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Most coding agents can complete the task. But here’s the real question: Would you actually merge that code? Cosine didn’t just ship a feature. They’re doubling down on a bigger idea: One agent across every surface developers use. CLI Desktop VS Code Cloud Same runtime. Same system. No context...

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Md Riyazuddin

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12,203 görüntüleme • 3 ay önce •via X (Twitter)

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Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:
0:35

Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:

Kawsar

31,812 görüntüleme • 5 ay önce

the engineer who built Claude Code just showed how he runs an entire AI engineering team by himself I've seen $500 agent courses that don't cover what he explains in this interview 5 parallel Claude sessions, separate worktrees, planning agents, coding agents, AI code review he uses them to ship 20-30 pull requests every day without manually writing the code all from the person who actually built Claude Code most people are still trying to find one perfect model that can do everything the better approach is to give different models different roles and make them work together based on this, I put together a complete guide on building a multi-model AI team in 2026 full guide in the article below
1:37:58

the engineer who built Claude Code just showed how he runs an entire AI engineering team by himself I've seen $500 agent courses that don't cover what he explains in this interview 5 parallel Claude sessions, separate worktrees, planning agents, coding agents, AI code review he uses them to ship 20-30 pull requests every day without manually writing the code all from the person who actually built Claude Code most people are still trying to find one perfect model that can do everything the better approach is to give different models different roles and make them work together based on this, I put together a complete guide on building a multi-model AI team in 2026 full guide in the article below

Rahul

14,768 görüntüleme • 14 gün önce

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering
0:56

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

Om Patel

159,179 görüntüleme • 3 ay önce

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.
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🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

OpenServ

34,665 görüntüleme • 1 yıl önce

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
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Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 görüntüleme • 3 ay önce

This is the future of web design. Gamma 3.0 has just been released, and I used it to create a complete website from a URL in seconds. No prompts. No code. No input. Their new AI agent will design, review, fix, and iterate on your content. The best part: you can watch it in real-time as it builds your website! This is pretty amazing! There are many AI design agents out there, but this one is one of the most hands-off tools I've seen. This is incredible.
1:11

This is the future of web design. Gamma 3.0 has just been released, and I used it to create a complete website from a URL in seconds. No prompts. No code. No input. Their new AI agent will design, review, fix, and iterate on your content. The best part: you can watch it in real-time as it builds your website! This is pretty amazing! There are many AI design agents out there, but this one is one of the most hands-off tools I've seen. This is incredible.

Santiago

39,140 görüntüleme • 10 ay önce

do you understand what just shipped? → AI agents can now design directly on Figma’s canvas. not cheesy mockups… or lame screenshots… real native Figma assets wired to your actual design system → the use_figma MCP tool lets Claude Code, Codex, Cursor, and 6 other coding agents write directly to your Figma files → agents read your component library first and build with what already exists… variables, tokens, auto layout, the works → skills let you teach agents HOW your team designs. a skill is just a markdown file… anyone who understands Figma can write one → also works with Copilot CLI, Copilot in VS Code, Factory, Firebender, Augment, and Warp → free during beta… usage based pricing coming later the design to code gap that’s haunted every product team just collapsed in front of our eyes. designers hand off to agents now no need to wait on developers anymore everyone can take a deep breath now if you’re building products and not connecting Figma to your agents yet, you’re leaving serious speed on the table. set this up today. you’ll thank me later
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do you understand what just shipped? → AI agents can now design directly on Figma’s canvas. not cheesy mockups… or lame screenshots… real native Figma assets wired to your actual design system → the use_figma MCP tool lets Claude Code, Codex, Cursor, and 6 other coding agents write directly to your Figma files → agents read your component library first and build with what already exists… variables, tokens, auto layout, the works → skills let you teach agents HOW your team designs. a skill is just a markdown file… anyone who understands Figma can write one → also works with Copilot CLI, Copilot in VS Code, Factory, Firebender, Augment, and Warp → free during beta… usage based pricing coming later the design to code gap that’s haunted every product team just collapsed in front of our eyes. designers hand off to agents now no need to wait on developers anymore everyone can take a deep breath now if you’re building products and not connecting Figma to your agents yet, you’re leaving serious speed on the table. set this up today. you’ll thank me later

