Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions

In the entrepreneurial world, much has been said about famous startup founder Paul Graham’s mantra: “Do things that don’t scale.” This wisdom often goes unheeded, especially when it comes to practical implementation in the realm of coding and technology. Over the past eight months, while developing my AI podcast platform, I’ve arrived at a straightforward yet powerful framework that I call the 3-Month Rule. The essence? Every workaround or temporary solution that doesn’t scale is given a lifespan of just three months to prove its worth or meet its end.

The Dilemma of Scalability in Startups

As engineers, we frequently find ourselves entrenched in a mindset geared towards creating scalable systems right from the outset. Techniques involving design patterns, microservices, or distributed architectures are undoubtedly impressive, designed to handle vast user bases. However, this approach often leads to a kind of costly procrastination in the startup environment. More often than not, we spend considerable time optimizing for hypothetical users who may not even exist yet, all while missing out on the real requirements of our current user base.

My 3-month rule encourages me to focus on simple, straightforward solutions—allowing me to ship code quickly while gaining invaluable insights into the specific needs of my users.

My Infrastructure Insights and Tactical Decisions

1. Consolidating Resources on One VM

Currently, my entire operation—including the database, web server, background jobs, and Redis—runs on a single virtual machine that costs just $40 a month. While this may seem risky due to the lack of redundancy and manual backups, it’s actually a strategic move. Within just two months, I learned about my resource consumption more effectively than any theoretical capacity planning exercise could have provided. As it turns out, my AI-driven platform routinely requires around 4GB of RAM, far less than the elaborate Kubernetes infrastructure I nearly established. Each crash—notably a couple so far—has been enlightening, revealing unexpected weaknesses in the system.

2. Simplifying Configuration Management

In my code, configuration seems haphazard: constants like PRICE_TIER_1 = 9.99 or MAX_USERS = 100 are scattered throughout instead of being confined to configuration files or environment variables. This direct method might seem a bit reckless, but it grants me the ability to quickly search for any config value across the codebase. Each adjustment I