LAB / AI-ASSISTED DEVELOPMENT
Building this portfolio like a product.
I designed and built this site using AI-assisted development. I used product strategy, art direction, iterative prototyping and QA to shorten the distance between an idea and something I could inspect, test and improve.
THE PROBLEM
A résumé can show what shipped. It doesn't show how I think nearly as well: what I noticed, which trade-offs mattered, how I worked with technical constraints, and why one path was chosen over another.
I wanted a portfolio that could do both jobs: let a recruiter understand the signal quickly, while giving a product leader or engineer enough depth to see the reasoning underneath the outcome.
The constraint was that the site also had to stay coherent as the content grew. Seven case studies could not become seven unrelated microsites.
THE PRODUCT BRIEF
Design for the skim. Reward the deeper read.
The primary audience was hiring teams: recruiters, product leaders and engineers evaluating whether the work was credible and whether I could operate at senior scope.
That led to a simple design rule: every case needed a fast path through problem → decision → trade-off → outcome, with enough evidence underneath for someone who wanted to keep reading.
KEY DECISION
Don't use AI to skip thinking. Use it to shorten the build–learn loop.
Optimize for output first and hope the system becomes coherent later.
Use AI to accelerate prototyping, implementation and iteration inside clear constraints.
THE LOOP
AI was most useful when it compressed the distance between a decision and something tangible enough to critique. The loop stayed familiar; the handoff time inside the loop changed.
AI-ASSISTED, NOT AI-OWNED
The tool accelerated execution. Accountability stayed human.
Front-end implementation
Copy and visual critique
Repetitive implementation work
QA assistance and code inspection
Case-study narrative and evidence
Art direction and visual-system decisions
Factual accuracy and confidentiality
Final implementation and quality decisions
QUALITY CONTROL
The biggest risk with fast generation is accepting something because it looks finished. I treated every output as a draft until it survived the same questions I would ask of a product artifact.
WHAT CHANGED
WHAT I LEARNED
AI shouldn't replace product judgment. It should shorten the distance between an idea, an artifact and what you can learn from it.
The useful shift for me was not “I can generate a website.” It was realizing how much closer a product person can now stay to the thing being built. That still means respecting the expertise of designers and engineers and being clear about where the tool ends and accountability begins.
THE POINT OF THE EXPERIMENT
Get closer to
the thing being built.
AI accelerated the loop. Product judgment still decided what deserved to ship.
See the product work →