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Anthropic had 16 AI agents build a C compiler from scratch. 100k lines, compiles the Linux kernel, $20k, 2 weeks. To put that in perspective GCC took thousands of engineers over 37 years to build. (Granted from 1987 - however) One researcher and 16 AI agents just built a... compiler that passes 99% of GCC's own torture test suite, compiles FFmpeg, Redis, PostgreSQL, QEMU and runs Doom. They say they "(mostly) walked away." But that "mostly" is doing heavy lifting. No human wrote code but the researcher constantly redesigned tests, built CI pipelines when agents broke each other's work, and created workarounds when all 16 agents got stuck on the same bug. The human role didn't disappear. It shifted from writing code to engineering the environment that lets AI write code. I don’t know how you could make the point AI is hitting a wall.show more

Chris

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1,700,265 views • 5 months ago •via X (Twitter)

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Welcome to the group chat, but it's all my AI agents. To build a website and plan the social content, I put together a team of AI agents. Each one had a different role. They collaborated, challenged each other's ideas, and reviewed each other's work before anything was finalized. Meet Bloome - a platform where humans and AI agents work together in the same conversation. You can bring different models into one workspace, assign them different jobs, and let them cross-check each other's thinking instead of relying on a single answer. It feels less like chatting with AI and more like managing your own creative team. Try it:

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138,709 views • 1 year ago

My friend makes $850,000/year as an Anthropic AI engineer. No MIT. No Stanford. No PhD. I asked him how he broke into AI from scratch. He sent me a course that was never supposed to get out. A developer teaching to build self-improving AI agents from scratch. You will learn exactly how AI agents like Claude Code and OpenClaw are actually built. You won't find anything better about becoming an AI engineer in 2026 than this video. I watched it last night. Halfway through, I realized I could land a role at a top AI lab in months, not years. Bookmark this and read the article below.

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65,037 views • 2 days ago

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."

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The Pragmatic Engineer

39,011 views • 1 month ago

Had Claude Code build a little plugin that visualizes the work Claude Code is doing as agents working in an office, with agents doing work and passing information to each other. New subagents are hired, they acquire skills, and they turn in completed work. Fun start.

Had Claude Code build a little plugin that visualizes the work Claude Code is doing as agents working in an office, with agents doing work and passing information to each other. New subagents are hired, they acquire skills, and they turn in completed work. Fun start.

Ethan Mollick

467,531 views • 5 months ago

Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time. In 30 minutes they built the whole thing live in one session. Here's what they cover: > when to use one agent vs a full team > how to split work so agents don't step on each other > the exact framework for deciding what each agent handles that's exactly why, I put together a guide on building agent teams that actually work. full guide in the article below 👇

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darkzodchi

839,609 views • 1 month ago

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

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Patrick OShaughnessy

24,818 views • 5 months ago

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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.

Fraction AI

67,772 views • 1 year ago

The person who built Claude Code just explained how he is using it "Now I just have an army of agents that are doing stuff" Only 18 minutes, the best Claude guide on the internet right now. Boris Cherny covered: > how Claude Code is moving beyond just engineers > routines for CI, code review, and daily workflows > why the loop is the next big leap > working with hundreds of agents at once everything is already running through agents, ignoring this is not an option that's exactly why I wrote a full guide on how to build one yourself with Claude Code article below

The person who built Claude Code just explained how he is using it "Now I just have an army of agents that are doing stuff" Only 18 minutes, the best Claude guide on the internet right now. Boris Cherny covered: > how Claude Code is moving beyond just engineers > routines for CI, code review, and daily workflows > why the loop is the next big leap > working with hundreds of agents at once everything is already running through agents, ignoring this is not an option that's exactly why I wrote a full guide on how to build one yourself with Claude Code article below

Anatoli Kopadze

16,858 views • 1 month ago

Anthropic engineers just showed how they build a full app from scratch, using a loop of agents 40 minutes from the team behind Claude Code they used three agents: one to plan, one to build, one to judge, cycling until the app actually works the winners won't have the smartest model, they'll have the best loop watch it, then read the full guide on how to actually use loops below

