Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Development

In the ever-evolving world of tech startups, the common advice often echoes the sentiment of Paul Graham: “Do things that don’t scale.” However, a pivotal question remains unanswered: how can we practically apply this mindset in our coding practices?

Over the past eight months while developing my AI podcast platform, I have found clarity in adopting a straightforward framework: every unscalable solution is given a lifespan of three months. After this period, it either proves its worth and gets developed further, or it is phased out.

Typically, as engineers, our training emphasizes the development of scalable systems right from the outset. We delve into microservices, design patterns, and sophisticated architectures capable of accommodating millions of users. However, such thinking can be a trap in the context of startups where scalable solutions can act as a costly form of procrastination. Often, we’re optimizing for potential users who may never arrive or dealing with challenges that don’t yet exist. My 3-month rule compels me to write straightforward, even “inefficient” code that is deployable, providing invaluable insights into user needs.

Current Infrastructure Innovations and Their Strategic Wisdom

1. Consolidated on a Single Virtual Machine

Currently, my entire setup—database, web server, background jobs, and caching—runs on a single virtual machine costing just $40 a month. It lacks redundancy, and I manage backups manually.

This design might seem rudimentary at first glance, but it has allowed me to gain a profound understanding of my actual resource requirements. In just two months, I’ve gleaned more insight than through any complex capacity-planning document. I discovered that my AI-centric platform typically peaks at 4GB of RAM. The intricate Kubernetes infrastructure I was on the verge of implementing would have merely been a futile exercise in resource management. Each time the system crashes (and it has a couple of times), I am learning exactly what goes wrong—often revealing unexpected insights.

2. Hardcoded Configurations

Instead of using configuration files or environment variables, I’ve opted for hardcoded constants that look like this:

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

While some might view this as a risky approach, it has its advantages: I can search