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

In the startup world, we often hear the seasoned advice from Paul Graham: “Do things that don’t scale.” However, integrating this philosophy into the coding process is rarely discussed. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework that I call the “3-Month Rule.” This principle dictates that any unscalable solution I implement will have a lifespan of three months. At the end of this period, the solution must either demonstrate its worth and be refined, or it will be discarded.

As engineers, we are conditioned to create scalable solutions from the outset. We focus on sophisticated design patterns, microservices, and distributed systems that can easily handle vast user bases. However, in the early stages of a startup, such scalable code can often serve as an expensive form of procrastination. Instead of focusing on users who aren’t quite there yet and solving hypothetical problems, my 3-Month Rule compels me to write straightforward and purposeful code that delivers insights into the true needs of my users.

Current Infrastructure Hacks: Ingenious Solutions for Immediate Learning

1. Consolidated Operations on a Single Virtual Machine

I run my entire operation—a database, web server, background jobs, and Redis—on a single, budget-friendly $40/month virtual machine. While it may seem risky with no redundancy and manual backups, here’s the upside: I have quickly gauged my resource needs. In just two months, I’ve discovered that my platform, which I initially labeled as “AI-heavy,” only peaks at 4GB of RAM. This experience has spared me from the complexities of an intricate Kubernetes setup that would have spent resources managing idle containers. Crashes? They’ve occurred, but they’ve provided invaluable data on what genuinely goes awry—quite often, it’s not what I anticipated.

2. Hardcoded Configurations

My code features hardcoded constants instead of configuration files or environment variables. For example:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
When I change a value, it requires a redeployment, which might sound cumbersome. However, this approach affords me the advantage of quickly searching my entire codebase for any configuration, with every change meticulously tracked in version control. I’ve modified these constants merely three times over three months. It’s