Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Development
In the world of software development, the mantra “Do things that don’t scale,” popularized by Paul Graham, is often echoed but rarely unpacked with practical steps for implementation. Having spent the past eight months developing an AI podcast platform, I have devised a straightforward yet powerful framework: each unscalable solution I implement receives a trial period of three months. After this period, the effectiveness of the solution is evaluated—if it proves its worth, it is refined and properly integrated; if not, it is discarded.
This approach illustrates a key challenge many engineers face: we are often conditioned to prioritize scalability from the outset. We become enamored with design patterns, microservices, and distributed systems—all of which are essential for large-scale applications. However, in the context of a startup, pursuing scalable solutions prematurely can hinder progress and consume resources on hypothetical user bases and needs.
The three-month rule compels me to create straightforward, albeit imperfect, code that is functional and insights-driven, allowing me to uncover real user requirements. Below are some real-world applications of this framework that have proven to be effective:
Current Infrastructure Strategies That Are Surprisingly Effective
1. Consolidating Functions on a Single Virtual Machine
My entire application infrastructure—including the database, web server, background jobs, and Redis—runs on a single, cost-effective $40/month virtual machine with no redundancy. While this setup might seem reckless at first glance, it has provided invaluable insights into my resource requirements in just two months. I learned, for instance, that my platform’s peak usage caps at 4GB of RAM, highlighting that my anticipated need for a complex Kubernetes architecture would have been misguided. The occasional crashes have yielded practical data about failure points, which often differ from my initial expectations.
2. Embracing Hardcoded Configuration
Instead of using configuration files or environment variables, I have opted for hardcoded constants throughout my code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This decision allows me to quickly locate any configuration value across my entire codebase. Every change is documented in version control, and the time saved on setting up a configuration service far outweighs the benefits, given that I’ve modified these values a mere three times in
