Hacker News front pageApr 24, 2026, 14:48 UTC+8

Show HN: How LLMs Work – Interactive visual guide based on Karpathy's lecture

The author published an interactive web guide that walks through the LLM pipeline, using example figures of 15T training tokens, 405B parameters, 44TB of text, and a 100K-token vocabulary. The post breaks down Common Crawl data collection, BPE tokenization, Transformer training, temperature-based sampling, and base-model behavior; this is not a new research release but an operational teaching resource based on Karpathy's lecture.

Why it matters: HKR-H and HKR-K pass: the interactive guide turns data collection, tokenization, training, and sampling into a clickable walkthrough with concrete figures. HKR-R is weaker because this is an adaptation, not a new release or claim, so it sits at the low featured edge.

Read the original ↗Export Markdown

Take

This is useful because it turns the usual LLM explainer into a runnable mental model. The page anchors the pipeline with 15T tokens, 405B parameters, 44TB of text, and a 100K-token vocabulary, then lets readers poke through Common Crawl filtering, BPE, Transformer training, and temperature sampling. The loss demo moving from 11.2 to 2.4 is exactly the kind of concrete hook most intro material lacks.

I’d be careful with the numbers. They smell like a Llama 3.1 405B-scale example, but the article does not claim a real training recipe. Karpathy’s lecture works as a simplified engineering map; the interactive layer makes it easier to mistake that map for the territory. Post-training, data mix, eval contamination, serving systems, and product behavior are where teams bleed time, and this guide only gestures at them.

Source: Hacker News front page · ynarwal.github.io
Published: Apr 24, 2026, 14:48 UTC+8 · Apr 24, 2026