Documenting code, one commit at a time.
Sometimes, the most challenging bugs are not in the core logic, but in the tooling around it. Recently, while working on the diagram rendering service for a project, I encountered an issue where invalid Mermaid syntax would cause the entire process to hang. Here's how I tackled it.
The diagram rendering service, used for generating visualizations, relied on an external script to
Ensuring consistent code quality and documentation can be a challenge. One area where consistency is crucial is in the generation of diagrams for technical documentation. This post discusses how we've improved the consistency and validity of Mermaid diagrams generated as part of our technical writing process for the devlog-ist/landing project, which provides a landing page for
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:
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.
