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

In the realm of entrepreneurship, an age-old piece of advice from Paul Graham resonates strongly: “Do things that don’t scale.” While this is often discussed in theory, its practical application within the coding landscape remains largely uncharted. As I navigate the development of my AI podcast platform, I’ve adopted a simple yet effective framework: any unconventional or unscalable solution I implement is given a lifespan of precisely three months. After this period, it must either prove its worth and evolve, or it will be phased out.

Breaking Away from the Scalable Mindset

In the world of engineering, we are trained to prioritize scalability from the outset—thinking of sophisticated design patterns, microservices, and distributed systems that can handle a vast user base. This approach is often essential for larger companies. However, in a startup environment, overly focusing on scalability can lead to unnecessary complexities and delays.

In my experience, scaling code prematurely can resemble expensive procrastination, as it prepares for users who are yet to arrive and addresses issues that may never materialize. My 3-month rule encourages me to create straightforward, albeit imperfect, solutions that are quick to deploy. This enables me to gather critical insights about user behavior and needs in real-time.

My Current Infrastructure: Unconventional Yet Insightful

1. Consolidated Infrastructure on a Single VM

I currently operate my entire platform—a database, web server, background jobs, and Redis—on one $40/month virtual machine. While this lacks redundancy and relies on local manual backups, the advantages are significant. Within just two months, I’ve gained invaluable insight into my resource requirements, discovering that my platform peaks at a mere 4GB of RAM. Had I pursued a complex Kubernetes setup instead, I would only be managing idle containers. When the VM crashes (which has happened twice), it provides real data regarding what genuinely fails—often not what I anticipated.

2. Simplified Configuration Management

My setup involves hardcoded constants for key configurations, such as:

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

While this approach may seem crude at first glance, it allows for instant codebase searches and detailed change tracking via Git history. Given that I’ve modified these