Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Software Development

In the world of software engineering, the wisdom of Paul Graham’s advice, “Do things that don’t scale,” often gets overlooked when it comes to practical implementation, especially in coding. After spending the last eight months developing an AI podcast platform, I’ve come up with an effective framework that I like to call the “3-Month Rule.” This approach allows me to harness the power of unscalable hacks while keeping my development process agile.

An Engineer’s Tendency to Over-Optimize

As engineers, we’re often conditioned to create scalable solutions right from the start. We indulge in the allure of advanced design patterns, microservices, and distributed systems — the glamorous architecture capable of handling millions of users. However, this line of thinking can sometimes lead startups into the trap of premature scaling. By focusing on users who haven’t yet arrived, we risk wasting valuable resources building unwarranted complexities.

Implementing my 3-month rule means that any unscalable hack has a life expectancy of just three months. At the end of this period, it either proves its worth and transforms into a robust solution or it gets phased out. This method encourages me to produce straightforward, albeit imperfect, code that I can release quickly and learn from in real-time.

Current Infrastructure Hacks: Smart Choices or Reckless Risks?

1. Consolidated Operations on One VM

Currently, my entire infrastructure—including the database, web server, background jobs, and Redis—runs on a single virtual machine that costs just $40 a month. This setup lacks redundancy, and I handle backups manually on my local machine.

The brilliance of this approach lies in its simplicity. I’ve gained critical insights into my actual resource needs within a mere two months — insights that no extensive capacity planning document could ever reveal. For instance, my platform’s peak usage only requires 4GB of RAM. The Kubernetes architecture I considered would have likely been an unnecessary complication, managing empty containers instead of focusing on user needs.

2. Streamlined Hardcoded Configurations

My configuration is simplistic, with values like PRICE_TIER_1 = 9.99 integrated directly into the code. There are no separate config files or environment variables to manage. Any changes necessitate a redeployment.

This seemingly inefficient method has a silver lining: it allows for rapid searching within the codebase and easy tracking of changes through git history