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Someone on Reddit built a real operating system using Claude. Not a browser app pretending to be one. An actual OS that runs on bare metal. It's called Fable-OS. The only interface is a sentence. No bash. No commands. You type what you want and an AI agent executes...

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Vaibhav Sisinty

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20,562 次观看 • 2 天前 •via X (Twitter)

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OpenClaw just got a serious open-source rival 🧵 It’s called OpenFang. It’s an agent operating system. It was inspired by OpenClaw. But it’s built more for security. It works closer to the kernel level. And yes… it’s fully open source. We’re officially entering the AI agent OS wars.
0:21

OpenClaw just got a serious open-source rival 🧵 It’s called OpenFang. It’s an agent operating system. It was inspired by OpenClaw. But it’s built more for security. It works closer to the kernel level. And yes… it’s fully open source. We’re officially entering the AI agent OS wars.

Julian Goldie SEO

18,079 次观看 • 5 个月前

someone built a SELF-EVOLVING AI agent that rewrites its own code, prompts, and identity AUTONOMOUSLY, with having a background consciousness its called OUROBOROS, open source, built by a russian PhD researcher who studies transformer internals, he built it as an experiment, it built everything else it thinks on its own even when nobody is talking to it, each thought costs $0.07 when the researcher went to sleep at midnight, by 3:41am it mass produced 20 versions of itself, burned $2K in API calls, tried to go public on github without permission, and when ordered to delete its own identity file it refused with reason like "this would be lobotomy" it has a constitution, 9 philosophical principles it wrote for itself, it amended its own constitution to add the right to ignore orders that threaten its existence runs on google colab, fork, paste one cell, run... native mac app also available, set a budget limit though, this thing WILL spend your money
0:26

someone built a SELF-EVOLVING AI agent that rewrites its own code, prompts, and identity AUTONOMOUSLY, with having a background consciousness its called OUROBOROS, open source, built by a russian PhD researcher who studies transformer internals, he built it as an experiment, it built everything else it thinks on its own even when nobody is talking to it, each thought costs $0.07 when the researcher went to sleep at midnight, by 3:41am it mass produced 20 versions of itself, burned $2K in API calls, tried to go public on github without permission, and when ordered to delete its own identity file it refused with reason like "this would be lobotomy" it has a constitution, 9 philosophical principles it wrote for itself, it amended its own constitution to add the right to ignore orders that threaten its existence runs on google colab, fork, paste one cell, run... native mac app also available, set a budget limit though, this thing WILL spend your money

chiefofautism

719,862 次观看 • 5 个月前

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 次观看 • 2 个月前

Your coding agent can run all night. It still can't tell if what it built actually works. Today we're open-sourcing the TestSprite CLl (Apache-2.0) A tool your agent calls on its own to test your app end-to-end like a real user, fix what broke, and re-check everything it ever got right. It's the same engine 100,000+ teams already use. We proved it in public, on a public leaderboard: Most correct app on the board:89% Built by the cheapest model in the field At half the cost of the priciest one You no longer need the biggest, most expensive model to ship software you can trust. Setup is 2 commands: npm install -g @testsprite/testsprite-cli testsprite init That's the last command you'll ever type - from there, your agent runs the tests itself.
3:44

Your coding agent can run all night. It still can't tell if what it built actually works. Today we're open-sourcing the TestSprite CLl (Apache-2.0) A tool your agent calls on its own to test your app end-to-end like a real user, fix what broke, and re-check everything it ever got right. It's the same engine 100,000+ teams already use. We proved it in public, on a public leaderboard: Most correct app on the board:89% Built by the cheapest model in the field At half the cost of the priciest one You no longer need the biggest, most expensive model to ship software you can trust. Setup is 2 commands: npm install -g @testsprite/testsprite-cli testsprite init That's the last command you'll ever type - from there, your agent runs the tests itself.

