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

In the world of startups, the wisdom often shared by Paul Graham resonates deeply: “Do things that don’t scale.” While this advice is widely acknowledged, the practical application of it within the realm of programming and development often remains unspoken. Drawing from my experiences of building an AI podcast platform over the past eight months, I’ve devised a straightforward yet effective framework: any unscalable approach is given a lifespan of three months. At the end of this period, it must prove its worth and undergo refinement, or it will be phased out.

As engineers, we tend to gravitate toward designing scalable architectures from the outset. We think in terms of sophisticated design patterns, microservices, and distributed systems, creating impressive infrastructures capable of supporting millions of users. This mentality, however, is more in line with larger corporations, and all too often leads to over-engineering at the startup level.

In a startup scenario, focusing on scalability can frequently turn into a costly form of procrastination, as it means preparing for user bases that may never materialize, while grappling with problems that don’t yet exist. By implementing my three-month rule, I am encouraged to write straightforward, if imperfect, code that actually gets deployed, allowing me to glean valuable insights into what users truly need.

Current Infrastructure Hacks: Leveraging Simplicity for Insight

1. Unified Environment on a Single VM

Currently, my entire environment—from the database and web server to background jobs and caching—is hosted on a single $40/month virtual machine. This approach lacks redundancy, with manual backups conducted locally.

Here’s the brilliance of this setup: within just two months, I gained more understanding about my resource requirements than any meticulous planning document could have provided. I discovered that my platform’s most demanding points merely peak at 4GB of RAM. The complex Kubernetes environment I initially contemplated would have meant managing an array of empty containers.

When crashes occur (which they have a couple of times), I gather real data about failure points—often surprising insights that I wouldn’t have anticipated.

2. Simplified Configuration Management

In my codebase, configuration values are hardcoded:

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

Rather than relying on configuration files