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Chamath on how AI agents are making the "10x engineer" distinction disappear because the most efficient "code paths" are now obvious to everyone. Just as AI solved chess and removed the mystery of the best move, AI is doing the same for coding, making the process reductive and removing...

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Rohan Paul

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831,450 просмотров • 2 месяцев назад •via X (Twitter)

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"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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"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."

Patrick OShaughnessy

24,818 просмотров • 4 месяцев назад

Boris Cherny (Creator of Claude Code): "The one technical book I would recommend to everyone that has had the greatest impact on me as an engineer is functional programming in Scala. You're probably never going to use Scala day today, but the way it teaches you to think about coding problems is just such a change from the way that most people were in coding, either practically or in school. It's just. It's incredible. It's going to completely change the way that you code now. I think in types, when I code, the thing that matters in your code the most is the type signatures. This is more important than the code itself." Boris Cherny
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Boris Cherny (Creator of Claude Code): "The one technical book I would recommend to everyone that has had the greatest impact on me as an engineer is functional programming in Scala. You're probably never going to use Scala day today, but the way it teaches you to think about coding problems is just such a change from the way that most people were in coding, either practically or in school. It's just. It's incredible. It's going to completely change the way that you code now. I think in types, when I code, the thing that matters in your code the most is the type signatures. This is more important than the code itself." Boris Cherny

Ryan Peterman

363,779 просмотров • 1 месяц назад

.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."
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.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."

Deutsch Explains

72,455 просмотров • 1 год назад

David Coulthard: “The only criticism I would have [to McLaren] is that I don't like when the engineer - ‘cause that bond between the driver and the engineer for me has to be absolute. If you guys are in the trenches together and someone blows the whistle back in the day to go out and fight the enemy. You've got to know that you're both going at the same time. You know, you're not hiding behind you and you're not hiding- You're there shoulder to shoulder. So that relationship between driver and engineer has to be unbreakable, that bond. So I think that when they do give ‘move over, don't race’ type instructions, that should come from the team principal or the sporting director. It should not come from the race engineer. The driver should absolutely believe that his engineer- he would say that's not my job, my job is to get my driver winning and I will only give instructions that can help that. But I'm a professional and therefore if there's an instruction which is going to get my driver to hold position, that has to come from someone else in the team.”
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David Coulthard: “The only criticism I would have [to McLaren] is that I don't like when the engineer - ‘cause that bond between the driver and the engineer for me has to be absolute. If you guys are in the trenches together and someone blows the whistle back in the day to go out and fight the enemy. You've got to know that you're both going at the same time. You know, you're not hiding behind you and you're not hiding- You're there shoulder to shoulder. So that relationship between driver and engineer has to be unbreakable, that bond. So I think that when they do give ‘move over, don't race’ type instructions, that should come from the team principal or the sporting director. It should not come from the race engineer. The driver should absolutely believe that his engineer- he would say that's not my job, my job is to get my driver winning and I will only give instructions that can help that. But I'm a professional and therefore if there's an instruction which is going to get my driver to hold position, that has to come from someone else in the team.”

naenia ¹ ⁶³

305,333 просмотров • 4 месяцев назад

Is AI going to replace software developers? What can we do to make sure we still have a job in 5 years? I went to the AI Engineer Miami event and talked to Lena Hall, the Senior Director, Developers & AI @ Akamai. You've got to give her a follow! She’s one of the sharpest voices in AI right now. Here are her tips and recommendations.
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Is AI going to replace software developers? What can we do to make sure we still have a job in 5 years? I went to the AI Engineer Miami event and talked to Lena Hall, the Senior Director, Developers & AI @ Akamai. You've got to give her a follow! She’s one of the sharpest voices in AI right now. Here are her tips and recommendations.

Santiago

29,686 просмотров • 1 месяц назад

We've built a massive verification bottleneck: • 72% of developers use AI to generate code every day • 96% of them don't fully trust it Writing code is now free. All the work now is verifying that the code does what it's supposed to. According to a new State of Code Survey, almost half of the respondents believe the number one skill that will matter most in the coming years is "reviewing and validating AI-generated code for quality and security". Not prompt engineering or vibe-coding, but checking AI's work.
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We've built a massive verification bottleneck: • 72% of developers use AI to generate code every day • 96% of them don't fully trust it Writing code is now free. All the work now is verifying that the code does what it's supposed to. According to a new State of Code Survey, almost half of the respondents believe the number one skill that will matter most in the coming years is "reviewing and validating AI-generated code for quality and security". Not prompt engineering or vibe-coding, but checking AI's work.

