Cutting the product down
to what's worth building.
A solo-built AI job-search coach — shipped, live, and better for losing two of its five features.
Origin
40+ applications in flight —
my own job search.
Six disconnected tools: a resume editor here, salary data there, a tracking spreadsheet, prep notes scattered across apps. The product idea was the frustration itself:
"Why isn't there one place that coaches you through the whole journey?"
V1
Five pillars. I was proud of the scope.
01
AI job search
02
Resume optimization
03
Interview story bank
04
Market insights
05
Application tracking
Reality · blow zero
The feature I was proudest of.
AI job search — personalized matching that ran your full profile against live listings, with a reason attached to every match.
The most ambitious build in v1. The technical centerpiece. The feature I showed people first.
The pitch
Your profile × live listings
= a reason for every match
Reality
The reality check.
Blow 1 · user testing
Too slow — and not better.
Processing a profile against listings takes time, and the results weren't better than LinkedIn — whose data moat I can't match.
Blow 2 · unit economics
Burning tokens on every search.
Personalized search is expensive to run. A solo builder on freemium can't carry a feature that costs money on every use.
Method
AI builds whatever you ask.
It never pushes back on scope.
Saying no stayed my job. So instead of asking AI to build, I asked it to evaluate — turning the machine that never questions scope into the scope's harshest critic.
The prompt that reshaped the product
"Rank every feature I have by market uniqueness vs. cost to run. Predict which ones people can already get elsewhere."
Decision
Killing a feature that worked.
My second-proudest feature: the resume editor. AI-assisted, line by line. It shipped, it functioned, users liked it. Nothing about it argued for its own death.
Then the market spoke: Teal — $20M in funding, 4M users — a crowded, well-funded category doing exactly this. A solo builder is always behind.
Working isn't a reason to keep something —
being worth building is.
Research
Every competitor is strong in 1–2 pillars — weak in the rest.
Decision
The cut.
01 · LinkedIn owns the data moat
AI job search
✕02 · Teal: $20M funding, 4M users
Resume optimization
✕03 · no one offers structured STAR stories
Interview story bank
04 · woven into coaching, not a destination
Market insights
05 · the spine connecting the other two
Application tracking
Sub-story · v2, the atmosphere
The cut decided what to build.
Then: how should it feel?
Walk into a room — you feel something before you read anything. Every interface is a space; its light, materials and noise set a mood before any feature loads. For a product people open on their worst days, the mood is the emotional value. So the v2 redesign started from a moodboard, not a wireframe.
Space
The interface as a room
↓
Atmosphere
Light, material, noise
↓
Mood
The emotional value of the product
Sub-story · where v2 started
The moodboard came before the wireframes.
Sub-story · method
Teaching the machine to feel the room.
An AI can't be told "make it cozy." It can be shown a space and taught to extract what makes the atmosphere — held as a system, while the taste calls stay human.
Dopamine Bauhaus · 70/20/10
Sub-story · mood → function
Every response corrects your position.
The mood earns its keep in function. Tracking isn't a filing cabinet — every answer that comes back, rejection or next step, is a data point about your standing. The market feature accumulates them into a continuously corrected position: where you actually are, updated as the market keeps talking.
APPLIED3
INTERVIEW1
CLOSED2
Your market position
Design decision
JD storage.
The interview call comes weeks later — and by then the job posting is gone. Jobpilot saves the JD at application time and builds the prep page around it.
"Save this now — future you will thank you."
Principle
Design for the high-anxiety moment, not the entry moment.
Design decision
No sign-up wall.
The full product works anonymously — job seekers are drowning in logins, and value comes before the ask. Sign-up appears only when it buys the user something: opening your story bank on another device. Build stories on your phone, rehearse from your laptop.
The cost: dual-mode storage — local-first, with a seamless merge on account creation. The user never feels it.
Worth noticing
Here, the design decision and the technical decision were the same decision.
Opportunity
Why these two are the killer features.
The habit loop
JD storage & story bank
Both demand frequent return visits — check, update, rehearse. Not one-shot tools: a loop that builds toward a moment.
The moment AI can't take
The interview itself.
Real-time human interaction. AI can prepare you for it; it can't do it for you.
Reflection
The product got better
when it got smaller.
AI made it easy to say yes to everything — the hard part was saying no. And the atmosphere chapter is the same lesson from the other side: once the product was small enough to be honest, it could afford to feel like somewhere.
The question I now ask first
Where in this product is the irreplaceable human moment?
How I build
Live, instrumented, from day one.
User-testing data loops back in, and AI reviews scope against market uniqueness and running cost before anything gets built. Design keeps the veto.