Brady Wilfahrt

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.

Hello guys,

i'm planning to do a quick ad for Murmell, something like beetween 1min and 1min30 and higgsfield prices are very expensive, do you think it's worth it for this kind of videos ?

I ve been thinking a lot about the difference between personalization and real adaptation in fitness software.

A profile with age, goal and experience level is useful, but it still treats each workout request almost like a new session.

A more interesting model is longitudinal: remember what the person actually completed, which movements and muscle groups were trained recently, how difficult the exercises felt, what restrictions exist, and how nutrition has looked over the previous days.

Then the next recommendation is based on that history instead of starting from zero.