I'm afraid of spiders. So I made AI look at 2k of them
← All postsSeptember 11, 2026Benchmarks5 min readI’m Afraid of Spiders. So I Made AI Look at 2,000 of Them.By Dawid KiełbasaIn this article +First, approximately ten seconds of biologyHow the benchmark worksLet’s look at a few examplesOkay, who knew the spiders?How wrong is wrong?Accuracy versus costIs getting half wrong actually bad? A lot. 🕷️ My identification system currently consists of “the one with long legs” and “the short but big one.” There’s also “where the fuck did it go,” but that’s more of an emergency than a classification.
- ▪← All postsSeptember 11, 2026Benchmarks5 min readI’m Afraid of Spiders.
- ▪So I Made AI Look at 2,000 of Them.By Dawid KiełbasaIn this article +First, approximately ten seconds of biologyHow the benchmark worksLet’s look at a few examplesOkay, who knew the spiders?How wrong is wrong?Accuracy versus costIs getting
- ▪A lot. 🕷️ My identification system currently consists of “the one with long legs” and “the short but big one.” There’s also “where the fuck did it go,” but that’s more of an emergency than a classification.
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,795 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 | Labqoat |
| Canonical URL | https://labqoat.com/blog/how-well-can-ai-identify-spiders |
| Publication time | Mon, 21 Sep 2026 09:00:37 +0000 |
| Retrieval time | 2026-09-21T09:38:48.015Z |
| Last seen | 2026-09-21T09:38:48.015Z |
| 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 | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| 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
← All postsSeptember 11, 2026Benchmarks5 min readI’m Afraid of Spiders. So I Made AI Look at 2,000 of Them.By Dawid KiełbasaIn this article +First, approximately ten seconds of biologyHow the benchmark worksLet’s look at a few examplesOkay, who knew the spiders?How wrong is wrong?Accuracy versus costIs getting half wrong actually bad? I’m afraid of spiders. A lot. 🕷️ My identification system currently consists of “the one with long legs” and “the short but big one.” There’s also “where the fuck did it go,” but that’s more of an emergency than a classification. So I got curious: how well could AI actually identify them? I tested nine models on the same 2,000 spider photos to see how often they got the species right.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Labqoat.
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