Show HN: FKS2G – LLM-backed metrics for deciding how closely to review code
FKS2G is a tool designed to assist developers in determining the level of scrutiny required during code reviews. It utilizes various metrics, including LLM assessments and historical data, to evaluate the risk associated with code changes. The software aims to streamline the review process and reduce the likelihood of shipping problematic code.
- ▪FKS2G helps developers decide how closely to review code changes.
- ▪It collects data such as cosine similarity and LLM judgments to inform review depth.
- ▪The tool is currently in a 0.X software stage, indicating it is still in development.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,516 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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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/kmdupr33/fks2g |
| Publication time | Thu, 21 May 2026 05:47:48 +0000 |
| Retrieval time | 2026-05-21T05:55:03.490Z |
| Last seen | 2026-05-21T05:55:03.490Z |
| 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 | qY4iKayloj2G |
| 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
fks2g Since code review is the bottleneck now, fks2g helps developers decide how closely to review code. Its for the devs who have already tried this method of reviewing code: And for devs who have realized that this code review strategy leads to a finger pointing situation when bugs or bad architecture gets shipped: To inform how closely to review a code change, the CLI collects: cosine similarity between file-name embeddings and configurable project text sources an LLM judgment about whether the closest files are likely to change soon based on source documents recent bug-fix commits classified by an LLM file change frequency from git history an LLM final risk assessment based on the collected evidence Usage OPENAI_API_KEY=<KEY> npx fks2g analyze -- --repo ../react --github-repo…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.
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