Embracing Imperfection: The 3-Month Framework for Building Non-Scalable Solutions

In the world of tech startups, the adage “Do things that don’t scale” — famously shared by Paul Graham — is often embraced but rarely unpacked, especially when it comes to implementation in coding. As I’ve ventured into building my AI podcast platform over the past eight months, I’ve devised a technical framework that has become vital to my workflow: the 3-Month Rule. This approach allocates a fixed period for every unscalable solution to prove its worth. After this time, if it hasn’t demonstrated significant value, it is retired.

Traditionally, engineers focus on creating scalable architectures from the outset. We often become enamored with complex design patterns, microservices, and distributed systems, which are ideal for handling millions of users. However, in the startup environment, aiming for scalability too soon can lead to unnecessary delays and inflated costs. My 3-Month Rule encourages me to prioritize direct, simple code that can be deployed quickly, ultimately helping me to identify the genuine needs of my users.

Current Practices: Simplifying for Success

1. Centralized Operations on a Single VM

I have consolidated all operations — including the database and web server — on a single $40/month virtual machine, with no redundancy in place and manual backups. This may seem reckless, but it has provided me invaluable insights into my resource requirements within just two months. My platform, which I presumed would be resource-intensive, only peaks at 4GB of RAM. The extensive Kubernetes setup I nearly implemented would have left me managing unutilized containers. Each system failure has taught me which components truly require attention, often revealing surprises in the process.

2. Hardcoded Constants for Configuration

Instead of using configuration files or environment variables, I’ve opted for hardcoded constants throughout my code — something like this:

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

This method allows me to quickly search and change parameters through the entire codebase, tracking revisions in Git history. Over the past three months, I’ve only modified these values three times, saving countless hours that would have been spent developing a configuration service.

3. Leveraging SQLite for Production

Surprisingly, I’m utilizing SQLite for my multi-user application, with