Ribosome traffic model shows Jev triaging run summaries for review

Kurt @0xkurtOriginally posted 2026.10.25

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I CHANGED ONE RULE. OUTPUT FELL FROM 73 TO 29. THE JAM BUILT ITSELF. I built a small ribosome-inspired traffic model with two lanes, a rule log and a control you can change. The moving particles get their positions from the model state. Both lanes have 72 positions. Both attempt to launch a particle every four ticks. Each particle needs three positions of space, and nothing can pass through the particle ahead. Then I changed the dwell at position 35 from one tick to eight. Everything else stayed the same. After 360 model ticks, 73 particles had finished the baseline lane. Only 29 had finished the changed lane. The rule engine counted 2,385 blocked move attempts. Nobody had to place the queue on a timeline: particles reached occupied space and couldn't move. Change the dwell back to one and the lanes match again. The same code produces the positions, the completion counts and the event log, so you can inspect why a particle stopped. That's what makes an AI-built model interesting to me. Change an input and see which outcome changes. Check the rules. Inspect the trace. A convincing image becomes much more useful when there's a system you can question underneath it. This is a teaching model with designer-set delays, not a real gene or a validated prediction of protein synthesis. It doesn't model frameshifts, folding or measured tRNA availability. The cell is a stylized view of the experiment. In a larger agent workflow, Jev could triage run summaries for review while Claude investigates the flagged cases. Here, the execution and the checks are local code; no model API is running behind the counters. The Jev article below explains that decision layer. What would you change first: the launch interval or the slow position?

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