System One Lite – typed decisions from a local LLM, with no generated tokens
System One Lite A tiny project that turns a normal local LLM into a typed decision engine. It needs no fine-tuning, text generation, or parser. Language models are brilliant at producing text.
- ▪System One Lite A tiny project that turns a normal local LLM into a typed decision engine.
- ▪It needs no fine-tuning, text generation, or parser.
- ▪Language models are brilliant at producing text.
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,190 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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/snellingio/system-one |
| Publication time | Wed, 16 Sep 2026 14:31:30 +0000 |
| Retrieval time | 2026-09-16T14:38:41.582Z |
| Last seen | 2026-09-16T14:38:41.582Z |
| 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 | wqrn2E04QBMe · 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
System One Lite A tiny project that turns a normal local LLM into a typed decision engine. It needs no fine-tuning, text generation, or parser. Read the docs. Stop asking language models to write. Start making them decide. Language models are brilliant at producing text. Software does not want text. It wants a route, a score, a yes or no, and an honest signal when the answer is unclear. So why are we still asking models to write tiny essays? We parse those essays back into data, validate the data, retry failures, and hope nothing goes off the rails. System One Lite deletes the essay. Send one unstructured state plus up to 64 typed questions. A local model scores only the answers you allow and returns a probability distribution for every question. Unstructured state in.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.