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
One Comment
Thank you for sharing this practical and thought-provoking approach. I appreciate how the 3-Month Rule emphasizes the importance of rapid experimentation and real-world insights over premature scalability concerns. It resonates with the idea that in early-stage development, simplicity often leads to faster learning, better customer understanding, and more informed decisions about future infrastructure.
The example of consolidating functions on a single VM is particularly compelling—it reminds us that a minimal, cost-effective setup can serve as a crucial testing ground and data source before investing in complex architectures. Additionally, embracing hardcoded configurations for rapid iteration makes sense in the context of a startup, where agility takes precedence over flexibility.
One thought to extend this framework might be to incorporate a regular feedback process—beyond the three-month window—to see if certain unscalable practices should be retained or gradually restructured as the product scales. Also, capturing quantitative metrics during the trial period can help objectively evaluate whether the solution truly warrants the effort to scale or refine. Overall, this approach balances pragmatism with strategic foresight—an excellent methodology for early-stage development.