Embracing the 3-Month Rule: A Practical Guide to Technical Experimentation
In the world of software development, particularly in startup environments, one piece of advice stands out: “Do things that don’t scale.” While this wisdom, famously shared by Paul Graham, is often discussed, the implementation of such an approach—especially in coding—remains less explored. After eight months of building my AI podcast platform, I’ve formed a straightforward framework that I call the “3-Month Rule.” This strategy allows any unscalable hack I develop a trial period of three months; within this timeframe, each solution must either demonstrate its practicality and be developed further or be discarded.
Understanding the 3-Month Rule
As developers, we are conditioned to prioritize scalable solutions right out of the gate. We often gravitate towards complex designs involving microservices and distributed architectures intended for high user demand. However, in the startup arena, focusing on scalability can sometimes lead to unnecessary expenses and delays in progress. My 3-Month Rule compels me to adopt a more straightforward approach by allowing me to create simple, albeit temporary, code that can be shipped swiftly and provides immediate insights into user requirements.
Current Infrastructure Tactics: Simple but Effective
Let me share some of my current tactical decisions that may appear rudimentary at first glance but have proven to be intelligent moves for my project.
1. Consolidated Infrastructure on a Single VM
For $40 a month, everything—database, web server, background tasks, and Redis—operates on one virtual machine. This setup lacks redundancy and relies on manual backups, yet it has provided more valuable insights regarding my resource needs in just two months than any elaborate planning documentation could have. It turns out my platform, which is heavily reliant on AI, requires only 4GB of RAM at peak times. Had I implemented a complex Kubernetes infrastructure, I would have been managing empty containers without realizing it.
2. Hardcoded Configuration Variables
Instead of using configuration files or environment variables, critical parameters such as pricing and user limits are hardcoded directly into my application. While this may seem inefficient, it allows for quick and effortless tracking of changes via Git history. Over the past three months, I’ve altered these values just three times, illustrating that extensive configuration services are not always necessary.
3. Utilizing SQLite in Production
I’m using SQLite for a multi-user web application—all 47MB of it. This database manages up to 50 concurrent users