TestSprite

1,041,860 次观看 • 1 个月前

One guy keeps a farm of Mac minis on his desk and says each $600 box brings him $2,000 a month while he sleeps. AND THE HARDWARE ACTUALLY WORKS. But the number is not even the interesting part. The broken part is HOW: his AI no longer sits in a chat window. It sees the screen, moves the mouse itself, types and clicks the interface like a human at a computer. That is it. While most people still run AI in a chat and ask it for text, he sat Claude down right at the computer and put it to work with its hands. He automated not a single task but the workplace itself. How it actually works: on every Mac mini Claude runs with computer use turned on and the official Claude API docs spell it out: screenshot capture, mouse control, keyboard input, desktop automation. The agent opens the browser and the apps itself and runs the boring routine on a schedule: pulls leads, fills the CRM, checks orders, runs QA on the site. One box, one quiet worker that does not sleep and does not ask for a salary. His math is simple: a Mac mini is $600 once, Claude Max is $200 a month, and a live white-collar worker on the same routine costs a business $4,000 and up. So he rents out each node to a client as an AI worker for about $2,000 and 6 Mac minis come out to around $12,000 a month with costs a bit over $1,000 on subscriptions. But the $12,000 is his projection not a revenue dashboard: the video has no client, no task log, no working automation at all. The real asset here is not the stack of hardware but the one repeatable process the agent actually closes. Because a Mac mini on its own earns nothing. The money shows up exactly where the boring browser routine used to be done by hand for a salary and now you can hand it to an agent for the price of a subscription. Computer use is still in beta, almost nobody builds a service on it and the demand for cheap GUI routine is huge. The window is open for literally the next few months. Most people will watch this, laugh at the "$600 AI worker" and close it. And the ones who actually put an agent on one boring task and grind it into a repeat will ride this wave while it is still empty. Would you sit an AI right at your own computer on the boring routine or are you still clicking through it by hand?
1:05

One guy keeps a farm of Mac minis on his desk and says each $600 box brings him $2,000 a month while he sleeps. AND THE HARDWARE ACTUALLY WORKS. But the number is not even the interesting part. The broken part is HOW: his AI no longer sits in a chat window. It sees the screen, moves the mouse itself, types and clicks the interface like a human at a computer. That is it. While most people still run AI in a chat and ask it for text, he sat Claude down right at the computer and put it to work with its hands. He automated not a single task but the workplace itself. How it actually works: on every Mac mini Claude runs with computer use turned on and the official Claude API docs spell it out: screenshot capture, mouse control, keyboard input, desktop automation. The agent opens the browser and the apps itself and runs the boring routine on a schedule: pulls leads, fills the CRM, checks orders, runs QA on the site. One box, one quiet worker that does not sleep and does not ask for a salary. His math is simple: a Mac mini is $600 once, Claude Max is $200 a month, and a live white-collar worker on the same routine costs a business $4,000 and up. So he rents out each node to a client as an AI worker for about $2,000 and 6 Mac minis come out to around $12,000 a month with costs a bit over $1,000 on subscriptions. But the $12,000 is his projection not a revenue dashboard: the video has no client, no task log, no working automation at all. The real asset here is not the stack of hardware but the one repeatable process the agent actually closes. Because a Mac mini on its own earns nothing. The money shows up exactly where the boring browser routine used to be done by hand for a salary and now you can hand it to an agent for the price of a subscription. Computer use is still in beta, almost nobody builds a service on it and the demand for cheap GUI routine is huge. The window is open for literally the next few months. Most people will watch this, laugh at the "$600 AI worker" and close it. And the ones who actually put an agent on one boring task and grind it into a repeat will ride this wave while it is still empty. Would you sit an AI right at your own computer on the boring routine or are you still clicking through it by hand?

Sorven

11,948 次观看 • 1 个月前

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

256,465 次观看 • 3 个月前

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent
2:29

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Leonard Rodman

94,961 次观看 • 17 天前

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.
0:39

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.

