I m trying to understand the key information that architects, contractors, and fabricators should review before approving a

.

I have noticed something about how I use AI for important decisions. I start with a point of view. I explain the context. AI gives me a thoughtful response. I challenge it a little. And eventually, I often walk away feeling more confident about what I already wanted to do.

Which made me wonder: did AI actually stress-test my decision, or did it just help me rationalize it better? With people, we have social biases. Friends, colleagues and mentors may soften their advice, bring their own biases, or tell us what they think we want to hear.

There's a specific phase and I think a lot of people are in it right now without realizing it.

You've added enough AI to your workflow that the maintenance cost starts eating into the time you were trying to save.

I keep running into this while building.

There s always another feature that sounds useful on its own.

Another setting.
Another metric.
Another automation.
Another smart layer.

But at some point, the product starts asking the user to understand the system instead of the system understanding the user.

Feels like every time we launch a small bug fix two other random features break immediately after.

We end up trapped in this annoying cycle of testing fixing and re-testing all over again.

It's eating up way too much of our development time each week.

Do you guys use simple automated checks for this or are you still testing everything manually before going live?

I've just made my free tier worse for a group of people who did nothing wrong, and I can't work out what I owe them.

Background, anonymised as best I can. Until this week you could use the core function of the thing I build without an account at all turn up, get a result, look at it. You only had to identify yourself if you wanted to keep the output. This week I put a free account in front of it. Same allowance, same features, no card, but you now sign up before you get anything.

The reasons were dull and real. I couldn't attribute anything to anyone. I couldn't tell a returning person from a new one, so I had no idea whether people were coming back. And an anonymous endpoint that costs me money on every call was an abuse problem I was going to have to solve eventually regardless.

What I didn't think through is what to say about it. Three things I'm stuck on:

I ve been paying closer attention to onboarding flows lately. A pattern I keep noticing is that products often try to explain everything before the user has done anything. Feature tours, tooltips, welcome screens, setup questions all before the user has experienced the core value. I used to assume more explanation meant less confusion. Now I m not so sure. The products I remember most clearly tend to get me to one useful action first, then explain things when they become relevant. I d be interested in how other makers think about this: At what point does helpful onboarding become friction?

I've been exploring better ways to validate early-stage product ideas not just from gut feel, but through meaningful market signals.

In your experience:

• How do you go about validating a new product idea or GTM direction?

• Do you lean on user interviews, market data, experiments, or something else?

• What s worked well (or not so well) for you?

As a product person myself, I ve always found this step to be the trickiest especially when time and budgets are tight.

Hey makers

RevenueCat's Shipaton (their mobile hackathon) ended on September 30 with 3,336 projects on Devpost and 2,139 published apps. I went through all the public pages to see what people built. Some of what I found: