Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the startup ecosystem, the popular advice from Paul Graham—“Do things that don’t scale”—is frequently echoed, yet the actual execution of this principle in the world of software development is rarely discussed. Over the past 8 months while developing my AI podcast platform, I’ve created a straightforward framework that I call the “3-Month Rule.” This approach mandates that every unscalable solution is evaluated over a fixed lifespan of three months. After this period, each solution either demonstrates its worthiness and evolves into a robust build or is abandoned.
As engineers, we tend to emphasize the construction of scalable systems from the outset. We gravitate toward design patterns, microservices, and complex architecture suitable for handling thousands, if not millions, of users. However, this mentality is often more applicable to established companies rather than startups. At the early stage, chasing scalability can be a costly form of procrastination—focused on hypothetical users while neglecting the immediate needs of existing customers.
By adopting my 3-month rule, I have been forced to create straightforward, albeit “poorly” engineered, solutions that actually go live. This process provides deep insights into user behavior and requirements.
Ingenious Infrastructure Hacks for Immediate Insights
1. Consolidating Everything on One Virtual Machine
Currently, my entire operation runs on a single $40/month virtual machine that houses the database, web server, background jobs, and Redis—all without redundancy. Some may view this approach as reckless, but I’ve gleaned more about my operational resource needs in these two months than any elaborate planning document could provide. It turns out my resource usage peaks at just 4GB of RAM. The intricate Kubernetes configuration I had considered setting up would have required managing unused containers.
When the system crashes—an occurrence I’ve encountered twice thus far—I gain invaluable information about the true points of failure, typically revealing surprises along the way.
2. Hardcoded Configuration for Simplicity
You might be surprised to learn that I have hardcoded essential configurations like pricing tiers and user limits scattered throughout my code. This lack of configuration files or environment variables means that any adjustments require a redeployment.
The true brilliance of this method lies in its simplicity: I can quickly search through my entire codebase for configuration values, and every change is logged in Git history. In essence, I’ve tracked changes to these values only three
