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Andrej Karpathy
Senior Director of Artifical Intelligence at Tesla
Andrej Karpathy is a Slovak-Canadian computer scientist who has made significant contributions to the field of artificial intelligence, particularly in deep learning and computer vision.1 He was the director of AI and Autopilot Vision at Tesla.1 Karpathy is also a co-founder and former member of OpenAI.1 As of July 16, 2024, Karpathy announced that he started a new AI+Education company called Eureka Labs.1
Early Life and Education::
- Karpathy was born in Bratislava, Czechoslovakia (now Slovakia), on October 23, 1986.1
- At age 15, he moved with his family to Toronto, Canada.1
- He obtained his bachelor's degrees in Computer Science and Physics from the University of Toronto in 2009.1
- In 2011, he earned a master's degree from the University of British Columbia, where he researched physically simulated figures with advisor Michiel van de Panne.1
- He completed his PhD at Stanford University in 2015 under Fei-Fei Li, specializing in natural language processing, computer vision, and deep learning models.1
Career and Research::
- Stanford: Karpathy authored and instructed Stanford's first deep learning course, CS 231n: Convolutional Neural Networks for Visual Recognition, which grew to be one of the largest classes at Stanford.1
- OpenAI: He was a founding member and research scientist at OpenAI from 2015 to 2017.1
- Tesla: In June 2017, Karpathy became Tesla's Director of Artificial Intelligence, reporting to Elon Musk.1 He led the Autopilot computer vision team.23 After a sabbatical, he left Tesla in July 2022.12
- Return to OpenAI: It was reported that Karpathy returned to OpenAI in February 2023 but left a year later.1
- Eureka Labs: Karpathy started a new AI+Education company called Eureka Labs on July 16, 2024.1 According to Eureka Labs, their first product will be the AI course, LLM101n.1
Karpathy was named one of MIT Technology Review's Innovators Under 35 for 2020.1 He likes to train deep neural nets on large datasets.3
Highlights
Weekend hack: HackerTube. I was annoyed with YouTube's recommendations so I had Claude filter down 43M comments over 20 years of HN to 6,454 videos that keep being recommended. Significantly better viewing experience, ~2 hours with Opus 5.5: https://t.co/JIhXN7OSh1
- Excellent recs without any logins (state is kept in local browser session)
- Thumbnail is a representative frame from middle of the video instead of the super duper A/B test hyper-optimized user uploads
- Underlying data from the comment analysis as one zip file, point your Claude at it and ask for your own version
- HackerTube Longs ✊
A bit more on the process: I use Claude Code (desktop app for Mac), Opus 5.5 on Medium effort as my default. I only talk to Claude and have it do everything, the only "manual work" I helped with in tandem was in the browser (reminiscent of MenuGen): 1) I had to create some kind of API key from Google Cloud UI to query YouTube for video metadata, 2) I booted up a VPS box for serving and 3) I bought the domain name. Once the box was up I gave Claude the IP and told it update, secure, harden, deploy and optimize the code, and it did the whole thing.
Many awesome projects that would have normally taken weeks/months even can now be few-shotted for breakfast. What else are we not thinking of.

Dusting off this tweet from April because this gap in shared understanding of LLM capability is widening. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
- The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
- The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
- Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
