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

In the realm of technology and startups, a well-known piece of advice from Paul Graham resonates: “Do things that don’t scale.” While this advice is frequently highlighted, the practical application of it—especially in coding—remains largely uncharted territory.

After spending the past eight months developing my AI podcast platform, I have devised a straightforward framework: each unscalable tactic is granted a lifespan of three months. At the end of this period, it either demonstrates its worth and is built out for sustainability, or it’s discarded.

A Common Dilemma: The Scalable Solution Trap

As developers, we are often conditioned to focus on scalable solutions right from the outset. We delve into complex design patterns, microservices, and distributed systems, preparing for the influx of millions of potential users. However, this mindset can be counterproductive in a startup environment, where scalability can sometimes lead to costly delays. More often than not, we find ourselves optimizing for hypothetical scenarios rather than addressing current needs.

My 3-month rule compels me to write straightforward, less-than-perfect code that can be deployed quickly. This practice provides invaluable insights into the actual requirements of my users.

The Advantages of My Current Infrastructure Choices

1. Consolidated Resources: One VM to Rule Them All

Rather than spreading my infrastructure across multiple resources, I run my entire stack—database, web server, background workers, and Redis—on a single virtual machine costing just $40 a month. While this approach lacks redundancy and involves manual backups to my local drive, the knowledge gained in just two months about my resource needs has been enlightening. My platform peaked at 4GB of RAM, revealing that the complex Kubernetes architecture I nearly implemented was unnecessary, as it would have dealt with idle resources.

When the system has crashed (a total of two times), I’ve gained real-world insights into failure points and the unexpected nature of issues that arise.

2. Hardcoded Configurations: A Hidden Superpower

My configuration is hardcoded directly into the codebase—constants like:

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

While this setup lacks flexibility and requires redeployment for changes, it has its merits. I can quickly