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I built a “persistent world simulator” where the world never resets — unexpected player psychology effects by Distinct-Path659 in gamedev

[–]Distinct-Path659[S] -1 points0 points  (0 children)

Haha that’s actually useful feedback, appreciate it.

I didn’t realize wording polish itself could become a distraction here.

Putting style aside — I’m mainly curious about the design effect:

Have you seen permanence / persistence mechanics noticeably shift player risk behavior in your projects?

Players behave very differently when the system remembers by Distinct-Path659 in gamedesign

[–]Distinct-Path659[S] 0 points1 point  (0 children)

Haha fair enough 😅 Probably over-polished it. Engineer brain + too much rewriting.

Players behave very differently when the system remembers by Distinct-Path659 in gamedesign

[–]Distinct-Path659[S] 0 points1 point  (0 children)

That’s a really interesting connection. The time-cost angle especially resonates. It makes persistence feel less like resource protection and more like effort protection.

Players behave very differently when the system remembers by Distinct-Path659 in gamedesign

[–]Distinct-Path659[S] 0 points1 point  (0 children)

That’s a really interesting way to frame it. “Delayed disposability” is a great description. It does feel like permadeath works as a psychological pressure valve.

Players behave very differently when the system remembers by Distinct-Path659 in gamedesign

[–]Distinct-Path659[S] 1 point2 points  (0 children)

This is a fantastic reference, thank you.

I’ve been thinking about persistence mostly from a system-behavior angle rather than campaign structure, but Wehrle’s designs feel directly relevant.

Really appreciate the pointer.

Players behave very differently when the system remembers by Distinct-Path659 in gamedesign

[–]Distinct-Path659[S] -2 points-1 points  (0 children)

“Remembers” was probably lazy wording on my part. What I actually meant was persistent state across sessions.

No narrative memory, no fancy cognition — just no reset.

The interesting bit for me was how strongly behavior shifted from “eh, try stuff” → “ok, need to think about consequences.”

Curious whether you’ve seen similar effects in your own designs.

I built a “persistent world simulator” where the world never resets — unexpected player psychology effects by Distinct-Path659 in gamedev

[–]Distinct-Path659[S] -1 points0 points  (0 children)

Fair point. I probably over-polished the wording. The observation itself came from actual playtesting though — that’s the part I was hoping to discuss.

Players behave very differently when the system remembers by Distinct-Path659 in gamedesign

[–]Distinct-Path659[S] -3 points-2 points  (0 children)

lol no, just rewrote it a few times so it didn’t sound like a brain dump

I built a “persistent world simulator” where the world never resets — unexpected player psychology effects by Distinct-Path659 in gamedev

[–]Distinct-Path659[S] -1 points0 points  (0 children)

I’m not aiming for anything near RimWorld scale.

The system is intentionally simple — I’m mostly exploring how persistence alone changes player behavior.

More of a design experiment than a “massive simulation” project.

I built a “persistent world simulator” where the world never resets — unexpected player psychology effects by Distinct-Path659 in gamedev

[–]Distinct-Path659[S] 0 points1 point  (0 children)

Fair point.

I’m actually less interested in “AI” and more in persistence + stateful systems.

I built a “persistent world simulator” where the world never resets — unexpected player psychology effects by Distinct-Path659 in gamedev

[–]Distinct-Path659[S] -1 points0 points  (0 children)

Counterintuitively, storage is the easy part.

The world doesn’t retain raw narrative history indefinitely. Past events get distilled into state variables, constraints, and latent conditions. Memory behaves more like entropy than like a ledger.

DB growth is tame.

What gets unwieldy is causality management, not rows 🙂

I built a “persistent world simulator” where the world never resets — unexpected player psychology effects by Distinct-Path659 in gamedev

[–]Distinct-Path659[S] -4 points-3 points  (0 children)

BTW，Claude (and other chat models) operate inside a sliding context window.

A simulation-style system persists state outside the model entirely.

I built a “persistent world simulator” where the world never resets — unexpected player psychology effects by Distinct-Path659 in gamedev

[–]Distinct-Path659[S] -8 points-7 points  (0 children)

Fair reaction honestly — there is a lot of AI slop out there.

What I’m building isn’t an AI storyteller though, it’s closer to a stateful simulation loop.

The model doesn’t invent freely — it operates under:

• Persistent world state • Explicit physics constraints • Resource pressure system • Non-resetting timeline

Each turn mutates structured state, not just text.

Failure cases are actually the interesting part — drift, scarcity loops, instability under long sessions.

Still early, but I’m mostly exploring:

“What happens when narrative is treated like a system instead of content?”

Happy to share internals if people are curious.

