u/FactivalUniverse

I’m researching this for a thriller and want the institution to behave plausibly rather than like a giant conspiracy.

I’m especially interested in how a compromised system can continue functioning when only a relatively small number of people are actively corrupt.

In real organisations, what mechanisms allow that to happen?

For example, can corruption persist through things like compartmentalised information, fear of retaliation, career incentives, procedural obedience, selective enforcement, management pressure, people assuming someone else has checked something, or employees seeing only one small part of a larger pattern?

I’m also interested in the people who are not corrupt themselves. How might an honest employee unknowingly help a corrupt outcome happen simply by following normal procedure?

And what tends to happen when someone inside such a system begins noticing inconsistencies but does not yet have enough evidence to prove deliberate wrongdoing?

I’m looking for real-world organisational dynamics rather than advice on plotting the story.

I’ve been experimenting with a workflow where AI is deliberately kept away from final creative authority and used more as an external audit layer.

Instead of asking one model to “improve” a manuscript, I’ve been testing the same material across multiple models and comparing where they agree or disagree on things like:

• continuity
• character motivation
• pacing
• repeated exposition
• physical realism
• information leaks
• causality
• scene logic

What I’m finding interesting is that the models are often more useful when they disagree than when they agree.

If five models all flag the same problem, that’s useful. But if only one model spots something the others miss, that can also be valuable — especially if it’s a concrete continuity or logic issue rather than a taste preference.

The harder part is deciding what to do with conflicting recommendations without letting the AI collectively rewrite the author’s intent.

For people using AI seriously in long-form fiction:

Do you get more value from AI as a critic/auditor than as a co-writer?

And if you use multiple models, how do you separate:

• genuine recurring weaknesses,
• model-specific taste,
• hallucinated problems,
• and useful outlier catches?

I’d be especially interested in workflows where the human author keeps final authority rather than treating model consensus as automatically correct.

Random Karachi memory question.

Did you grow up hearing some version of the Karsaz ki Dulhan story?

I’ve realised people seem to remember very different versions of it — bride on the road, accident story, apparition, late-night warning, etc.

I’m less interested in whether the story was “real” and more interested in what version people in Karachi actually remember hearing.

So:

What were you told?
When did you first hear it?
And what did people call her?

Would be fun to compare how much the story changed from family to family and generation to generation.

I’m working on a multi-scene visual project where the subject, location and time period can change completely from one sequence to the next.

Individual AI-generated shots can look convincing on their own, but the harder problem is making them feel as though they belong to the same film.

I’ve been trying to control things like:

• color palette and grade
• lighting philosophy
• lens / camera language
• composition
• texture and production design
• overall visual density

But when the subject changes radically, there’s still a point where the sequence starts feeling like a collection of separate generated images rather than one cinematic world.

For those of you working on multi-scene AI films:

What do you treat as the strongest continuity anchor when characters, locations and even time periods keep changing?

Do you primarily lock the camera language? Lighting? Grade? Production design? Or is there another layer that has made the biggest difference for you?

I’ve been looking closely at AI-generated human characters across different communities, and one thing keeps standing out: a character can look “similar” from image to image and still not feel like the same person.

For me, consistency seems to break in several different ways:

• facial proportions drift even when the general likeness survives;
• age appears to change slightly between shots;
• hairstyle and hairline move around;
• body proportions shift;
• wardrobe details mutate;
• lighting changes the perceived identity too aggressively;
• and once the character moves into a different camera angle, the illusion often falls apart.

What I’m interested in is the point where a recurring AI model stops being a sequence of attractive images and starts feeling like an actual persistent character.

For those of you building recurring models here, what has mattered most in practice?

Identity references? LoRAs? Fixed wardrobe? Camera discipline? Prompt structure? Training data? Manual correction?

And which dimension tends to fail first when you move from a single portrait into a real multi-shot sequence?

I’d be especially interested in hearing from people who have maintained the same character across different angles, locations and lighting setups.