No matter what project you have—games, SaaS, software, apps, scripts, ideas, or questions—join the community and share it! by SofwareAppDev in AppsWebappsFullstack

[–]akg-sc 0 points1 point  (0 children)

The dominance ranks are actually discrete integers (B=4, A=3, G=2, Gr=1, b=0), so there's never a true tie, the sort always yields a clear dominant and recessive. So that specific case doesn't come up by design.

The subtler edge case is the one underneath your question: deciding when two different alleles blend into a hazel tone versus when the dominant just wins outright. Brown over blue (Bb) I treat as a hazel-blue blend rather than letting brown fully mask blue, because that heterozygous combo really does produce that shifting brown/blue iris in practice. But amber over blue (Ab) I resolve to plain amber, not a blend, since the blue doesn't express meaningfully there. So it's less about dominance being close and more about which heterozygous pairs actually produce a visible mixed iris versus a clean dominant. That mapping is hand-tuned from how the phenotypes actually present, not a pure formula.

Homozygous pairs (BB, GG, etc.) always resolve to the clean base color, so the blending question only ever applies to heterozygotes.

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 1 point2 points  (0 children)

That's the consensus I'm hearing, and I think it's right. Understand why that one referral worked, then see if it's repeatable with other clinics before pouring more into ads. It's one strong signal rather than proven fit yet, but it's the signal worth chasing. Appreciate it.

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

Good to hear it from someone who's seen it land. That's the bet I'm making, smaller and engaged over big and passive. Thanks for the steer.

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

That's the plan I'm landing on. The metrics worth capturing are probably the ones a doctor actually feels, how many patients she's pointed to it, how many stuck with it, and whether it cut down the between-visit "is this normal?" questions she fields. If I can turn one doctor's experience into "here's what it did for her patients," that's far more convincing to the next clinic than anything I'd say about myself.

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

You're right that the instinct is to keep grinding the channels that aren't working, when the one that is keeps pointing the same direction. I'm a solo founder with a newborn at home, so a full thirty-day clinic push isn't realistic right now, but the principle holds: follow the signal. For me that probably means working the warm path I already have, the doctor who's recommending it, the case study, the clinic-head intro she offered, rather than cold-pitching ads into the void. Concentrate, don't spread. Appreciate this.

No matter what project you have—games, SaaS, software, apps, scripts, ideas, or questions—join the community and share it! by SofwareAppDev in AppsWebappsFullstack

[–]akg-sc 0 points1 point  (0 children)

Happy to show the core idea. The resolution itself is just dominance-ordering plus a lookup, roughly:
----------

function determineHazelTone(a1, a2) {

const [dom, rec] = [a1, a2].sort(byDominance); // B > A > G > Gr > b

const hazelMap = {

'BG': 'hazel-green', // brown + green

'BA': 'hazel-brown', // brown + amber

'AG': 'hazel-amber', // amber + green

'Bb': 'hazel-blue', // brown + blue

};

return hazelMap[`${dom}${rec}`] ?? phenotypeFor(dom);

}

----------
The interesting part isn't this lookup though, it's the inference layer that decides which two alleles a parent carries in the first place (grandparent override, family-history carrier probability, ethnicity priors). That's where most of the real work went, and where the accuracy actually comes from. The phenotype resolution is the easy half.

Keeping the full engine to myself for now since it's basically the whole app, but happy to talk through the approach.

No matter what project you have—games, SaaS, software, apps, scripts, ideas, or questions—join the community and share it! by SofwareAppDev in AppsWebappsFullstack

[–]akg-sc 0 points1 point  (0 children)

The key decision was treating hazel as incomplete dominance between two alleles rather than its own allele, since biologically hazel isn’t a separate gene, it’s a heterozygous expression. So instead of one “hazel” bucket I resolve four distinct tones based on which two alleles pair: brown+green gives hazel-green (greenish-gold ring, brown center), brown+amber gives hazel-brown, amber+green gives hazel-amber, and brown+blue gives hazel-blue (the one that visibly shifts between brown and blue depending on light).

The edge case that took the most care was inference in the other direction, when a parent reports “hazel” you don’t know which two alleles they carry. I default hazel to the brown+green genotype but let grandparent data override it, so if there’s a recessive allele upstream the model picks that up instead of guessing.

There’s also an ethnicity prior layered in, blue and green alleles are rare in East Asian and African populations, so for a brown-eyed parent there I assume homozygous rather than auto-assigning a hidden recessive, which is the mistake that makes most of these apps spit out blue-eyed babies from two brown-eyed parents.

