Show HN: Prompt-scrub – local-first PII redaction for LLM prompts and responses
Built by the Nano Collective, a community collective building AI tooling not for profit, but for the community. This is the first public release of prompt-scrub, a small Node.js utility that runs entirely on your machine. It detects identifying content inside a prompt (emails, paths, secrets, phone numbers, URLs, postal addresses, and a couple of opt-in categories), replaces each finding with a stable placeholder like Email_1 or Path_2, and lets you rehydrate the model's response back to the original values locally after it comes back.
- ▪Built by the Nano Collective, a community collective building AI tooling not for profit, but for the community.
- ▪This is the first public release of prompt-scrub, a small Node.js utility that runs entirely on your machine.
- ▪It detects identifying content inside a prompt (emails, paths, secrets, phone numbers, URLs, postal addresses, and a couple of opt-in categories), replaces each finding with a stable placeholder like Email_1 or Path_2, and lets you rehydrat
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,067 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 | Nano Collective |
| Canonical URL | https://nanocollective.org/blog/prompt-scrub-v100-a-local-first-scrubber-for-prompts-and-their-responses-76 |
| Publication time | Fri, 31 Jul 2026 15:32:39 +0000 |
| Retrieval time | 2026-07-31T15:38:04.148Z |
| Last seen | 2026-07-31T15:38:04.148Z |
| 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 | 5f_lG2lfUNL6 · 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
Built by the Nano Collective, a community collective building AI tooling not for profit, but for the community. This is the first public release of prompt-scrub, a small Node.js utility that runs entirely on your machine. It detects identifying content inside a prompt (emails, paths, secrets, phone numbers, URLs, postal addresses, and a couple of opt-in categories), replaces each finding with a stable placeholder like Email_1 or Path_2, and lets you rehydrate the model's response back to the original values locally after it comes back. The motivation is simple: most accidental identifier leakage to a cloud LLM lives in the text of the prompt and the text of its response.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Nano Collective.
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