Embracing the 3-Month Rule: A Practical Guide to Non-Scalable Solutions in Software Development

In the world of startups, the wisdom of Paul Graham’s mantra, “Do things that don’t scale,” often gets overlooked when it comes to writing code. As I embark on the journey of building my AI podcast platform, I’ve formulated a straightforward yet effective framework known as the Three-Month Rule. This principle dictates that every unscalable workaround I implement is given a three-month trial period to prove its value. If it’s valuable, I’ll invest in its development; if not, it will be retired.

The Challenge of Scalability in Startups

As engineers, we are conditioned to aim for scalable solutions right from the outset. Our learning gravitates towards sophisticated design patterns, microservices, and distributed architectures capable of sustaining hundreds of thousands of users. However, this kind of big-picture thinking can lead to unnecessary complexities in a startup scenario. Here, focusing on scalable code often equates to costly delaying tactics—prioritizing future users over current realities.

My three-month constraint compels me to create simpler, more agile code that allows me to ship products quickly and gather genuine feedback on user needs.

Insights from My “Less-Than-Perfect” Infrastructure Simplifications

1. One VM to Rule Them All

I’ve consolidated all components—database, web server, background jobs, and caching—onto a single, $40 monthly virtual machine. While this setup lacks redundancy and employs manual backups, it has delivered invaluable insights. Over just two months, I’ve acquired a clear understanding of my resource requirements. Notably, my AI platform peaks at just 4GB of RAM, revealing that the complex Kubernetes environment I considered would have merely wasted resources.

Each time the system crashes (which has happened twice), I gather real-time data about the failure—often revealing surprises about what truly breaks.

2. Hardcoded Configurations

My code features hardcoded values for key parameters such as pricing and user thresholds, all embedded directly within the source files. This setup may seem primitive—requiring a redeployment for any change—but it has major advantages. I can quickly search through my code base for constants, and every update is recorded in Git history, fostering a straightforward review process.

Rather than investing significant time to establish a configuration management service, I’ve spent just 15 minutes redeploying after three changes over the past three months.

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