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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 views • 3 months ago •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.
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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.

Julian Goldie SEO

18,079 views • 8 months ago

On Omarchy for the last few days. Wanted a dock, so I built it with an agent. Wanted an infinite canvas, so I built it with an agent. Want a new file manager, being built now. Yes it's not super polished yet. But there is no going back to a locked down OS. You build your own apps. You build your own OS. PS: Posting from a MBPro because it's 2:43AM and I can't begin to learn Kdenlive now.
0:30

On Omarchy for the last few days. Wanted a dock, so I built it with an agent. Wanted an infinite canvas, so I built it with an agent. Want a new file manager, being built now. Yes it's not super polished yet. But there is no going back to a locked down OS. You build your own apps. You build your own OS. PS: Posting from a MBPro because it's 2:43AM and I can't begin to learn Kdenlive now.

Zsolt Kacso

122,103 views • 2 months ago

You must be tired of seeing game demos built with AI..so how about an ONLINE MULTIPLAYER game that runs fully in the browser. It includes: - Online matchmaking - Custom SFX Engine - Custom classes - Killstreaks - In-game chat Angelo 🇵🇷 built it over the past couple of weeks, with GPT-6 Astra helping bring it across the finish line by building the map from scratch Built entirely through prompting and runs on Railway (Sound on)
4:20

You must be tired of seeing game demos built with AI..so how about an ONLINE MULTIPLAYER game that runs fully in the browser. It includes: - Online matchmaking - Custom SFX Engine - Custom classes - Killstreaks - In-game chat Angelo 🇵🇷 built it over the past couple of weeks, with GPT-6 Astra helping bring it across the finish line by building the map from scratch Built entirely through prompting and runs on Railway (Sound on)

Mahmoud

23,858 views • 2 months ago

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

720,665 views • 8 months ago

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

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

Rohit

120,600 views • 5 months ago

THIS IS FCK***** DANGEROUS THIS GUY JUST FOUND AN OPEN SOURCE AI THAT MAKES ENTIRE YOUTUBE VIDEOS FROM START TO FINISH It’s basically a full production team in one AI. → Give it a YouTube channel idea and one goal → It writes the script → Creates the storyboards → Finds the footage → Generates the visuals and voiceovers → Edits everything → Renders the final video No editor. No production team. No scriptwriter. Just AI. It also comes with 100+ production tools, 700+ agent skills, reference based creation, and built in live web research. He gave it a channel idea and it generated high quality videos on its own. Bookmark this so you have it when you’re ready to test it.
0:37

THIS IS FCK***** DANGEROUS THIS GUY JUST FOUND AN OPEN SOURCE AI THAT MAKES ENTIRE YOUTUBE VIDEOS FROM START TO FINISH It’s basically a full production team in one AI. → Give it a YouTube channel idea and one goal → It writes the script → Creates the storyboards → Finds the footage → Generates the visuals and voiceovers → Edits everything → Renders the final video No editor. No production team. No scriptwriter. Just AI. It also comes with 100+ production tools, 700+ agent skills, reference based creation, and built in live web research. He gave it a channel idea and it generated high quality videos on its own. Bookmark this so you have it when you’re ready to test it.

Force🦅

53,867 views • 8 days ago

ONE PROMPT AND ONE BUTTON NOW BUILDS A FULL SYSTEM, DATABASE, BACKEND, AND FRONTEND INCLUDED Most people still picture AI as something you ask questions to, then copy the answer into your own code by hand. A builder wired Claude into Obsidian through MCP instead, so a single written prompt becomes the actual spec for the system. Claude breaks that prompt into database schema, UI components, and API endpoints on its own, then writes, runs, and fixes the code until it actually works. MCP is what makes this possible, it lets the agent see the filesystem, installed packages, and running services before it starts building anything. The output isn't a code snippet sitting in a chat window, it's a live local app running on its own server, ready to open in a browser. What used to need a backend developer, a DevOps engineer, and two weeks now finishes in under fifteen minutes. See the full architecture and prompt structure below👇
0:21

