The 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Software Development

In the entrepreneurial world, it’s common to hear Paul Graham’s advice: “Do things that don’t scale.” Yet, the challenge many developers face is translating this philosophy into actionable coding practices. After eight months of refining my AI podcast platform, I’ve devised a straightforward strategy: each unscalable solution gets a lifespan of just three months. At the end of this period, I evaluate its effectiveness—either it gets upgraded into a more robust system, or it’s phased out completely.

Embracing Imperfection in Software Design

Many engineers are trained to think in terms of scalability from the outset. The emphasis is often on intricate design patterns, microservices, and distributed systems—all suited for handling vast user bases. However, in a startup environment, devoting time to build scalable architecture can sometimes yield little more than a costly delay, focusing efforts on hypothetical problems and future users who may never materialize.

This is where my 3-month rule shines. It encourages me to develop straightforward, sometimes imperfect code that can be deployed quickly, offering real insights into user needs.

Insights from My Current Practices

1. Consolidated Operations on a Single Virtual Machine

I host my database, web server, and background jobs all on one $40/month virtual machine without redundancy and perform manual backups to my local setup.

This choice turned out to be a strategic one. In just two months, I’ve gained invaluable insights into my actual resource demands. It turns out that my platform, primarily driven by AI, only peaks at 4GB of RAM use. The complex Kubernetes architecture I contemplated would have meant managing inefficiencies rather than addressing real needs.

When the system has crashed—twice so far—I’ve received actionable data on failure patterns, often revealing surprises that I hadn’t anticipated.

2. Hardcoded Configuration for Simplicity

Instead of using configuration files or environment variables, I’ve opted for straightforward hardcoded values like:

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

Altering any of these constants requires a redeployment, but this approach has its advantages. I can quickly search for any configuration across the entire codebase and track price modifications in git history. What would take days to build—a