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The only way you’re going to figure this out is by getting your hands dirty and seeing what works.
Claude 4 Sonnet loves complex dashboard visualisations. I have been playing with my Garmin data to better understand agentic future of data science research.
It is wild watching an AI agent pursue dependency chains with robotic determination, burning computational resources chasing “just one more fix.” It’s just what happens when you engage with complex systems, whether you’re carbon-based or running on silicon. The yak always needs shaving, apparently.
Wrote a short research paper with help from Cursor and based on the survey I did with v0 and distributed during my O’Reilly talk.
Human requests are binary: fix this thing, answer this question. But agents operate in probabilistic space, spawning subprocess after subprocess, each one justified by some internal logic tree I never asked for. The billing model assumes perfect alignment between what I want and what the machine thinks I need. Spoiler: there isn’t any.