Full-Stack AI Engineer
Now I build AI products while keeping my day job as VP—and I write about the journey. The wins, the failures, and everything in between.
What brings you here?
Frameworks, operating models, and a complete course for senior leaders
Explore →From zero coding to three shipped AI products — the full roadmap
Explore →Credit, healthcare, savings, and relocation guides from a fellow expat
Explore →Course, STRATUM, DIALOGUE — three AI products
Explore →Thoughts on AI, learning, and building
Two weeks after my original comparison, both tools shipped major updates. Codex challenged my product strategy in ways Claude Code did not. Claude Code shipped Agent Teams and AutoMemory. The result: I am cutting my $200/month Max plan — and getting better output for less money.
Most conversations about AI and team design start with headcount. I think that is the wrong starting point. The better question is which functions your team needs — and those turn out to be the same whether you have four people or forty.
DIALØGUE supports 7 languages, but the real multilingual work was not translating strings. It was fixing audience-local dates, TTS consistency, UI language drift, and deciding where quality mattered enough to slow down.
I spent years in advertising watching teams confuse motion with progress. Then I started building AI marketing tools and realized the problem was getting worse: faster execution, weaker judgment.
AI can now produce media plans, performance summaries, measurement frameworks, and campaign setups at impressive speed. The problem is not that the output is obviously bad. The problem is that it is often good enough to pass a casual review while missing the business context that actually matters.
Most teams still ask which model to use. From my experience, that is no longer the main question. If your AI system forgets the client or the brand, the category, and what good looks like, the smartest model in the world still starts every conversation from zero.