The 3-Month Experiment: My Tactical Approach to Non-Scalable Coding

In the realm of startups, many of us have encountered the familiar guidance from Paul Graham: “Do things that don’t scale.” While this advice is well-recognized, the specifics of applying it to coding practices often remain undisclosed.

Having spent the past eight months developing my AI podcast platform, I’ve established a straightforward strategy: every temporary, unscalable solution is given three months to demonstrate its utility. After this period, it must either validate its worth through effective performance or be discarded.

As engineers, we are ingrained with the notion of creating scalable solutions from the outset. We often find ourselves entangled in design patterns, microservices, and sophisticated architectures, all aimed at accommodating millions of users. However, this line of thinking tends to cloud our judgment in a startup environment.

In the early stages of a venture, focusing excessively on scalable architecture can lead to costly delays in decision-making. It’s essentially preparing for users who may not even exist and tackling problems that might never arise. My 3-month rule compels me to embrace simple, sometimes suboptimal code that is deployable, allowing me to understand my users’ genuine needs.

My Current Infrastructure Hacks: Maximizing Learning Through Simplicity

1. Single Virtual Machine Setup

Everything for my project—from the database to the web server and background tasks—runs on one $40-per-month virtual machine. There’s no redundancy, and I handle manual backups.

Here’s why this is actually smart: in just two months, I’ve gleaned more insight into my resource requirements than any planning document could provide. My so-called “AI-heavy” platform peaks at merely 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have meant managing non-essential resources.

Whenever it crashes (which has happened twice so far), I gain valuable insights into the actual points of failure. And surprisingly, these are rarely what I anticipated.

2. Hardcoded Values

Consider this:

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

I operate without configuration files or environment variables, relying instead on constants throughout my codebase. To make any changes, I redeploy the code.

The advantage? I can quickly search my entire base for any constant, and every price