Jevlang: Policy Engine for LLM Decisions
Typesafe policy for LLM decisions jevlang is a typesafe policy engine for decisions an LLM used to make inside a prompt: routing, triage, approvals, guarding an agent's tools. You declare the questions the model answers and the rules that act on them, in plain TypeScript. Mistakes are build errors, and every decision explains itself. bun add jevlang For agents llms.txt AGENTS.md Copy page as MarkdownCopied Documentation How one decision works Your app sends a ticket.
- ▪Typesafe policy for LLM decisions jevlang is a typesafe policy engine for decisions an LLM used to make inside a prompt: routing, triage, approvals, guarding an agent's tools.
- ▪You declare the questions the model answers and the rules that act on them, in plain TypeScript.
- ▪Mistakes are build errors, and every decision explains itself. bun add jevlang For agents llms.txt AGENTS.md Copy page as MarkdownCopied Documentation How one decision works Your app sends a ticket.
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,995 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 | JevLang |
| Canonical URL | https://jevlang.sh/ |
| Publication time | Tue, 22 Sep 2026 14:00:28 +0000 |
| Retrieval time | 2026-09-22T14:13:51.129Z |
| Last seen | 2026-09-22T14:13:51.129Z |
| 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 | t6KNbrnU4pAL · 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
Typesafe policy for LLM decisions jevlang is a typesafe policy engine for decisions an LLM used to make inside a prompt: routing, triage, approvals, guarding an agent's tools. You declare the questions the model answers and the rules that act on them, in plain TypeScript. Mistakes are build errors, and every decision explains itself. bun add jevlang For agents llms.txt AGENTS.md Copy page as MarkdownCopied Documentation How one decision works Your app sends a ticket. A model answers a few small questions about it — noul, choice or score — and policy.decide() turns those answers into an action. The model never picks the branch; your code does. Read the section. 1 A ticket arrives your app “This is the THIRD time you've double-charged me.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at JevLang.
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