Embracing the 3-Month Rule: A Technical Framework for Non-Scalable Solutions

In the realm of startups and software development, the mantra “Do things that don’t scale,” popularized by Paul Graham, resonates with many. Yet, implementing this advice in the coding world often remains uncharted territory. After eight months of building my AI podcast platform, I’ve crafted a straightforward technical framework: every unscalable solution is given a lifespan of just three months. At the end of this period, we assess its value—either it evolves into a robust solution or it gets phased out.

As engineers, we’re often conditioned to prioritize scalable solutions from the start. We engage with design patterns, microservices, and distributed systems—architectural marvels geared toward handling millions of users. However, this type of thinking typically suits larger companies, leaving startups in danger of suffering from premature optimization. In many cases, developing scalable code can turn into costly procrastination, focusing on hypothetical users while neglecting the real ones currently engaging with the product. My three-month rule has pushed me to create simple, straightforward code that ships quickly, providing valuable insights into what users actually need.

Current Infrastructure Practices: Smart Hacks for Efficient Learning

1. Unified Infrastructure on One Virtual Machine

Everything—database, web server, background jobs, and Redis—runs on a single, modestly priced $40/month virtual machine (VM). There’s no redundancy, and backups are done manually to my local machine.

Why is this a masterstroke rather than a blunder? In a mere two months, I’ve gained insights into my actual resource requirements that far exceed what any capacity planning document could offer. My supposedly “AI-heavy” platform operates comfortably within a peak of 4GB of RAM. With the complicated Kubernetes setup I nearly adopted, I’d have found myself managing empty containers instead of addressing real usage patterns.

When the system crashes (and it has, twice), I glean authentic data on what fails—inevitably revealing surprising truths.

2. Hardcoded Configuration Throughout

In my codebase, configurations like price tiers and maximum users are hardcoded as constants. There are no configuration files or environment variables; any modification requires a redeployment.

The brilliance of this approach? I can swiftly search my entire codebase to find any configuration value. Each price adjustment is tracked in Git history, and every change goes through a personal code review. The thought of creating a configuration service without any immediate need was a