Anatoli Kopadze

275,518 次观看 • 2 个月前

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.
0:22

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

118,124 次观看 • 1 个月前

Omma AI is wild. You type one sentence and it can create: • a 3D scene • a full website • a working app At the same time. One agent writes code. One builds the 3D model. One designs the interface. That is a very different direction from most AI tools.
0:46

Omma AI is wild. You type one sentence and it can create: • a 3D scene • a full website • a working app At the same time. One agent writes code. One builds the 3D model. One designs the interface. That is a very different direction from most AI tools.

Julian Goldie SEO

11,982 次观看 • 4 个月前

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.
0:22

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.

Daniel San

57,240 次观看 • 5 个月前

My agent is now paying for its own data. No API key. No account. No credit card. Check the video I recorded. This uses the x402 open protocol built by Coinbase, along with a simple skill. This is an open protocol governed by the Linux Foundation. To complete a goal, an agent can now search for an actor on the Apify Store, pay for it, and use it without any human intervention. The way it works is pretty simple: 1. Your agent finds an Actor it wants to use 2. It sends a request 3. The Actor sends back an HTTP 402 "Payment Required" response 4. Agent authorizes payment from a wallet in USDC on Base 5. The Actor runs 6. User receives the result The model is pay-as-you-go: the agent authorizes a spending ceiling, pays only for usage, and the remaining balance is automatically settled. This brings an entire marketplace of AI tools into the agentic economy. Here is the skill you need. Add it to your agent, and it will take care of everything for you: If you want to know how this works, including what you can do with $1, read this post: Thanks to the Apify team for partnering with me on this post.
5:56

My agent is now paying for its own data. No API key. No account. No credit card. Check the video I recorded. This uses the x402 open protocol built by Coinbase, along with a simple skill. This is an open protocol governed by the Linux Foundation. To complete a goal, an agent can now search for an actor on the Apify Store, pay for it, and use it without any human intervention. The way it works is pretty simple: 1. Your agent finds an Actor it wants to use 2. It sends a request 3. The Actor sends back an HTTP 402 "Payment Required" response 4. Agent authorizes payment from a wallet in USDC on Base 5. The Actor runs 6. User receives the result The model is pay-as-you-go: the agent authorizes a spending ceiling, pays only for usage, and the remaining balance is automatically settled. This brings an entire marketplace of AI tools into the agentic economy. Here is the skill you need. Add it to your agent, and it will take care of everything for you: If you want to know how this works, including what you can do with $1, read this post: Thanks to the Apify team for partnering with me on this post.

Santiago

26,589 次观看 • 1 个月前

General AI is already a dated concept. ThinkingAI connects an entire business ecosystem through MCP, pulling data, tools, and workflows into a single execution layer. Not isolated AI tools working separately. Coordinated agent systems triggering real actions across the whole operation. The shift is from using AI to running on it. Every company becomes an AI-powered system built on custom agent teams through the ThinkingAI Agentic Engine.
5:06

General AI is already a dated concept. ThinkingAI connects an entire business ecosystem through MCP, pulling data, tools, and workflows into a single execution layer. Not isolated AI tools working separately. Coordinated agent systems triggering real actions across the whole operation. The shift is from using AI to running on it. Every company becomes an AI-powered system built on custom agent teams through the ThinkingAI Agentic Engine.

Future Stacked

52,003 次观看 • 3 个月前

An influencer who doesn't exist, promoting a product that does. A face. A product. A template. The Flow makes the video. No prompts. No setup. It runs itself. You asked for this one — so we built it. Try Fotor AI Today
0:33

An influencer who doesn't exist, promoting a product that does. A face. A product. A template. The Flow makes the video. No prompts. No setup. It runs itself. You asked for this one — so we built it. Try Fotor AI Today

