u/SERIOUS-OG — reddlx

I built an iOS revenue estimator, then tested it against 16 apps that disclosed real MRR. It underestimated indie subscription apps by up to 20–40x. Here's why.

I've been building an app-store revenue estimation engine solo. Most estimators (mine included) key off review velocity → downloads → revenue. So I pulled 16 apps whose founders publicly disclosed real MRR (via RevenueCat/Stripe on TrustMRR) and checked my engine blind.

Result: it was consistently low — sometimes 20–40x — on small, low-review, high-ARPU subscription apps. An app with ~300 reviews doing $16k/mo looks tiny to a review-count model, but its paywall converts hard.

Two lessons: (1) review-count → revenue breaks for subscription apps with strong paywalls; (2) worldwide MRR vs US-only iOS scope explains maybe 2x of it, but not the rest. Working on it in the open.

Curious if others estimating app revenue hit the same wall — how are you handling low-review, high-conversion subs?

reddit.com

u/SERIOUS-OG — 1 day ago

▲ 2 r/microsaas

a competitor cloned my whole app-data tool. took me a week to realize it doesn't matter

solo founder here, been building an iOS app market intelligence thing for a few months. quick story plus a couple things i figured out that might help other small saas folks.

so a few weeks back i find out a competitor basically reverse-engineered my whole approach and shipped something really similar. spent like a week genuinely stressed about it.

then it clicked: the code was never the hard part. the hard part is the pile of real, verified numbers i've been collecting and folding in every day. anyone can copy how the thing works. nobody can copy the data you've actually accumulated. if you're doing anything data-heavy, that's your moat. not the code.

second thing, the weird one. every tool in my space shows you ONE confident number for what an app earns. like "$612,400/mo". looks precise. it's also often way off and they never warn you.

i went the other way:

what everyone else shows:

$612,400/mo one number, looks exact, often 3x wrong

what i show:

$410k |----●----| $520k 87% sure -> act on it

$90k |--●----------------| $640k low -> just a direction

everyone told me people only want the single number. turns out a lot of them trust the ranged version MORE, because it's not pretending. if your product spits out estimates or ai output, showing the uncertainty is a feature.

on pricing: killed freemium. one tier, 3 day trial, done. for a niche tool freemium is mostly support pain from people who'll never pay.

still early, still hunting my first real paying users. anyone else building data-type saas wrestle with the "is my data the moat" thing? how'd you think about it?

reddit.com

u/SERIOUS-OG — 3 days ago

▲ 0 r/iOSProgramming

I built a free tool to check what any iOS app really earns — it shows a confidence band instead of a fake-exact number

As an indie dev I could never justify Sensor Tower / enterprise pricing just to sanity-check a market before building. And the cheaper tools all hand you one confident number that's often wildly off with zero indication of it.

So I built the thing I wanted: download & revenue estimates for any App Store app, each shown as a low–high range with how confident the read is — plus cross-platform demand checks where public Android data exists, keyword/ASO, ads and review mining.

It's free to look up your own app (paid tier for the deeper workspace, but the read itself is open).

I'd really value this community's eyes on the honesty of the numbers — if you check your own app and the range feels wrong, tell me, that feedback is gold. app-dex.com — I'm the solo dev, happy to answer anything.

u/SERIOUS-OG — 3 days ago

▲ 1 r/alphaandbetausers+1 crossposts

I was tired of face-rating apps spitting out a different score every scan, so I built one that measures your face in actual millimeters with the TrueDepth camera

Hey r/SideProject 👋

I'm a solo dev and I just shipped Caliper, an iOS app for facial

self-improvement. It's live on the App Store now and I'd love honest

feedback — especially the skeptical kind.

── Why I built it ──

The "looksmaxxing" app category (Umax, LooksMax AI, etc.) blew up, but

almost all of them do the same thing: take a 2D selfie, send it to a

vision model, and hand you a single "attractiveness score." Two problems

bugged me as a user:

Scan the SAME photo twice and you get a different number. That's not a

measurement, it's a vibe.
The "advice" is generic and uncited — you have no idea if any of it is real.

I wanted to see if I could build the opposite: something that actually

*measures*, gives the same answer every time, and shows its work.

── How it works (the part this sub will care about) ──

• Uses ARKit + the TrueDepth camera to build a 1,220-vertex 3D mesh of

your face — real depth, not a flat photo. Measurements come out in

millimeters with confidence intervals (e.g. canthal tilt, gonial angle,

facial thirds/fifths, nasal projection, symmetry).

• Deterministic scoring: same input → same output, every time. No random

LLM number.

• Multi-frame median + quality gates (distance / pitch / roll / lighting)

to reject bad captures instead of scoring garbage.

• Every recommendation in the 66-day plan is tied to a real cited paper

(Farkas anthropometric norms for percentiles, etc.) — there's an

in-app research library so you can check the source.

• All measurement runs on-device. The only thing that leaves the phone

is the optional AI "future self" render.

── What's in it ──

A scan → a full report (your measurements vs population norms) → a

66-day coaching plan (mewing, skincare, grooming, jaw exercises) →

progress tracking with before/after + a "future self" AI render and a

hairstyle studio.

── Stack ──

SwiftUI (iOS 17, ARKit/TrueDepth, on-device Vision +

CoreML, a Cloudflare Worker proxy in front of the AI calls (rate-limited),

RevenueCat for subscriptions, Vertex/Gemini for the renders. XcodeGen for

the project. Solo, ~6 months.

── Honest limitations (where I want your feedback) ──

• "Attractiveness" is partly subjective — I treat the score as a

*measurement-derived* signal, not gospel, and I'm careful with the

framing. Tell me if it still feels overclaimed.

• Needs a TrueDepth device (iPhone X+). No Android.

• It's 18+ with a body-image/BDD disclaimer baked in — I genuinely don't

want this to be another insecurity-farming app. Curious if that comes

across.

• Freemium: first few days/scans are free, then it's a subscription.

Happy to take shots at the paywall timing.

Link: https://apps.apple.com/app/id6779588686

What would make you trust a tool like this more (or less)? The

determinism + citations were my attempt at "the non-scammy one" — does

that land, or does the whole category make you roll your eyes?

u/SERIOUS-OG — 14 days ago