The 3-Month Rule: A Practical Approach to Unscalable Solutions in Tech

In the ever-evolving landscape of technology, advice from industry leaders often provides valuable insight. One piece that resonates with many, particularly startups, is Paul Graham’s adage: “Do things that don’t scale.” However, the real challenge lies in figuring out how to pragmatically apply this principle in software development.

Having worked on my AI podcast platform for the past eight months, I have crafted a straightforward guideline for myself: any unscalable solution I implement will be given a lifespan of just three months. Within this timeframe, the solution must either demonstrate its worth and be developed into a robust approach, or it will be retired.

The familiar training engineers receive encourages them to pursue scalable solutions right from the onset. Concepts like design patterns, microservices, and distributed systems emphasize creating robust frameworks designed to accommodate thousands or even millions of users. While these strategies are essential for large corporations, at the startup level, overly complex systems can turn into costly delays. This three-month rule compels me to produce simple, straightforward code that actually moves the project forward, helping me better understand user needs in the process.

Insights from My Current Infrastructure Approaches:

1. Unified Virtual Machine Operation

At present, my database, web server, background jobs, and Redis run seamlessly on a single $40/month virtual machine. This setup is devoid of redundancy, and I handle manual backups on my local machine.

The brilliance of this arrangement lies in the hands-on insight it has provided. After just two months, I’ve gained a more accurate understanding of my resource requirements than any planning document could offer. It turns out that my platform, centered on AI, only needs around 4GB of RAM at peak times. The complex Kubernetes environment I almost implemented would have simply been managing idle containers. Each crash (which has happened twice) has yielded crucial data, revealing what truly causes failures—often unexpected components.

2. Simplified Hardcoded Configuration

In lieu of complex configuration files or environment variables, my setup employs hardcoded constants like:

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

The strength of this approach is evident; it allows me to quickly search my codebase for configuration values, track changes in git history, and review configuration updates