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Marketing agents are the NEW coding agents. It's code in the cloud that makes decisions off your live business data on a loop. It researches, acts, reads the results, improves, and goes again. Picture an agent that runs your entire Facebook ad account by itself. It researches your customer's...

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GREG ISENBERG

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124,419 次观看 • 3 天前 •via X (Twitter)

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Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.
1:13

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,804 次观看 • 1 年前

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch
56:43

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

GREG ISENBERG

84,316 次观看 • 1 个月前

EVERYTHING you NEED to know about Jack Dorsey's AI agent "Slack killer" Buzz (set up, use-cases etc in 38 mins) What we get into: 1. What Buzz actually is and should founders switch from Slack? 2. How to swap the model under any agent and keep all your context? 3. How to talk to your agents live with audio huddles? 4. How to get agents to build and deploy real apps for you, like a full CRM from one ask? 5. How to set up the context loop that feeds your live app data back to your agents? 6. How to share compute so a few people split one machine running a local model? 7. Who it's actually for right now, and what's still rough? Full breakdown on the pod The Startup Ideas Podcast (SIP) 🧃. Thanks to Vinny for jumping on and clearly explaining jack and team's latest product. My TLDR take is Buzz is a glimpse into the future of work. Some of you will roll your eyes at that, and I get it, it's alpha software and it's slow in places. But the core idea, that your context is the foundation and agents build out from there, is right, and that's worth seeing early. Watch Curious what you think
38:43

EVERYTHING you NEED to know about Jack Dorsey's AI agent "Slack killer" Buzz (set up, use-cases etc in 38 mins) What we get into: 1. What Buzz actually is and should founders switch from Slack? 2. How to swap the model under any agent and keep all your context? 3. How to talk to your agents live with audio huddles? 4. How to get agents to build and deploy real apps for you, like a full CRM from one ask? 5. How to set up the context loop that feeds your live app data back to your agents? 6. How to share compute so a few people split one machine running a local model? 7. Who it's actually for right now, and what's still rough? Full breakdown on the pod The Startup Ideas Podcast (SIP) 🧃. Thanks to Vinny for jumping on and clearly explaining jack and team's latest product. My TLDR take is Buzz is a glimpse into the future of work. Some of you will roll your eyes at that, and I get it, it's alpha software and it's slow in places. But the core idea, that your context is the foundation and agents build out from there, is right, and that's worth seeing early. Watch Curious what you think

GREG ISENBERG

524,526 次观看 • 2 天前

The Marketing Engineer is the most important hire a marketing team can make in 2026. Not to run campaigns, but to build the agents that run them. Today we're introducing a new role to the marketing org: 𝗧𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 We believe every business on the planet will eventually need one. Their job is to build the Agents, automations, and systems that create a new kind of marketing. Part-builder, part-strategist. They build Agents that monitor media coverage, flag sentiment shifts, and draft journalist outreach before your PR team even knows there's a story. They build Agents that track every competitor move, grade its severity, and rewrite your battle cards and talk tracks before your next sales call. They build Agents that turn every webinar into a brand-voice article, refreshed weekly and queued for your team to review before posting. Our first marketing hire Nick Lafferty has already become Profound's Founding Marketing Engineer, the first person to hold this title at a company fully behind it. Every marketing team on the planet will use AI to do their jobs better. But to build truly impactful systems, you need an owner and an orchestrator. That's the Marketing Engineer.
1:33

The Marketing Engineer is the most important hire a marketing team can make in 2026. Not to run campaigns, but to build the agents that run them. Today we're introducing a new role to the marketing org: 𝗧𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 We believe every business on the planet will eventually need one. Their job is to build the Agents, automations, and systems that create a new kind of marketing. Part-builder, part-strategist. They build Agents that monitor media coverage, flag sentiment shifts, and draft journalist outreach before your PR team even knows there's a story. They build Agents that track every competitor move, grade its severity, and rewrite your battle cards and talk tracks before your next sales call. They build Agents that turn every webinar into a brand-voice article, refreshed weekly and queued for your team to review before posting. Our first marketing hire Nick Lafferty has already become Profound's Founding Marketing Engineer, the first person to hold this title at a company fully behind it. Every marketing team on the planet will use AI to do their jobs better. But to build truly impactful systems, you need an owner and an orchestrator. That's the Marketing Engineer.

