Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions

In the entrepreneurial landscape, the wisdom of Paul Graham resonates: “Do things that don’t scale.” However, the practical steps to apply this concept in the realm of software development are often overlooked. After spending the past eight months building an AI podcast platform, I’ve fine-tuned a straightforward framework: every unscalable tactic has a lifespan of three months. At the end of this period, each solution must either demonstrate its worth and evolve into a more robust architecture, or face elimination.

As developers, we are typically indoctrinated into the world of scalable design from the outset—think of grand systems, distributed architectures, and efficient microservices that can handle vast user bases. However, this is primarily a mindset suited for large-scale enterprises. At a startup, crafting solutions aimed at hypothetical future users can lead to unnecessary complexity and procrastination. My three-month rule encourages the development of straightforward, albeit imperfect, code that is delivered promptly, providing invaluable insights into actual user behaviors and needs.

My Current Infrastructure Adjustments and Their Strategic Contributions

1. Single VM for Everything

Everything—be it the database, web server, background jobs, or cache—operates on a single, economical $40/month virtual machine. There’s no redundancy and backups are handled manually.

Why is this approach effective? It has enabled me to comprehend my real resource requirements in just two months, beyond what any capacity planning document could have offered. I discovered that my platform peaks at 4GB of RAM during load testing. The Kubernetes setup I contemplated? It would have been a management nightmare for virtual containers that weren’t even in use.

When crashes occur, they provide genuine data about failures that are often unexpected.

2. Hardcoded Configurations

Instead of relying on complex configuration files or environmental variables, my setup utilizes constants strewn across the codebase, such as:

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

The benefit? It takes mere seconds to search for any configuration value across my codebase. Any changes are logged in git history and subject to a personal review process. Implementing a comprehensive configuration service would have consumed a week, while making updates has taken only 15 minutes total over three months.