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The creator of Ruby on Rails just said programmers may be worse at AI coding than people who cannot code. Lex Fridman asked David Heinemeier Hansson directly. His answer: 100%. Because once the AI handles the implementation, knowing how to write a loop is no longer the rare skill....

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Ihtesham Ali

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142,189 просмотров • 7 дней назад •via X (Twitter)

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CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below

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14,741 просмотров • 2 месяцев назад

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Sam Altman told the world exactly what skills will matter when AI takes over 30 to 40 percent of the global economy. He was asked what his own kids should do to survive it. His answer was surprisingly human. He said the single most valuable thing anyone can build right now is the meta-skill of learning how to learn. Not a degree or a certification but the raw ability to adapt when everything around you changes. He also said learning to understand what other people actually want and building useful things for them will be more valuable than almost any technical knowledge. That skill has never been automated and is not close to being automated. He said human creativity and the desire to express it are, in his words, limitless. Every major technological revolution increased the demand for creative, curious, and socially intelligent people, not decreased it. The Industrial Revolution is the clearest parallel. Machines replaced physical labor and people were terrified. The next generation took those machines and built industries, art forms, and institutions nobody had conceived of before. The people who thrived were not the ones who competed with the machines. They were the ones who learned to direct them toward something new. That dynamic is already playing out right now with AI. The practical implication is this, depth in a single rigid skill is becoming less valuable. The ability to move across domains, pick up new tools quickly, and apply judgment in ambiguous situations is becoming more valuable. Altman also pointed to something most career advice ignores entirely, learning how to interact with the world, build relationships, and earn trust from other people. Those are things AI can simulate but cannot replace. The honest opportunity in this moment is not to outrun AI. It is to focus on the things that make you irreducibly human. Curiosity, judgment, empathy and the ability to ask the right question before anyone knows what the right question is. The people who will matter most in an AI-driven economy are not necessarily the ones who understand the technology deepest. They are the ones who can figure out what the technology should actually be used for. Altman has spent his career betting on human potential in the face of technological disruption. Based on every historical precedent, that is still the right bet to make.

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376,873 просмотров • 5 месяцев назад

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Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Rohan Paul

16,584 просмотров • 3 месяцев назад

Sam Altman: "One person who knows how to use AI can do a week of work in 20 minutes." In 30 minutes he breaks down exactly what those people do differently, and how you can too. The gap isn't between AI and humans. It's between people who know AI and people who don't. Watch it, then read what knowing AI can actually do below
26:29

Sam Altman: "One person who knows how to use AI can do a week of work in 20 minutes." In 30 minutes he breaks down exactly what those people do differently, and how you can too. The gap isn't between AI and humans. It's between people who know AI and people who don't. Watch it, then read what knowing AI can actually do below

Mahax

17,829 просмотров • 18 дней назад

Max Tegmark says the way through the AI black box may be to deploy its outputs instead. A cat can learn, but it cannot export what it learned. A human scientist can. As intelligence rises, more knowledge may become externalized into code and proofs. Deploy what the mind can verify, not the mind itself.
2:16

Max Tegmark says the way through the AI black box may be to deploy its outputs instead. A cat can learn, but it cannot export what it learned. A human scientist can. As intelligence rises, more knowledge may become externalized into code and proofs. Deploy what the mind can verify, not the mind itself.

