HackerTube.dev

Deep Dive into LLMs like ChatGPT

Andrej Karpathy · 10.2M views · 2025-02-05 · 3:31:24

Open on YouTube ↗

If playback is restricted in your region, try opening the original video. Use Mark watched to add it to your marked videos.

Overview

Andrej Karpathy's three-hour-plus general-audience deep dive into how ChatGPT-style LLMs are built, covering the full training stack from pretraining to post-training. It also gives mental models for their quirks, like hallucination, and how to use them well.

On Hacker News

HN's most frequent recommendation for a clear, approachable foundation in how LLMs work without heavy math.

Recommended by 10 HN people across 8 months · HN mentions 2025–2026

Topics

Search by tags

What people said

etothet · 2026-03 · Strong recommendation

Even if you have some basic understanding of how LLMs work, I highly recommend Karpathy’s intro to LLMs videos on YouTube. - [VIDEO] - [LINK]

Read the original HN comment ↗
romanhn · 2026-02 · Strong recommendation

I highly recommend Andrej Karpathy's Deep Dive into LLMs like ChatGPT: [VIDEO] . It's a 3.5 hour general intro to the topic that I think does a great job of making sense of the tech and how it works under the hood, without needing to build up a foundation of math and programming.

Read the original HN comment ↗
pengaru · 2025-11 · Strong recommendation

This is like the LLM era version of the search bubble that prevented people from having the same search results for ostensibly identical searches. Also keep in mind that LLMs are stochastic by design. If you haven't seen it, Karpathy's excellent "deep dive into LLMs like chatgpt" video[0] explains and demonstrates this aspect pretty well: [0] [VIDEO]

Read the original HN comment ↗

Sources

Video by Andrej Karpathy, played through YouTube.

Data and selection notes

HN summaries and labels may contain AI errors. HN dates refer to discussions, not video publication.

HN Library: HackerTube export (2026-10-31) and open-index/hacker-news. Upstream summaries and classifications may contain AI errors.

Views, duration and playback status refresh separately from the published catalog. When an update fails, existing data remains visible.

Selection checks
  • SaaS & AI candidates: Matches the configured criteria
  • Editor-approved picks: Editorial review is required

Rule matches identify candidates. They do not guarantee video quality or financial results.

About the library and its sources