Building the Ed-O-Meter: Notes on Writing My Own LLM Benchmark
The article discusses the creation of Featherbench, a compact harness for testing large language models (LLMs) directly, comparing model quality, latency, cost, and refusal behavior. The author argues that public benchmark scores may not accurately measure a model's performance in specific domains, and that running one's own tests can provide more relevant results. By using Featherbench, users can run 28 fixed tasks against any model and grade them, with the goal of finding the smallest model that still meets their needs.
- ▪Featherbench is a single Python file that runs 28 fixed tasks against any model and grades them.
- ▪The Ed-o-meter LLM Leaderboard is produced by the Featherbench harness, providing a transparent and reproducible comparison of model performance.
- ▪Running one's own tests can help avoid the problem of benchmark maxxing, where models are optimized for public suites rather than real-world performance.
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Record
| Original publisher | Reinvently |
| Canonical URL | https://reinvently.co.uk/blog/building-the-ed-o-meter-llm-eval-harness/ |
| Publication time | Fri, 07 Aug 2026 14:02:46 +0000 |
| Retrieval time | 2026-08-07T14:05:44.990Z |
| Last seen | 2026-08-07T14:05:44.990Z |
| 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 | Qx9VIbHz8jO2 · 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
← All posts At a glance Featherbench is a compact, single-file harness for testing LLMs directly,comparing model quality, latency, cost and refusal behaviour. Provider and model variables are controlled, versioned so the comparison is reproducible. Refusals from models are counted as failures Its 28 tasks test simulate realworld usage scenarios rather than focusing on a single area. Task variety is prioritised over repeated trials: five categories weighted toward realworld use, each run at least once, with the resulting uncertainty shown honestly rather than smoothed away. Every model launch arrives with a wall of benchmark scores: MMLU, GPQA, SWE-bench, an alphabet of acronyms that grows with each release. The numbers are always fascinating.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Reinvently.
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