Show Notes
Choosing the right AI model shouldn’t feel like roulette. We sit down with Kate Catlin, the product manager from GitHub. Kate shares how model strengths and explains how auto mode aims to pick the right model for the job so developers can focus on outcomes.
We dig into practical tactics that cut through hype: start with a golden dataset, run evaluations early, and keep refining with real user prompts once your AI is live. If you’re overwhelmed by weekly model releases, you’re not alone—Kate outlines how to compare new options with scoring and selective manual review. She also tackles the enterprise challenge: slow model approvals that leave teams on outdated systems. With a disciplined eval pipeline, organisations can safely adopt newer, faster, and often cheaper variants that deliver better results.
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Bernat Agulló is a data platform engineer based in Barcelona who speaks Spanish, English, Japanese, and some German. Agulló has been a Microsoft MVP since 2022 and recently renewed the status for a fifth year. His professional background in data engineering began around 2018, following earlier experience with Excel macros, SQL, and data pipelines like SSIS. He gained recognition in the community by focusing on niche topics such as calculation groups and scripting, areas that were not widely covered by other presenters at the time.