Orcrist: A Coding Agent using LLM state machines
Orcrist is a DSL for state machines whose states are executed by an LLM, and a desktop coding agent that runs on it. Before touching anything it writes an Orcrist machine for that task, grounded in the grammar and the authoring guide in this repo, and then executes that machine one state at a time. Each state's prompt goes to the model, the model works with real tools, the runtime measures what it can and records what the state is declared to report, and the machine's guards decide what happens next.
- ▪Orcrist is a DSL for state machines whose states are executed by an LLM, and a desktop coding agent that runs on it.
- ▪Before touching anything it writes an Orcrist machine for that task, grounded in the grammar and the authoring guide in this repo, and then executes that machine one state at a time.
- ▪Each state's prompt goes to the model, the model works with real tools, the runtime measures what it can and records what the state is declared to report, and the machine's guards decide what happens next.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,603 of its stories.
Story provenance
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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/simone20a/Orcrist |
| Publication time | Sat, 12 Sep 2026 15:45:39 +0000 |
| Retrieval time | 2026-09-12T16:45:11.138Z |
| Last seen | 2026-09-12T16:45:26.603Z |
| 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 | Ne3BhasjS_3G · 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
Orcrist is a DSL for state machines whose states are executed by an LLM, and a desktop coding agent that runs on it. You give the agent a task. Before touching anything it writes an Orcrist machine for that task, grounded in the grammar and the authoring guide in this repo, and then executes that machine one state at a time. Each state's prompt goes to the model, the model works with real tools, the runtime measures what it can and records what the state is declared to report, and the machine's guards decide what happens next. The run ends when it reaches a final state. The point is the one the authoring guide makes: a to-do list only contains what was explicitly asked for, and has no answer for what happens when a step doesn't go as planned.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.