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

When it comes to startup culture, many of us have heard the advice from Paul Graham: “Do things that don’t scale.” While this principle is widely acknowledged, implementing it effectively when developing software can be quite challenging. After eight months of building my AI podcast platform, I’ve cultivated a straightforward framework that I call the “3-Month Rule.” This approach allows every unscalable solution to thrive for three months: if it proves its worth, it gets the proper development it deserves; if not, it’s time to say goodbye.

As developers, we are often conditioned to focus on scalable solutions from the outset, crafting intricate architectures complete with design patterns, microservices, and distributed systems. Yet, this mindset is more suited to larger organizations. In the startup environment, crafting scalable code can often lead to wasted resources and time, as it may address needs that don’t yet exist. By adhering to my 3-Month Rule, I’m able to prioritize straightforward, less-than-perfect code that is actually deployed and reveals what my users truly require.

Innovative Infrastructure Hacks: Why They Work for Me

1. Consolidated Operations on a Single VM

I currently run all functionalities—including the database, web server, background jobs, and Redis—on a single $40/month virtual machine. It may sound reckless to forego redundancy and rely on manual backups, but this has been a game-changer. In just two months, I’ve gained invaluable insights into my resource consumption. For instance, my “AI-heavy” platform has not exceeded 4GB of RAM, which means that my planned Kubernetes setup would have been an exercise in futility. Each time the system crashes—twice so far—I’m provided with real data about what actually fails, often leaving me surprised at which areas need improvement.

2. Simplistic Configuration Management

Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants scattered within my code, such as:

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

This may seem impractical, but the benefit lies in its simplicity. I can quickly search through my entire codebase for any configuration value using grep. Changes are tracked in version control, and every update undergoes a code review process—albeit done by myself.