code: BUILD85 Try my helper / Build your own on Sintra:

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Dave Portnoy says he paid $10,000 for an AI clone to narrate his audiobook for him — and it was “actually scary” how dead-on it sounded. His parents couldn’t even tell the difference. Then Audible shut it down, and he had to record every word himself. “I could only do 15 minutes [of recording a day] because it was that hard for me to read it. I hated it. I tried to get out of it. I paid $10,000 to do AI. I had this AI program for it.” “My parents couldn’t tell the difference. And it could literally just scan the book and go word-for-word.” “It was actually scary how it was dead-on. And it could have literally just done every single word in that book.” “[Audible] wouldn’t let me use it... I was told, ‘No, they don’t let you do that.’ So I had to [record the whole book myself]. I hated it. Hated it!”

The Vigilant Fox 🦊

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i spend my days testing ai tools. hundreds of them. but every time someone asked me “which one should i use?” i had no good answer. everything was scattered across bookmarks, notes, random threads so i decided to build one clean place. a marketplace where people could actually find the right ai tool for the job the problem? i’m not a developer. building and hosting something like that felt out of reach then i found Zero it gives your ai access to 14k+ real world services with no api keys, no subscriptions, no setup. your agent just… finds what it needs and uses it four prompts later, my marketplace was built AND live. real url. nothing to configure i didn’t touch a single config file that’s why i used it. i couldn’t have shipped this without it first 10k get $5 free: #Ad
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i spend my days testing ai tools. hundreds of them. but every time someone asked me “which one should i use?” i had no good answer. everything was scattered across bookmarks, notes, random threads so i decided to build one clean place. a marketplace where people could actually find the right ai tool for the job the problem? i’m not a developer. building and hosting something like that felt out of reach then i found Zero it gives your ai access to 14k+ real world services with no api keys, no subscriptions, no setup. your agent just… finds what it needs and uses it four prompts later, my marketplace was built AND live. real url. nothing to configure i didn’t touch a single config file that’s why i used it. i couldn’t have shipped this without it first 10k get $5 free: #Ad

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I built a FREE AI Agent that can browse the web, code websites, and automate tasks WITHOUT any technical setup I literally have my own AI assistant that works 24/7 In this video I'll show you how to easily set it up No coding experience required (Trust me, you want to bookmark this)

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THIS GUY JUST BUILT A DRONE THAT TRACKS TARGETS WITH A LASER USING CLAUDE > No robotics team > No engineering degree > No budget He used Claude Code to write the entire system in Python - a drone that locks onto a target, follows it with a laser and corrects its own aim automatically after every attempt. The AI gets more accurate the longer it runs because it learns from every single shot. He's still improving it and this is only the beginning. You have the same tool he used. The article below is about how to actually use it to its full potential.

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Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below

rari

27,639 görüntüleme • 13 gün önce

Most PMs still think in tasks. Write a PRD. Update JIRA. Review designs. AI native PMs think differently. They think in prompts. .Mike Bal calls it "thinking in prompts" and admits it sounded silly at first. But once you integrate AI into daily work, it becomes natural. Instead of "I need to write a PRD," they think: "What do I need to get done? What are the steps? What are the best tools?" They treat AI as an extension of themselves. Breaking down tasks into instructions and steps. "If you're thinking about AI as like an extension of yourself, you're thinking like, what are the instructions? What are the steps? And if you're reflective, like you probably do that to yourself at some level anyway, you have your inner dialogue, right?" The shift is from task-oriented to process-oriented thinking. It's how you already think internally. AI native PMs just make that process explicit. Lesson: AI native PMs don't just use AI tools. They fundamentally restructure how they think about work, breaking every task into steps and instructions that AI can execute.
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Most PMs still think in tasks. Write a PRD. Update JIRA. Review designs. AI native PMs think differently. They think in prompts. .Mike Bal calls it "thinking in prompts" and admits it sounded silly at first. But once you integrate AI into daily work, it becomes natural. Instead of "I need to write a PRD," they think: "What do I need to get done? What are the steps? What are the best tools?" They treat AI as an extension of themselves. Breaking down tasks into instructions and steps. "If you're thinking about AI as like an extension of yourself, you're thinking like, what are the instructions? What are the steps? And if you're reflective, like you probably do that to yourself at some level anyway, you have your inner dialogue, right?" The shift is from task-oriented to process-oriented thinking. It's how you already think internally. AI native PMs just make that process explicit. Lesson: AI native PMs don't just use AI tools. They fundamentally restructure how they think about work, breaking every task into steps and instructions that AI can execute.

