The 3-Month Rule: A Technical Approach to Unscalable Solutions

In the world of startup development, the renowned advice from Paul Graham rings true: “Do things that don’t scale.” However, the challenge lies in integrating this philosophy into the coding process effectively. Over the past eight months, while building my AI podcast platform, I stumbled upon a pragmatic framework that I call the “3-Month Rule.” This guideline dictates that every temporary, non-scalable solution gets a trial period of three months. After this timeframe, it’s either promoted to a well-structured implementation or retired for good.

As tech professionals, we are often conditioned to pursue scalable solutions from the outset. We’re drawn to elaborate design patterns, microservices, and the intricate architecture that can potentially support millions of users. Yet, in a startup context, this approach can lead to costly delays, as we optimize for users who may never materialize. Implementing the 3-Month Rule motivates me to produce straightforward, immediate solutions that expedite the learning process about my users’ needs.

Current Infrastructure Hacks and Their Surprising Benefits

1. Consolidated Architecture on a Single VM

All components — database, web server, background jobs, and caching services — operate on a single $40 monthly virtual machine. This setup comes with no redundancy and relies on manual backups to my local system.

This approach has proven advantageous. In just two months, I’ve gained insights into my actual resource requirements that traditional capacity planning could not provide. For instance, my “AI-heavy” platform peaked at just 4GB of RAM; the Kubernetes configuration I nearly set up would have ended up managing idle resources.

When the system crashes (which has occurred twice), I receive concrete feedback about the failure points. Interestingly, they were seldom what I initially anticipated.

2. Simplistic Configuration Management

I rely on hardcoded constants like:

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

No configuration files or environment variables clutter my codebase. Changing any parameter necessitates a redeployment.

The advantage of this method? I can instantly search my entire codebase for configuration values. Each price adjustment is documented in my Git history, and every change undergoes a code review (albeit self-imposed).

Constructing a full-fledged configuration service would have consumed a week