The 3-Month Rule: A Pragmatic Approach to Non-Scalable Development

In the world of startups, the mantra “Do things that don’t scale,” as famously coined by Paul Graham, is often echoed but rarely fleshed out, especially in the realm of programming. After eight months of developing my AI podcast platform, I’ve implemented a straightforward guideline that I call the “3-Month Rule.” This principle dictates that each unscalable workaround is granted a lifespan of just three months. Within this timeframe, it must either validate its worth and evolve into a more robust solution or be phased out.

Embracing Simplicity Over Complexity

As software engineers, we are conditioned to prioritize the creation of scalable architectures from the outset. This leads us down a path of intricate design patterns, microservices, and distributed systems—systems that are indeed impressive but often suited for large-scale enterprises. In a startup context, however, building these structures can turn into costly delays, as we risk optimizing for hypothetical users and scenarios that may never materialize.

My 3-Month Rule compels me to adopt a straightforward approach, allowing me to produce “bad” code that gets shipped quickly. This strategy helps me glean invaluable insights into actual user needs and behaviors.

Current Infrastructure Hacks: Smart Choices in Action

1. Single Virtual Machine Setup

Currently, my entire infrastructure—web server, database, background jobs, and Redis—operates on a single $40/month virtual machine, with no redundancy and manual backups to my local machine. This may seem reckless, but it has been immensely educational. I’ve gathered more actionable data regarding my resource consumption in just two months than any capacity-planning document would provide. My so-called “AI-heavy” application only peaks at 4GB of RAM. The Kubernetes setup I nearly built would have only managed idle containers.

When my system crashes—a scenario that has occurred twice—I receive real feedback on failure points, illuminating issues I hadn’t anticipated.

2. Hardcoded Configuration

I’ve opted for hardcoded constants throughout my codebase, such as:

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

Without configuration files or environment variables, any change necessitates a redeploy. The advantage? Tracking configuration changes is simplified, allowing for faster iterations and straightforward maintenance. In the