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After building AI workflows for the last 3 years, I realized something. The model is rarely the bottleneck. The bottleneck is giving it the ability to actually do things. Every new capability meant another service, another API key, another billing setup. I spent more time wiring tools together than building. Tried Zero this week, and it genuinely changed that workflow. My AI could discover and use services on demand without me manually setting everything up. That is a much bigger unlock than another benchmark. Worth trying if you build with Claude or ChatGPT. They are giving the first 10,000 users $5 in credits at 👉 #Ad

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138,969 görüntüleme • 1 ay önce

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.
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Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.

Santiago

13,712 görüntüleme • 4 ay önce

Most AI agent products still feel like you need to become an engineer before getting anything done. I spent way too much time messing around with Hermes and OpenClaw before trying Helio, and honestly the biggest difference was the setup experience. With most agent tools, you spend forever configuring workflows, prompts, APIs, and orchestration before the actual work even starts. Helio felt completely different. I gave it a task, left for a while, came back, and the AI teammates had already organized the work and started executing on their own. No complicated setup. No endless configuration. No babysitting agents. It feels less like operating AI tools and more like managing an actual AI workforce.
0:53

Most AI agent products still feel like you need to become an engineer before getting anything done. I spent way too much time messing around with Hermes and OpenClaw before trying Helio, and honestly the biggest difference was the setup experience. With most agent tools, you spend forever configuring workflows, prompts, APIs, and orchestration before the actual work even starts. Helio felt completely different. I gave it a task, left for a while, came back, and the AI teammates had already organized the work and started executing on their own. No complicated setup. No endless configuration. No babysitting agents. It feels less like operating AI tools and more like managing an actual AI workforce.

Manish Kumar Shah

28,242 görüntüleme • 2 ay önce

i asked an AI to break my app. it did. then fixed it. it was breaking the app on purpose and somehow made it better i used the new Replit ⠕ Agent 3 (anyone can try it right now ) i set it up to act like a QA tester told it to click through every page try every button and report anything weird it didn’t just test the app — it used an actual browser logged every step screenshot the UI and flagged two bugs i didn’t even know existed then it rewrote the code to fix them tested it again and sent me a summary like “fixed button logic on checkout flow — confirm working” all without me asking it ran for almost 2 hours autonomously while i did other work this is like a dev + tester rolled into one but without the back and forth i didn’t give it prompts i gave it a goal and that’s just one thing it can do it’s more like assigning work to a teammate not chatting with a bot we can now finally hand off part of our own work and products to AI without being nervous about the outcome you can try it here:
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i asked an AI to break my app. it did. then fixed it. it was breaking the app on purpose and somehow made it better i used the new Replit ⠕ Agent 3 (anyone can try it right now ) i set it up to act like a QA tester told it to click through every page try every button and report anything weird it didn’t just test the app — it used an actual browser logged every step screenshot the UI and flagged two bugs i didn’t even know existed then it rewrote the code to fix them tested it again and sent me a summary like “fixed button logic on checkout flow — confirm working” all without me asking it ran for almost 2 hours autonomously while i did other work this is like a dev + tester rolled into one but without the back and forth i didn’t give it prompts i gave it a goal and that’s just one thing it can do it’s more like assigning work to a teammate not chatting with a bot we can now finally hand off part of our own work and products to AI without being nervous about the outcome you can try it here:

The SamurAI

40,030 görüntüleme • 11 ay önce

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch
58:55

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

376,293 görüntüleme • 5 ay önce

AI is getting incredibly good. The problem is it still gets stuck whenever it needs to interact with the real world. Need to host a website? API keys. Need to send emails? Another service. Need to use external tools? More accounts and more setup. That's exactly why I wanted to try Zero. Zero gives Claude and ChatGPT access to **14,000+ real-world services**, so instead of stopping to ask for integrations, they can actually complete real tasks. I tested it by asking Cursor to build an AI healthcare startup. Within a single workflow it: → built and hosted a complete website → generated the assets it needed → emailed me the live URL → showed the total cost of the entire workflow No API keys. No juggling different services. No complicated setup. Just a prompt → a finished result. If you're building with Claude or ChatGPT, it's definitely worth trying. The first 10,000 users can claim $5 in free credits to get started: Try out now: #Ad
1:14

