Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Coding

In the entrepreneurial landscape, the legendary advice from Paul Graham, “Do things that don’t scale,” is widely acknowledged but often misunderstood, especially among developers. While the concept is simple, the execution within the realm of software development leaves many perplexed.

Over the past eight months while constructing my AI podcast platform, I’ve discovered a valuable framework that I like to call the “3-Month Rule.” The premise is straightforward: any unscalable hack I implement is given a lifespan of three months. By the end of this period, I evaluate whether it has demonstrated enough value to warrant a more robust design or if it will be unearthed from my codebase.

Rethinking Scalability

Traditionally, as engineers, we are conditioned to create scalable solutions right from the get-go. We gravitate towards elaborate design patterns, microservices, and distributed systems—architectures that are well-suited for large-scale applications. However, this mindset often poses challenges in a startup environment where scalability can sometimes translate to costly delays.

My 3-month rule compels me to focus on creating straightforward, albeit rudimentary, code that is capable of being deployed. This approach encourages real-time learning about user interactions and their genuine needs.

Insights from My Current Infrastructure Hacks

1. Consolidated Operations on a Single VM

Currently, my database, web server, background jobs, and Redis are all hosted on a single virtual machine, costing only $40 a month. This setup lack redundancy, supported by manual backups to my workstation.

Why is this approach smart? In just two months, I’ve gained more insights into my actual resource needs than any capacity planning document could provide. It turns out that my platform, which is designed for heavy AI usage, only spikes at 4GB of RAM. The complex Kubernetes architecture I almost implemented would have meant managing idle containers.

When my system has crashed—twice now—I have obtained valuable data regarding failures and, ironically, it’s rarely what I anticipated.

2. Hardcoded Configurations Across the Board

Instead of using configuration files or environment variables, I have constants directly in my code, making updates as simple as redeploying the application:

“`python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = “