Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech Development

In the realm of tech startups, the phrase “do things that don’t scale” often echoes among entrepreneurs. Yet, practical guidance on how to realistically apply this principle within coding practices remains scarce. Over the past eight months while developing my AI podcast platform, I’ve adopted an innovative strategy: every unscalable hack is granted a lifespan of three months. After this period, I evaluate its utility—if it proves its worth, it gets a robust overhaul; if not, it’s phased out.

As engineers, we’re trained to champion scalable solutions from the outset—leveraging design patterns, microservices, and distributed systems to ensure we are prepared for millions of users. However, this mindset can lead us astray in the startup ecosystem, where optimizing for future users often translates to spending resources on problems that might never arise. My 3-month rule compels me to create straightforward, albeit imperfect code that launches quickly, allowing me to gather insights into actual user needs.

Current Infrastructure Innovations: My Unconventional Yet Effective Approaches

1. Single VM Architecture

My setup includes a database, web server, background jobs, and Redis, all functioning on a single $40 per month virtual machine. Although there’s no redundancy and I rely on manual backups, this configuration has proven remarkably enlightening. In just two months, I have gauged my actual resource needs far more effectively than if I had relied on theoretical capacity planning. I learned my “AI-heavy” platform typically peaks at 4GB of RAM—all insights that would have been obscured by a complex Kubernetes infrastructure.

2. Hardcoded Configuration

With constants like PRICE_TIER_1 = 9.99 and MAX_USERS = 100 scattered throughout my codebase, I bypass traditional configuration files and environment variables entirely. While this might seem impractical, it allows me to quickly retrieve any configuration value and track changes effectively. This approach saved me significant development time—what could have taken a week to implement as a configuration service only required 15 minutes of redeployment.

3. Utilizing SQLite in Production

SQLite powers my multi-user web app, with a compact database of merely 47MB. Handling up to 50 concurrent users seamlessly has provided crucial learning experiences; my access patterns reveal 95% read operations, which aligns perfectly with SQLite’s strengths. Should I have opted