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

In the world of startups, the mantra of “Do things that don’t scale,” famously articulated by Paul Graham, is often heard yet rarely implemented effectively, especially in the realm of software development. After dedicating eight months to creating my AI podcast platform, I’ve devised a practical framework: every unscalable solution receives a three-month trial period. At the end of this timeframe, we either recognize its potential and develop it further, or we discontinue it.

Let’s face it: as engineers, we’re conditioned to aim for scalability from the outset. Whether it’s adhering to best practices or implementing complex architectures like microservices and distributed systems, we often get caught in the trap of building for a future that may never exist. In a startup environment, this focus on scalability can lead to unnecessary complexity and significant procrastination. My three-month rule encourages me to prioritize simple, actionable code that allows me to better understand and respond to real user needs.

Why My Current Platform Infrastructure is Working

Here’s a breakdown of the unconventional methods I’m using and why they’re proving to be advantageous:

1. Consolidation on a Single VM

Currently, my entire infrastructure – from the database to the web server and background jobs – operates on a modest $40/month virtual machine. There’s no redundancy and backups are performed manually.

This setup has been enlightening. In just a couple of months, I’ve gained insights into my true resource usage that no theoretical capacity plan could offer. My AI-driven platform fluctuates around a mere 4GB of RAM. The complex Kubernetes architecture I almost implemented would have been wasted on managing virtually idle containers. Whenever the system crashes (and it has happened a few times), I glean valuable data on the actual failure points, which often surprises me.

2. Hardcoded Configurations

Instead of relying on configuration files or environment variables, I use hardcoded constants, like so:

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

While it may seem counterintuitive, this approach has its advantages. I’ve streamlined the ability to track changes effectively, letting me monitor the history of configuration updates through version control. While a dedicated configuration service would take significant time to build, my approach has saved me countless