The 3-Month Rule: A Strategic Approach to Non-Scalable Development

In the world of startups and tech entrepreneurship, advice often echoes the words of Paul Graham: “Do things that don’t scale.” While this principle is widely recognized, the execution of such an approach in software development isn’t always clearly defined. After eight months of building my AI podcast platform, I’ve developed a straightforward framework that I call the “3-Month Rule.” This method provides a structured timeline for evaluating unscalable hacks: they get a lifespan of three months, after which they must either prove their value or be discarded.

Rethinking Scalability in Startups

As engineers, we are trained to think in terms of scalability from the outset. Advanced architectures, microservices, and distributed systems are all part of this mindset, aimed at preparing for a future with millions of users. However, in a startup environment, focusing on scalability too early can lead to costly delays. My 3-Month Rule compels me to produce simple, rapid solutions that not only ship but also yield insights into what my users actually need.

Current Infrastructure Strategies: Practical Examples

1. Single VM Architecture

Currently, my entire application — including the database, web server, background jobs, and Redis — runs on a single $40 per month virtual machine. This setup is deliberately devoid of redundancy, with manual backups carried out to my local system.

Why does this make sense? In just two months, I’ve gained a clearer understanding of my resource needs than through any formal capacity planning documents. For instance, I found that my “AI-heavy” platform only requires a peak of 4GB RAM. The complex Kubernetes architecture I almost implemented would have been an exercise in managing empty containers. Every crash (which has happened a couple of times) has offered me invaluable data about failure points I never anticipated.

2. Hardcoded Configurations

My configurations are simply hardcoded values scattered throughout the code:

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

While this may seem impractical, the considerable advantage lies in my ability to quickly search my codebase for configuration values in seconds. Each change is easily tracked through version control, with minimal overhead. Creating a centralized configuration service would take far longer than the few moments it takes for me to redeploy after