Every developer has that one story – the production bug that blindsided them, turning a routine release into a frantic scramble. For us, it was a harsh lesson, one that transformed our entire approach to shipping code.
It started with the best intentions. After a previous chaotic release, I was determined to ‘do everything right.’ I merged early, planned for testing, and prepared a thorough demo. Yet, the calm was deceptive. Just hours before a critical client presentation, a simple role switch on our application revealed a critical flaw: the dashboard wouldn’t load for standard users. What followed was a late-night debugging marathon, tracing the issue to a database query that, while lightning-fast on my development machine, crawled to a halt on the production server. The presentation was salvaged by transparent communication about the known issue, but the experience highlighted a fundamental problem in our development workflow.
The root of our predicament lay in a trap many developers fall into: the illusion of local success. We had been rapidly developing, incorporating a significant amount of AI-generated code. While initially efficient, this led to increasingly complex database queries with nested RLS policies and multiple joins. On our powerful local machines, these complexities were invisible; queries executed almost instantly. However, we failed to account for real-world conditions – network latency, actual user load, and production-scale data. Our local environment was a poor substitute for reality, and we paid the price when these complex queries choked under true operational demands.
The fix, once identified, was surprisingly straightforward and immensely liberating. By refactoring our database schema to store relevant IDs directly in tables, we entirely eliminated the problematic joins. This simplification resulted in a dramatic 96% reduction in query runtime and yielded cleaner, more robust code. It was a stark reminder that unchecked complexity, even if initially productive, inevitably erodes performance and maintainability.
This crisis underscored the absolute necessity of a staging environment. Previously considered a ‘nice-to-have,’ it became our lifeline. Operating without revenue meant we needed an affordable solution, so we innovated. We leveraged a second Supabase account’s free tier for a copy of our anonymized production schema and deployed our staging application on AWS Amplify, utilizing startup credits for virtually free hosting. This setup provided a realistic testbed for every developer, allowing us to log in, switch roles, and simulate actual user flows without ever touching live production data. It transformed debugging from a desperate race against time into a structured, confident process.
With our staging environment in place, our release process underwent a complete overhaul. Gone are the days of merging directly to the main branch, a quick test, and a hopeful deploy. Now, every release begins with a dedicated release branch, deployed to staging with our anonymized production data. The entire team collaboratively tests, thoroughly exercising their features and critical user paths. This proactive approach ensures issues are caught and resolved long before they ever reach a client, and performance optimizations can be validated safely. The outcome is a repeatable, stress-free release cycle, replacing heart-pounding anxiety with calm confidence.
Our journey from production chaos to a calm, structured release process taught us invaluable lessons: AI code, while powerful, demands rigorous review; unchecked complexity is a performance killer; local testing is never a substitute for real-world conditions; a well-implemented staging environment is a development superpower; and a clear, collaborative release process is paramount for team sanity and product quality. The next frontier for us is integrating automated tests to further fortify our confidence.
We invite you to share your own war stories from the trenches of production bugs or your strategies for staging and testing. Let’s learn from each other’s experiences.