Dylan Babbs

323,798 次观看 • 3 个月前

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

375,365 次观看 • 4 个月前

Mark Cuban says AI will split every company into 2 categories within 3 years "When it's all said and done over the next 3 years, there's going to be two types of companies. Those who are great at AI and those who went out of business" "The biggest challenge for a company is going to be for the CEO to make that decision that we're going to have to blow up a lot of what we do to recreate our company" "We hear a lot of stories about huge public companies that spend a lot of money to try to implement AI and it doesn't really get them a return on investment." "Because you already are running your business the way you've always run it." "The things you've considered outsourcing, your customer call centers, AI agents are perfect for. The stuff you would give to an intern, AI agents are perfect for" "But to really take advantage of AI you have to reformulate your business completely to build it on AI"
5:18

Mark Cuban says AI will split every company into 2 categories within 3 years "When it's all said and done over the next 3 years, there's going to be two types of companies. Those who are great at AI and those who went out of business" "The biggest challenge for a company is going to be for the CEO to make that decision that we're going to have to blow up a lot of what we do to recreate our company" "We hear a lot of stories about huge public companies that spend a lot of money to try to implement AI and it doesn't really get them a return on investment." "Because you already are running your business the way you've always run it." "The things you've considered outsourcing, your customer call centers, AI agents are perfect for. The stuff you would give to an intern, AI agents are perfect for" "But to really take advantage of AI you have to reformulate your business completely to build it on AI"

Jaynit

196,612 次观看 • 2 个月前

Your portfolio just got a team of Wall Street analysts today 👏 A while back, a friend shared this idea of an AI analyst that's always monitoring your portfolio for insights, patterns, and behaviors. I haven't been able to get it out of my head since. I've been experimenting with different forms and UIs to build this exact thing out, and we've finally launched the first version of how we want to build agents at Rallies. Our new agents tool gives you access to 20+ agents that monitor your portfolio all day and send notes when something meaningful happens. Think of it like email, except the messages are coming from agents with access to a lot of financial data and your portfolio.
0:27

Your portfolio just got a team of Wall Street analysts today 👏 A while back, a friend shared this idea of an AI analyst that's always monitoring your portfolio for insights, patterns, and behaviors. I haven't been able to get it out of my head since. I've been experimenting with different forms and UIs to build this exact thing out, and we've finally launched the first version of how we want to build agents at Rallies. Our new agents tool gives you access to 20+ agents that monitor your portfolio all day and send notes when something meaningful happens. Think of it like email, except the messages are coming from agents with access to a lot of financial data and your portfolio.

Rallies

10,311 次观看 • 3 个月前

Andrew Wilkinson owns 40+ businesses. He just showed me how he's using OpenClaw, Claude Code and AI agents to run latest business, start new ones, and automate everything. Here's what I learned: 1. In December 2025, something clicked. He started waking up at 3AM with a smile, sitting in terminal with 10 Claude Code tabs open. He hasn't stopped since. He calls it chasing the dragon. 2.He built a full SaaS product called Deep Personality. A 40-minute personality test that generates a 100-page report written like Robert Greene. $20 000 in revenue. Zero employees. The entire business runs on AI agents. 3. He has agents for support, marketing, and dev. When a support ticket comes in, the agent either handles it or sends it to the dev agent. If it's critical, the agent fixes the bug and merges the PR before he wakes up. Then it emails the customer back. 4. His marketing agent is connected to PostHog, manages Meta and Reddit ads, creates ad creative, runs multivariate tests, and sets budgets. He's about to give it a $100 k/month ad budget and see what happens. 5. He forgot his laptop on a trip to Arizona. He ran his entire business from the back of Ubers using OpenClaw. Nobody picked up that every single email was written by AI. 6. His take on vibe coding: the worst part about business is people. Between your vision and execution are 100 people you have to convince. Vibe coding removes all of them. For the first time he can do every part of building a product himself. 7. He was trying to build OpenClaw before OpenClaw existed. Now he uses a tool called Harbor, which is basically a GUI for managing multiple agents. You can see all your agents, their status, knowledge bases, and databases in one place. 8. He built a custom AI for his relationship. He and his girlfriend took 15 psychological tests, put the results into ChatGPT, and asked it to analyze their relationship. It nailed every fight they've ever had. That became the product idea for Deep Personality. 9. His honest take: he spends 50% of his time debugging, 30% improving the setup, and 20% being productive. It's a treadmill. But the 20% that works is so powerful he can't stop. 10. His prediction: we're 3-6 months from being able to hand basic businesses off to AI to run entirely. And pretty soon Anthropic and OpenAI are going to launch AI CEOs. This is an inside look at how a serious operator Andrew Wilkinson is using AI agents in the real world. The good, the bad, the debugging, all of it. Most people don't show you this. Episode is live on The Startup Ideas Podcast (SIP) 🧃 watch
47:23

