Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech

In the entrepreneurial world, there’s a widely recognized piece of advice from Paul Graham: “Do things that don’t scale.” However, the challenge many developers face is figuring out how to apply this principle effectively, especially when it comes to coding and infrastructure.

After dedicating eight months to developing my AI podcast platform, I’ve formulated a straightforward framework that I call the “3-Month Rule.” This method allows any non-scalable hack or quick solution a trial period of three months. At the end of this timeframe, it either demonstrates its utility and earns a proper implementation, or it is discarded.

As software engineers, we often operate under the belief that we should design scalable solutions from the very beginning. This perspective brings to mind complex concepts like design patterns, microservices, and elaborate distributed systems—an approach typically associated with established companies.

In the startup environment, however, focusing on scalability too early can often lead to wasted resources and unnecessary complications. By adhering to my 3-month framework, I prioritize simple, direct, and often imperfect code that enables me to quickly learn about user needs.

Current Infrastructure Choices: Smart Hacks That Work

1. Consolidating on a Single Virtual Machine

All elements of my project—including the database, web server, and background jobs—operate from a single $40-a-month virtual machine. This setup lacks redundancy, and backups are conducted manually.

This approach has been surprisingly beneficial. Within just two months, I’ve gained more insights into my actual resource requirements than any conventional capacity planning report could provide. My platform, which is geared toward AI, peaks at only 4GB of RAM. The complex Kubernetes system I nearly implemented would have meant managing resources that were hardly utilized.

When the machine does experience issues (which has occurred twice), I gather valuable data about the real pain points, often revealing that the failures weren’t where I initially thought.

2. Using Hardcoded Values for Configuration

My application contains hardcoded constants for configuration such as pricing tiers and user limits. There’s no reliance on configuration files or environment variables—just straightforward constants embedded in the code.

Here’s the unexpected advantage: it allows me to search my entire codebase for any configuration quickly. Changes in pricing are documented in Git history, and every configuration update is subject to code review (even if it’s conducted by me). The time saved by not building a complicated configuration