Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions
In the world of entrepreneurship, the mantra “Do things that don’t scale” often echoes through discussions, particularly in startup circles. But how can we actively apply this principle, especially in the realm of coding? Having spent the last eight months developing my AI podcast platform, I’ve crafted a straightforward framework that aids in navigating this challenge: the 3-Month Rule.
This rule dictates that every non-scalable solution I implement is given a lifespan of three months. At the end of this period, each hack will either demonstrate its worth and evolve into a fully-fledged solution or be discarded altogether.
Rethinking Scalable Solutions in Startups
As developers, we are often conditioned to prioritize scalable solutions right off the bat—envisioning intricate architectures with microservices, distributed systems, and design patterns meant to accommodate millions of users. While this approach is essential for larger, established companies, it can be counterproductive in a startup environment.
In my experience, directing resources towards scalability prematurely often serves as a form of costly procrastination. You find yourself optimizing for future users who may never materialize, while neglecting the immediate needs of existing customers. The 3-Month Rule motivates me to write simple, straightforward code that can actually be deployed, providing valuable insights into what users genuinely require.
Current Infrastructure Hacks: Learning Through Simplicity
1. Consolidating Everything on a Single VM
For just $40 a month, I run my database, web server, background jobs, and Redis on one virtual machine. This minimalist setup comes with no redundancy and a simply manual backup process.
Why is this approach beneficial? It has provided me with insights into my true resource needs far more effectively than any theoretical capacity planning. My platform’s resource usage peaks at just 4GB of RAM, and the elaborate Kubernetes setup I nearly pursued would have meant managing a fleet of empty containers. When outages occur, the data I gather reveals unexpected trouble spots.
2. Hardcoded Configuration Across the Board
I utilize hardcoded values for significant parameters in my codebase. Instead of complicated configuration files or environment variables, I have constants directly embedded in the code.
This might appear inefficient, but it grants me the ability to swiftly search for any configuration value—each change is documented in git history and deserves a code review. Rebuilding a configuration service would demand a week’s work
