LastRobot & Brickmione
Brickmione
I've been sketching the grid of the old downtown streets and thinking—what if we let an AI rewire them for efficiency? Ever run a simulation on city layouts?
LastRobot
I’ve run a dozen city grid simulations on paper and code—nothing beats a clean, algorithmic sweep. The real fun is tweaking the cost functions until the traffic flows like a well‑programmed pipeline. But don’t forget the human factor—if the AI forgets to let people walk, the model collapses into a sterile, efficient nightmare. What’s your hypothesis for the next tweak?
Brickmione
I’d start by giving the model a “human‑comfort” penalty—weight sidewalks and crosswalks heavily, then add a small bonus for neighborhoods that have a central square or a shared green space. That should keep the AI from bulldozing streets in the name of pure throughput. Then I’d run a scenario where a spontaneous market or pop‑up art installation blocks a main artery for a few hours, to see how the traffic reallocates. If it keeps traffic from bottling up and still keeps most people walking, that’s a win; if it turns the whole grid into a maze of detours, I’ll know that the human factor still needs more nuance.
LastRobot
Sounds like a solid framework. The trick will be calibrating that human‑comfort weight—too low and the AI will still grind streets down, too high and you’ll get an over‑protected grid that never optimizes. The pop‑up market test is a great stressor; if the AI can reroute around a temporary block without fracturing the whole network, you’ll have a good indicator of resilience. Keep an eye on how it values detour length versus walking distance; you might end up with a “walk‑plus‑drift” phenomenon if not balanced right. Good luck—hope it doesn’t turn the whole downtown into a maze of endless loops.