Kimiya Razdar

Technical Challenge: Real-time visualization caused performance lag with large datasets. Implemented data windowing and throttled updates to maintain 60fps while processing 1000+ events per second, same optimization strategies used in production monitoring systems.

Technical Challenge: localStorage size limits failed for power users with 500+ tasks. Migrated to IndexedDB for scalable storage, taught me to plan for scale from the start, which I apply when designing AI prompt libraries for production environments.

Technical Challenge: Training data was heavily imbalanced (80% password resets, 5% network issues). Implemented SMOTE resampling and weighted classes to improve minority category accuracy. Learning: Real-world data is 80% of ML work.

Technical Challenge: OpenAI API rate limits caused workflow failures during bulk processing. Implemented exponential backoff retry logic and request queuing. Learning: Production AI workflows need robust error handling APIs fail, and you have to plan for it.

Technical Challenge: Keyboard shortcuts conflicted with browser defaults. Solved with event delegation and input debouncing, same pattern I use for managing rapid AI API calls without overwhelming rate limits.

Technical Challenge: Floating-point precision errors produced incorrect conversions. Implemented rounding strategies and boundary testing—same approach I use when validating AI output for accuracy before deployment.