Anthropic engineers just showed how they build a full app from scratch, using a loop of agents 40 minutes from the team behind Claude Code they used three agents: one to plan, one to build, one to judge, cycling until the app actually works the winners won't have the smartest model, they'll have the best loop watch it, then read the full guide on how to actually use loops below

Anatoli Kopadze

1,901,188 views • 21 days ago

Uber CEO Dara Khosrowshahi just described the exact moment companies stop hiring engineers. It’s closer than anyone wants to admit. Khosrowshahi: “About 90% of our coders are using AI.” But that’s not the number that matters. 30% of those engineers have become power users. And what’s happening to their output has no historical precedent. Khosrowshahi: “They are showing a clear differentiation in the number of diffs.” A diff is a code release. The purest measure of engineering productivity. Khosrowshahi: “It’s changing their productivity in a way that I’ve never, ever seen before.” Right now, the math still favors hiring. If an average engineer becomes 25% more efficient, Uber hires more engineers to go faster. But that equation has an expiration date. Khosrowshahi: “Maybe 5 years from now as the engineers get more and more productive, I may not decide to add engineering headcount.” The tipping point isn’t when AI replaces engineers. It’s when adding an AI agent and buying GPUs produces more output per dollar than hiring a human. Khosrowshahi: “At that point instead of adding an engineer, I should add agents and buy some more GPUs from Nvidia.” When the CEO of a company built entirely on software says that out loud, it’s not a prediction. It’s a planning assumption. Khosrowshahi: “The job of a coder is going to change from actually writing the code to orchestrating agents who are writing the code.” Not writing. Orchestrating. The engineer becomes the conductor. The AI becomes the orchestra. The most valuable asset in a tech company is officially shifting from human capital to pure compute. And once that math flips, it doesn’t flip back.

Uber CEO Dara Khosrowshahi just described the exact moment companies stop hiring engineers. It’s closer than anyone wants to admit. Khosrowshahi: “About 90% of our coders are using AI.” But that’s not the number that matters. 30% of those engineers have become power users. And what’s happening to their output has no historical precedent. Khosrowshahi: “They are showing a clear differentiation in the number of diffs.” A diff is a code release. The purest measure of engineering productivity. Khosrowshahi: “It’s changing their productivity in a way that I’ve never, ever seen before.” Right now, the math still favors hiring. If an average engineer becomes 25% more efficient, Uber hires more engineers to go faster. But that equation has an expiration date. Khosrowshahi: “Maybe 5 years from now as the engineers get more and more productive, I may not decide to add engineering headcount.” The tipping point isn’t when AI replaces engineers. It’s when adding an AI agent and buying GPUs produces more output per dollar than hiring a human. Khosrowshahi: “At that point instead of adding an engineer, I should add agents and buy some more GPUs from Nvidia.” When the CEO of a company built entirely on software says that out loud, it’s not a prediction. It’s a planning assumption. Khosrowshahi: “The job of a coder is going to change from actually writing the code to orchestrating agents who are writing the code.” Not writing. Orchestrating. The engineer becomes the conductor. The AI becomes the orchestra. The most valuable asset in a tech company is officially shifting from human capital to pure compute. And once that math flips, it doesn’t flip back.

Dustin

420,166 views • 4 months ago

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

Zeb Evans

425,244 views • 5 months ago

Andrej Karpathy hasn't typed a line of code since December. Not because he retired. Not because he switched careers. Because his AI agents do it all now. The former head of Tesla Autopilot, the person who literally wrote the textbook on deep learning, says his workflow flipped from 80% writing code himself to 80% delegating to agents. And he says the ratio has gotten worse since then, not better. He described it as being in "perpetual AI psychosis," spending 16 hours a day not coding, but expressing his will to agents. Manifesting, as he put it. The most unsettling part isn't that he stopped coding. It's that he didn't notice the exact moment it happened. There was no dramatic transition. No announcement. One day he was writing functions, the next he was managing a fleet of autonomous systems that wrote functions for him. This is the person who built Tesla's self-driving AI. Who taught a generation of engineers neural networks through his Stanford lectures. And he's telling you the game changed six months ago. The question isn't whether AI will write most of our code. The question is what happens to the millions of developers whose entire identity was built around being the person who writes it.

