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

In the tech industry, the wisdom of Paul Graham resonates loud and clear: “Do things that don’t scale.” Yet, when it comes to practical application in software development, this advice often remains uncharted territory. Over the course of the past eight months, while building my AI podcast platform, I’ve cultivated a straightforward strategy: each unscalable workaround is permitted to exist for a maximum of three months. Beyond that, it either validates its worth through performance and refinement or is retired.

The startup Dilemma: Chasing Scale Too Soon

As developers, we frequently fall into the trap of designing for scalability right from the outset. The allure of sophisticated architectural patterns, such as microservices and distributed systems, captivates many of us. However, this mindset often comes from a big-company perspective that doesn’t necessarily align with the realities of startup life.

At startups, the pursuit of scalability can often equate to wasted time and resources—essentially procrastination towards resolving issues that may never arise. Guided by my 3-month rule, I intentionally write straightforward, albeit “imperfect,” code. This method allows me to deliver quickly and gain valuable insights into what users actually require.

Key Infrastructure Hacks and Their Strategic Value

1. Single Virtual Machine Setup

All components—database, web server, background jobs, and Redis—operate on a single virtual machine costing $40 per month. This setup lacks redundancy and relies on manual backups.

Why is this a savvy move? I’ve gained a deeper understanding of my resource consumption in just two months than I ever would have through standard capacity planning. It turns out my platform requires only 4GB of RAM at peak usage. The intricate Kubernetes architecture I nearly implemented would have mostly involved managing unused containers. When downtime occurs—which has happened twice—I obtain real insights into potential failure points, which often surprise me.

2. Hardcoded Configurations

In my code, configuration values like pricing tiers and user limits are hardcoded directly. Modifying any of these necessitates redeployment.

This design choice has a surprising advantage: it allows me to swiftly search my entire codebase. Each price adjustment is logged in git history, and every update undergoes a review process. Instead of spending a week developing a configuration service—only to adjust the values a handful of times—I save countless engineering hours. A quick