The first tool in the toolchain proper: a system that scores app ideas, niches and markets before I commit any build time — so I only build things worth building.
The problem
Deciding what to build is the most expensive decision I make, and for years I made it on gut feel. I'd get attached to an idea, research it just enough to confirm what I already wanted to believe, and only discover it was a bad market after I'd sunk weeks into building. The costly failure in product isn't a bad build — it's building the wrong thing well. What I needed wasn't more ideas; it was a way to kill the bad ones cheaply, before a line of code, with evidence instead of enthusiasm.
The manual version of that took the better part of a week per idea — trawling listings, reviews, forums and search data by hand — and still came out subjective. I was both the bottleneck and the bias.
My approach
I treated idea selection like moneyball: replace the gut call with a repeatable, evidence-scored funnel that judges a whole field the same way every time. Cast a wide, cheap net; rank everything by a transparent score; spend real effort only on the handful at the top; and put each finalist through a rubric that is deliberately hard to please and defaults to "no."
The fuel is data, kept deliberately broad: I point the engine at interesting signals — what people already use and complain about, what they're actually searching for, and which businesses have already won — and run them through four systems that get progressively deeper, and more expensive, as an idea earns the right to more scrutiny.
What I built
Four connected systems, arranged as a funnel from cheap-and-wide to expensive-and-deep.
App Radar is the front door. It sweeps a whole field of apps wide and cheaply, then ranks every one by an opportunity score — a blend of real demand, a weak incumbent, and a neglected, out-of-date product, on the theory that the best openings are big markets served badly. The sweep is deliberately cheap and refuses to spend on the costly analysis until I've shortlisted the few candidates worth it.
App Analysis is the deep dive on a shortlisted app. It mines real user complaints into structured pain points, checks each one against genuine search demand, then runs a "death-trap" screen — can it actually make money, can I afford to acquire the users, and can this realistically be built — before scoring the whole opportunity against a Real·Win·Worth rubric. The rubric is calibrated to be skeptical (most proven, popular apps come out a no for a solo builder), the final verdict is computed rather than guessed, and a single fatal flaw can veto an idea however well it scores elsewhere.
Niche Analysis zooms out from one app to a whole market. It builds demand-and- competitor dashboards that answer a single question — is there a defensible wedge a solo builder could enter and own, or is it a treadmill where any fast-follower catches you? The verdict — wedge, conditional, or no wedge — is deterministic, tied to real demand and how beatable the leader is.
Case Studies keeps the whole thing honest. It's a library of businesses that already won, reverse-engineered into a common structure and distilled into a playbook of what actually worked. That real-world precedent is what calibrates every score above — it's the reason the engine can confidently tell me a popular idea is still a bad bet.
Two things tie them together: the same Real·Win·Worth judgment runs across both individual apps and proven case studies, and every heavy step runs as a background job — so I can start a sweep, close the laptop, and come back to a ranked, scored field.
Why it mattered
Validation went from roughly a week of biased manual digging to an afternoon of evidence — the outcome I most wanted from the whole toolchain. But the real change was what I chose to build next. The engine doesn't tell me what to build; it kills the losers cheaply and forces every survivor to earn its slot, so my build time goes to ideas that have already passed a hard, consistent test. It's the front door of everything downstream: nothing gets built without going through it first.
What I learned
The hard part of product isn't having ideas — it's killing them, and a good idea-filter is one that disappoints you on purpose. The change that made it trustworthy was making the verdict deterministic: the model supplies the evidence, but a fixed rubric computes the answer, so I can't quietly argue a score into a yes the way I would in my own head. And the moneyball lesson held — consistent, transparent scoring across a whole field beats a brilliant gut call on a single idea, every time.