Overllm – flags where you're paying an LLM to do a regex's job
overllm Catch the LLM/AI calls you didn't need. overllm is a small, fast linter with one job: find the places in your code where you call an AI model to do something plain code does better. You called a model to extract JSON that json.loads already handles. You are paying latency, money, and nondeterminism for a regex.
- ▪overllm Catch the LLM/AI calls you didn't need. overllm is a small, fast linter with one job: find the places in your code where you call an AI model to do something plain code does better.
- ▪You called a model to extract JSON that json.loads already handles.
- ▪You are paying latency, money, and nondeterminism for a regex.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,336 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 | GitHub |
| Canonical URL | https://github.com/theadamdanielsson/overllm |
| Publication time | Sat, 04 Jul 2026 18:45:16 +0000 |
| Retrieval time | 2026-07-04T19:00:17.958Z |
| Last seen | 2026-07-04T19:00:17.958Z |
| 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 | nNRYYM3spkEs |
| 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
overllm Catch the LLM/AI calls you didn't need. overllm is a small, fast linter with one job: find the places in your code where you call an AI model to do something plain code does better. You called GPT to parse a date. You called a model to extract JSON that json.loads already handles. You are paying latency, money, and nondeterminism for a regex. It reads your code with a real parser: Python through the standard-library ast, and JavaScript and TypeScript through tree-sitter. No model runs, no network, no API key. Same code in, same result out. Fast enough for a pre-commit hook. Everyone else lints the code the AI wrote. overllm catches where you are paying an AI to do what a library already does.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.