Here I can
Notebook entries tagged workflow.
…one of the biggest differences I've seen between people that really know how to build agentic workflows compared to people that are less effective at it is the ability to drive a disciplined development process, specifically one focused on evals and error analysis.
If you're going to really exploit the capabilities of these new tools, you need to be operating at the top of your game. You're not just responsible for writing the code - you're researching approaches, deciding on high-level architecture, writing specifications, defining success criteria, designing agentic loops, planning QA, managing a growing army of weird digital interns who will absolutely cheat if you give them a chance, and spending so much time on code review.
- You learn more per unit time because you make contact with reality more frequently
- Going fast makes you focus on what's important; there's no time for bullshit
- "Slow is fake"
- A week is 2% of the year
- Time is the denominator
“Scaling laws are real, ofc. ++ model power keeps raising the ceiling. But capability ≠ adoption. The moat in workflow AI is bottling institutional context and making it useful in the moment of work.”
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“Pretrained models are powerful, but not turnkey. Extracting Quinn’s win rate, UCSF’s pathways, or Zappos’s ethos is closer to RL than pretraining. You shape behavior with constraints and feedback until it fits the environment. Workflow products do that shaping.”
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“The Bitter Lesson is obvi right: scaling keeps raising the baseline. But raw capability isn’t adoption, because no user wants to give superintelligence full context all the time. App winners are the ones who bottle context into systems trusted at the point of action.”