AI helps me build software. Product judgment is still the work.
Most ideas start with something I notice: a complaint, an awkward workflow, or an existing product that feels one feature short. I search the App Store for alternatives, then ask Claude or ChatGPT to challenge the idea. I want the reasons it could fail, not automatic encouragement.
- 01
Notice
Start with a complaint, awkward workflow, or missing feature.
- 02
Challenge
Research alternatives and ask AI to make the case against the idea.
- 03
Scope
Define the user, the central feature, the screens, and the business model.
- 04
Build and test
Run it on real devices, inspect failures, revise, and decide whether to ship.
When something fails, I start with the evidence: the build error, the Xcode console, the n8n execution log, the Supabase query. The logs usually tell me which layer broke, whether that’s the data, an API call, the model, or the UI. Once I can describe the problem precisely, I bring AI in, test the fix, and keep going until the real behavior changes.
AI writes a lot of my code. I design how the pieces fit together: Ripple’s 11-source news pipeline, the streaming that cut its wait time from roughly 13–14 seconds to about 2–3 seconds, and Yapper’s call flow through telephony, AI, webhooks, billing, and the customer dashboard. I own the product decisions, implementation, quality bar, testing, release, marketing, and the outcome when an idea does not work.