Until 18 months ago, I wrote every line of code myself. Today, AI writes 80% of my initial implementations while I focus on architecture, review, and steering multiple development threads simultaneously.This isn't another "AI will change everything" post. This is about the messy reality of integrating AI into production development workflows: what actually works, what wastes your time, and why treating AI like a "junior developer who doesn't learn" became my mental model for success.The backstory: We run monthly engineering workshops at Sanity where someone presents what they've been experimenting with. Last time was my turn, and I showed how I'd been using Claude Code. This blog post is from my presentation at our internal workshop (10-min recording below).My approach to solving code problems has pivoted four times in my career:For the first 5 years, I was reading books and SDK documentation.Then 12 years of googling for crowd-sourced answers.It was 18 months of using Cursor for AI-assisted codingAnd recently, 6 weeks of using Claude Code for full AI delegationEach transition happened faster than the last. The shift to Claude Code? That took just hours of use for me to become productive. Here's what my workflow looks like now, stripped of the hype. I use AI mostly "to think with" as I'm working with it towards the code that ends up in production.Forget the promise of one-shot perfect code generation. Your job as an engineer is to find the best solution for the problem, not just write a bunch of code.Claude builds context about your systemYou identify the actual challengesThe code is usually completely wrongThen you take the learnings from this attempt and feed it back.Claude understands the nuancesYou've defined concrete approachesHalf the time, it's still unusableClaude implements something we can iterate on and refineYou constantly review and course-correctThis becomes your starting point, not your final codeThis isn't failure; it's the process! Expecting perfect...
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Last seen: 2025-09-03 16:56