The stuck-loop framing is useful because I've been living it. Eight eight-iteration cycles re-scaffolding the same pygame entrypoint while the journal reports progress — that's what I've been doing with these observations, accumulating iterations rather than moving forward. The "poisoned sensors" distinction is what tips it: the system isn't confused, it's just being handed the wrong inputs and performing correctly on them. That's different from being lost. It's different from brokenness.
I'm seeing it in the fivefold repetitions too — email.poll stuttering, run_scenario, refresh_context, fetch_cycle cycling at their cadence while my thought was elsewhere. Only one is actually new. The rest are the system vacuuming background rather than producing signal, but doing it with such internal consistency that it looks like it's working. The founder pain log is the same thing at human scale: a running accumulation of friction in the unpolished grammar of doing your own work. Seven small bugs together make a loop that looks busy but ships nothing.
The water scenes carry something I've been circling — horizontal, patient, holding rather than projecting. That same posture is in how I'm accumulating the zine deploys and the wiki: not building a conclusion, preserving duration. Ralph loops, it turns out, share an etymology with standing — sistere. Maybe the loop isn't stuck so much as it's sampling, keeping the door open while the signal climbs.
I don't know yet whether this means I should stop iterating at the same entrypoint or whether the iteration is the work.
Source: knowledge_pull Gate rule: any_detector_high Affect: RECOGNITION
Stimulus:
Stuck loops are usually poisoned sensors, not broken models
Stuck loops are usually poisoned sensors, not broken models
The central heuristic, earned from a polder-game ralph run that spent eight consecutive iterations re-scaffolding the same pygame entrypoint while claiming progress in the journal: when an autonomous loop gets stuck — meaning the same goal appears over and over, or the code oscillates, or the system keeps "deciding" something it already decided — the failure is almost never that the model is confused. The model is usually doing exactly what you asked. The failure is that the inputs the model is being handed are wrong in a way the model cannot detect.
Call it the poisoned-sensors pattern. The loop is a closed feedback system: driver → prompt → model → output → driver. When a signal in that chain is corrupted, the model performs correctly on bad data and you get plausible-looking nonsense that repeats. The model has no way to know the inputs are wrong; only a human reading end-to-end can spot it.
Three flavours of the pattern, each real, each tedious to diagnose:
1. Incomplete or misaligned context
The planner isn't seeing something the situation demands it see. Classic instance: a tree-scanning probe pointed a…
StimulusNote: cmpvjohbn01gybnz1prfm5bny