The Three-Month Rule: A Practical Approach to Unscalable Solutions in Tech Development

In the tech landscape, the mantra “Do things that don’t scale” popularized by Paul Graham resonates widely. However, practical applications of this advice, particularly in coding, are often overlooked. As I embarked on building my AI podcast platform over the last eight months, I adopted a straightforward methodology: any non-scalable workaround is given a finite lifespan of three months. After this period, the solution must either validate its worth and be developed into a robust system or it will be discarded.

As engineers, we are conditioned to focus on creating scalable technologies right from the outset. We become enamored with elegant design patterns, microservices, and distributed architectures — the kind of frameworks anticipated to accommodate millions of users. Yet, this approach often dominates larger enterprises and can stifle innovation in a startup context.

In the early stages of a startup, striving for scalability can become an exercise in futility, often leading to delays and resource allocation for problems that may not even arise. My three-month rule compels me to prioritize rapid deployment of straightforward, albeit “imperfect,” code that allows me to gather real user feedback and insights.

Current Infrastructure Strategies: Embracing Simplicity for Efficiency

1. All-in-One Virtual Machine (VM)

My entire infrastructure operates on a single $40/month VM, which hosts the database, web server, background jobs, and Redis services. This setup lacks redundancy and relies on manual backups to my local device.

You might wonder how this could be beneficial. In just two months, I’ve gained a deeper understanding of my resource requirements than any traditional capacity planning document could provide. The platform’s peak usage only demands 4GB of RAM, rendering my plans for a complex Kubernetes architecture unnecessary. Every time the system crashes—an occurrence that has happened twice—I receive invaluable insights into actual malfunctions, dispelling my initial assumptions about what would fail.

2. Simplified Configuration Management

In my codebase, configuration values are hardcoded as constants:

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

There are no external config files or environment variables to manage, which can be seen as a drawback. However, this method offers a streamlined ability to locate and modify values quickly. I can search my