I’ve been working on a harness called MOOLLM (MOO + LLM), applying Sims‑style object advertisements and github repo as microworld in filesystem to Cursor. I've written up the approach here and given some examples:

MOOLLM Repo:

https://github.com/SimHacker/moollm/blob/main

The Eval Incarnate Framework:

https://github.com/SimHacker/moollm/blob/main/designs/eval/E...

Text Adventure Approaches:

https://github.com/SimHacker/moollm/blob/main/designs/text-a...

This is a practical attempt to make memory and world state explicit, inspectable, and cheap.

Quick replies to a few points:

@nitwit005 / @pflenker / @woggy / @zetalyrae

Training data could include transcripts, but salience is weak. The bigger failure mode I’ve seen is harness design: long transcript vs. structured state. I try to make it observable and auditable so it doesn’t rely on accidental recall.

@mnky9800n / @apples_oranges / @throwway262515 / @falcor84

I don’t think “return to symbolic AI” is a retreat. It’s scaffolding. LLMs do the fuzzy interpretation, but the symbolic layer keeps state, causality, and constraints visible. MOOLLM’s bias is “combine, don’t choose.”

@daxfohl

Backtracking in mazes is exactly why I externalize geometry and action affordances. If the map is a file and exits are explicit, the agent can stop re‑discovering dead ends. It also separates “solve the maze” from “play the maze.”

@lukev / @skybrian / @twohearted / @fragmede

Agreed: memory is a tool, not a dump. I use file‑based memory types (characters, maps, rooms, inventory, goals, episodic summaries) and explicit affordances (cards with The Sims style "advertisements", like CLOS generic dispatch meets Self multiple prototypical inheritance). It’s closer to “human with tools” than “human on a whiteboard.”

@CephalopodMD / @wktmeow / @imiric

I think periodic summaries + structured memory work better than full transcript reuse. Cache helps with cost, but structure helps with reasoning. If a model can ask for “full history” occasionally, that’s a nice escape hatch.

The cursor-mirror skill can search and query text chats and sqlite databases that Cursor uses to store chat state as structured intertwingled data.

https://github.com/SimHacker/moollm/tree/main/skills/cursor-...

The thoughtful-commitment skill composes with the cursor-mirror tool to reflect on the cursor chat history, and write git commit messages and prs that relate cursor activity, prompts, thinking, file editing, and problem solving with git commits -- persisting transient cursor state into git comments explaining the prompts and thoughts and context that went into each commit.

https://github.com/SimHacker/moollm/tree/main/skills/thought...

@tibbon / @brimtown / @kaiokendev

Love these experiments. I’m trying to make the harness composable in Cursor with inspectable context, so you can understand why it did what it did. That’s where cursor‑mirror fits.

@PaulHoule

Yes. Models are trained on transcripts, not on custom memory tools. So the memory tool has to be shaped like a game object—explicit state, small interface, clear affordances. If you want to see the Cursor introspection tooling: skills/cursor-mirror/ in the repo. It shows what the agent read, what tools fired, and how context was assembled.

On the visual side, I documented the full “vision feedback stack” in a session with Richard Bartle. It’s a 10‑stage loop: narrative → incarnation → ads → prompt crystallization → prompt synthesis → render → context‑aware mining → YAML‑fordite layering → slideshow synthesis → photos as actors. The key idea is “YES, AND”: generated images become canon, mining extracts coherent meaning, and the slideshow locks the narrative and visual continuity for future turns. Full write‑up: Visual Pipeline Demonstration.

Visual Pipeline Demo to Simulated Familiar of Richard Bartel (MOO):

https://github.com/SimHacker/moollm/blob/main/examples/adven...

Slideshow Index:

https://github.com/SimHacker/moollm/blob/main/examples/adven...

Master Synthesis Slideshow (threading multiple parallel slideshows happening at the same time):

https://github.com/SimHacker/moollm/blob/main/examples/adven...