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

When it comes to startup culture, one piece of advice stands out: “Do things that don’t scale.” This insight, popularized by Paul Graham, tends to be overlooked in the technical realm, particularly in software development. As I’ve been building my AI podcast platform over the past eight months, I’ve crafted a straightforward framework that I like to call the “3-Month Rule.” This method allows each unscalable hack to thrive for three months, at which point it either proves its worth and gets refined, or it is discarded.

In the engineering field, we often rush toward creating scalable solutions right from the start. We immerse ourselves in design patterns, microservices, and distributed systems, all aimed at accommodating potentially millions of users. However, this approach may lead startups down a path of costly procrastination—optimizing for a user base that doesn’t yet exist and addressing problems that may not ever arise.

By adhering to the 3-Month Rule, I prioritize simple, effective code that delivers tangible results. Here are some of the infrastructure decisions and hacks I’m currently employing, along with the invaluable lessons they’ve taught me.

Current Infrastructure Hacks: Smart Choices in Action

1. Consolidated Operations on a Single VM

All essential services—database, web server, background jobs, and caching—run on a single virtual machine costing $40 per month. This means no redundancy, and I rely on manual local backups.

What might seem like a reckless decision has actually been incredibly insightful. In just two months, I’ve gained a clearer understanding of my resource needs than any capacity planning document could provide. It turns out my AI-centric platform barely requires 4GB of RAM. The complex Kubernetes architecture I considered would have involved managing idle containers.

Experiencing the inevitable crashes (twice so far) has handed me real data about what really causes failures—most of which surprised me.

2. Hardcoded Configurations

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

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

There are no configuration files or environment variables; just constants spread across my code. Any change requires a redeployment.

This unconventional approach offers a superpower