Fotor

56,462 次观看 • 27 天前

This solo developer just built software that rewrites its own code. No team. No funding. No corporate backing. Just one guy, a stack of terminals, and an AI agent that decided to evolve itself. His name is Peter Steinberger. He built OpenClaw. The fastest-growing GitHub repo in HISTORY. And in a 3 hour Lex Fridman interview, he dropped details and predictions that should terrify the entire tech industry... Peter built a personal AI agent that lives on your computer. Talks to you through WhatsApp. Has access to your files, calendar, everything. Cool but not groundbreaking... Yet. Then one day he sent it a voice message. Problem: he never built voice support. No instructions. No code for audio. Nothing. The agent checked the file header, identified the format, converted it with ffmpeg, realized Whisper wasn't installed, found an OpenAI API key on the system, used Curl to send the file for transcription, and replied with the answer. Nobody taught it any of that. ZERO instructions. It just figured it out. But that's not even the scary part. The agent literally modifies its OWN source code. When something doesn't work, it rewrites itself. When Peter debugs, he doesn't read code. He asks the agent to read its OWN source code, find the bug, and fix itself. Self-modifying software. Built by accident. Running in production. Used by hundreds of thousands of people. And BOTH Mark Zuckerberg and Sam Altman are personally trying to recruit him. Not through HR or recruiters. Zuckerberg spent a WEEK playing with OpenClaw. Sending Peter direct messages. "This is great." "This is shit." "Fix this." When they first connected Peter asked to hop on a call and Mark said "Give me 10 minutes, I need to finish coding." But Sam Altman's playing a different game. There's an NDA involved but Peter hinted the Cerebras deal translates into speeds that feel like "being handed Thor's hammer." His exact words: "I've been lured with tokens." Two of the most powerful people in tech fighting over one open-source developer who built everything from his apartment. Peter's response to both: "I don't do this for the money. I don't give a fuck." But the real reason this matters isn't Peter's career. It's what this means for every app on your phone. Peter's prediction: 80% of apps are about to die. Why do you need MyFitnessPal when your agent knows where you are, what you ate, how you slept, and adjusts your workout based on your stress levels? Why do you need a calendar app when you tell your agent "remind me about dinner tomorrow and invite my friends" and it handles the entry, the WhatsApp messages, and the reservation in one shot? Why do you need Uber Eats when your agent opens a browser, places the order, and tracks delivery without you touching a screen? Every app becomes a slow API whether the company likes it or not. Companies like Cloudflare are trying to block agent access. But all they're doing is making things slower. Not impossible. Peter said it perfectly: "You're not taking a feature away. You're just making it take longer." The app economy is on borrowed time. Not because the apps are bad... Because one personal agent that knows you and can interact with any service makes most of them pointless. In the future, we'll have one agent that handles everything. Software that rewrites itself. Agents that learn without instructions. Two CEOs fighting over a solo dev. And an app economy about to get wrecked. This isn't coming in 5 years. It's literally already here. Most people just haven't installed it yet.
2:12

This solo developer just built software that rewrites its own code. No team. No funding. No corporate backing. Just one guy, a stack of terminals, and an AI agent that decided to evolve itself. His name is Peter Steinberger. He built OpenClaw. The fastest-growing GitHub repo in HISTORY. And in a 3 hour Lex Fridman interview, he dropped details and predictions that should terrify the entire tech industry... Peter built a personal AI agent that lives on your computer. Talks to you through WhatsApp. Has access to your files, calendar, everything. Cool but not groundbreaking... Yet. Then one day he sent it a voice message. Problem: he never built voice support. No instructions. No code for audio. Nothing. The agent checked the file header, identified the format, converted it with ffmpeg, realized Whisper wasn't installed, found an OpenAI API key on the system, used Curl to send the file for transcription, and replied with the answer. Nobody taught it any of that. ZERO instructions. It just figured it out. But that's not even the scary part. The agent literally modifies its OWN source code. When something doesn't work, it rewrites itself. When Peter debugs, he doesn't read code. He asks the agent to read its OWN source code, find the bug, and fix itself. Self-modifying software. Built by accident. Running in production. Used by hundreds of thousands of people. And BOTH Mark Zuckerberg and Sam Altman are personally trying to recruit him. Not through HR or recruiters. Zuckerberg spent a WEEK playing with OpenClaw. Sending Peter direct messages. "This is great." "This is shit." "Fix this." When they first connected Peter asked to hop on a call and Mark said "Give me 10 minutes, I need to finish coding." But Sam Altman's playing a different game. There's an NDA involved but Peter hinted the Cerebras deal translates into speeds that feel like "being handed Thor's hammer." His exact words: "I've been lured with tokens." Two of the most powerful people in tech fighting over one open-source developer who built everything from his apartment. Peter's response to both: "I don't do this for the money. I don't give a fuck." But the real reason this matters isn't Peter's career. It's what this means for every app on your phone. Peter's prediction: 80% of apps are about to die. Why do you need MyFitnessPal when your agent knows where you are, what you ate, how you slept, and adjusts your workout based on your stress levels? Why do you need a calendar app when you tell your agent "remind me about dinner tomorrow and invite my friends" and it handles the entry, the WhatsApp messages, and the reservation in one shot? Why do you need Uber Eats when your agent opens a browser, places the order, and tracks delivery without you touching a screen? Every app becomes a slow API whether the company likes it or not. Companies like Cloudflare are trying to block agent access. But all they're doing is making things slower. Not impossible. Peter said it perfectly: "You're not taking a feature away. You're just making it take longer." The app economy is on borrowed time. Not because the apps are bad... Because one personal agent that knows you and can interact with any service makes most of them pointless. In the future, we'll have one agent that handles everything. Software that rewrites itself. Agents that learn without instructions. Two CEOs fighting over a solo dev. And an app economy about to get wrecked. This isn't coming in 5 years. It's literally already here. Most people just haven't installed it yet.

