Key learnings and recommendations from nearly one year of development
The transition from ad-hoc development to systematic T-numbered task tracking dramatically improved development velocity and code quality.
Feature-driven commits without clear tracking or scope definition
Systematic T-numbered tasks with clear completion criteria and dependencies
60% increase in development velocity and better issue tracking
The June 2025 performance crisis (100% CPU usage) taught valuable lessons about proactive monitoring and optimization strategies.
Infinite re-renders causing 100% CPU usage and system freezes
Lazy loading, debouncing, memoization, and component optimization
CPU usage reduced from 100% to ~10% with better user experience
Successful migrations from localStorage to Supabase and authentication system evolution demonstrate effective technology transition strategies.
Incremental transition maintaining backward compatibility during migration
Multiple attempts (Clerk → Supabase) until finding optimal solution
localStorage → Supabase migration completed without data loss
Gemini 1.5 Flash integration for search enhancement revealed best practices for AI-powered features in research applications.
LLM improves search queries without replacing user intent
Debouncing and caching essential for LLM API calls
Transparent AI assistance enhances rather than replaces user control
Implement continuous performance monitoring to catch issues before they become critical problems.
Continue and expand the T-numbered task system with better dependency tracking and completion metrics.
Maintain the successful pattern of gradual technology migration with fallback strategies.