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

In the realm of startup culture, the concept of “doing things that don’t scale,” famously championed by Paul Graham, often resonates deeply. However, translating this philosophy into actionable technical strategies—particularly in coding—remains underreported. After dedicating eight months to building my AI podcast platform, I’ve formulated a straightforward framework: any unscalable workaround is afforded a trial period of three months. Should it prove valuable, it gets refined into a robust solution; otherwise, it is promptly discarded.

Rethinking Scalability in Startups

As engineers, our training typically emphasizes designing scalable solutions from the outset. We’re often immersed in the intricacies of design patterns, distributed systems, and microservices—impressive architectures aiming to support millions of users. However, for startups, lean coding practices can sometimes prevent us from getting bogged down with unnecessary optimization that addresses hypothetical future users and problems. By adhering to my 3-month rule, I’m encouraged to produce straightforward, albeit imperfect, code that can be shipped quickly, providing me with invaluable insights into user needs.

My Ingenious Infrastructure Hacks

Here are the unconventional yet strategic choices I’ve made that have yielded significant learning experiences:

1. Consolidated Resources on a Single VM

I’ve opted to host my entire infrastructure—database, web server, background jobs, and caching—on a $40-per-month virtual machine (VM). This means no redundancy and manual backups to my local machine.
– Why it Works: In just two months, I’ve gained more insight into my resource requirements than any theoretical planning document could offer. My “AI-heavy” platform, contrary to expectations, only peaks at 4 GB of RAM. The complex Kubernetes setup I nearly implemented would have merely been an exercise in managing empty resources. When issues arise—twice so far—I receive direct data regarding the actual points of failure, which are never what I anticipated.

2. Hardcoded Configurations

Instead of relying on configuration files or environment variables, I’ve hardcoded values directly into my codebase:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
– The Advantage: This approach allows me to quickly search my codebase for any configuration value,