Embracing the 3-Month Rule: A Pragmatic Approach to Building in Tech

In the entrepreneurial world, Paul Graham’s advice to “do things that don’t scale” resonates widely, yet there is a notable gap when it comes to practical application in software development. Over the past eight months, while developing my AI podcast platform, I’ve come to adopt a framework that simplifies this concept: every non-scalable hack has a life span of just three months.

After this period, each solution must either demonstrate its value and evolve into a robust architecture, or it must be discarded.

The Engineer’s Dilemma: Scalability vs. Reality

As engineers, we often fall into the habit of creating solutions designed for scalability from the outset. We apply sophisticated design patterns and build highly intricate systems intended to manage millions of users. Yet for startups, this approach can be an expensive form of procrastination, focusing on potential future users rather than the real issues at hand. The 3-month rule encourages me to produce straightforward, even “subpar” code that can be deployed quickly, ultimately teaching me more about user needs.

Current Infrastructure Strategies That Are Surprisingly Effective

1. Unified Virtual Machine (VM) Setup

Currently, my entire operation—including the database, web server, background jobs, and caching—is consolidated on a single VM costing just $40 a month. While there’s no redundancy and backups are done manually to my local machine, this setup has provided invaluable insights. Within two months, I’ve gained a clearer understanding of my actual resource requirements, discovering that my platform peaks at just 4GB of RAM. That elaborate Kubernetes architecture I almost implemented? It would have been managing unused resources, ultimately revealing that the real points of failure were entirely unexpected.

2. Directly Hardcoded Configuration

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

Instead of using config files or environmental variables, my configuration values are hardcoded throughout the codebase. Although redeploying requires effort every time a change is needed, the advantage lies in my ability to quickly search and track changes across my entire project using Git. Building a separate configuration service would have consumed a week, but I’ve changed these values only three times in the past three months—spending a mere 15 minutes redeploying versus 40 hours