Embracing the 3-Month Rule: A Practical Approach to Unscalable Development

In the world of tech entrepreneurship, the advice from Paul Graham resonates deeply: “Do things that don’t scale.” While this principle is widely acknowledged, implementing it within a software development context often remains a challenge. After eight months of development on my AI podcast platform, I’ve devised a streamlined framework to navigate this dilemma: I allocate a strict time frame of three months for any unscalable solution. If it demonstrates its worth within that period, it gets refashioned into a robust product; if not, it’s phased out.

The crux of the matter is that as engineers, we’re often conditioned to prioritize “scalable” architectures from the outset—think microservices or distributed systems designed to accommodate an influx of users. Yet in the startup environment, investing time and resources into scalable solutions can often lead to costly delays, imposing risks on our burgeoning product. The essence of my three-month rule is to encourage the creation of straightforward, albeit imperfect, code that can be deployed rapidly, offering genuine insights into user needs.

Current Infrastructure Strategies: My Smart “Hacks”

1. One Virtual Machine for Everything

By hosting my database, web server, background jobs, and Redis on a single $40/month virtual machine, I’ve opted for zero redundancy and rely on manual backups to my local environment. While this may seem risky, it has afforded invaluable learning opportunities about resource requirements in just a short span. My presumed “AI-heavy” operations averaged at 4GB of RAM use—if I had gone through elaborate capacity planning, I would have wasted resources managing unnecessary complexities.

Every crash—of which there have been two—has provided concrete data about operational weaknesses, revealing that failures rarely align with my initial assumptions.

2. Hardcoded Configurations

In my codebase, configuration occurs through hardcoded constants:

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

While this bypasses traditional configuration files and environment variables, it allows for swift searches across my entire codebase and a clear record of changes through git history. Rather than investing a week to develop a configuration service, I’ve made minor updates thrice in three months, translating to just 15 minutes of redeployment—a huge efficiency gain compared to hours of engineering.