John Ta

Elon musk, on lex fridman last month: "anyone not running a swarm of agents by the end of 2026 will look like someone who refused to use email in 1998" this is what he meant, in one screen recording: one guy. 50 claude agents. a full working system shipped in 30 minutes. he did not write a single line of code. he gave the orders and watched them execute in parallel, each one handling a different piece of the build meanwhile 90% of people reading this are still using claude the same fucking way they used chatgpt in 2023. one chat window. one task at a time. asking the same question in the same thread until it finally gets it right the setup costs you one evening to learn and pays back forever. whoever learns it this month will be running 10x what their coworkers ship by january. the ones who do not will still be slow-typing into a blank chat like it is still 2023 i wrote the full breakdown of how the 50-agent setup works, what each agent is told to do, and the exact claude code config behind it. the article is below the gap between the person who runs agents and the person who runs chats is about to become the only gap that matters. do not land on the wrong side of it

holy f*cking sh*t Karpathy put an entire AI degree on github for free while you are still paying $200/month to watch youtube tutorials from a guy with 3000 followers 7 repos. 306,741 stars combined. in the right order they take you from "how a neuron learns" to "agents that run research overnight while you sleep" every repo you skip is a layer of understanding your competition at work already has and you do not. and every month you keep "planning to start" is another month someone on your team gets handed the AI project you should have been running here is the exact order. do not reshuffle it 1. micrograd → github.com/karpathy/microgra… how a neural net actually learns. backpropagation in about 100 lines of python. this is where every AI engineer on earth started. if you cannot read this file, every other repo on the list is going to feel like black magic to you 2. nn-zero-to-hero → github.com/karpathy/nn-zero-… his full video course. lecture notebooks that walk you from a single neuron to GPT. the thing people pay $2000 for from AI "bootcamps" that teach a fraction of what is in this one free repo 3. minbpe → github.com/karpathy/minbpe how text actually turns into tokens. the tokenizer that sits inside every LLM, in clean minimal code. this is the part 95% of self-taught people skip, and it is why their models never work right 4. nanoGPT → github.com/karpathy/nanoGPT your own GPT. the simplest repo in existence to train and fine-tune one. after this file you stop asking "how does ChatGPT work" because you have literally built the smaller version with your own hands 5. llm.c → github.com/karpathy/llm.c the same training with nothing hidden. raw C and CUDA, no PyTorch wrapper doing the hard parts for you. this is where you stop being a prompt engineer and start being an actual engineer 6. nanochat → github.com/karpathy/nanochat a full chat model end to end. GPT-2 level training that cost ~$43,000 in 2019. in this repo it costs about $48. let that sit for a second. the thing people called a breakthrough is now a weekend project 7. autoresearch → github.com/karpathy/autorese… agents that do the research for you. the agent edits your training code, trains for 5 minutes, keeps whatever improved, discards whatever did not, and repeats all night while you sleep. this is where the people reading this list stop being students and start being whatever comes after that the brutal truth most people avoid: train one GPT from scratch and you become the person who gets handed the AI project at work, along with the raise that comes with it. keep "planning to start" and you become the person watching that happen to someone else the classroom half of the same degree, what Stanford actually teaches about AI for real, is below