Embracing the 3-Month Rule: A Pragmatic Approach to Building Scalable Solutions

In the realm of startups, there’s a renowned piece of advice by Paul Graham: “Do things that don’t scale.” However, implementing this strategy in the world of coding can sometimes be overlooked. After eight months of developing my AI podcast platform, I’ve crafted a framework that I like to call the “3-Month Rule.” This simple yet effective guideline allocates a three-month lifespan to every unscalable solution I implement, allowing me to evaluate its worthiness for further development or to retire it altogether.

It’s a common tendency among engineers to construct scalable architectures from the outset—leaning into complex design patterns and distributed systems to prepare for millions of potential users. However, this approach can lead to costly delays and missed opportunities, especially in a startup environment where the focus should be on immediate learning rather than abstract optimization for hypothetical users. My 3-Month Rule compels me to adopt a more hands-on, straightforward coding style that yields actionable insights and real user needs.

Current Infrastructure Hacks: Learning Through Simplicity

1. Consolidated Operations on One Virtual Machine

My entire infrastructure, including the database, web server, and background jobs, resides on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it provides invaluable learning experiences regarding my resource demands. Over the past two months, I have gained clearer insights into my actual needs, discovering that my so-called heavy platform peaks at a mere 4GB of RAM. The complex Kubernetes architecture I envisioned now seems unnecessary, as I’ve already learned exactly what causes my system to crash—insights that surprise me each time.

2. Simplified Hardcoded Configurations

Instead of utilizing configuration files or environment variables, I employ hardcoded constants within my codebase, ensuring that any adjustments require redeployment. This may seem limiting, but it empowers me to quickly locate and modify values, as each change is documented within my version control history. In just three months, I’ve altered these constants a mere three times, demonstrating how this straightforward approach saves me substantial engineering hours.

3. SQLite in a Production Environment

Surprisingly, I’ve opted to run SQLite for my multi-user web application, which currently operates smoothly with a total database size of only 47MB. By monitoring access patterns, I’ve determined that 95% of database interactions are reads, indicating that SQLite is a perfect fit