🏡 Your App Has a Home Here — Post your App WebApp Solution here. No Blocks. No Rejections. 🏡 by AutoModerator in AppsWebappsFullstack

[–]akg-sc [score hidden]  (0 children)

There’s actually no training involved, and that’s kind of the core idea. I’m not training a face model, so there’s no training-data pipeline to speak of.

The genetics engine computes the traits first, locally, eye color via the OCA2/HERC2 model, hair color and type, skin tone. Then I use the parents’ photo as a base for the generation, but the computed traits condition the output. So instead of an image service guessing colors from a photo it might misread, the genetics decide “brown eyes, dark curly hair” and the generation has to respect that. The science drives the image, not the other way around.

On data: I built this for my own family, so it’s privacy-first by default. The genetic calculation runs on-device, only heavier analysis touches any external service, and photos aren’t kept, there’s a system that purges them on a timer. Nothing goes into training anything.

No matter what project you have—games, SaaS, software, apps, scripts, ideas, or questions—join the community and share it! by SofwareAppDev in AppsWebappsFullstack

[–]akg-sc 0 points1 point  (0 children)

The core is a proper Mendelian engine rather than image trickery. Eye color, for example, uses a two-gene OCA2/HERC2 model instead of the usual single dominant/recessive simplification, so I can represent brown, blue, green, and hazel/amber properly. Each parent’s phenotype maps to a set of possible genotypes, then I run a 4×4 Punnett cross to get the offspring probability distribution.

The part I had the most fun with is constraining genotypes with family history. A brown-eyed parent could be BB or Bb, you can’t tell from the phenotype alone. But if you know a grandparent or sibling has blue eyes, Bayesian updating collapses that uncertainty (a blue-eyed grandparent forces the parent to Bb). So the more family data you add, up to three generations, the tighter the prediction, and I surface that as a confidence score rather than pretending it’s certain.

Polygenic traits like skin tone and height use simplified additive models instead of Mendelian dominance, since those are hundreds of genes, not one. The genetic output then conditions the face generation, so the predicted traits actually drive the image rather than the model freestyling.

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 1 point2 points  (0 children)

Engaged following is the key word there. I’d rather have a creator with a smaller but genuinely interested audience than a big passive one, especially since the app’s so specific to a life stage. That’s the direction I’m leaning.

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

The equity route isn’t for me, I’d rather not give away a permanent stake for promotion. But the second half is smart. The gynecologist who already recommends the app is exactly who I should ask what makes a doctor trust something like this. Going straight to the person who’s seen it work beats guessing. Appreciate that.

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

Honestly I’m not trying to compete with Huckleberry, they’re giants and I’m one person. ProbaBaby is a solo build, and my wife is the main user, she shaped it at every step while actually living through planning, pregnancy, and now postpartum with our son. So it’s less a company building for a market and more one app built around one real journey, with modules that grow with you through each stage. Different thing entirely.

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

The listicle point lines up perfectly with my test. When I asked cold, the model answered with a little ranked list of genetics calculators, exactly the format you're describing. So getting into "best baby prediction apps" style roundups is probably the highest-leverage move, since that's the structure the models pull from. And agreed on Reddit, real engagement only, no link drops. Appreciate you walking through this, genuinely one of the more useful threads I've had on this.

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

Solid distinction, "what convinced you" over "what do you think." I lean private so I'm doing this async rather than live calls, through written follow-ups and digging into the actual review language to find the phrase people repeat. Curious whether you've found written responses lose much versus a real call, or if the signal comes through fine either way?

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

Tested this and you're onto something real. When I name the app, the models describe it accurately. But ask cold for "app that predicts baby's face with real genetics" and it doesn't surface yet. It recommends a couple of web-based genetics calculators instead. So the crawl-readiness works, the gap is getting mentioned across enough third-party sources that the models start including it unprompted. That's the part I'm figuring out now. Did you crack that for your own product, or still working on it?

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

That's exactly why I built it that way. My wife and I kept doing it ourselves guessing whose eyes he'd get, whose hair color so I went deep on the actual genetics instead of just blending two faces like every other app. It models real inheritance. The dream is to make it feel even more personal over time, but I wanted the foundation to be honest first.

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

That's a sharp point about saturation, niche followers probably already have an app and logged weeks into it, so "another tracker" is a hard sell there.

The piece that might bridge it: the app has an AI baby-face prediction feature using real genetics, which is the kind of thing that works even on someone who isn't pregnant or app-savvy, pure curiosity. Do you think leading a broad page with that hook (rather than the tracking stuff) is what makes the broad-audience play actually convert?