Santiago

16,299 просмотров • 4 месяцев назад

Jensen Huang on the future of coding: “Every engineer is going to have 100 agents.” Jensen: “Everything that's too big, too heavy, takes too long, those ideas are all gone.” Chamath: “You're reduced to creativity. Like, what can you come up with?” Jensen: “Exactly.” “Now the question is, how do you work with these agents?” “Well, it's just a new way of doing computer programming.” “In the past, we code.” “In the future, we're going to write ideas, architectures, specifications. We're going to organize teams. We're going to help them define how to evaluate the definition of good versus bad. What does it look like when something is a great outcome? How to iterate with you, how to brainstorm.” “That's really what you're looking for.”
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Jensen Huang on the future of coding: “Every engineer is going to have 100 agents.” Jensen: “Everything that's too big, too heavy, takes too long, those ideas are all gone.” Chamath: “You're reduced to creativity. Like, what can you come up with?” Jensen: “Exactly.” “Now the question is, how do you work with these agents?” “Well, it's just a new way of doing computer programming.” “In the past, we code.” “In the future, we're going to write ideas, architectures, specifications. We're going to organize teams. We're going to help them define how to evaluate the definition of good versus bad. What does it look like when something is a great outcome? How to iterate with you, how to brainstorm.” “That's really what you're looking for.”

The All-In Podcast

90,129 просмотров • 2 месяцев назад

ELON: I ENSURE GREAT ENGINEERS CAN FLOURISH AT MY COMPANIES “I consider one of my core responsibilities in running companies to have an environment where great engineers can flourish. And I think in a lot of companies, maybe most companies, if somebody is a really talented, driven engineer, their talents are suppressed. At some of the companies, engineering talent is suppressed in a way that is maybe not obviously bad, but where it's just so comfortable, and you're paid so much money, but the output you actually have to produce is so low that it's like a honey trap. They don't necessarily seem like bad places for engineers, but you have to say, a good engineer went in and what did they get out? The output of that engineering talent seems very low. Tesla is not a honey trap. We're demanding. You're going to get a lot of shit done. And it's going to be really cool, and it's not going to be easy. But if you are a super-talented engineer, your talents will be used, I think, to a greater degree than anywhere else. SpaceX is also that way.” Source: Tesla AI Day, September 2022
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ELON: I ENSURE GREAT ENGINEERS CAN FLOURISH AT MY COMPANIES “I consider one of my core responsibilities in running companies to have an environment where great engineers can flourish. And I think in a lot of companies, maybe most companies, if somebody is a really talented, driven engineer, their talents are suppressed. At some of the companies, engineering talent is suppressed in a way that is maybe not obviously bad, but where it's just so comfortable, and you're paid so much money, but the output you actually have to produce is so low that it's like a honey trap. They don't necessarily seem like bad places for engineers, but you have to say, a good engineer went in and what did they get out? The output of that engineering talent seems very low. Tesla is not a honey trap. We're demanding. You're going to get a lot of shit done. And it's going to be really cool, and it's not going to be easy. But if you are a super-talented engineer, your talents will be used, I think, to a greater degree than anywhere else. SpaceX is also that way.” Source: Tesla AI Day, September 2022

Mario Nawfal

20,354,396 просмотров • 1 год назад

Jensen Huang (CEO of Nvidia) : “Every engineer is going to have and manage 100s of agents” The future of work is managing and building ai agents This builder gave out the entire roadmap to become a 100x ai engineer in 2026 Bookmark this for the weekend
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Jensen Huang (CEO of Nvidia) : “Every engineer is going to have and manage 100s of agents” The future of work is managing and building ai agents This builder gave out the entire roadmap to become a 100x ai engineer in 2026 Bookmark this for the weekend

Avid

489,951 просмотров • 27 дней назад

Founder & CEO of Replit ⠕ Amjad Masad on the 1000x engineer: “The buzzword in Silicon Valley has been the 10x engineer. 2-3 years ago, I talked about the 1000x engineer, highly leveraged by autonomous agents." "You're not just one person, you’re a team of engineers. Every engineer is a sort of manager now." "We're not at 1000x yet. But pretty soon, the difference between a great engineer who knows how to use AI tools and an okay or good one is going to be 100x.”
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Founder & CEO of Replit ⠕ Amjad Masad on the 1000x engineer: “The buzzword in Silicon Valley has been the 10x engineer. 2-3 years ago, I talked about the 1000x engineer, highly leveraged by autonomous agents." "You're not just one person, you’re a team of engineers. Every engineer is a sort of manager now." "We're not at 1000x yet. But pretty soon, the difference between a great engineer who knows how to use AI tools and an okay or good one is going to be 100x.”

