Sonkei: AI Shipped Product · Mobile
Match With the
Right Martial Arts
Training Partners
Martial artists traveling to new cities have no reliable, trust-layered way to find
vetted training partners.
Role
Solo Designer / Builder
Stack
Claude (product thinking +
prompt engineering +
prototyping) → Lovable
(React/Vite build) → Vercel
(deployment) + Microsoft
Clarity + Tally
Timeline
3 weeks
Year
2026
SONKEI - FILTERS
The Constraint
Safety couldn't be a feature layered
on top; it had to be the model.
Every design decision here was a trust decision first, and a feature decision second.
Sonkei couldn't treat safety as a feature layer added on top of a working product.
Every design decision - whether users can message immediately, whether
win/loss records appear, how many onboarding screens there are - was a trust
decision underneath. That reframe changed everything that followed.
3 Key Decisions
Three calls that built consent
into the product, not around it.
Each decision traded the faster pattern for the one that respected
what's actually at stake before two strangers train together.
Request-gating over open messaging.
Messaging unlocks only after both users accept a session request. Not a safety feature
bolted on; a model of bidirectional consent before physical contact. It also changes
behavior: a user who explicitly accepted your request shows up differently than one who just received a cold message.
Removed win/loss records from the data model.
Not just hidden; gone. A practitioner with a 0-0 record who is an excellent drilling partner
shouldn't be filtered out. A practitioner with a 12-3 record who is dangerous to train with
shouldn't be surfaced. The stats show Exp, Stance, and Belt. They communicate
compatibility without rewarding the wrong behavior.
Vertical scroll over swipe cards.
Choosing a training partner is a multi-variable decision: discipline, experience, availability,
location, belt rank. Swipe cards force a binary judgment before you have context. Vertical
scroll lets you compare. Less exciting, more honest about what the decision actually is.
The AI-Specific Insight
The spec document isn't
documentation; it's the design artifact.
“
AI-generated UI is locally coherent but globally
inconsistent without explicit upfront contracts.
Working with Claude and Lovable surfaced a specific failure mode: AI-generated UI is
locally coherent but globally inconsistent without explicit upfront contracts. The fix wasn't
better prompting; it was declaring a canonical state machine before writing a single
prompt, and referencing it by name in every subsequent one.
The spec document isn't documentation. It's the primary design artifact.
SONKEI - PROFILE
Outcome
Testers understood it without
instruction; and shared it unasked.
3 / 10
Shared Unprompted
Testers forwarded the link to someone
else without being asked; the
strongest validation signal on the page.
8 / 10
Completed Core Loop
Moderated testers completed the core
loop without instruction.
3 Weeks
Concept → Shipped
Solo, end to end: product thinking,
prompt engineering, build, and
deployment.
Most-cited insight: availability overlap visualization reduced evaluation cost before
any profile tap.
One Thing I'd Do Differently
Travelers and local seekers aren't the same user.
I'd validate the use-case split between travelers and local seekers before building the feed; they have different retention curves and the product needed to account for both from the start.
ALONSO ROSADO
AI-first Product Designer shipping full
products, end to end.