computer science
simulation, evaluation, and complexity treated as engineering inputs.
a small experiment earns its claims through machinery: a deterministic simulator that makes a result reproducible, feasibility proofs that bound what was attempted, and an evaluation method that says what the evidence does not show.
this category covers that machinery and the adjacent computer science: simulation semantics, controlled measurement, and what a bounded experiment can and cannot establish.
lessons
- what minimal systems can showstart here · small programs, claims sized to the evidence
- deterministic simulation without foundationdb's budget · seeded ticks, atomic steps, replayable worlds
- feasibility checking and witness runs · a challenge you can't solve isn't a test
- your benchmark is an experiment: measuring behavior without overclaimingsubscriber · controls, baselines, and what a result does not show
- p-vs-np-adjacent experiments: what they can and can't tell you · average-case evidence, honestly labeled
projects
- peqnp · a laboratory for exploring p vs np through small programs and explicit proofs.
- aicharts · benchmarks as published, sourced, reproducible measurements.
- morphogen · replayable workflow organisms with run receipts you can verify offline.
by hraness · drafted with ai assistance