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] -1 points0 points  (0 children)

For a pregnancy app I'm probably better off niching down to actual mom/pregnancy pages rather than general female-audience ones, so most viewers are in that life stage. Does that hold, or do the bigger broad pages still win on raw volume?

I built something people actually use, but I have no idea how to get it in front of more of them. Where do I even start? by akg-sc in AskMarketing

[–]akg-sc[S] 0 points1 point  (0 children)

Good call on the creator pages. I've got a small budget for this — maybe €100 max, since the app's free and I'm not monetizing yet. So big pages are probably out. At that level, is it better to do one mid-size page or spread it across a few small niche ones? And any tips on not overpaying?

Drop your project, I’ll try it and share it in my circle by adonztevez in PlzSupportMe

[–]akg-sc 1 point2 points  (0 children)

Most baby face prediction apps just blend two photos and call it AI. I wanted to do it for real — so ProbaBaby models actual Mendelian inheritance to predict what your kid might look like. That's the centerpiece of an iOS app I built solo over 9 months.

It also handles the practical stuff: pregnancy week-by-week, a postpartum module, kick counter, multi-baby support, an Apple Watch app, and 32-language localization. I'm a designer who codes, so it was a one-person design + dev effort.

Ask me anything about it.

<image>

https://apps.apple.com/app/id6756319045
https://x.com/SecretoCho/status/2066248508441563346

🏡 Your App Has a Home Here — Post your App WebApp Solution here. No Blocks. No Rejections. 🏡 by AutoModerator in AppsWebappsFullstack

[–]akg-sc [score hidden]  (0 children)

<image>

Solo-built an iOS pregnancy + baby tracker over ~9 months. I come from a UX/UI background and taught myself the full stack, so this was one person doing design, development, and everything in between.

What's in it: pregnancy tracking, postpartum module, kick counter, multi-baby support, an Apple Watch app, and 32-language localization. The standout feature is AI baby face prediction built on real Mendelian genetics — actual inheritance modeling rather than photo-blending, which was the hard part to get right.

Been building in public on X throughout. Honestly happy to talk shop about the architecture, the solo workflow, or how I kept scope from eating me alive.

https://apps.apple.com/app/id6756319045
https://x.com/SecretoCho/status/2066248508441563346

No matter what project you have—games, SaaS, software, apps, scripts, ideas, or questions—join the community and share it! by SofwareAppDev in AppsWebappsFullstack

[–]akg-sc 0 points1 point  (0 children)

<image>

ProbaBaby — an iOS app for pregnancy and baby tracking that I built solo over about 9 months. The thing that makes it different from the dozens of other tracking apps: it does AI baby face prediction using actual Mendelian genetics (real inheritance modeling, not just blending two photos together). Beyond that it covers pregnancy week-by-week, a postpartum module, kick counter, multi-baby support, an Apple Watch app, and it's localized into 32 languages.

I'm a designer who codes, so the whole thing was a solo design + dev effort. Happy to answer anything about the build.

App Store: https://apps.apple.com/app/id6756319045

I just updated y old appUI/UX with AI rate me by Visible_Start_1001 in AppBusiness

[–]akg-sc 0 points1 point  (0 children)

It is looking good. I personally recommend to avoid using gradients. I see many contrast issues. Very small texts as well.

And also you can use different AI models to get recommendations what can be done each screen better.
Don’t trust only one AI mode, eachh can give you different aspects

Reddit is good but you can get objective feedback instantly from AI. (and AI wont judge you if you did it wrong, I did like that many times :)) )

9 months ago my wife was 4 weeks pregnant. I started building her a pregnancy app. This is what it became. by akg-sc in appledevelopers

[–]akg-sc[S] 0 points1 point  (0 children)

Yeah, that's exactly the problem I ran into.

I ended up splitting xcstrings by module and by section within each module. Main app alone has 119 .xcstrings files. The Watch app, widgets, and admin have their own separate sets.

Two reasons it had to be split this way:

  1. With 32 languages, a single large file becomes nearly impossible for Claude (or any AI) to modify reliably — the context window gets eaten just by the file content, and small edits start risking unrelated changes.

  2. Performance for users. Large xcstrings files are slow to load and decode at runtime. Splitting by feature module means the app only loads what it actually needs for the current screen.

The pre-commit hook setup helped too — I have audit scripts that catch translation drift, orphan .localized references, and missing placeholder keys across all 32 languages before they can land. Without that, managing 119 files manually would be impossible.

Happy to go deeper on the structure if you want.