Curious how well this holds up across different traces. Instruments data can be pretty noisy and context-dependent. @jlreyes - do the derived views generalize, or do you end up encoding a lot of assumptions about what “jank” looks like?
Where the AI trips up is getting distracted by aggregate signals instead of digging deep into root causing specific frame drops, but I see humans and existing tooling getting distracted by that too.
Root causes are often context-dependent, but they tend to cluster into a handful of common issues. If you're able to enable the new swiftui instrument (from WWDC 2025), the entire attribute graph is encoded, and it can get you to the precise issue quite well.
> Where the AI trips up is getting distracted by aggregate signals
this shows up everywhere in agent loops. anytime I hand Claude a wide slice of anything it starts reasoning in averages. the moment I narrow to ~10 rows it locks onto the actual root cause. so derived views sound like exactly the right shape. how big do real traces get in your duckdb format? does a 5min scroll-heavy trace stay manageable =)