The 3-Month Experiment: A Pragmatic Approach to Non-Scalable Practices in Tech Development

In the world of technology startups, there’s a well-known adage from Paul Graham: “Do things that don’t scale.” However, the discussion seldom ventures into the practical execution of this philosophy, especially in the realm of software development.

After dedicating the last eight months to building an AI podcast platform, I’ve devised a straightforward framework that I call the “3-Month Rule.” The principle behind this method is simple: any unscalable solution I implement is given a lifespan of three months. At the end of this period, it either becomes a proven asset and receives the proper development it deserves or it is retired.

As engineers, we’re often conditioned to prioritize scalability from the outset. We immerse ourselves in advanced frameworks, complex architectures, and systems capable of supporting thousands, if not millions, of users, which is mostly relevant for established organizations. At a startup, however, the pursuit of scalable solutions can lead to wasted resources, delaying essential groundwork while trying to prepare for problems that may never arise. The 3-Month Rule compels me to adopt a more straightforward approach—writing direct and “imperfect” code that gets deployed and reveals the true needs of my users.

My Current Simplified Infrastructure: Clever Choices in Design

1. Single Virtual Machine Deployment

I’ve consolidated all essential services—database, web server, background processing, and caching—onto a single virtual machine that costs just $40 a month. Although it lacks redundancy and relies on manual backups, this configuration provides invaluable insights into my resource consumption. I’ve learned that my platform typically only requires 4GB of RAM, saving me from overengineering solutions like a sophisticated Kubernetes setup that would have been simply managing idle containers.

When a crash occurs (yes, it’s happened a couple of times already), I gather concrete data about unforeseen issues, often revealing surprising weak points in my application.

2. Hardcoded Variables

Instead of utilizing configuration files or environment variables, I prefer defining constants across my codebase, such as:

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

Although this might seem archaic, it allows me to easily search my entire codebase for any parameter in seconds. Changes require a redeployment, but I’ve only modified