In the world of AI-native tooling, it’s easy to guess that a model or an API is "fast enough." But as someone working deep in RLHF (Reinforcement Learning from Human Feedback), I’ve learned that assumptions are the enemy of production-grade systems.
I don't just describe tools; I measure them. Whether I'm auditing model responses or benchmarking API latency, my workflow is built around creating "rigs"—reproducible, data-driven scripts that reveal how systems actually behave under the hood.
My core philosophy:
Data over opinion: If I can't measure it, I can't improve it.
Reproducibility is non-negotiable: If my benchmark rig isn't shareable and reproducible, it doesn't count as research.
Honest results: If a tool fails a test, the data shows it. I publish the unflattering truths alongside the successes.
Building this mindset into my daily routine allows me to move faster without sacrificing the technical integrity that engineers trust. You can see my latest benchmark rig on my GitHub, where I compare real-world API latencies.
Rigorous research is slow, but it’s the only way to build software that actually scales.
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