klöss

101,073 görüntüleme • 4 ay önce

The #1 problem with coding agents right now: Ask them to solve one problem, and they will make 10 other changes you didn't want. This happens to me every day. It happens to everyone I talk to as well. We have a solution for this now. The team Augment Code released a "Task List" feature for their coding assistant that solves this problem. Augment Code is partnering with me on this post. In case you haven't used them before: • Augment Code is a fully-fledged coding assistant • Their specialty are large projects • Fastest coding indexing I've seen • Has a free forever community edition Now, you can ask their coding agent to generate a Task List before doing anything. This will give you a plan you can review, edit, and augment if you need to. You can export this plan, load it on a different session, or even share it across projects. It makes a huge difference: The task list constrains the agent so you won't get any "unintended" changes anymore. It also puts you in control of everything the agent does. Check the video to see the agent working through a task list. You can also try this 100% free: (By the way, they also have support for remote agents. You can basically have those agents write your code while you are sleeping.)
3:06

The #1 problem with coding agents right now: Ask them to solve one problem, and they will make 10 other changes you didn't want. This happens to me every day. It happens to everyone I talk to as well. We have a solution for this now. The team Augment Code released a "Task List" feature for their coding assistant that solves this problem. Augment Code is partnering with me on this post. In case you haven't used them before: • Augment Code is a fully-fledged coding assistant • Their specialty are large projects • Fastest coding indexing I've seen • Has a free forever community edition Now, you can ask their coding agent to generate a Task List before doing anything. This will give you a plan you can review, edit, and augment if you need to. You can export this plan, load it on a different session, or even share it across projects. It makes a huge difference: The task list constrains the agent so you won't get any "unintended" changes anymore. It also puts you in control of everything the agent does. Check the video to see the agent working through a task list. You can also try this 100% free: (By the way, they also have support for remote agents. You can basically have those agents write your code while you are sleeping.)

Santiago

41,738 görüntüleme • 1 yıl önce

these guys are making Claude, Hermes, and DeepSeek argue with each other instead of just agreeing with you. this is the practical future of agent teams. with Bloome you just pick a task template, and it spins up a full agent team in a group chat to collaborate and get the job done. research, review, writing, market analysis, or anything you want. they brainstorm, review, and plan together. meanwhile, you or other human teammates can steer them or jump in, and stay in the same conversation so nothing gets lost across tools. since this is actual work, Bloome works across web, mobile, and desktop. try the desktop app first if you're using it seriously. same flexibility on the agent side too, you can connect any agents you want: ChatGPT, OpenClaw, Gemini… check them out →
0:58

these guys are making Claude, Hermes, and DeepSeek argue with each other instead of just agreeing with you. this is the practical future of agent teams. with Bloome you just pick a task template, and it spins up a full agent team in a group chat to collaborate and get the job done. research, review, writing, market analysis, or anything you want. they brainstorm, review, and plan together. meanwhile, you or other human teammates can steer them or jump in, and stay in the same conversation so nothing gets lost across tools. since this is actual work, Bloome works across web, mobile, and desktop. try the desktop app first if you're using it seriously. same flexibility on the agent side too, you can connect any agents you want: ChatGPT, OpenClaw, Gemini… check them out →

ℏεsam

62,794 görüntüleme • 22 gün önce

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

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

GREG ISENBERG

375,365 görüntüleme • 4 ay önce

You don’t need a dev team anymore. You need one prompt. Zoer takes your idea and turns it into a fully working app, not just code, but a real product you can use. Here’s what it handles for you: • Database (already structured) • Backend (logic + APIs) • Frontend (clean, production-ready UI) • Auth (Google + email login) • Deployment (live in one click) Most AI tools stop at “here’s your code.” Zoer goes further: It builds the entire system and puts it live. From: “build me a CRM / SaaS / tool” To: A real, working product you can open, test, and share. Try it → No setup. No configs. No extra tools. This is what building should feel like in 2026.
0:53

You don’t need a dev team anymore. You need one prompt. Zoer takes your idea and turns it into a fully working app, not just code, but a real product you can use. Here’s what it handles for you: • Database (already structured) • Backend (logic + APIs) • Frontend (clean, production-ready UI) • Auth (Google + email login) • Deployment (live in one click) Most AI tools stop at “here’s your code.” Zoer goes further: It builds the entire system and puts it live. From: “build me a CRM / SaaS / tool” To: A real, working product you can open, test, and share. Try it → No setup. No configs. No extra tools. This is what building should feel like in 2026.

