Show HN: Building an LLM from Scratch in TypeScript
I wanted to understand transformers at a level where nothing is magic, so I'm building a ChatGPT-style model from first principles in TypeScript. Why not Python? I've found that reimplementing concepts in a different language forces me to grasp the nuance of the implementation. Chapters cover dataloaders, embeddings, and the attention mechanism so far. Feedback welcome if anything's unclear.
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Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Hacker News: Front Page |
| Canonical URL | https://book.humanexperiences.io/ |
| Publication time | Wed, 14 Oct 2026 10:29:56 +0000 |
| Retrieval time | 2026-10-14T10:38:35.783Z |
| Last seen | 2026-10-14T10:38:35.783Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher description |
| Excerpt method | Publisher-supplied description / RSS summary field. |
| Summary | None yet |
| Summary source text | description |
| Citation coverage | No WeSearch summary has been generated for this story yet. |
| Cluster | A1hHTWh12n6O · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.