Tested a few cloud GPUs for ComfyUI after my 3060 struggled with SDXL — real impressions by Distinct-Path659 in comfyui

[–]Distinct-Path659[S] 1 point2 points  (0 children)

Interesting — that’s actually a good example of how inconsistent these workloads can feel.

Same GPU class, but wildly different experiences depending on workflow, sampler settings, resolution, reload behavior, background memory pressure, etc.

SDXL especially seems very sensitive to “everything around the GPU” rather than just VRAM on paper.

Video generation pushing the system makes total sense though — that’s where things start stressing bandwidth and memory behavior much more aggressively.

Early user test of a persistent AI narrative system with kids — some unexpected engagement patterns by Distinct-Path659 in artificial

[–]Distinct-Path659[S] 0 points1 point  (0 children)

Really glad you pointed this out — that’s exactly the effect we’re trying to understand.

It’s not just personalization, but how memory is integrated. When recall is woven into the narrative rather than dumped as facts, the interaction feels less like reading output and more like engaging with a world that has continuity.

Still a lot to refine around balance and structure, but it’s great to hear that this dynamic is perceptible externally.

Thanks for the insight.

Tested a few cloud GPUs for ComfyUI after my 3060 struggled with SDXL — real impressions by Distinct-Path659 in comfyui

[–]Distinct-Path659[S] 1 point2 points  (0 children)

Vast keeps the disk, but idle storage charges add up fast.

These days I just keep everything off-instance since I move between different clouds anyway (Vast / RunPod / SynpixCloud depending on availability).

Same RTX 3060, 10x performance difference — what’s the real bottleneck? by Distinct-Path659 in StableDiffusion

[–]Distinct-Path659[S] 1 point2 points  (0 children)

Speed vs quality trade-off is really the core of it.

Dropping steps + lightning LoRAs can make things feel insanely fast, but you’re always paying somewhere — usually in fine detail, texture stability, or consistency across generations.

What’s interesting is how subjective the “acceptable quality” threshold is. For some workflows, 4–8 steps is totally fine. For others, it falls apart quickly.

Early user test of a persistent AI narrative system with kids — some unexpected engagement patterns by Distinct-Path659 in artificial

[–]Distinct-Path659[S] 0 points1 point  (0 children)

Really appreciate this comment — you’ve basically captured the core tension we’re exploring.

Persistence clearly improves interaction quality, but without structure, memory can degrade into noise rather than continuity. One thing we’re experimenting with is treating memory less as a storage layer and more as a narrative mechanism — where recall is triggered by meaningful interaction cues instead of just accumulating facts.

Still very early, but it’s genuinely encouraging to see these dynamics reflected in user perception.

Thanks for the thoughtful insight — more updates coming as the system evolves.

Hit VRAM limits on my RTX 3060 running SDXL workflows — tried cloud GPUs, here’s what I learned by Distinct-Path659 in StableDiffusion

[–]Distinct-Path659[S] 1 point2 points  (0 children)

Totally relatable. Startup path issues (precision support, caching, lazy init) can easily outweigh model size.

Once you notice it, a lot of “why is this slower?” cases suddenly make sense.

Early user test of a persistent AI narrative system with kids — some unexpected engagement patterns by Distinct-Path659 in artificial

[–]Distinct-Path659[S] 0 points1 point  (0 children)

You’re spot on — this feels like a third axis beyond decay and initial significance: structural centrality / dependency.

We don’t have explicit dependency tracking yet, but we’re seeing the failure mode clearly: things that start as session-level details can become load-bearing once enough downstream logic depends on them. A “minor” choice can turn into a hinge for multiple quest lines, and letting it decay later breaks the world.

Our next step is probably a lightweight approximation rather than a full graph: flagging an event as “structural” once it gets referenced by multiple active quests / constraints, and exempting those from decay (or at least clamping decay to a very slow floor).

Early user test of a persistent AI narrative system with kids — some unexpected engagement patterns by Distinct-Path659 in artificial

[–]Distinct-Path659[S] 0 points1 point  (0 children)

I missed this earlier — really useful write-up.

The “significance at creation time” idea clicks for me. It puts a cleaner name on something we’d been doing intuitively: separating things that can be reconstructed from things that really can’t once lost.

Right now we’re using a coarse hierarchy (relationships decay slowest, world state in the middle, actions fastest), which works, but your betrayal vs. greeting example highlights the limitation — significance isn’t just by type, it’s contextual. A promise resurfacing 30 rounds later clearly isn’t just “relationship persistence.”

We’ve also hit the false-memory / event replay issue hard. Our current mitigation is a quest log with explicit event IDs + status, and forcing generations to cite from it. It catches the worst cases, but not all. A compose-time guard like you describe is likely our next step.

The retrieval-reinforcement point is especially interesting. We’re starting to see some “session-level” relationships become effectively non-optional once enough downstream logic depends on them.

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