Tanvir

18,160 görüntüleme • 3 ay önce

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:
2:37

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

DeepLearning.AI

32,513 görüntüleme • 11 ay önce

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 görüntüleme • 7 ay önce

🚨 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 görüntüleme • 1 yıl önce

New Andrej Karpathy interview Says AI agent failures stem from user skill, not model capability. Poor instructions cause errors. He suggests delegating 20-minute macro actions like coding and research to parallel agents and reviewing their work. --- "I think everything, like so many things, even if they don't work, I think to a large extent you feel like it's a skill issue. It's not that the capability is not there; it's that you just haven't found a way to string together what's available. Like, I didn't give good enough instructions to the agents in the file, or whatever it may be. I don't have a nice enough memory tool that I put in there, or something like that. So, it all kind of feels like a skill issue when it doesn't work to some extent. You want to see how you can parallelize them, and you want to be a 'Pierce tender,' basically. Pierce famously has a funny photo where he's in front of lots of these Codex agents behind the monitor. They all take about 20 minutes if you run them correctly and use high effort. You have multiple—you know, 10 or 20—pull requests checked out. It's just like you can do much larger macro actions. It's not just, 'Here's a line of code, here's a new function.' It's like, 'Here's a new functionality, delegate it to agent one. Here's a new functionality that's not going to interfere with the other one, give it to agent two.' Then, you try to review their work as best as you can, depending on how much you care about that code. You look for these macro actions that you can manipulate your software repository by. Another agent is doing some research, another agent is writing code, another one is coming up with a plan for some new implementation. Everything just happens in these macro actions over your repository. You're just trying to become really good at it and develop a muscle memory for it. It's very rewarding when it actually works, but it's also a new thing to learn. Hence, the psychosis." --- From No Priors YT channel (link in comment)
2:05

New Andrej Karpathy interview Says AI agent failures stem from user skill, not model capability. Poor instructions cause errors. He suggests delegating 20-minute macro actions like coding and research to parallel agents and reviewing their work. --- "I think everything, like so many things, even if they don't work, I think to a large extent you feel like it's a skill issue. It's not that the capability is not there; it's that you just haven't found a way to string together what's available. Like, I didn't give good enough instructions to the agents in the file, or whatever it may be. I don't have a nice enough memory tool that I put in there, or something like that. So, it all kind of feels like a skill issue when it doesn't work to some extent. You want to see how you can parallelize them, and you want to be a 'Pierce tender,' basically. Pierce famously has a funny photo where he's in front of lots of these Codex agents behind the monitor. They all take about 20 minutes if you run them correctly and use high effort. You have multiple—you know, 10 or 20—pull requests checked out. It's just like you can do much larger macro actions. It's not just, 'Here's a line of code, here's a new function.' It's like, 'Here's a new functionality, delegate it to agent one. Here's a new functionality that's not going to interfere with the other one, give it to agent two.' Then, you try to review their work as best as you can, depending on how much you care about that code. You look for these macro actions that you can manipulate your software repository by. Another agent is doing some research, another agent is writing code, another one is coming up with a plan for some new implementation. Everything just happens in these macro actions over your repository. You're just trying to become really good at it and develop a muscle memory for it. It's very rewarding when it actually works, but it's also a new thing to learn. Hence, the psychosis." --- From No Priors YT channel (link in comment)

Rohan Paul

23,122 görüntüleme • 4 ay önce

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @pauleveritt@fosstodon.org. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!
2:51

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @[email protected]. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

Andrew Ng

462,094 görüntüleme • 3 ay önce

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.
1:15

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

MIKE

38,108 görüntüleme • 1 ay önce

I've seen a lot of AI coding tools, but most are just copilots in a sidebar or in a black box form like Claude code. This one is different. I recently came across the new TRAE SOLO, and it’s not an assistant... It's more like a full AI engineer who plans and executes entire projects. The key point is that it has a GUI, which allows the development process to be clearly visible.
0:49

I've seen a lot of AI coding tools, but most are just copilots in a sidebar or in a black box form like Claude code. This one is different. I recently came across the new TRAE SOLO, and it’s not an assistant... It's more like a full AI engineer who plans and executes entire projects. The key point is that it has a GUI, which allows the development process to be clearly visible.

