Exploring Claude/GPT Knowledge Cutoffs and Pre-Training Timelines
While all labs operate slightly differently, what you might see (~page 44) is:The “pre-training” team kicks off and babysits a multi-month run to get a base checkpoint. These pre-training models often, but not always, imply major versions of released models (GPT-4 → GPT-5).While that’s happening, the “capability” and “post-training” teams will run experiments for how to improve on the most recent base model. Advancements in post-training and capabilities often manifest as minor versions of released models.
- ▪While all labs operate slightly differently, what you might see (~page 44) is:The “pre-training” team kicks off and babysits a multi-month run to get a base checkpoint.
- ▪These pre-training models often, but not always, imply major versions of released models (GPT-4 → GPT-5).While that’s happening, the “capability” and “post-training” teams will run experiments for how to improve on the most recent base mode
- ▪Advancements in post-training and capabilities often manifest as minor versions of released models.
Hacker News (Front Page) files mainly under programming. We currently carry 1,379 of its stories. Top-voted stories on Hacker News.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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://blog.sshh.io/p/exploring-claudegpt-knowledge-cutoffs |
| Publication time | Mon, 10 Aug 2026 14:20:41 +0000 |
| Retrieval time | 2026-08-10T16:15:47.885Z |
| Last seen | 2026-08-10T16:15:47.885Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | rEktQlOT9OpM · 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.
Opening excerpt (first ~120 words) tap to expand
Exploring Claude/GPT Knowledge Cutoffs & Pre-training TimelinesAn analysis of what models know and what it tells us about how they were trained.Shrivu ShankarAug 10, 2026ShareWe can learn hidden facts about how frontier models were trained by “probing” them with carefully curated requests.By scoring them on niche facts we can approximate how many parameters models like GPT-5 and Opus have, using “Incompressible Knowledge Probes”By measuring how the models break down tokens we can reveal facts about the datasets mixtures they used to train the model (or at least the tokenizer) using “Data Mixture Inference”By scoring them on date or self-identification related questions you can also estimate training timelines (this post)Everything here is an estimate.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (Front Page).
Discussion
0 commentsMore from Hacker News (Front Page)
