Meta's Muse is fantastic for web scraping
Meta’s Muse is fantastic for web scrapingOct 1, 2026Scott Cooperaiweb-scrapingfullsetsI haven’t found much use for the personal assistant part of Muse. People on X (Twitter) keep using these assistants to order DoorDash or something. I order DoorDash like five times a year, so that’s not going to speed anything up for me.
- ▪Meta’s Muse is fantastic for web scrapingOct 1, 2026Scott Cooperaiweb-scrapingfullsetsI haven’t found much use for the personal assistant part of Muse.
- ▪People on X (Twitter) keep using these assistants to order DoorDash or something.
- ▪I order DoorDash like five times a year, so that’s not going to speed anything up for me.
Hacker News (Front Page) files mainly under programming. We currently carry 2,390 of its stories. Top-voted stories on Hacker News.
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 | sigh.dev |
| Canonical URL | https://sigh.dev/posts/metas-muse-is-fantastic-for-web-scraping/ |
| Publication time | Fri, 02 Oct 2026 04:56:02 +0000 |
| Retrieval time | 2026-10-02T05:25:43.918Z |
| Last seen | 2026-10-02T05:25:43.918Z |
| 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 | VSiDnsteJNPQ · 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
Meta’s Muse is fantastic for web scrapingOct 1, 2026Scott Cooperaiweb-scrapingfullsetsI haven’t found much use for the personal assistant part of Muse. People on X (Twitter) keep using these assistants to order DoorDash or something. I order DoorDash like five times a year, so that’s not going to speed anything up for me. I also don’t get many emails, and managing my appointments is easy enough already. What I do need is a bunch of data from Reddit and YouTube and a boatload of cheap AI tokens. That’s where Muse comes in handy. I’m building fullsets.fm, a side project that aggregates professionally recorded concerts (festival livestreams, Tiny Desk, etc.), organizes them by artist, and extracts tracklists. I’m looking for complete performances with good audio and video.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at sigh.dev.
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