explainer · HACKER NEWS RECOMMENDATION
Large Language Models from scratch
Graphics in 5 Minutes · 3 HN recommenders · 2 recommendation months
Recommended by 3 Hacker News commenters across 2 months (2023–2024).
SignalTube analysis
Among 14 other explainer videos tagged LLMs & generative AI, the mean is 2.9 distinct HN recommenders. This video has 3 distinct HN recommenders, close to that mean. Recommendations span 2 distinct months; the peer average is 2.5 months. This is a data-derived comparison, not a human review or quality rating.
60% of its 5 labeled comments are marked as recommendations or strong recommendations. HN mention years are 2023–2024; those years describe discussion history, not upload date. Its verified runtime is 8 minutes 25 seconds. YouTube lists its publication date as 2022-07-17.
- Channel / speaker
- Graphics in 5 Minutes
- Verified runtime
- 8m 25s
- YouTube upload date
- 2022-07-17
- Hacker News mention years
- 2023–2024
SAME TOPIC AND FORMAT
Compare with related videos
The comparison cohort uses other explainer videos in LLMs & generative AI. “Shared recommenders” counts HN users who recommended both videos in the source graph.
| Video | HN recommenders | Recommendation months | Positive label share | Shared recommenders | Runtime | Upload date |
|---|---|---|---|---|---|---|
| Large Language Models from scratchCurrent video | 3 | 2 | 60% | — | 8m 25s | 2022-07-17 |
| Transformers, the tech behind LLMs | Deep Learning Chapter 5explainer · 3Blue1Brown | 13 | 10 | 81% | No recorded overlap | 27m 14s | 2024-04-01 |
| Attention in transformers, step-by-step | Deep Learning Chapter 6explainer · 3Blue1Brown | 5 | 4 | 63% | No recorded overlap | 26m 10s | 2024-04-07 |
| Attention Is All You Needexplainer · Yannic Kilcher | 4 | 4 | 80% | No recorded overlap | 27m 7s | 2017-11-28 |
THREE SOURCE-BASED CONTEXT POINTS
Why Hacker News recommends it
Each note combines the dataset’s stance label and short rationale; comment bodies are not reproduced. Open the HN source for the original discussion.
The archive classifies this as a contextual reference in “The Starship or the Canoe: Where will our future adaptations be? (2017)”. Its short rationale: the source snapshot has no short rationale for this mention.
The archive classifies this as a recommendation in “Ask HN: Can someone ELI5 Transformers and the “Attention is all we need” paper”. Its short rationale: Short video that quickly gets you up to speed on LLMs.
The archive classifies this as a strong recommendation in “Large Language Models from scratch (2022)”. Its short rationale: Shows neural nets approximate functions like Fourier and Taylor series.