Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech

In the world of startup culture, we frequently hear the revered advice from Paul Graham: “Do things that don’t scale.” Yet, the challenge remains—how does one translate this philosophy into tangible practices in the realm of coding?

After eight months of developing my AI podcast platform, I’ve devised a straightforward framework that I like to call the “3-Month Rule”. Each non-scalable solution or hack is given a trial period of three months. At the end of this period, the solution either demonstrates its worth and is transformed into a robust system or is discarded.

This approach contrasts sharply with the conventional mindset that engineers typically adopt, where the focus is often on crafting scalable solutions from the outset. We’re taught to rely on intricate design patterns, microservices, and distributed systems to accommodate millions of users, an ideal more aligned with large corporations than budding startups.

In the startup ecosystem, building scalable code can often lead to costly delays. You risk wasting time optimizing for hypothetical users while overlooking the real needs of those you currently serve. My 3-month rule insists that I create simple, sometimes less-than-perfect code, allowing me to ship quickly and discover what my users genuinely require.

Here’s a Glimpse at My Current Technical Hacks and the Wisdom Behind Them:

1. Consolidated Infrastructure on a Single VM

Currently, my entire setup—database, web server, background jobs, and Redis—operates on a single $40/month virtual machine. While this approach lacks redundancy and relies on manual backups, it has proven invaluable. Within two months, I’ve gained more insights into my actual resource utilization than any extensive planning document could provide. Interestingly, my platform’s “AI-heavy” usage peaks at just 4GB of RAM. The complicated Kubernetes architecture I almost pursued would have been a case of over-engineering for non-existent traffic.

When the system hiccups (which has happened twice), I receive tangible insights into what malfunctioned, surprising me with the findings each time.

2. Hardcoded Configuration for Simplification

Instead of using configurations stored in files or environment variables, I utilize hardcoded constants throughout my codebase:

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

While this might