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

In the tech world, there’s a well-known principle often touted by Paul Graham: “Do things that don’t scale.” While this advice is fairly popular, applying it to software development tends to be overlooked. After spending the last eight months building my AI podcast platform, I’ve established a pragmatic framework that might just revolutionize how we approach unscalable solutions—the 3-Month Rule.

Understanding the 3-Month Rule:

The premise is straightforward: every unscalable hack or workaround I implement is given a lifespan of three months. During this time, it must demonstrate its worth—either by proving effective enough to be developed further or by getting discarded entirely.

As engineers, we find ourselves trained to think in terms of scalable solutions right from the outset. We delve into advanced design patterns, microservices, and intricate distributed systems—all engineered to accommodate millions of users. However, this mindset often aligns more with large corporations than with the nimble nature of startups.

In the world of a budding startup, writing scalable code can often equate to unnecessary delays, prioritizing users that may never materialize, and solving problems we might never encounter. My 3-Month Rule explicitly encourages the crafting of straightforward, minimalistic code that directly addresses real user feedback and needs.

My Effective Infrastructure Hacks

Here are a few of the strategic yet non-scale solutions I’ve implemented that, upon closer inspection, provide invaluable insights:

1. One Virtual Machine for Everything

I’ve consolidated my database, web server, background jobs, and Redis on a single $40 monthly VM. Admittedly, this approach lacks redundancy and relies on manual backups. Yet, this has proven to be a game changer—I’ve gleaned more about my resource necessities within just two months than any extensive planning documentation could provide. For instance, I’ve discovered that my platform requires a mere 4GB of RAM at peak usage. Had I set up a complex Kubernetes architecture at the outset, I would have found myself managing countless empty containers.

When the system has crashed—a couple of times—I’ve received actual data on what fails. Unsurprisingly, it’s rarely what I initially anticipated.

2. Hardcoded Configurations Galore

Consider the configuration:
“`python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL