Embracing Imperfection: The 3-Month Principle for Startups

In the startup world, a common piece of advice resonates: “Do things that don’t scale.” However, the implementation of this ethos in the realm of coding remains less discussed. Over the past eight months while developing my AI podcast platform, I’ve crafted a straightforward framework that I like to call the “3-Month Rule.” This principle states that any unscalable hack or workaround is granted three months of utility. After that period, it either demonstrates its worth and is refined into a robust solution, or it’s set to rest.

As engineers, we often find ourselves gravitating toward creating scalable solutions right from the outset. With our minds trained in design patterns, microservices, and sophisticated architecture meant to support millions of users, it’s easy to slip into the mindset of larger enterprises. However, this line of thinking can lead to excessive worries about users who have yet to materialize and challenges that may never arise. My 3-Month Rule advocates for straightforward, albeit rudimentary, code that can be deployed quickly, allowing me to gather real-world insights into user needs.

Ingenious Infrastructure Hacks

Here are some of the unorthodox strategies I’m currently deploying and the reasons they make sense:

1. Consolidation on a Single VM

My entire architecture—including the database, web server, background processes, and Redis—operates on a single $40/month virtual machine. While this setup lacks redundancy and mandates manual backups, the advantages are revealing. Within just two months, I’ve accurately assessed my resource needs; my application’s peak demand is merely 4GB of RAM. Had I pursued a complex Kubernetes setup, I would have been managing empty containers instead of focusing on real usage patterns. Each crash (I’ve experienced two) provides invaluable data on performance issues, revealing unexpected breakdown points.

2. Hardcoded Configuration for Agility

For configuration, I opted for a simple approach: constants scattered across my codebase. Rather than setting up elaborate config files or environment variables, changing a value requires a straightforward redeployment. This technique allows me to quickly grep my entire codebase for any config, track changes through Git history, and conduct self-reviews for any updates. The time saved is substantial—three updates in three months compared to what would have been over a week of engineering for a complicated configuration service.

3. Adopting SQLite for Production

Currently, my