Implementing the 3-Month Rule: A Practical Approach for Scalable Coding in Startups

In the realm of startups, advice that encourages “doing things that don’t scale” is often heard, particularly from influential figures like Paul Graham. However, the actual implementation of this principle in the coding process is less frequently discussed.

Over the past eight months of developing my AI podcast platform, I have established a straightforward framework: every non-scalable hack is granted a lifespan of three months. At the end of this period, we evaluate whether it has demonstrated value and warrants further development or if it should be retired.

The reality is, as engineers, we are conditioned to prioritize scalable solutions from the outset—think design patterns, microservices, and distributed systems. While these sophisticated architectures can effectively cater to millions of users, they often represent an overly ambitious mindset for startups.

In many cases, investing time into scalable code can amount to expensive procrastination. We end up optimizing for future users who may never materialize and addressing potential problems that might not even exist. By adhering to my 3-month rule, I focus on crafting simple and direct “imperfect” code that can be deployed quickly, allowing me to gain insights into what users genuinely need.

My Current Infrastructure Hacks: Simplicity Over Complexity

1. Consolidation of Resources on a Single VM

I currently run my entire infrastructure—database, web server, background jobs, and caching—on a single virtual machine costing $40 per month. This setup has no redundancy and involves manual backups to my local system.

Why is this approach beneficial? Within just two months, I’ve gleaned more about my actual resource needs than any capacity planning document could offer. My platform, presumed to be AI-intensive, only peaks at 4GB of RAM. The Kubernetes architecture I nearly implemented would have led to managing empty containers instead.

When issues arise (which they have on two occasions), I gain valuable data about failure points—surprisingly, they’re never what I anticipated.

2. Hardcoded Configuration for Efficiency

By employing hardcoded constants across my code, such as:

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

I eliminate the complexities of config files and environment variables. Modifying any value requires redeploying, which might seem cumbersome but offers significant advantages. I