The core idea is simple: you play a text-based boss encounter against a character called the Architect, set in a strange library. You can fight, sneak, persuade, or try something I haven't thought of. Turns are mechanically resolved with d100 rolls, conditions track injuries instead of HP, and objects in the world have physical properties the LLM reasons about. The "engine" is property-based instead of tables of rules, and I've found that to yield some novel gameplay.
The part I'm most interested in exploring is the learning. The Architect builds impressions from what it actually perceived during an encounter, stores them as vector embeddings, and retrieves relevant ones at the start of future encounters. It's lossy on purpose — more like human memory than a database lookup. If a tactic keeps working, the Architect starts recognizing the pattern. If you sneak past undetected, it remembers losing but not how.
The technical foundation for all of this is a dual-LLM turn loop. Each turn makes two model calls: an engine model that sees full game state and resolves mechanics, then an architect model that only receives what it has actually perceived (line of sight, noise, zone proximity). The "information asymmetry" is structural and deliberate — the architect model literally cannot access state the engine doesn't pass through the perception filter.
I tried the single-LLM approach first and it didn't work. No matter how carefully you prompt a model to "forget" information sitting in its context window, it leaks. Not to mention the Architect had the habit of adopting God Mode. So splitting the roles made the whole thing feel honest in a way prompt engineering alone couldn't.
This is my first HN post, and this is a real launch on modest infrastructure (single Fly.io instance, small Supabase project), so if it gets any traffic I might hit some rough edges. There's a free trial funded by a community pool, or you can grab credits for $5/$10 if you want to keep going. It's best experienced in a full desktop browser, but it's passable on the two mobile devices I've tested it on.
Playable here: https://www.theaugur.ai/
I'm happy to go deeper on any of the internals — turn flow, perception gating, memory extraction, cost model, whatever is interesting.
thutch76•1h ago
If you want to dig deeper on the architecture, I wrote up the dual-LLM design and the memory system on my engineering blog:
- https://www.conecrows.com/blog/augur-soft-launch — covers the dual-LLM turn loop, perception gating, and why the single-LLM approach failed - https://www.conecrows.com/blog/augur-memory-v1 — covers impression extraction, vector embeddings, and lossy synthesis
The stack is Fastify, Next.js, Supabase (Postgres + Auth), and both OpenAI and Anthropic models for the engine and analysis. Encounters cost roughly $0.08–$0.15 per turn (internal cost) depending on state complexity and encounter length.