Show HN: Scrubbed – fast native web-data cleaning for LLM training
scrubbed One native binary for turning raw web pages into clean training and evaluation data. Why Demo Benchmarks Install Usage Status Source README Why Encoding damage, boilerplate, personal information (PII), and duplicate pages quietly degrade training data. Fixing them often means chaining several Python tools.
- ▪scrubbed One native binary for turning raw web pages into clean training and evaluation data.
- ▪Why Demo Benchmarks Install Usage Status Source README Why Encoding damage, boilerplate, personal information (PII), and duplicate pages quietly degrade training data.
- ▪Fixing them often means chaining several Python tools.
Hacker News (AI / LLM) files mainly under ai. We currently carry 7,207 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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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 | Github |
| Canonical URL | https://schancel.github.io/scrubbed/ |
| Publication time | Thu, 01 Oct 2026 18:19:10 +0000 |
| Retrieval time | 2026-10-01T18:22:31.805Z |
| Last seen | 2026-10-01T18:22:31.805Z |
| 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 | OmFEfK4isjp1 · 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
scrubbed One native binary for turning raw web pages into clean training and evaluation data. Why Demo Benchmarks Install Usage Status Source README Why Encoding damage, boilerplate, personal information (PII), and duplicate pages quietly degrade training data. Fixing them often means chaining several Python tools. Their dependencies may conflict and force separate environments. Deployment can also involve building or pulling large container images before the first document is processed. scrubbed puts encoding repair, main-content extraction, and PII scanning in one native binary, with language ID and near-duplicate decisions as optional stages. That means one artifact to deploy and fewer handoffs between tools.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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