Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Coding

In the startup world, we often hear the renowned advice from Paul Graham: “Do things that don’t scale.” However, implementing this concept within the realm of programming can be a challenging endeavor that many overlook.

After eight months of developing my AI podcast platform, I’ve established a dynamic framework: every quick fix or unscalable solution is given exactly three months to prove its worth. After this period, it must either demonstrate its value through results and receive a proper build-out, or it will be discarded.

The startup Paradigm Shift

As engineers, we are often conditioned to pursue scalable solutions from the outset—think complex architectures, microservices, and distributed systems designed to handle massive user loads. However, this mindset can hinder startups. In the early stages, focusing too heavily on scalability can lead to unnecessary procrastination and resources spent on problems that may not even exist. This is where my 3-month rule comes into play, pushing me to write straightforward, sometimes “imperfect” code that is functional and provides real insights into user needs.

Current Infrastructure Hacks: Proven Strategies

Let’s delve into some of my current non-scalable strategies and why they are effective:

1. Simplifying with a Single VM

All of my infrastructure—including the database, web server, background tasks, and caching—operates on a single virtual machine costing $40 a month. While this approach lacks redundancy and relies on manual backups, it has offered invaluable lessons about actual resource requirements. Within just two months, I’ve determined that my “AI-heavy” platform only requires 4GB of RAM during peak usage. Any elaborate solutions I considered, like Kubernetes, would have resulted in unnecessary complexity, managing resources that aren’t needed.

2. Hardcoding Configuration Values

Rather than employing extensive configuration files or environment variables, I use hardcoded constants throughout my codebase. This method may seem rudimentary, but it allows me to quickly search and track changes in configuration values. In the past three months, I’ve needed to change these constants only three times, saving countless hours of engineering time on building an intricate configuration service.

3. Leveraging SQLite for Production

My application currently employs SQLite, and surprisingly, it runs smoothly, handling 50 users simultaneously with a database size of only 47MB. The data access patterns I’ve observed—95% reads and 5% writes—