#andrejkarpathy#software2.0#software3.0
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Intermediate. Assumes working knowledge.
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AI and machine learningTools and practiceTransformers and LLMsAI agents and tool useAI for coding
Andrej Karpathy outlines the evolution of software from 1.0 to 3.0, explaining how LLMs act as operating systems and introduce vibe coding.
Andrej Karpathy's keynote on June 17, 2025 at AI Startup School in San Francisco. Slides provided by Andrej: drive.google.com/file/d/1a0h1mkwfmV2PlekxDN8isMrDA5evc4wW/vi… Chapters: - Intro - Software evolution: From 1.0 to 3.0 - Programming in English: Rise of Software 3.0 - LLMs as utilities, fabs, and operating systems - The new LLM OS and historical computing analogies - Psychology of LLMs: People spirits and cognitive quirks - Designing LLM apps with partial autonomy - The importance of human-AI collaboration loops - Lessons from Tesla Autopilot & autonomy sliders - The Iron Man analogy: Augmentation vs. agents - Vibe Coding: Everyone is now a programmer - Building for agents: Future-ready digital infrastructure - Summary: We’re in the 1960s of LLMs — time to build Drawing on his work at Stanford, OpenAI, and Tesla, Andrej sees a shift underway. Software is changing, again. We’ve entered the era of “Software 3.0,” where natural language becomes the new programming interface and models do the rest. He explores what this shift means for developers, users, and the design of software itself— that we're not just using new tools, but building a new kind of computer. More content from Andrej: youtube.com/@AndrejKarpathy Thoughts (From Andrej Karpathy!) - Imo fair to say that software is changing quite fundamentally again. LLMs are a new kind of computer, and you program them *in English*. Hence I think they are well deserving of a major version upgrade in terms of software. - LLMs have properties of utilities, of fabs, and of operating systems → New LLM OS, fabbed by labs, and distributed like utilities (for now). Many historical analogies apply - imo we are computing circa ~1960s. - LLM psychology: LLMs = "people spirits", stochastic simulations of people, where the simulator is an autoregressive Transformer. Since they are trained on human data, they have a kind of emergent psychology, and are simultaneously superhuman in some ways, but also fallible in many others. Given this, how do we productively work with them hand in hand? Switching gears to opportunities... - LLMs are "people spirits" → can build partially autonomous products. - LLMs are programmed in English → make software highly accessible! (yes, vibe coding) - LLMs are new primary consumer/manipulator of digital information (adding to GUIs/humans and APIs/programs) → Build for agents! Some of the links: - Software 2.0 blog post from 2017 karpathy.medium.com/software-2-0-a64152b37c35 - How LLMs flip the script on technology diffusion karpathy.bearblog.dev/power-to-the-people/ - Vibe coding MenuGen (retrospective) karpathy.bearblog.dev/vibe-coding-menugen/ Apply to Y Combinator: ycombinator.com/apply Work at a startup: workatastartup.comWhat people say. Highly recommended for its forward-looking perspective on how LLMs and vibe coding are reshaping the software engineering landscape.
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