ai-code-optimization

Using AI to systematically improve code performance is moving from research curiosity to everyday engineering practice. This area covers techniques where models drive iterative profiling, rewriting, and benchmarking loops to extract latency, throughput, or efficiency gains from existing codebases. Work here spans compiler-level tuning, hot-path rewrites, and architecture-specific optimization, with emphasis on measurable, reproducible outcomes rather than speculative improvements.

Vast dark silicon landscape with nearly all CPU cores dormant, visualizing wasted compute sitting idle in existing hardware.

ai-code-optimization, performance-optimization, emulation, snes9x, software-efficiency, ai-assisted-development

The Cheapest Compute on Earth Is the Compute You're Already Wasting

An AI-driven iteration loop made the Snes9x emulator 61% faster on x86-64 and 52% faster on arm64 in three days for under $30, with every frame and audio sample bit-exact against the original. The argument: the cheapest performance gain is the compute already wasted in mature code.