LLM-friendliness as a metric: porting 20 languages with an LLM
My original goal was to compare performance and memory usage across languages — and the results are available at LangArena. Along the way, I collected a lot of data — code size, compilation times, and so on. One of the tables I put together was an Expressiveness metric.
- ▪My original goal was to compare performance and memory usage across languages — and the results are available at LangArena.
- ▪Along the way, I collected a lot of data — code size, compilation times, and so on.
- ▪One of the tables I put together was an Expressiveness metric.
Hacker News (AI / LLM) files mainly under ai. We currently carry 6,261 of its stories.
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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 | Github |
| Canonical URL | https://kostya.github.io/LangArena/llm_friendliness.html |
| Publication time | Thu, 24 Sep 2026 10:15:08 +0000 |
| Retrieval time | 2026-09-24T10:35:25.648Z |
| Last seen | 2026-09-24T10:35:25.648Z |
| 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 | Jf3YCPQ5xUWw · 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
LLM-friendliness as a metric: porting 20 languages with an LLM What code size reveals about working with AI I recently completed a large experiment: I took a benchmark suite written in Crystal (51 tests covering sorting, parsing, algorithms, compression) and ported it to 19 other programming languages with the help of an LLM. The process took about two months. My original goal was to compare performance and memory usage across languages — and the results are available at LangArena. Along the way, I collected a lot of data — code size, compilation times, and so on. One of the tables I put together was an Expressiveness metric. After a while, I realized this metric was unexpectedly revealing about the experience of working with different languages.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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