Eval Lab exists to document how AI systems behave under measurement. The focus is practical: benchmark teardowns, evaluation design, research notes, and implementation trade-offs that are easier to build from than vendor marketing or vague product claims.

I use this site to write through the details that usually get skipped: what a benchmark is actually testing, which scores transfer to real workflows, where agent architectures add useful structure, and where they mostly add latency and complexity.

Our point of view

The most useful evaluation writing is specific. Instead of asking whether a model is "the best," I care more about what kind of failure a benchmark can reveal, what inference setup produced a score, and what trade-offs appear when you recreate the workflow yourself. The goal is to make the reasoning inspectable enough that you can adapt it to your own stack.

What we cover

• Benchmark deep-dives — ARC-AGI, Humanity's Last Exam, SWE-bench, LiveCodeBench, GPQA, and other benchmarks explained in terms of the capability they isolate and the shortcuts they fail to catch.
• Evaluation design — practical notes on internal eval sets, LLM-as-judge pipelines, cost-and-latency trade-offs, and what to log when you want results you can trust.
• Agent architecture notes — control loops, memory design, graph orchestration, tool-use policies, and the failure modes that show up once an agent starts acting instead of only answering.
• R&D paper reviews — research papers rewritten for builders who want the method, the insight, and the limitations without reading around the topic for three days.
• State-of-the-field reports — periodic snapshots of where public benchmarks are still informative and where the leaderboard has turned into noise.

Who it’s for

This site is for practitioners who need a sharper mental model of evaluation: engineers building agent systems, data scientists designing experiments, and technical decision-makers who want to understand what a benchmark score really buys them.

How the articles are written

• Show the mechanism — explain what the benchmark or system is doing, not just whether it scored well.
• Show the trade-offs — include cost, latency, brittleness, and where an architecture stops being worth it.
• Show the messy middle — note the failure modes, false starts, and implementation details that usually disappear from polished summaries.