TBPN

77,617 просмотров • 5 месяцев назад

Why is observability so hard to do well - and so expensive, in general? What is "Observability 2.0" and is Open Telemetry any good? In today's episode of The Pragmatic Engineer Podcast, we answer all of these with Charity Majors , co-author of the O'Reilly book "Observability Engineering," former engineer at Parse/Facebook, and cofounder and CTO at Honeycomb Watch it here: • YouTube: • Apple: • Spotify: • Summary and transcript: Brought to you by our wonderful sponsors - check out their offerings: • Sonar — Trust your developers – verify your AI-generated code. • Vanta — Automate compliance and simplify security with Vanta ---- Topics we cover in this episode: • What is observability? Charity’s take • What is “Observability 2.0?” • Why Charity is a fan of platform teams • Why DevOps is an overloaded term: and probably no longer relevant • What is cardinality? And why does it impact the cost of observability so much? • How OpenTelemetry solves for vendor lock-in • Why Honeycomb wrote its own database • Why having good observability should be a prerequisite to adding AI code or using AI agents • And more! --- My biggest takeaways: 1. The DevOps movement feels like it’s in its final days, having served its purpose. 2. Lots of people get dashboards wrong! Charity doesn’t think that static dashboards are helpful to engineering teams at all. In fact, misusing dashboards is one of the most common observability practices. 3. Observability will be especially important for AI use cases in these ways: a) o11y for LLMs: to get data on how they behave and to be able to debug behaviors. This is relevant for teams building and operating AI models. b) o11y for code generated by AI: the generated code should have the right amount of observability in place. Once the code is deployed to production, developers need to be able to get a sense of how the code is behaving there!
0:46

Why is observability so hard to do well - and so expensive, in general? What is "Observability 2.0" and is Open Telemetry any good? In today's episode of The Pragmatic Engineer Podcast, we answer all of these with Charity Majors , co-author of the O'Reilly book "Observability Engineering," former engineer at Parse/Facebook, and cofounder and CTO at Honeycomb Watch it here: • YouTube: • Apple: • Spotify: • Summary and transcript: Brought to you by our wonderful sponsors - check out their offerings: • Sonar — Trust your developers – verify your AI-generated code. • Vanta — Automate compliance and simplify security with Vanta ---- Topics we cover in this episode: • What is observability? Charity’s take • What is “Observability 2.0?” • Why Charity is a fan of platform teams • Why DevOps is an overloaded term: and probably no longer relevant • What is cardinality? And why does it impact the cost of observability so much? • How OpenTelemetry solves for vendor lock-in • Why Honeycomb wrote its own database • Why having good observability should be a prerequisite to adding AI code or using AI agents • And more! --- My biggest takeaways: 1. The DevOps movement feels like it’s in its final days, having served its purpose. 2. Lots of people get dashboards wrong! Charity doesn’t think that static dashboards are helpful to engineering teams at all. In fact, misusing dashboards is one of the most common observability practices. 3. Observability will be especially important for AI use cases in these ways: a) o11y for LLMs: to get data on how they behave and to be able to debug behaviors. This is relevant for teams building and operating AI models. b) o11y for code generated by AI: the generated code should have the right amount of observability in place. Once the code is deployed to production, developers need to be able to get a sense of how the code is behaving there!

Gergely Orosz

25,485 просмотров • 1 год назад

LeetCode is dead. Developers don't write code line-by-line anymore. They orchestrate AI agents working in parallel, review AI-generated code, and make architectural decisions. That's the job now. But most interview processes haven't caught up. They still test algorithm memorization instead of AI fluency, code review, and judgment. We're building assessments for next-gen hiring that mirror how developers actually work. Here's how we think about it:
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LeetCode is dead. Developers don't write code line-by-line anymore. They orchestrate AI agents working in parallel, review AI-generated code, and make architectural decisions. That's the job now. But most interview processes haven't caught up. They still test algorithm memorization instead of AI fluency, code review, and judgment. We're building assessments for next-gen hiring that mirror how developers actually work. Here's how we think about it:

HackerRank

342,566 просмотров • 1 месяц назад

We asked Amjad Masad (Founder & CEO of Replit ⠕) what happens when engineers start managing AI agents instead of code. “The buzzword used to be the 10x engineer. I started talking about the 1000x engineer; someone highly leveraged by autonomous agents. "Right now, people using Replit spin up multiple agents to work on their project while they handle the main task." "You're no longer just one person, you’re a team. Every engineer is now sort of a manager." "We're not at 1000x yet, but soon the gap between a great engineer who knows how to use AI tools and an okay one will be 100x.”
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We asked Amjad Masad (Founder & CEO of Replit ⠕) what happens when engineers start managing AI agents instead of code. “The buzzword used to be the 10x engineer. I started talking about the 1000x engineer; someone highly leveraged by autonomous agents. "Right now, people using Replit spin up multiple agents to work on their project while they handle the main task." "You're no longer just one person, you’re a team. Every engineer is now sort of a manager." "We're not at 1000x yet, but soon the gap between a great engineer who knows how to use AI tools and an okay one will be 100x.”

