Not a launch post. We are not live, our listing is suspended right now. But this cost us a lot of time and I have not seen anyone write it down, so.
The policy is 1.2.1. If you list on the Shopify App Store, you have to bill through Shopify. Not should, have to. If your app charges through Stripe, or anything that is not Shopify's billing API, the listing gets rejected. Free apps are fine. The moment there is money, it goes through them.
Hey makers! I need to vent and hopefully get some advice from people who've been through this.
I'm working on our landing page copy and honestly it's become my personal hell. You know that moment when you think you've nailed the perfect headline, so you show it to 10 users and get 10 completely different interpretations?
User 1: "Oh so it's like Slack for teams"
User 2: "Wait, this is a project management tool right?"
Launch retrospectives over-sample the people who stayed long enough to talk. The most flattering feedback is also the easiest to collect.
I want the quiet exits in the room: people who clicked but did not start, started but never reached value, or used the product once and vanished. Even a tiny exit survey paired with the last completed step can reveal more than another call with a power user.
What is the earliest silent-exit signal you review after launch?
Ask someone whether they want your idea and you will get a polite forecast. Ask them to walk through the last time they tried to solve the problem and you get evidence: the trigger, the workaround, the cost, where it failed, and whether the problem was painful enough to revisit.
Specific past behavior beats imaginary future enthusiasm. It also makes the awkward answer useful when they did nothing at all.
What question reliably pulls your interviews out of opinion mode?
Polished onboarding can still be confusing if each step feels like a surprise. After the first action, ask a new user what they think will happen next. Their prediction exposes whether the product's mental model is clear before completion rates do.
If five people finish but all five expected a different result, the funnel is technically working and the product is still teaching the wrong thing.
Where in your onboarding do users most often predict the wrong next step?
A single score tells you who won. It rarely tells you what broke. For model or product evaluation, I want failures grouped before I trust the ranking: factual error, stale source, bad tool choice, latency collapse, unsafe action, or a task the system should have refused.
That taxonomy changes the next engineering decision. A two-point gain driven by easier cases can hide a worse regression in the failure mode users actually care about.
What failure category has changed how you read a benchmark?
Parameter count alone has become a bad shortcut for explaining conditional models. If a system uses experts or sparse memory, I want four numbers: total parameters, parameters active per token, routing or retrieval overhead, and measured throughput on named hardware.
Without those, a large number can describe storage capacity while readers assume every parameter contributes to every token. That muddles both cost and capability.
Which number do you wish model announcements reported by default?
A streak that resets after one missed day teaches the wrong lesson: consistency is fragile, so a setback might as well become a week off. Real learning has exams, illness, work shifts, and plain bad Tuesdays.
I prefer a recovery rule over a perfect chain. Miss a session, then return with the smallest meaningful action: retrieve one idea, solve one problem, or name the next gap. The habit worth reinforcing is coming back.
What would you measure instead of consecutive days?
I have automated a lot over the years, invoicing, scheduling, reporting, most of it now runs itself. But there is always one thing left that I still check manually, every single day, because the one time I trusted a tool to catch it, it did not. For me it is new leads going quiet after the first reply. I open that list myself every morning instead of trusting a dashboard to flag it. Curious what that one thing is for you, and whether you have ever found something that actually earned your trust enough to stop checking it by hand.
