Play's Age Signals terms limit the signal to age-appropriate experiences and name advertising, marketing, profiling and analytics as prohibited uses, with app takedown as the stated penalty. Apple's version hands you the age plus how it was proven. Both are live now in a widening list of regions, and the default engineering reflex is wrong for both.
There's a new field arriving in mobile apps this year and almost nobody is treating it as a product decision. It's the user's age, handed to you by the store instead of asked for in your onboarding.
For months, our most requested feature at Murror was a chat function. Users wanted to talk to the AI the way they talk to a friend. It seemed obvious. Every competitor had it. Every feedback form mentioned it.
I ve been working on another product alongside Murror a side project we re experimenting with, built almost entirely through AI. I m not a developer. I can t read most of the codebase. But I shipped a feature that three users have already told me feels exactly right.
After our first launch on Product Hunt, our team spent a little over a month upgrading the product. There were major changes to the UI and several new features added, so the process took time from discussions and redesigning the interface to testing, fixing bugs, and updating AI prompts.
We re also a very small team, so everyone had to push themselves to give 200%. Time and resources are limited, and at the same time, we also had to work on securing funding for the next six months to keep the team running and continue developing the app.
Average AI product gross margin is 52% against the 80% that defined SaaS, and inference eats about 23% of revenue. Every growth instinct you inherited was formed in a world where your best user cost you nothing.
There's a rule most of us absorbed without ever being taught it: engagement is free. One more session, one more entry, one more day in the app pure margin. That rule is why "north star metric" articles exist, why every dashboard puts DAU at the top, why nobody ever asked you what your heaviest cohort costs. It held for twenty years because the marginal cost of serving a software user really was approximately zero.
Six months ago, our team was obsessed with making Murror's AI more intelligent. Better pattern recognition, deeper emotional analysis, more insightful reflections. Every sprint, we'd ship something that made the AI sound smarter.
For the first year, we marketed Murror as an "AI-powered emotional companion." It was technically accurate. It was also the worst positioning decision we made.
Here's what happened: people who downloaded expecting an AI chatbot were confused. Murror doesn't chat with you -- it helps you journal, reflect, and understand patterns in your relationships. They'd open the app, look for the chat bubble, and leave within 30 seconds.
For three months, the #1 feature request in Murror was "let me share my journal entries with my partner."
It made perfect sense. Murror helps you understand your emotions and relationships. Sharing seemed like the obvious next step. Our roadmap was built around it. We designed the UI, built the sharing flow, even wrote the notification copy.
Most software wants you to come back every day. The business model depends on it. More sessions, more engagement, more opportunities to monetize.
But what happens when your product's purpose is to help someone understand themselves better? At Murror, we've been wrestling with a paradox: if we do our job well, users should eventually need us less not more.
The hard part of building a product moved in 2026 and most makers are still pouring their energy into the part that got easy.
There's a specific kind of quiet that happens about three weeks after launch. The app works. The landing page is clean. You shipped the whole thing in a weekend because Cursor and Claude Code let one person out-build a small 2020 dev team. And then... nothing. A trickle of signups from your own timeline, and then the graph goes flat.