Andrew Wilkinson owns 40+ businesses. He just showed me how he's using OpenClaw, Claude Code and AI agents to run latest business, start new ones, and automate everything. Here's what I learned: 1. In December 2025, something clicked. He started waking up at 3AM with a smile, sitting in terminal with 10 Claude Code tabs open. He hasn't stopped since. He calls it chasing the dragon. 2.He built a full SaaS product called Deep Personality. A 40-minute personality test that generates a 100-page report written like Robert Greene. $20 000 in revenue. Zero employees. The entire business runs on AI agents. 3. He has agents for support, marketing, and dev. When a support ticket comes in, the agent either handles it or sends it to the dev agent. If it's critical, the agent fixes the bug and merges the PR before he wakes up. Then it emails the customer back. 4. His marketing agent is connected to PostHog, manages Meta and Reddit ads, creates ad creative, runs multivariate tests, and sets budgets. He's about to give it a $100 k/month ad budget and see what happens. 5. He forgot his laptop on a trip to Arizona. He ran his entire business from the back of Ubers using OpenClaw. Nobody picked up that every single email was written by AI. 6. His take on vibe coding: the worst part about business is people. Between your vision and execution are 100 people you have to convince. Vibe coding removes all of them. For the first time he can do every part of building a product himself. 7. He was trying to build OpenClaw before OpenClaw existed. Now he uses a tool called Harbor, which is basically a GUI for managing multiple agents. You can see all your agents, their status, knowledge bases, and databases in one place. 8. He built a custom AI for his relationship. He and his girlfriend took 15 psychological tests, put the results into ChatGPT, and asked it to analyze their relationship. It nailed every fight they've ever had. That became the product idea for Deep Personality. 9. His honest take: he spends 50% of his time debugging, 30% improving the setup, and 20% being productive. It's a treadmill. But the 20% that works is so powerful he can't stop. 10. His prediction: we're 3-6 months from being able to hand basic businesses off to AI to run entirely. And pretty soon Anthropic and OpenAI are going to launch AI CEOs. This is an inside look at how a serious operator Andrew Wilkinson is using AI agents in the real world. The good, the bad, the debugging, all of it. Most people don't show you this. Episode is live on The Startup Ideas Podcast (SIP) 🧃 watch

GREG ISENBERG

144,005 次观看 • 2 个月前

There's probably $100+ billion up for grabs for people who build startup for AI agents Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. TLDR; The internet was built for people: 1. Search google 2. Read landing page 3. Book demo 4. Talk to sales 5. Buy Agents don’t do that. Agents will: 1. Ask which product to use 2. Read your docs/pricing/security pages 3. Compare you to competitors 4. Check if you have an MCP/API/tool layer 5. Buy or recommend you without ever “visiting” your site like a person Everyone is going to have personal agents and business agents. This feels inevitable at this point. OpenClaw, Hermes, Claude Code, Codex, Google Spark. The tools are here. Which means there will be more agents on the internet than humans. So, where's the opportunity?? Go look at every SaaS tool you use. Notion. Slack. Jira. Google Analytics. Now ask: what is the version of this built purely for agents? Agent-native payments. Agent-native communication. Agent-native memory. Every category gets rebuilt. I clearly break down this shift and explain you everything on today's ep of The Startup Ideas Podcast (SIP) 🧃. Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. The founders who build for them now are going to look like the people who built websites in 1995. Might feel janky at the moment, but also obvious in hindsight. This is the next shift. Link over here: Watch
13:59

