Embracing the 3-Month Rule: A Pragmatic Approach to Software Development

In the tech world, the well-known directive from Paul Graham advises budding entrepreneurs to “do things that don’t scale.” However, the challenge arises when it comes to actual implementation, particularly in the realm of coding. After dedicating eight months to developing my AI podcast platform, I’ve fashioned my own practical framework that I call the 3-Month Rule: every temporary hack that lacks scalability is given a lifespan of three months. Post that period, it either secures its permanence through evidence of its effectiveness or it meets its end.

As engineers, we often default to crafting scalable solutions from the very beginning. We get caught up in sophisticated design patterns, microservices, and distributed systems—all that flashy architecture designed to cater to millions of users. This mentality often favors larger enterprises, but in a startup environment, such approaches can translate to costly delays as we strive to serve potential users who haven’t yet arrived. My 3-Month Rule compels me to write straightforward, albeit “imperfect” code that is quick to deploy, allowing for insights into what users truly need.

Current Infrastructure Strategies That Work Wonders

1. Consolidated Resources on a Single VM

Imagine running your database, web server, background jobs, and Redis on a single $40-per-month virtual machine, with zero redundancy and manual backups. While this may seem reckless to some, I’ve gleaned more understanding of my actual resource requirements within just two months than any extensive capacity planning could provide. Surprisingly, my AI-dedicated platform peaks at only 4GB RAM, rendering an intricate Kubernetes setup unnecessary—only to manage idle containers. Each crash (which has happened twice) offers me enlightening data about the points of failure.

2. Hardcoded Configuration

Instead of sophisticated configuration setups, I’ve opted for hardcoded parameters sprinkled throughout my code, such as:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

This simplistic approach enables me to quickly search the codebase for configuration values and keeps a history of changes through Git. While crafting a dedicated configuration service would span a week, I’ve made only three configuration changes in three months. This amounts to a mere 15 minutes of redeployment time compared to the 40 hours it would take to implement a sophisticated solution