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

In the tech startup landscape, amid the flurry of best practices and architectural ideals, one piece of advice stands out: “Do things that don’t scale.” You’ve likely heard this gem from Paul Graham, but the discussion often skews towards theory without addressing practical application, especially in the realm of coding.

After spending eight months building my AI podcast platform, I’ve developed a straightforward framework that guides my approach to unscalable solutions: the 3-Month Rule. Each unoptimized solution is given a trial period of three months. At the end of this timeframe, if it proves its worth, I’ll invest in refining and scaling it; if not, it gets the axe.

As engineers, we are largely conditioned to craft solutions that can handle immense growth right from the outset. We dive into sophisticated design patterns, microservices, and distributed systems that are meant to accommodate millions of users—a mentality that suits larger companies but often leads to unnecessary complexity in a startup environment. In many cases, creating scalable applications is simply postponing essential decisions and layering over problems that may never arise.

My 3-month framework encourages me to write straightforward, albeit sometimes messy, code that can be rolled out quickly, thereby providing insight into user needs.

Current Infrastructure Hacks: Why They Work

1. Consolidated Operations on a Single VM

My entire setup—including the database, web server, background jobs, and even Redis—operates on a single virtual machine for just $40 a month. This means zero redundancy and manual backups to my local machine.

Why is this approach sound? It has forced me to gain a deep understanding of my actual resource requirements in a mere two months. My platform, contrary to my initial expectations, only requires 4GB of RAM at peak use. The complex Kubernetes architecture I considered would have only managed idle containers. When the system crashes (which has happened twice), I gather valuable data about failure points—almost always different from what I anticipated.

2. Hardcoded Configuration Parameters

Rather than scattering configuration values across various files and relying on environment variables, I’ve opted for hardcoded constants, such as:

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

What’s the advantage of this seemingly outdated method? It results in