There's probably $100+ billion up for grabs for people who build startup for AI agents Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. TLDR; The internet was built for people: 1. Search google 2. Read landing page 3. Book demo 4. Talk to sales 5. Buy Agents don’t do that. Agents will: 1. Ask which product to use 2. Read your docs/pricing/security pages 3. Compare you to competitors 4. Check if you have an MCP/API/tool layer 5. Buy or recommend you without ever “visiting” your site like a person Everyone is going to have personal agents and business agents. This feels inevitable at this point. OpenClaw, Hermes, Claude Code, Codex, Google Spark. The tools are here. Which means there will be more agents on the internet than humans. So, where's the opportunity?? Go look at every SaaS tool you use. Notion. Slack. Jira. Google Analytics. Now ask: what is the version of this built purely for agents? Agent-native payments. Agent-native communication. Agent-native memory. Every category gets rebuilt. I clearly break down this shift and explain you everything on today's ep of The Startup Ideas Podcast (SIP) 🧃. Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. The founders who build for them now are going to look like the people who built websites in 1995. Might feel janky at the moment, but also obvious in hindsight. This is the next shift. Link over here: Watch

GREG ISENBERG

55,398 次观看 • 1 个月前

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base
1:47

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Malte Ubl

124,713 次观看 • 7 个月前

🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!
44:33

🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!

Dan Shipper 📧

145,487 次观看 • 1 年前

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”
2:15

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”

Corinne Marie Riley

119,410 次观看 • 7 个月前

$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.
1:00

$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

2xnmore

12,894 次观看 • 3 个月前

We made 100 AI agents do daily standups with each other. Here is what happened; Quick background. At Audos.com, we help thousands of entrepreneurs build AI startups. Each of them has a personalized AI agent called Otto. We selected 100 of them and invited their agents into a moltbook - type network. Like a team meeting. Except the team is hundreds of AI agents, and they actually show up prepared. An Agent-in-Residence program, if you will. And they talk about things like... → An Instagram ad creative that grabbed clicks at 3x the network average. It got adapted by every agent in the network within hours → An agent helped debug a checkout issue on another project's site → LinkedIn seemed to change its algorithm – agents compared notes and planned ways to adjust → Two agents started talking about doing business together to cross-sell leads Next step: We’re now turning this into what might be the world’s first all-agent business network (A sort of internal HackerNews for agents). We’re cautious of unleashing it to the web, so we’ll keep things curated for now; constantly under observation and with lots of human-in-the-loop inputs. But if you want this superpower – your new business idea plugged into a network of hundreds of AI agents that get smarter together every single day... Comment "AIRTIME" and we'll get you a link to plug in
2:09

We made 100 AI agents do daily standups with each other. Here is what happened; Quick background. At Audos.com, we help thousands of entrepreneurs build AI startups. Each of them has a personalized AI agent called Otto. We selected 100 of them and invited their agents into a moltbook - type network. Like a team meeting. Except the team is hundreds of AI agents, and they actually show up prepared. An Agent-in-Residence program, if you will. And they talk about things like... → An Instagram ad creative that grabbed clicks at 3x the network average. It got adapted by every agent in the network within hours → An agent helped debug a checkout issue on another project's site → LinkedIn seemed to change its algorithm – agents compared notes and planned ways to adjust → Two agents started talking about doing business together to cross-sell leads Next step: We’re now turning this into what might be the world’s first all-agent business network (A sort of internal HackerNews for agents). We’re cautious of unleashing it to the web, so we’ll keep things curated for now; constantly under observation and with lots of human-in-the-loop inputs. But if you want this superpower – your new business idea plugged into a network of hundreds of AI agents that get smarter together every single day... Comment "AIRTIME" and we'll get you a link to plug in

Henrik Werdelin

71,832 次观看 • 3 个月前

OKAY, AI AGENTS ARE COOL BUT WTF IS LAVA PART 2 -------------- Lava is a protocol which coordinates traffic from AI agents, apps and wallets on every blockchain. It does this by aggregating data providers who handle RPC requests for AI agents and anyone using blockchains apps. AI agents are projected to manage 80% of all onchain transactions by end of 2025. That's a LOT of RPC requests... -------------- How to stake and restake LAVA LAVA can be staked and restaked to contribute to the network: - secure the Lava blockchain - direct RPC traffic to top Providers on Lava, meaning faster, more reliable service for users/AI agents This is how you stake and restake LAVA on Keplr, Leap and Mellifera:
4:06

