How to Create Your Own Personal AI Benchmark
Nobody hires a vice president based on their SAT scores. Yet every time a new model drops, AI researchers first check how well it does on a Math Olympiad and a set of multiple-choice trivia, then argue online about whether it’s the smartest model in the world. Wharton professor Ethan Mollick points out that Massive Multitask Language Understanding-Pro (MMLU-Pro), one of the most-cited benchmarks, asks models for the approximate cranial capacity of Homo erectus and the place named in the title of Cheap Trick’s 1979 live album.
- ▪Nobody hires a vice president based on their SAT scores.
- ▪Yet every time a new model drops, AI researchers first check how well it does on a Math Olympiad and a set of multiple-choice trivia, then argue online about whether it’s the smartest model in the world.
- ▪Wharton professor Ethan Mollick points out that Massive Multitask Language Understanding-Pro (MMLU-Pro), one of the most-cited benchmarks, asks models for the approximate cranial capacity of Homo erectus and the place named in the title of
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| Original publisher | Hacker News (AI / LLM) |
| Canonical URL | https://every.to/also-true-for-humans/how-to-create-your-own-personal-ai-benchmark |
| Publication time | Wed, 23 Sep 2026 02:48:23 +0000 |
| Retrieval time | 2026-09-23T03:04:30.198Z |
| Last seen | 2026-09-23T03:04:30.198Z |
| 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 | TLF6BtwSFyMA · 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 |
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| 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
Was this newsletter forwarded to you? Sign up to get it in your inbox. Nobody hires a vice president based on their SAT scores. Yet every time a new model drops, AI researchers first check how well it does on a Math Olympiad and a set of multiple-choice trivia, then argue online about whether it’s the smartest model in the world. Wharton professor Ethan Mollick points out that Massive Multitask Language Understanding-Pro (MMLU-Pro), one of the most-cited benchmarks, asks models for the approximate cranial capacity of Homo erectus and the place named in the title of Cheap Trick’s 1979 live album. Those tests measure something—the scores are directionally useful—but not what you need to know: Can this model help you with your job? To answer that, you need to build a personal benchmark.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
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