Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions

In the startup world, there’s a familiar mantra often echoed by Paul Graham: “Do things that don’t scale.” While this advice rings true, the practical implementation of this philosophy in software development is less frequently discussed. After eight months of working on my AI podcast platform, I have arrived at a straightforward framework that I’ve dubbed the “3-Month Rule.” Essentially, every unscalable solution I implement has a lifespan of just three months. At the end of this period, it either proves its worth and is refined into a robust solution, or it is discarded.

The Coding Paradigm Shift

As software engineers, we are conditioned to aim for scalability from the outset. We obsess over design patterns, microservices, and complex architectures that can manage vast user bases. However, in the context of a startup, this pursuit of scalability can often lead to unnecessary delays. It encourages us to craft solutions for users who may never exist and solve issues that aren’t pertinent to our current state.

By adopting my 3-month rule, I compel myself to focus on writing simple and, at times, imperfect code that can be deployed quickly. This approach allows me to better understand the actual needs of my users.

Current Infrastructure Insights

Let’s dive deeper into the infrastructure hacks I’ve implemented, along with the thoughtful reasoning behind them:

1. Unified Server Setup

Currently, I host my entire application—database, web server, background jobs, and caching—on a single virtual machine costing $40 per month. While this setup lacks redundancy and relies on manual backups, the insights I’ve gained over the past two months have been invaluable. I have learned that my AI-driven platform’s resource requirements peak at just 4GB of RAM, which would have been obscured by an unnecessarily complicated setup like Kubernetes.

Every time the server crashes, I gather firsthand data on what actually fails, which often surprises me.

2. Simplistic Configuration Approach

I’ve opted to implement hardcoded variables throughout my codebase like this:

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

There are no configuration files or environment variables; changes require a redeployment. While this may seem inefficient, it allows for swift adjustments