Embracing the 3-Month Rule: A Practical Framework for Rapid Development

In the world of startup culture, the well-known mantra from Paul Graham, “Do things that don’t scale,” often resonates with entrepreneurs and developers alike. However, few delve into how this philosophy can be applied in the realm of coding and product development.

After spending the last eight months building an AI podcast platform, I have developed a straightforward yet effective framework that I call the “3-Month Rule.” It dictates that any unscalable hack I implement is given just three months to demonstrate its utility. If it doesn’t prove its value by then, it gets phased out.

As engineers, we often fall into the trap of designing scalable solutions from the get-go—think intricate architecture like distributed systems and microservices designed for handling millions of users. While this approach is essential for large organizations, it can be a costly delay for startups. Instead of solving problems that may not even exist, I’ve found that my 3-Month Rule compels me to write straightforward, albeit “raw,” code that can be shipped quickly and teaches me a great deal about user behavior and needs.

Current Infrastructure Innovations: Why They Work

1. Consolidated Resources on a Single VM

I’m running my entire platform—database, web server, background jobs, and Redis—on one $40/month virtual machine. Admittedly, it’s a barebones setup with no redundancy, relying on manual backups.

Why is this method effective? Over the past two months, I’ve gained insights into my actual resource requirements that no capacity planning document could provide. My application, which I anticipated would be “AI-heavy,” actually only peaks at 4GB of RAM. The complex Kubernetes infrastructure I almost implemented would have resulted in managing idle containers instead.

And when the system crashes—something that’s happened twice—I receive concrete data on what truly fails, often revealing unexpected issues.

2. Directly Hardcoded Configurations

In my code, configurations like pricing tiers and user caps are hardcoded:

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

This approach, devoid of config files and environment variables, may seem simplistic, but it allows me to quickly search my codebase for any configuration value. Every alteration is meticulously documented in Git history and code-reviewed, even