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

In the entrepreneurial landscape, Paul Graham’s famous mantra to “do things that don’t scale” is prevalent, yet the practical application of this principle in coding often goes unaddressed. After eight months of developing my AI podcast platform, I’ve formulated an approach that revolves around a simple guideline: each non-scalable hack is allotted a timeframe of three months. Following this period, the solution must either demonstrate its value and be developed further or be discarded.

As developers, we often focus on crafting scalable solutions immediately—think design patterns, microservices, and distributed systems. These sophisticated architectures are ideal for enterprises but can create unnecessary complexity in a startup environment. In many cases, writing scalable code is merely a distraction, anticipating user demands that may never become reality. My three-month rule compels me to produce straightforward, albeit imperfect, code that can be shipped quickly, allowing me to glean valuable insights into user needs.

Innovative Infrastructure Hacks: Why They Work

1. Single Virtual Machine Operation

Currently, my setup operates on a single virtual machine (VM) for $40 a month, hosting everything from the database to web servers and background jobs. Although some may view this as a reckless approach lacking redundancy, it has provided invaluable lessons regarding my resource requirements over the last two months.

I’ve learned, for instance, that my AI-focused platform only requires about 4GB of RAM at peak times. Had I pursued a more intricate Kubernetes infrastructure, I would have spent time managing containers that would ultimately sit idle. Each time my VM crashes (a phenomenon that has occurred twice), I receive clear feedback regarding the root causes—information I wouldn’t have gathered from traditional planning methods.

2. Straightforward Hardcoded Configurations

I utilize hardcoded constants for configuration instead of external files or environment variables. For example:

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

While this may seem overly simplistic, there’s an efficiency to it. I can quickly perform a search across my entire codebase to pinpoint any configuration value, and each change is documented in my Git history. In the past three months, I’ve adjusted these values only three times, effectively saving myself significant engineering hours by opting for a quick redeployment rather