Our free invoice generator takes a JSON post with no account behind it and hands back a PDF. So it has limits on everything.

At most a hundred line items. At most twenty taxes on any one item. A length cap on every text field, a byte cap on the logo, and checks that run before anything expensive is decoded. Each limit was chosen by asking how large one field can reasonably be, and each one was sensible on its own.

Then we measured a request that respected every single one of them: a hundred items, each carrying twenty taxes. It answered 200, and it held a worker for about 62 seconds. The same hundred items with one tax each took about 2.4.

Nothing was broken. The limits multiplied. A hundred items times twenty taxes is two thousand line and tax pairs, and each one is printed in its line and, when the names differ, again as its own row in the totals. The item limit counts items. The per item tax limit counts what sits inside one item. Nothing counted the document.

A small diff helps, but it doesn't tell you why an agent chose that approach.

For a change that touches validation, authorization and a database write, I'd want to understand the decisions before looking at a green test result.

I'm interested in what makes that handoff useful in practice. A short explanation of the tradeoffs, a list of the tests that actually ran, and a walkthrough of the risky paths all seem useful.

The useful part would be enough detail to check the work without replaying the whole conversation.

We've been stuck in merchant verification for almost a month.

The provider's site says a few days.

First the company took weeks to verify. Then the identity of the founder. Now they won't enable payment processing because they can't see the connection between our marketing site and our app, which lives on a subdomain of the same domain.

Same brand, same company, same documents they already have. One is the website, the other is the product. That's how software has worked for about fifteen years.

Hey Product Hunt,

I just launched the closed beta of CoLaunchly for waitlisted users. It s a marketing co-pilot for indie devs that creates personalized launch plans, content templates, and channel strategies based on your app and target audience.

Now that it s out in the wild, I m realizing how hard it is to actually get feedback from early users. Even the ones who signed up seem quiet.

A few years ago, the biggest challenge for building a software product was usually technical skills.

Today, with AI coding tools, someone with limited coding experience can create a functional MVP in days or even hours.

But I m starting to wonder if the bottleneck has simply moved somewhere else.

Building is becoming easier, but:

My dashboard is beautiful. Signups, activation, weekly actives, retention curves, the whole thing. And I've started to suspect it's telling me a story I want to hear.

I chose the definitions. I chose the date range. I chose which cohorts to compare. Every one of those choices was defensible on its own, and together they might add up to a portrait of a business that doesn't exist.