vitrupo

49,717 просмотров • 4 месяцев назад

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓
1:08:17

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

slash1s

236,758 просмотров • 3 месяцев назад

Dario Amodei just announced the end of software engineering as a profession. The timeline is 6 to 12 months. Amodei: “I have engineers within Anthropic who say, I don’t write any code anymore. I just let the model write the code. I edit it.” Not a prediction. Current reality inside the frontier lab. The engineers who built the most advanced AI in the world have stopped writing code. They supervise. They edit. They manage architecture. The craft they spent careers mastering has been handed to the system they built. Amodei says models will do most, maybe all, of what software engineers do end-to-end within six to twelve months. Not assisting. Not autocompleting. Handling the entire development process independently. If you are learning syntax today, you are learning a dead language. Amodei: “Then it’s a question of how fast does that loop close?” The loop is this. AI writes code. Code builds better AI. Better AI writes better code. Faster. Without sleep. Without the cognitive limits that cap how quickly any human engineer can work. Once that loop closes, technological progress stops being constrained by human output. It becomes self-sustaining. Exponential. Operating at a pace no human workforce can match or direct. Software engineering isn’t ending. It’s becoming supervision. The developers who survive won’t be the best coders. They’ll be the best supervisors. The ones who can direct AI output, catch its failures, and architect what it builds toward. The skill that matters stops being implementation. It becomes judgment. Most developers are still optimizing for a skillset about to become as obsolete as stenography. While the people who built the systems replacing them already stopped doing the work themselves. The window to develop that judgment before the loop closes is exactly as long as Amodei’s timeline. Six to twelve months.
0:26

Dario Amodei just announced the end of software engineering as a profession. The timeline is 6 to 12 months. Amodei: “I have engineers within Anthropic who say, I don’t write any code anymore. I just let the model write the code. I edit it.” Not a prediction. Current reality inside the frontier lab. The engineers who built the most advanced AI in the world have stopped writing code. They supervise. They edit. They manage architecture. The craft they spent careers mastering has been handed to the system they built. Amodei says models will do most, maybe all, of what software engineers do end-to-end within six to twelve months. Not assisting. Not autocompleting. Handling the entire development process independently. If you are learning syntax today, you are learning a dead language. Amodei: “Then it’s a question of how fast does that loop close?” The loop is this. AI writes code. Code builds better AI. Better AI writes better code. Faster. Without sleep. Without the cognitive limits that cap how quickly any human engineer can work. Once that loop closes, technological progress stops being constrained by human output. It becomes self-sustaining. Exponential. Operating at a pace no human workforce can match or direct. Software engineering isn’t ending. It’s becoming supervision. The developers who survive won’t be the best coders. They’ll be the best supervisors. The ones who can direct AI output, catch its failures, and architect what it builds toward. The skill that matters stops being implementation. It becomes judgment. Most developers are still optimizing for a skillset about to become as obsolete as stenography. While the people who built the systems replacing them already stopped doing the work themselves. The window to develop that judgment before the loop closes is exactly as long as Amodei’s timeline. Six to twelve months.

Dustin

44,304 просмотров • 6 месяцев назад

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.
0:31

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.

Dustin

536,136 просмотров • 5 месяцев назад

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

376,293 просмотров • 5 месяцев назад

do developers really hate marketing? In this episode of The West Coast Builders podcast, I sat down with Sam Bhagwat, founder & ceo Mastra things we discussed in this episode : - who is actually building with mastra today? - which agent framework should developers choose? - what would be the right roadmap to build a coding agent today? - has the AI memory layer been solved? - how does mastra stay relevant against competitors? - what is Sam's number one challenge as ceo? - what's mastra's gtm and distribution strategy? - Is enterprise the endgame for AI startups? - how should founders delegate without abdicating? - how do you spot a high agency when hiring? and much more...
0:59

do developers really hate marketing? In this episode of The West Coast Builders podcast, I sat down with Sam Bhagwat, founder & ceo Mastra things we discussed in this episode : - who is actually building with mastra today? - which agent framework should developers choose? - what would be the right roadmap to build a coding agent today? - has the AI memory layer been solved? - how does mastra stay relevant against competitors? - what is Sam's number one challenge as ceo? - what's mastra's gtm and distribution strategy? - Is enterprise the endgame for AI startups? - how should founders delegate without abdicating? - how do you spot a high agency when hiring? and much more...

Mayank Gupta🧑🏻‍💻

13,983 просмотров • 15 дней назад