Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech Development

In the world of tech startups, the adage “Do things that don’t scale” rings true, but the execution often remains elusive. Many developers, including myself, find themselves tangled in the intricate web of scalable architecture from the get-go. However, after eight months of developing my AI podcast platform, I’ve crafted an approach I like to call the “3-Month Rule,” a technique for navigating the murky waters of unscalable solutions.

My framework requires each unscalable hack to undergo a three-month trial. At the end of this period, we’ll assess whether the implementation is worth evolving into a robust solution or if it should be phased out altogether.

Breaking Free from the Scalable Mindset

As engineers, we are conditioned to prioritize scalability—think microservices, distributed systems, and elaborate architectures designed for millions of users. While this mindset serves large enterprises well, it often leads startups to chase problems that may never surface. In many cases, writing “bad” code that promptly delivers results is far more valuable than trying to optimize for hypothetical users and challenges.

Some Current Infrastructure Strategies That Demonstrate This Philosophy

1. Consolidated Operations on a Single VM

Currently, my entire infrastructure—be it the database, web server, or background jobs—operates on a single $40/month virtual machine. While this setup lacks redundancy and relies heavily on manual backups, it allows me to glean insights about actual resource demands quickly.

In the span of two months, I’ve discovered that my platform’s demands peak at just 4GB of RAM. The complex Kubernetes framework I initially considered would have led me to manage resources that remained unused, making it an unnecessary endeavor. Moreover, any crashes provide genuine data on system vulnerabilities—I’ve experienced two incidences, and the root causes were not what I anticipated.

2. Simplified Hardcoded Configurations

Instead of employing configuration files or environment variables, I’ve opted for straightforward constants within the code base:

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

Any change requires a redeployment, but the benefits are numerous. The ability to search through my entire code base for these values in seconds has proven invaluable, allowing me to keep