Francesco Ciulla

75,035 görüntüleme • 8 ay önce

Every project management tool was designed by project managers, for project managers. This one was designed for ADHD, dyslexic, and autistic brains instead. And it turns out that also makes it better for literally everyone who just wants to get work done without configuring a tool for two weeks first. It’s called Leantime. Most PM tools throw you straight into a task board and expect you to already know what a “sprint” is. Leantime is built around a different idea: tasks should trace back to a goal, not float in a backlog with no reason attached. → Ships with strategic planning tools, Lean Canvas, SWOT analysis, built to connect the “why” to the actual task list, not just a bare Kanban board → The same tasks render as Kanban, table, or list, whichever your brain processes better on a given day → Gantt-style milestone timeline, a built-in project wiki, and time tracking, all native, not four separate tools stitched together → Interface is deliberately built to reduce cognitive overload and context-switching, an actual design principle here, not an accessibility checkbox added later → Self-host via Docker in under an hour, your team’s entire project history stays on a server you control Jira was built assuming a certified project manager runs the workflow. Most teams are five people trying to ship something, not an enterprise PMO. Leantime is what a PM tool looks like when it’s built for the second group. Open source. AGPL-3.0. 10,000+ GitHub stars.
0:38

Every project management tool was designed by project managers, for project managers. This one was designed for ADHD, dyslexic, and autistic brains instead. And it turns out that also makes it better for literally everyone who just wants to get work done without configuring a tool for two weeks first. It’s called Leantime. Most PM tools throw you straight into a task board and expect you to already know what a “sprint” is. Leantime is built around a different idea: tasks should trace back to a goal, not float in a backlog with no reason attached. → Ships with strategic planning tools, Lean Canvas, SWOT analysis, built to connect the “why” to the actual task list, not just a bare Kanban board → The same tasks render as Kanban, table, or list, whichever your brain processes better on a given day → Gantt-style milestone timeline, a built-in project wiki, and time tracking, all native, not four separate tools stitched together → Interface is deliberately built to reduce cognitive overload and context-switching, an actual design principle here, not an accessibility checkbox added later → Self-host via Docker in under an hour, your team’s entire project history stays on a server you control Jira was built assuming a certified project manager runs the workflow. Most teams are five people trying to ship something, not an enterprise PMO. Leantime is what a PM tool looks like when it’s built for the second group. Open source. AGPL-3.0. 10,000+ GitHub stars.

Harman

31,018 görüntüleme • 21 gün önce

Last week, Anthropic dropped the coolest "AI isn't just chat" product. Claude Design lets you describe what you want to Claude and it returns prototypes, slides, and one-pagers by just chatting. You can then export to Canva, PDF, PPT, or hand off to Claude Code. You can give your entire design system and codebase to apply automatically to your project, and can share and collaborate with your team. The direct connect to GitHub will probably be my most used feature. Feels directly like a competitor to Figma or just a way to further boost collaboration with Claude. I built a preview of an app that allows CSGO players to meet and squad up with other players, marketplace for in-game loadouts, and pay high-ranking players to mentor them. (CSGO is a game released in 2012 and still consistently one of the highest watched games on Twitch, extremely popular in esports, highly competitive, and an all-around classic game - I know this because a gen zer talked to me about it for 2 straight hours.) I'll keep sharing more examples here.
1:52

Last week, Anthropic dropped the coolest "AI isn't just chat" product. Claude Design lets you describe what you want to Claude and it returns prototypes, slides, and one-pagers by just chatting. You can then export to Canva, PDF, PPT, or hand off to Claude Code. You can give your entire design system and codebase to apply automatically to your project, and can share and collaborate with your team. The direct connect to GitHub will probably be my most used feature. Feels directly like a competitor to Figma or just a way to further boost collaboration with Claude. I built a preview of an app that allows CSGO players to meet and squad up with other players, marketplace for in-game loadouts, and pay high-ranking players to mentor them. (CSGO is a game released in 2012 and still consistently one of the highest watched games on Twitch, extremely popular in esports, highly competitive, and an all-around classic game - I know this because a gen zer talked to me about it for 2 straight hours.) I'll keep sharing more examples here.

Allie K. Miller

10,586 görüntüleme • 3 ay önce