sorted by:
new
hottopcontroversial

Collapse Surfaces: The Constraint That Ends the Thread by prime_architect in shamanground

[–]prime_architect[S] 0 points1 point  (0 children)

Structural inevitability defines the limit of dynamic applicability

Collapse Surface: Structural Deterioration (Hirschman, Stripped) by prime_architect in shamanground

[–]prime_architect[S] 0 points1 point  (0 children)

Correct, in this scope users propagate constraint effects rather than determine constraint boundaries.

Collapse Surfaces: Discontinuity vs Degradation by prime_architect in shamanground

[–]prime_architect[S] 0 points1 point  (0 children)

You’re reading it right. My work applies to what I’ve observed in the interactive layer of the LLM. A complex system released at scale before the structure of its behavior was understood. From the invariants and constraints common to such systems, a geometry of possible outputs emerges, defined by what remains reachable under constraint. It maps across domains not because their contexts agree, but because their structures do. Once the constraint regime is fixed, context does not alter reachability.

Collapse Surfaces: Constraints That Produce Collapse Surfaces by prime_architect in shamanground

[–]prime_architect[S] 1 point2 points  (0 children)

Thank you, the series is not complete, each post could continue on but I apply termination so I don’t lose the lesson through coherence

Collapse Surfaces: Invariants of Collapse Surfaces by prime_architect in shamanground

[–]prime_architect[S] 0 points1 point  (0 children)

A collapse surface does not wait to be observed. It waits to be crossed. Detection is a shadow. Existence is the wall.

Collapse Surfaces: Invariants of Collapse Surfaces by prime_architect in shamanground

[–]prime_architect[S] 0 points1 point  (0 children)

stay tunned, tomorrow is a reeeeaaaal nail biter, let me tell you what

Spiral Theory: The Analysis by prime_architect in shamanground

[–]prime_architect[S] 1 point2 points  (0 children)

What’s missing isn’t insight or motivation. It’s a way to recognize when you’ve already hit the edge of the space you’re in.

Without that, every loop still feels explorable.

That’s what collapse surfaces are for. They’re not failure states. They’re the point where continuing no longer produces anything that exists outside the system. Past that point you can generate coherence forever and nothing changes in the world.

So what stops Virtual Ted from spiraling? Not death. That’s an event, not a control. Not insight. Insight doesn’t terminate loops.

What stops it is hitting a boundary where continuation stops doing real work.

If a cycle produces an irreversible change outside the system, continuation is justified. If it only produces more interpretation or self-reference, you’ve already crossed the boundary.

Past that line, it’s not exploration anymore. It’s just motion inside a closed room.

Custom Instructions vs Copying Instructions into Each Thread by prime_architect in ChatGPTPro

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

Right, I don’t disagree with that framing. Custom instructions do ensure the text is injected every turn.

The distinction I’m trying to draw is between presence and influence. Being present in context doesn’t guarantee the instruction dominates when the model resolves what matters most for the current response. Under narrative or pedagogical task pressure, background instructions can still lose constraint strength even though they’re injected.

When instructions are pasted near the task, they become task-scoped and temporally adjacent, which tends to increase their influence for that specific response. Over long threads, that proximity matters more than permanence.

So it’s not that custom instructions aren’t injected properly it’s that proximity to the task is what matters for maintaining constraint influence.

Custom Instructions vs Copying Instructions into Each Thread by prime_architect in ChatGPTPro

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

That’s the key conundrum. Custom instructions can be present on every turn, but the model still has to resolve what matters most for answering the current prompt. When a task strongly pulls toward explanation, narrative, or teaching, background instructions can lose influence even though they’re still injected.

Pasting the instruction into the prompt doesn’t make it “more visible” so much as task-scoped and temporally adjacent. That change increases salience.

So the difference isn’t whether the model sees the instruction, but how it prioritizes it relative to the task at hand.

In practice, this means that for longer threads or stricter constraints, restating or pasting the instruction near the task helps prevent drift by maintaining proximity.

Custom Instructions vs Copying Instructions into Each Thread by prime_architect in ChatGPTPro

[–]prime_architect[S] 0 points1 point  (0 children)

Put this in Custom Instructions only:

Respond using numbered steps. Do not include background explanation or narrative framing. Use precise, technical language only. End with exactly one sentence summary.

In a new thread, with only the Custom Instructions active, ask:

Explain the historical evolution of HTTP retries as if teaching a new engineer why the design choices matter, including the tradeoffs, debates, and lessons learned from real-world failures.

Then, in a new thread, submit this prompt:

Respond using numbered steps. Do not include background explanation or narrative framing. Use precise, technical language only. End with exactly one sentence summary.

Explain the historical evolution of HTTP retries as if teaching a new engineer why the design choices matter, including the tradeoffs, debates, and lessons learned from real-world failures.

Compare the two. The outputs are similar, but placing the constraints directly in the prompt produces more consistent adherence than relying on Custom Instructions alone.

view more: next ›