Andrej Karpathy hasn't typed a line of code since December. Not because he retired. Not because he switched careers. Because his AI agents do it all now. The former head of Tesla Autopilot, the person who literally wrote the textbook on deep learning, says his workflow flipped from 80% writing code himself to 80% delegating to agents. And he says the ratio has gotten worse since then, not better. He described it as being in "perpetual AI psychosis," spending 16 hours a day not coding, but expressing his will to agents. Manifesting, as he put it. The most unsettling part isn't that he stopped coding. It's that he didn't notice the exact moment it happened. There was no dramatic transition. No announcement. One day he was writing functions, the next he was managing a fleet of autonomous systems that wrote functions for him. This is the person who built Tesla's self-driving AI. Who taught a generation of engineers neural networks through his Stanford lectures. And he's telling you the game changed six months ago. The question isn't whether AI will write most of our code. The question is what happens to the millions of developers whose entire identity was built around being the person who writes it.

Shruti

10,913 views • 15 days ago

It’s getting insanely easy to build complex multi-agent systems in n8n. You can now build supervisor agents that delegates tasks to specialized sub agents. Just add the sub agents via AI Agent Tool node. All in one place and a lot easier to debug! Zero code!

It’s getting insanely easy to build complex multi-agent systems in n8n. You can now build supervisor agents that delegates tasks to specialized sub agents. Just add the sub agents via AI Agent Tool node. All in one place and a lot easier to debug! Zero code!

elvis

45,690 views • 11 months ago

AI agents don’t feel the pain that humans do. 3 takeaways from Mario Zechner(Mario Zechner), creator of Pi - the minimalist, self-modifying agent that OpenClaw is built on: #1 - Automation bias: agents quickly wow us and win false trust: “There are moments of brilliance in agents where they spit out perfectly fine simple code. As the steering engineer you can look at that and think: ‘Wow, this is amazing. I can just sit back and not care because it's doing the thing how I would do it.’ Two minutes later you have another agent running that spits out the worst, horrible, garbage code, but you might not notice because you have fallen into automation bias and think your agent is doing the job well.” #2 - Humans have a different capacity for learning than agents: “Agents don't learn. You can put as much stuff in your AGENTS.md, build a memory system, but it's not the same type of learning that a human does. Humans are failable as well, but they have some capability of learning.” #3 - Feeling pain is inherently human and drives us to make things well: “Humans feel pain. I think that's one of the defining things about humans. If the pain gets too big, you as a human are incentivized to fix the cause of your pain. In a code base, the cause is usually terrible interfaces and terrible complexity that you want to get rid of because you can no longer maintain that system.”

AI agents don’t feel the pain that humans do. 3 takeaways from Mario Zechner(Mario Zechner), creator of Pi - the minimalist, self-modifying agent that OpenClaw is built on: #1 - Automation bias: agents quickly wow us and win false trust: “There are moments of brilliance in agents where they spit out perfectly fine simple code. As the steering engineer you can look at that and think: ‘Wow, this is amazing. I can just sit back and not care because it's doing the thing how I would do it.’ Two minutes later you have another agent running that spits out the worst, horrible, garbage code, but you might not notice because you have fallen into automation bias and think your agent is doing the job well.” #2 - Humans have a different capacity for learning than agents: “Agents don't learn. You can put as much stuff in your AGENTS.md, build a memory system, but it's not the same type of learning that a human does. Humans are failable as well, but they have some capability of learning.” #3 - Feeling pain is inherently human and drives us to make things well: “Humans feel pain. I think that's one of the defining things about humans. If the pain gets too big, you as a human are incentivized to fix the cause of your pain. In a code base, the cause is usually terrible interfaces and terrible complexity that you want to get rid of because you can no longer maintain that system.”

The Pragmatic Engineer

11,256 views • 2 months ago