Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions

In the entrepreneurial landscape, one piece of advice stands out: “Do things that don’t scale.” This concept, popularized by Paul Graham, is frequently discussed, yet the practical steps for integrating it into coding processes are often overlooked.

After dedicating eight months to developing an AI podcast platform, I have devised a straightforward framework: every unscalable tactic is granted a lifespan of three months. Within this timeframe, the chosen approach must either demonstrate its worth and evolve into a robust piece of infrastructure or be discarded altogether.

The startup Paradox and the Need for Simplicity

As engineers, we are often conditioned to prioritize scalable solutions from the outset. We dive into the world of design patterns, microservices, and distributed systems—all brilliant methodologies that are more fitting for established enterprises. However, in a startup context, premature optimization can become an expensive form of procrastination. We find ourselves crafting solutions for hypothetical users and hypothetical problems.

The 3-month rule compels me to implement straightforward, even imperfect, code that allows for rapid deployment and real-time feedback. This approach provides invaluable insights into actual user needs, rather than mere assumptions.

My Ingenious Infrastructure Hacks

Here’s a look at my current infrastructure techniques, which may appear unorthodox but serve a clear purpose:

1. Consolidated Services on a Single VM

I run my database, web server, background jobs, and caching system on a single $40/month virtual machine. There’s no redundancy, and I perform manual backups.

This setup has proven beneficial; in just two months, I have gained deeper insights into my actual resource requirements than I ever did through extensive capacity planning. It turns out, my platform’s peak usage is around 4GB of RAM. The elaborate Kubernetes architecture I nearly pursued would have merely managed empty containers.

When issues arise—as they have on two occasions—I receive practical data about what really fails, which is rarely what I initially anticipated.

2. Hardcoded Configuration

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

My approach involves utilizing hardcoded constants scattered throughout my code without complex configuration files or environment variables.

Why this is advantageous: I can quickly search for any configuration value across my entire codebase.