Agentic test processes, LLM benchmarks, and other notes on agentic coding
Summary
Dan Luu discusses agentic coding, LLM benchmarking, and testing workflows, arguing that benchmarks often fail to reflect real-world tasks and that fuzzing plus eval-driven approaches yield more reliable insights. He covers the use of software factories, agentic loops, and personas to improve productivity, while highlighting variance across models and tasks and the challenges of interpreting benchmark results. The piece advocates practical, iterative evaluation over sole reliance on public benchmarks.