Moving business logic and pricing rules to the backend is definitely the right move for mobile apps.

The biggest mistake we made early on was hardcoding feature flags inside the binary which meant a simple UI toggle required a full release cycle.

Shifting everything to remote config files changed our deployment speed completely.

Have you ever pushed a backend config change that accidentally broke the app layout on older screen sizes?

When vibe coding, asking AI to add a new feature can sometimes change code that was already working

Suddenly, a small update breaks another part of the app and you have to spend time fixing it

Using Git helps, but it doesn t always prevent these problems from happening

For developers building with AI, what workflow do you use to keep changes safe?

Adding AI features to indie products often brings unexpected API cost spikes. I ve been testing user-level rate limits, token quotas and cached responses to keep spending predictable.

I m curious to hear practical approaches from other builders:

1. Do you enforce hard monthly caps for every user?

2. What fallback workflows do you offer once AI quotas are exhausted?

3. Which caching strategies work best for repeated AI requests?

Many generic guides skip these operational details, so real production experience would be really helpful.

Moving a date is tough but doing it for financial flow or security is always the right move. I ve made the mistake before of rushing out a launch just to stick to an arbitrary deadline and we spent the next month putting out fires instead of getting feedback.

People forget a late launch but they don't forget a broken checkout experience.

How did your team and early waitlist users react when you broke the news about the delay?

We had a 5-step setup wizard with no progress indicator. Completion rate: 62%.

We added a progress bar showing "Step 1 of 5." Completion dropped to 51%.

We ran it again with a bar that showed "2 minutes remaining" instead of step count. Completion rose to 71%.

The step count was telling users how much work was left. The time estimate was telling them when it would be over.
Same wizard, same steps, different frame. Users were not avoiding the work. They were avoiding the unknown.

It's really frustrating when an AI assistant repeats a bug you fixed two days ago. Chat context clears between sessions so past corrections just vanish into thin air.

Saving issue notes directly in the repo helps but the AI can still ignore them unless forced. Setting up hard rules or custom scripts that run during every response ensures the model actually abides by past lessons instead of drifting back to bad habits.

Do you rely on custom system prompts to maintain rules or do you use external scripts to enforce constraints on your agent?