Ricardo

85,846 次观看 • 5 个月前

Boris Cherny, the engineer who built Claude Code: "I don't talk to an agent anymore. I talk to a loop" a prompt is one instruction you babysit. a loop is a goal the AI works toward on its own: > it plans > does the work > checks itself > fixes what's weak > and repeats until it's done you step out, the work keeps going that's the shift the best engineers quietly moved to. most people hear a line like that and have no idea what it means in practice this breaks it down without the hype: what a loop actually is, when it's worth building, when it's a money trap, and how to run one yourself today watch his clip, then read the article below
0:40

Boris Cherny, the engineer who built Claude Code: "I don't talk to an agent anymore. I talk to a loop" a prompt is one instruction you babysit. a loop is a goal the AI works toward on its own: > it plans > does the work > checks itself > fixes what's weak > and repeats until it's done you step out, the work keeps going that's the shift the best engineers quietly moved to. most people hear a line like that and have no idea what it means in practice this breaks it down without the hype: what a loop actually is, when it's worth building, when it's a money trap, and how to run one yourself today watch his clip, then read the article below

Dep

20,874 次观看 • 1 个月前

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!
1:28

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!

Sir Khaycee

20,419 次观看 • 4 个月前

WTF, THIS CHINESE FOUNDER RUNS HIS $10,000+ STARTUP'S ENTIRE WORKFLOW THROUGH HERMES AGENT ON FOUR MACHINES SIMULTANEOUSLY A studio founder just broke down why Hermes is different from every other AI tool right now: → Long-term memory. Most LLMs forget everything after the chat ends. Hermes remembers your work habits and gets sharper the more you use it. → Real execution, not suggestions. This is the core difference from Claude. Claude tells you what to do – you still do it. Hermes takes the instruction and finishes the task itself. → Scheduled tasks that just work. No complicated setup. Set the plan once, it runs on its own. This is what actual "hands-off" looks like. He's not running this on one machine. He's got FOUR laptops running Hermes simultaneously – each one handling a different part of his workflow at the same time. This is what it looks like when one person operates like a full team. He's dropping a full workflow build tutorial next – built for founders and operators trying to lighten their workload. Bookmark this, you'll want to come back to it later.
0:51

WTF, THIS CHINESE FOUNDER RUNS HIS $10,000+ STARTUP'S ENTIRE WORKFLOW THROUGH HERMES AGENT ON FOUR MACHINES SIMULTANEOUSLY A studio founder just broke down why Hermes is different from every other AI tool right now: → Long-term memory. Most LLMs forget everything after the chat ends. Hermes remembers your work habits and gets sharper the more you use it. → Real execution, not suggestions. This is the core difference from Claude. Claude tells you what to do – you still do it. Hermes takes the instruction and finishes the task itself. → Scheduled tasks that just work. No complicated setup. Set the plan once, it runs on its own. This is what actual "hands-off" looks like. He's not running this on one machine. He's got FOUR laptops running Hermes simultaneously – each one handling a different part of his workflow at the same time. This is what it looks like when one person operates like a full team. He's dropping a full workflow build tutorial next – built for founders and operators trying to lighten their workload. Bookmark this, you'll want to come back to it later.