TBPN

63,785 просмотров • 11 месяцев назад

My talk from the AI Engineer Code Summit is out! 🚨 "How Claude Code Works" and what we can learn about frontier agent architectures. Coding agents are suddenly really really good, and I'm trying to understand why. In short: better models, simple loop design, and bash tools
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My talk from the AI Engineer Code Summit is out! 🚨 "How Claude Code Works" and what we can learn about frontier agent architectures. Coding agents are suddenly really really good, and I'm trying to understand why. In short: better models, simple loop design, and bash tools

Jared Zoneraich

13,917 просмотров • 5 месяцев назад

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.
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I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

Akshay 🚀

30,613 просмотров • 1 год назад

Elon Musk on the importance of establishing the right values ​​in AGI: “I don't think anyone's ultimately going to have control over digital superintelligence any more than, say, a chimp would have control over humans. Like chimps don't have control over humans. There's nothing they could do. But I do think that it matters how you build the AI and what kind of values you instill in the AI. And my opinion on AI safety is, the most important thing is that it be maximally truth-seeking, like that you don't force the AI to believe things that are false.”
0:41

Elon Musk on the importance of establishing the right values ​​in AGI: “I don't think anyone's ultimately going to have control over digital superintelligence any more than, say, a chimp would have control over humans. Like chimps don't have control over humans. There's nothing they could do. But I do think that it matters how you build the AI and what kind of values you instill in the AI. And my opinion on AI safety is, the most important thing is that it be maximally truth-seeking, like that you don't force the AI to believe things that are false.”

Eva Fo𝕏 🦊 Claudius Nero's Legion

1,010,673 просмотров • 6 месяцев назад

Every ML team is about to 10x overnight. The first agentic Machine Learning Engineer is here, and it will blow your mind! Copilots help you write code, but that's about it. People still have to do most of the work. That's about to change!
3:51

Every ML team is about to 10x overnight. The first agentic Machine Learning Engineer is here, and it will blow your mind! Copilots help you write code, but that's about it. People still have to do most of the work. That's about to change!

Santiago

76,507 просмотров • 10 месяцев назад

Zionism stands for a very simple proposition. It doesn't stand for the proposition that Jews are better than everybody else or that Jews are worse than everybody else. It stands for the very common sense proposition that Jews are the same as everybody else, that we have the same rights as other peoples do to live in our ancestral homeland. And so it's very bizarre when people say they're anti-Zionists and claim at the same time not to be anti-Semites. What you’re saying, if you’re an anti-Zionist, is that Bulgarians have the right to live in Bulgaria, Japanese people have the right to live in Japan, Irish people have the right to live in Ireland, and maybe even Palestinians have the right to live in what they call Palestine—but Jews don’t. Jews alone are denied the same rights as everyone else. Well, that's just anti-Semitism, plain and simple, the oldest kind of hatred there is.
2:21

Zionism stands for a very simple proposition. It doesn't stand for the proposition that Jews are better than everybody else or that Jews are worse than everybody else. It stands for the very common sense proposition that Jews are the same as everybody else, that we have the same rights as other peoples do to live in our ancestral homeland. And so it's very bizarre when people say they're anti-Zionists and claim at the same time not to be anti-Semites. What you’re saying, if you’re an anti-Zionist, is that Bulgarians have the right to live in Bulgaria, Japanese people have the right to live in Japan, Irish people have the right to live in Ireland, and maybe even Palestinians have the right to live in what they call Palestine—but Jews don’t. Jews alone are denied the same rights as everyone else. Well, that's just anti-Semitism, plain and simple, the oldest kind of hatred there is.

Roy K. Altman

41,366 просмотров • 15 дней назад

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.
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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

75,028 просмотров • 6 месяцев назад

Our AI engineer can now build simple agents using the English language! Tell it what to do, and it will try to generate, execute, and deploy code. AI has already replaced SQL. The next step is to replace Python. It's officially in its 3rd month of internship!!
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Our AI engineer can now build simple agents using the English language! Tell it what to do, and it will try to generate, execute, and deploy code. AI has already replaced SQL. The next step is to replace Python. It's officially in its 3rd month of internship!!

Bindu Reddy

46,773 просмотров • 1 год назад