I am sitting in Cairo airport right now, waiting for a flight, and I just saw this trending repo that does something I have been struggling with for weeks.
Basically, the problem: I am building a small SaaS tool that calls Claude API for prompt transformations. My free tier users hit rate limits fast, and I was trying to batch requests or queue them properly, but it felt messy. The repo I found uses a simple prompt chaining approach instead.
Here is what clicked for me. Instead of trying to handle rate limiting at the application level, you write a meta-prompt that tells Claude to itself suggest when to batch vs when to execute. You pass back the AI's own reasoning about pacing into your next call. It sounds weird but it actually reduces your total API hits by about 40 percent because the model learns which operations can be parallelized and which should wait.
I tested this yesterday with Sonnet 4.6 on my test data. Instead of 150 API calls for a user workflow, I got it down to 92. Still going to optimize more, but the improvement was real.
The thing that surprised me is how much of this is just asking the model for help with the architecture decision, not fighting against it. I was thinking of the rate limit as a system problem to hide from the API. But if you involve the model in the solution, it becomes smarter about itself.
I know this is not revolutionary, but I wanted to share because if anyone else is trying to ship something with Claude API on a small budget, this approach might help. The repo has good examples, thank you to whoever wrote it.
Also, does anyone have thoughts on Stripe Atlas vs Egyptian company registration for tax purposes? I need to figure this out before I launch properly.