tdalaaJan 2023Strongly recommends
Anyone seriously interested in this question should listen to the Lex Friedman interview of Andrej Karpathy: ▶ this video
in “Will Tesla Ever “Solve” Full Self Driving? (2022)” · View on HN
Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI | Lex Fridman Podcast #333
Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI | Lex Fridman Podcast #333 on HackerTube: recommended by 3 Hacker News commenters across 3 separate months, 2022–2025.
Lex Fridman
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Lex Fridman's long conversation with Andrej Karpathy on Tesla's self-driving approach, Optimus, neural networks, transformers, AGI and more. It also touches on his OpenAI work and working with Elon Musk.
Why Hacker News recommends it
Cited for Karpathy's first-hand explanations of camera-only self-driving, transformers and ML trends.
- people recommended it
- 3
- separate months
- 3
- strong recommendations
- 1
- discussed on HN
- 2022–2025
“Anyone seriously interested in this question should listen to the Lex Friedman interview of Andrej Karpathy”
What HN commenters said
11 Hacker News comments link this video. Each one links back to its thread.
mekenDec 2022Recommends
My understanding is the transformer layer in the LLM is basically doing something akin to message passing, it’s like a mini computer. In predicting the next word, it has to understand a lot about a lot of different kinds of topics My understanding is kinda fuzzy because I haven’t coded it up myself, but this was the takeaway I got from this explanation (starts at 36:21) ▶ this video
in “” · View on HN
jcimsNov 2022Recommends
Andrej Karpathy was recently on Lex Fridman's podcast and covered this to some extent. He has the same perspective on this topic and expanded on it quite a bit. Great listen overall IMHO - ▶ this video I like his idea of finding 0-days in physics. :)
in “Study urges caution when comparing neural networks to the brain” · View on HN
kamranjonJul 2025Cites it
The part that I always found difficult to square was not that Elon Musk towed this “no need for LiDAR” line so hard, but that Andrej Karpathy, who I generally consider a very reliable voice in this space, was also in strong agreement that cameras were all that was needed. Does anyone know if he still believes cameras is all you need? Edit: Here is a link to Karpathy discussing the trade-offs: ▶ this video
in “How Tesla is proving doubters right on why its robotaxi service cannot scale” · View on HN
nomelFeb 2025Cites it
This, obviously, is one of the ongoing efforts and goals of Tesla AI, and the reason they collect so much data. There's some talk about it in the Lex Friedman podcast(s)[1]. [1] ▶ this video
in “Tesla Cybertruck Drives Itself into a Pole, Owner Says 'Thank You Tesla'” · View on HN
yregAug 2023Cites it
Karpathy explains the reasoning on Lex's podcast[1]. This was after he left Tesla, but of course he's hardly impartial. [1] ▶ this video
in “Camera Crushes Lidar, Claims Startup” · View on HN
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TepixMar 2023Cites it
Yeah, check out the Lex Friedman podcast episode #333 around minute 52 where Andrew Karpathy talks about the OpenAI project "World of Bits" that did this. ▶ this video
in “Microsoft Kosmos-1: A Multimodal Large Language Model” · View on HN
1970-01-01Mar 2023Cites it
>your boyfriend’s only in a wheelchair because he doesn’t want to kneel five times a day Within the roast context, this is a good line. Humor was one of the tests Karpathy would use as a benchmark for AI: ▶ this video
in “GPT-4 System Card [pdf]” · View on HN
nvernoNov 2022Cites it
If you want to get an idea, why not listen to some engineers that have worked with him? There are plenty of those around, for example, Andrej Karpathy talked a bit about working with Elon recently - ▶ this video
in “Twitter to employees: all office buildings closed, badge access suspended” · View on HN
nuccyNov 2022Cites it
As pointed out in [1], it seems machine learning takes the same path as physics already did. In the mid-20th century there was a "break" in physics, before individuals were making ground breaking discoveries in their private/personal labs (think Newton, Maxwell, Curie, Roentgen, Planck, Einstein, and many others) later huge collaborations (LHC/CERN, Icecube, EHT, et al.) are required, since the machinery, simulations, models are so complex, that groups of people are needed to create, comprehend and use them. 1. ▶ this video Lex Fridman podcast with Andrej Karpathy P.S. To counteract that (unintentionally actually, likely because of a simple optimization of instruments' duty cycle) in astronomy people come up with a concept of "observatory" (Like Hubble, JWST) instead of "experiment" (like LHC, HESS telescopes) where outside people can submit their proposals, and if selected get observational time. Along with raw data authors of the proposals get required expertise from the collaboration to process and analyze that data.
in “Stable Diffusion 2.0” · View on HN
jstx1Oct 2022Cites it
At 1:03:30 - ▶ this video > I think there's definitely scope in building a better search engine today. Google have all the tools, all the people, (...) all the data, it's just not obvious that they're capable as an organization to innovate on their search engine right now, and if they don't, someone else will. There's absolutely scope to build a better search engine based on these tools.
in “Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI” · View on HN
Topics & tags
Self-driving & autonomous vehiclesMachine learning & deep learningAI safety, AGI & impactandrej karpathytesla autopilottransformersagilex fridman