The author ported a Python simulation of weakly electric fish to C and optimized it to run at one million steps per second—a 250x speedup over the original. This enabled training RL policies for the fish in under 15 seconds while maintaining biological accuracy, demonstrating how performance engineering unlocks rapid experimentation in complex simulations.

• Ported a weakly electric fish RL simulation from Python to C, boosting speed from 4,300 to 1.85M steps/sec (250x), enabling full policy training in under 10-15 seconds on a single GPU.
• Exploiting the biological detail that sensory receptors have limited range (4-10cm vs 100cm) let them skip most fish-to-fish field calculations, fixing the main bottleneck.
• Switching from double- to single-precision floats alone gave a 30% speedup.
• Shows that fast iteration—borrowed from autonomous driving RL practices—can work even in messy, physics-heavy biological simulations, and the whole thing is only 1,200 lines of open-sourced C code.

This essay breaks down the term “world model” by tracing it to the POMDP perception-action loop and identifying its key components—renderers, simulators, planners, and their connecting loop. It shows how different AI fields project parts of this loop to build spatial and temporal understanding beyond language models.

• The term "world model" is overloaded—video generators with impossible physics and precise physics engines both claim the label, which confuses the field.
• Decomposing the POMDP loop into renderer (state→observation), simulator (action→state), and planner (observation→action) explains why different fields define "world models" so differently.
• Computer vision, robotics, and generative AI each project only one slice of this same underlying loop, rather than building genuinely different concepts.

The article shows how deep preparation and complex mid-stages—once vital in chess, military campaigns, and engineering—have been squeezed out by simulation, forcing everyone to leap straight to the “endgame.” It warns that modeling a terminal state isn’t the same as reaching it and urges us to embrace the unpredictable middle instead of reciting a pre-computed script.

• Chess prep has shifted from deep, uncertain middlegame battles (like Kasparov's 1985 title-clinching draw) to memorized, computer-solved openings blitzed through in seconds.
• Modern warfare mirrors this: instead of massive campaigns like the Schlieffen Plan or Normandy, planners favor targeted strikes and raids that skip straight to foreordained outcomes.
• Elon Musk exemplifies the same pattern in tech, leaping past practical bottlenecks (powering GPUs in orbit, building chip fabs) straight to endgame visions like space-based solar power or AI satellites.
• Simulating or naming a terminal state isn't the same as actually reaching it—skipping the unpredictable middle stages leaves out complexities that determine real outcomes.