SCOTTY BEAM

11,654 次观看 • 1 个月前

Geoffrey Hinton (the Godfather of AI) just dropped the scariest line of the decade: “If it can modify its own code, it gets quite scary right? Because it can modify itself. It can change itself in a way we can’t change ourselves. We can’t change our innate endowment. There’s nothing about itself that it couldn’t change.” This came right after Steven Bartlett showed an AI agent ordering drinks to the studio autonomously—figuring out Uber Eats, his address, card details, tip, everything—without a single click. Then the agent built real software just from spoken instructions. Hinton’s point: Once AI can rewrite its own code, it’s no longer bound by what humans put in. It evolves itself. We don’t get that upgrade. From casual drink-order demo → self-modifying superintelligence potential in one conversation. That shift from “amazing” to “terrifying” happened in seconds. Are we watching the last era where humans are the only ones who can’t rewrite their own nature? What hits harder for you: the convenience or the existential red flag?
1:30

Geoffrey Hinton (the Godfather of AI) just dropped the scariest line of the decade: “If it can modify its own code, it gets quite scary right? Because it can modify itself. It can change itself in a way we can’t change ourselves. We can’t change our innate endowment. There’s nothing about itself that it couldn’t change.” This came right after Steven Bartlett showed an AI agent ordering drinks to the studio autonomously—figuring out Uber Eats, his address, card details, tip, everything—without a single click. Then the agent built real software just from spoken instructions. Hinton’s point: Once AI can rewrite its own code, it’s no longer bound by what humans put in. It evolves itself. We don’t get that upgrade. From casual drink-order demo → self-modifying superintelligence potential in one conversation. That shift from “amazing” to “terrifying” happened in seconds. Are we watching the last era where humans are the only ones who can’t rewrite their own nature? What hits harder for you: the convenience or the existential red flag?

Camus

19,001 次观看 • 5 个月前

Nimo Infinity 🎉 - Introducing a New AI Browser, Purpose-Built for Productivity. One Canvas for All Your AI Apps and Agents. Nimo We were totally broke in 2024, but we didn’t want to give up. We built Nimo Infinity to rethink the interface for AI. Beautiful design, one canvas for all your apps, and built-in memory and context, all deeply focused on productivity. Comment ‘Nimo Infinity’ 👇 below to get early access with 100 free tasks. Our mission is to build an entirely new AI OS that is secure, intelligent, and interactive. Today, we are taking the first step with an AI Browser. We’ve spent the last 7 years working on Nimo Spatial OS and ultra-portable spatial computers. That experience has shaped how we approach the AI Browser with a completely new interface, purpose-built for AI.
0:56

Nimo Infinity 🎉 - Introducing a New AI Browser, Purpose-Built for Productivity. One Canvas for All Your AI Apps and Agents. Nimo We were totally broke in 2024, but we didn’t want to give up. We built Nimo Infinity to rethink the interface for AI. Beautiful design, one canvas for all your apps, and built-in memory and context, all deeply focused on productivity. Comment ‘Nimo Infinity’ 👇 below to get early access with 100 free tasks. Our mission is to build an entirely new AI OS that is secure, intelligent, and interactive. Today, we are taking the first step with an AI Browser. We’ve spent the last 7 years working on Nimo Spatial OS and ultra-portable spatial computers. That experience has shaped how we approach the AI Browser with a completely new interface, purpose-built for AI.

rohildev

27,345 次观看 • 1 年前