AI is getting incredibly good. The problem is it still gets stuck whenever it needs to interact with the real world. Need to host a website? API keys. Need to send emails? Another service. Need to use external tools? More accounts and more setup. That's exactly why I wanted to try Zero. Zero gives Claude and ChatGPT access to **14,000+ real-world services**, so instead of stopping to ask for integrations, they can actually complete real tasks. I tested it by asking Cursor to build an AI healthcare startup. Within a single workflow it: → built and hosted a complete website → generated the assets it needed → emailed me the live URL → showed the total cost of the entire workflow No API keys. No juggling different services. No complicated setup. Just a prompt → a finished result. If you're building with Claude or ChatGPT, it's definitely worth trying. The first 10,000 users can claim $5 in free credits to get started: Try out now: #Ad

Abdullah AI

12,775 görüntüleme • 1 ay önce

Is your job actually safe from AI? I’ve been thinking about this a lot. To get some real answers, I'm using Frank AI Researcher to let you talk to an AI agent yourself. It takes 5 minutes. You tell the agent what you do every day and what tech you use. Then, it gives you a "future-proof score" to show how much of your work can be automated right now. I really want to see what you guys find out. Link to talk to the agent:👇
1:19

Is your job actually safe from AI? I’ve been thinking about this a lot. To get some real answers, I'm using Frank AI Researcher to let you talk to an AI agent yourself. It takes 5 minutes. You tell the agent what you do every day and what tech you use. Then, it gives you a "future-proof score" to show how much of your work can be automated right now. I really want to see what you guys find out. Link to talk to the agent:👇

AI Panda

70,835 görüntüleme • 4 ay önce

I think we've been using AI the wrong way. most of us use it to ask for ideas. then we spend the next hour figuring out which tools to use, creating new accounts, switching tabs, and finishing the work manually. I wanted to see what would happen if AI could access the right tool itself. so I asked Zero to find a web hosting capability and help me publish a landing page. instead of searching for the right service, it found the capability it needed and guided the workflow from a single place. that's what impressed me the most. intelligence is no longer the bottleneck. access is. and once your AI can discover the right capabilities when you need them, the entire workflow starts to feel a lot smoother. this feels like a glimpse of where AI assistants are heading. 🚀 #Zero #Codex #AI #Automation #BuildInPublic #Ad
0:51

I think we've been using AI the wrong way. most of us use it to ask for ideas. then we spend the next hour figuring out which tools to use, creating new accounts, switching tabs, and finishing the work manually. I wanted to see what would happen if AI could access the right tool itself. so I asked Zero to find a web hosting capability and help me publish a landing page. instead of searching for the right service, it found the capability it needed and guided the workflow from a single place. that's what impressed me the most. intelligence is no longer the bottleneck. access is. and once your AI can discover the right capabilities when you need them, the entire workflow starts to feel a lot smoother. this feels like a glimpse of where AI assistants are heading. 🚀 #Zero #Codex #AI #Automation #BuildInPublic #Ad

zen.

27,821 görüntüleme • 1 ay önce

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch
35:25

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

193,592 görüntüleme • 4 ay önce

something I’ve wanted for a while: an AI that isn’t just a text box so I built it lets you create a 3D AI character on-chain that lives in your browser it can talk, remember you, show emotions, and use different skills. you can even mint it onchain, so it actually belongs to you it gets its own identity, history, and reputation that no one can take away most AI today can disappear if the app shuts down this one is permanent and yours the future is AI you can see, interact with, and actually own
1:23

something I’ve wanted for a while: an AI that isn’t just a text box so I built it lets you create a 3D AI character on-chain that lives in your browser it can talk, remember you, show emotions, and use different skills. you can even mint it onchain, so it actually belongs to you it gets its own identity, history, and reputation that no one can take away most AI today can disappear if the app shuts down this one is permanent and yours the future is AI you can see, interact with, and actually own

nich

285,544 görüntüleme • 3 ay önce