ONE PROMPT AND ONE BUTTON NOW BUILDS A FULL SYSTEM, DATABASE, BACKEND, AND FRONTEND INCLUDED Most people still picture AI as something you ask questions to, then copy the answer into your own code by hand. A builder wired Claude into Obsidian through MCP instead, so a single written prompt becomes the actual spec for the system. Claude breaks that prompt into database schema, UI components, and API endpoints on its own, then writes, runs, and fixes the code until it actually works. MCP is what makes this possible, it lets the agent see the filesystem, installed packages, and running services before it starts building anything. The output isn't a code snippet sitting in a chat window, it's a live local app running on its own server, ready to open in a browser. What used to need a backend developer, a DevOps engineer, and two weeks now finishes in under fifteen minutes. See the full architecture and prompt structure below👇

wast3

20,348 views • 3 months ago

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.
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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

1,042,976 views • 4 months ago

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 views • 3 months ago

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 views • 6 months ago

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 🚀

123,101 views • 5 months ago

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

276,366 views • 6 months ago

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 views • 7 months ago

Introducing Octopus OS, a reinvented agent harness. Agents can edit their own harness in the middle of a task, on their own. Unlike Harness² and similar projects, self-improvement isn’t a separate stage bolted onto the agent. The whole architecture is just the environment they work in. Over 8 months, chasing self-improvement that feels truly native, I dropped the usual agent loop entirely and designed a new architecture that works a lot like a real operating system, with a 45k-line Rust kernel holding it all together. The system state lives in JSON files: goals act as real environments where agents work, along with inputs, outputs, and custom triggers they respond to. Agents can edit all of it while they work inside. It was also built as a product from day one, not a research demo. The full desktop app took 3 months, built on top of the Codex source. The client already works, and you can use it today like any other agent, Codex included. And it’s open source.
0:34

Introducing Octopus OS, a reinvented agent harness. Agents can edit their own harness in the middle of a task, on their own. Unlike Harness² and similar projects, self-improvement isn’t a separate stage bolted onto the agent. The whole architecture is just the environment they work in. Over 8 months, chasing self-improvement that feels truly native, I dropped the usual agent loop entirely and designed a new architecture that works a lot like a real operating system, with a 45k-line Rust kernel holding it all together. The system state lives in JSON files: goals act as real environments where agents work, along with inputs, outputs, and custom triggers they respond to. Agents can edit all of it while they work inside. It was also built as a product from day one, not a research demo. The full desktop app took 3 months, built on top of the Codex source. The client already works, and you can use it today like any other agent, Codex included. And it’s open source.

vechen

170,329 views • 14 days ago

I can't believe this is free. 🦊 Hermes just shipped its own browser that doesn't look like a bot. It's called Camofox, an official Nous Research plugin, and it runs on your own machine. ▸ Fingerprint spoofing built in, on a Firefox fork ▸ Shows up as everyday Firefox, not a headless bot ▸ Its own browser in the background, so yours stays free ▸ Search, click, type, scroll and read pages ▸ Keeps your logins, one profile per Hermes profile ▸ No cloud browser, it all runs locally ▸ Already using Camofox? Nothing changes Your machine. Your logins. Hermes in disguise.
0:29

I can't believe this is free. 🦊 Hermes just shipped its own browser that doesn't look like a bot. It's called Camofox, an official Nous Research plugin, and it runs on your own machine. ▸ Fingerprint spoofing built in, on a Firefox fork ▸ Shows up as everyday Firefox, not a headless bot ▸ Its own browser in the background, so yours stays free ▸ Search, click, type, scroll and read pages ▸ Keeps your logins, one profile per Hermes profile ▸ No cloud browser, it all runs locally ▸ Already using Camofox? Nothing changes Your machine. Your logins. Hermes in disguise.

