Documenting code, one commit at a time.
We've recently refactored and optimized our AI token usage tracking to improve performance and maintainability. This involved extracting duplicated queries and enhancing user filtering.
The primary focus was on improving the efficiency of retrieving daily and monthly tenant AI usage data. This was achieved through two key changes:
This post discusses a recent update to our post generation process, shifting from hardcoded sections to a more adaptable, example-driven approach.
Previously, our post generation system rigidly enforced a predefined structure: Introduction, Development, Examples, and Conclusion. This inflexible format ignored the nuances and varied structures of different example posts,
This post addresses an issue where a billing system, specifically when using a service like Cashier, fails to correctly identify active subscriptions beyond the default type. We'll explore the problem and provide a solution to ensure all subscription types are properly recognized.
In many billing systems, especially those utilizing
Developers often strive for efficiency, but sometimes, optimizing too early can lead to more problems than solutions.
Optimizing code before understanding its performance bottlenecks can waste time and introduce unnecessary complexity. It's tempting to micro-optimize, but it's crucial to identify the actual performance-critical sections first.
This post delves into optimizing SQL queries for product ranking and data aggregation, focusing on common pitfalls and effective strategies to enhance performance and accuracy. We'll explore techniques to address memory errors, improve query speed, and ensure data integrity when dealing with complex relationships and large datasets.