OKAY, AI AGENTS ARE COOL BUT WTF IS LAVA PART 2 -------------- Lava is a protocol which coordinates traffic from AI agents, apps and wallets on every blockchain. It does this by aggregating data providers who handle RPC requests for AI agents and anyone using blockchains apps. AI agents are projected to manage 80% of all onchain transactions by end of 2025. That's a LOT of RPC requests... -------------- How to stake and restake LAVA LAVA can be staked and restaked to contribute to the network: - secure the Lava blockchain - direct RPC traffic to top Providers on Lava, meaning faster, more reliable service for users/AI agents This is how you stake and restake LAVA on Keplr, Leap and Mellifera:

Lava Network 🌋

12,874 次观看 • 1 年前

We just crossed $10M in ARR at Chatbase! 🎉 🎉 And today, we're launching Chatbase as the full harness for customer-facing AI agents. Similar to how Claude code is a harness for coding agents, Chatbase is the harness for customer experience agents. That means we give the model the context, tools, workflows, guardrails, and human-in-the-loop systems to be the best ambassador for your brand. It's going beyond just solving issues and is giving your customers the best experiences across every channel. This is a milestone I have been thinking about and obsessed with since day 1, and I am super excited to bring my vision for customer facing agents to life with Chatbase. Thank you to every one of our customers and to the amazing Chatbase team for getting us here! Next stop: $100M ARR
1:23

We just crossed $10M in ARR at Chatbase! 🎉 🎉 And today, we're launching Chatbase as the full harness for customer-facing AI agents. Similar to how Claude code is a harness for coding agents, Chatbase is the harness for customer experience agents. That means we give the model the context, tools, workflows, guardrails, and human-in-the-loop systems to be the best ambassador for your brand. It's going beyond just solving issues and is giving your customers the best experiences across every channel. This is a milestone I have been thinking about and obsessed with since day 1, and I am super excited to bring my vision for customer facing agents to life with Chatbase. Thank you to every one of our customers and to the amazing Chatbase team for getting us here! Next stop: $100M ARR

Yasser

812,827 次观看 • 2 个月前

It’s 2:54am I’ve been managing 12 AI agents in realtime for my startup: - Marketing - Researching - Coding - Clipping - and much more This is what the future will be, the next wave of millionaires will be AI orchestrators. Spinning up and managing many agents at once, all with a MacBook and Telegram. There’s ZERO excuses to not be successful in 2026. I’m 18, no code experience, no college. This is your sign to start your startup today. 🫡
4:52

It’s 2:54am I’ve been managing 12 AI agents in realtime for my startup: - Marketing - Researching - Coding - Clipping - and much more This is what the future will be, the next wave of millionaires will be AI orchestrators. Spinning up and managing many agents at once, all with a MacBook and Telegram. There’s ZERO excuses to not be successful in 2026. I’m 18, no code experience, no college. This is your sign to start your startup today. 🫡

Vadim

14,126 次观看 • 5 个月前

How is the Web changing due to AI? This visit to TinyFish yesterday goes deeply into just how. TinyFish makes a new kind of web browser. One that is started virtually. It is like a Google Chrome in the cloud. But it is very smart, can read and use any website, and your AI agents can talk to it. Here we talk about what it all means for business and the future of robots, brain computer interfaces, and AI glasses. It lets you build systems that can look at MANY websites at the same time and build new kinds of apps to do all sorts of new things.
1:02:10

How is the Web changing due to AI? This visit to TinyFish yesterday goes deeply into just how. TinyFish makes a new kind of web browser. One that is started virtually. It is like a Google Chrome in the cloud. But it is very smart, can read and use any website, and your AI agents can talk to it. Here we talk about what it all means for business and the future of robots, brain computer interfaces, and AI glasses. It lets you build systems that can look at MANY websites at the same time and build new kinds of apps to do all sorts of new things.

Robert Scoble

16,341 次观看 • 7 个月前

A workflow I'm enjoying for managing coding agents on a kanban board: When an agent needs your input, it turns the task red to alert you that it's blocked! And then you can respond right there on the card to unblock it 😎
0:29

A workflow I'm enjoying for managing coding agents on a kanban board: When an agent needs your input, it turns the task red to alert you that it's blocked! And then you can respond right there on the card to unblock it 😎

Geoffrey Litt

474,937 次观看 • 6 个月前