Hermes Release Watch

67,345 views • 14 days ago

THIS IS F**KING DANGEROUS FOUND AN AI RED TEAM THAT NEEDS ZERO HUMAN INPUT PentAGI. Scanner —> Exploiter —> Reporter —> three agents, working as a swarm, sharing results in real time. One does recon, another finds and runs the actual exploits, a third writes the professional vulnerability report at the end. FULLY AUTONOMOUS —> plans its own steps, executes through terminal, browser, and code editor, monitors its own progress. Runs entirely inside sandboxed Docker, integrated with 20+ real security tools (Nmap, Metasploit, SQLmap, and more). It has over 22k STARS on GitHub MIT licensed, self hostable —> free if you run it on local models, cloud LLM costs only if you plug in OpenAI or Claude. The tradeoff nobody skips past: this drops the cost of serious security testing for companies who couldn't afford it before and puts the same capability in everyone's hands. Cybersecurity is genuinely becoming AI vs AI. Authorized security testing only. Using this on systems you don't own is illegal. REPO BELOW
0:23

THIS IS F**KING DANGEROUS FOUND AN AI RED TEAM THAT NEEDS ZERO HUMAN INPUT PentAGI. Scanner —> Exploiter —> Reporter —> three agents, working as a swarm, sharing results in real time. One does recon, another finds and runs the actual exploits, a third writes the professional vulnerability report at the end. FULLY AUTONOMOUS —> plans its own steps, executes through terminal, browser, and code editor, monitors its own progress. Runs entirely inside sandboxed Docker, integrated with 20+ real security tools (Nmap, Metasploit, SQLmap, and more). It has over 22k STARS on GitHub MIT licensed, self hostable —> free if you run it on local models, cloud LLM costs only if you plug in OpenAI or Claude. The tradeoff nobody skips past: this drops the cost of serious security testing for companies who couldn't afford it before and puts the same capability in everyone's hands. Cybersecurity is genuinely becoming AI vs AI. Authorized security testing only. Using this on systems you don't own is illegal. REPO BELOW

MAX

38,475 views • 2 months ago

I BUILT GROK BOT SECOND BRAIN 26 agents, one core, running off a single rule my own memory never followed: capture everything, forget nothing, connect it before you even ask. A voice note on a coffee walk gets split into a task and an idea in under 6 seconds - the core already knows the difference between something you have to do and something you just thought of. An idea from three weeks ago gets linked to the one you just had, automatically, before you remember you'd had it the first time. That visual -the rotating profile, the 26 glowing lines firing out through the skull, each one labeled with the agent running it - isn't a rendered mockup. It's the actual runtime. Every line is a live agent doing its job in real time, right now, in your browser. This isn't a note app. Note apps hold what you give them. This is a nervous system for everything your brain was never built to hold onto in the first place. Full build - the 26 agents, how memory actually persists, what it costs to set one up for real - is in the article.
0:20

I BUILT GROK BOT SECOND BRAIN 26 agents, one core, running off a single rule my own memory never followed: capture everything, forget nothing, connect it before you even ask. A voice note on a coffee walk gets split into a task and an idea in under 6 seconds - the core already knows the difference between something you have to do and something you just thought of. An idea from three weeks ago gets linked to the one you just had, automatically, before you remember you'd had it the first time. That visual -the rotating profile, the 26 glowing lines firing out through the skull, each one labeled with the agent running it - isn't a rendered mockup. It's the actual runtime. Every line is a live agent doing its job in real time, right now, in your browser. This isn't a note app. Note apps hold what you give them. This is a nervous system for everything your brain was never built to hold onto in the first place. Full build - the 26 agents, how memory actually persists, what it costs to set one up for real - is in the article.

Aleiah

259,802 views • 2 months ago

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

26,981 views • 4 months ago

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

57,445 views • 8 months ago

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

56,701 views • 4 months ago