The 3-Month Rule: A Practical Approach for Implementing Non-Scalable Solutions in Tech

In the entrepreneurial tech landscape, advice from industry veterans like Paul Graham often resonates: “Engage in activities that may not be scalable.” However, the challenge lies in translating this philosophy into practical coding applications.

Having spent the past eight months developing my AI podcast platform, I’ve devised an effective framework that I call the “3-Month Rule.” This rule dictates that any temporary, non-scalable solution is granted three months to prove its worth. After this period, if it hasn’t demonstrated its value, it is phased out.

Understanding the Challenge

As engineers, we often focus on building solutions that are engineered for scalability right from the start. We immerse ourselves in design patterns, microservices, and distributed systems, all aimed at accommodating millions of users. However, this perspective is typically more aligned with large corporations than with startups.

At a startup, prioritizing scalability can lead to unnecessary expenditures, as it often involves addressing issues for hypothetical users—those who might never materialize. Through my 3-month rule, I encourage the development of straightforward, albeit potentially suboptimal code that can be deployed quickly, allowing us to uncover the genuine needs of our users.

Current Infrastructure Hacks: Insightful Simplifications

1. Consolidated Resources on a Single Virtual Machine

By running my database, web server, background processes, and Redis on a single $40/month virtual machine, I find myself without redundancy and conducting manual backups to my local machine.

This approach has proven beneficial. I’ve gained invaluable insights into my actual resource requirements in just two months—information that traditional capacity planning documents would never provide. My so-called “AI-driven” platform only requires 4GB of RAM. The sophisticated Kubernetes setup I nearly implemented would have meant managing empty containers.

When my system crashes (which has happened twice), I collect real-world data on the points of failure, which are rarely what I initially suspected.

2. Configuration Hardcoding

In my codebase, configurations such as price tiers and maximum user limits are hardcoded, requiring redeployment for updates rather than utilizing external configuration files or environment variables.

This seemingly primitive method has its advantages: it allows for rapid searches through the entire codebase for any configuration value. Changes are consistently tracked in version history, and every update undergoes code review, albeit by myself. Opting away from a dedicated configuration service has saved me tremendous time—