Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions

In the ever-evolving world of software development, there’s a popular mantra that resonates: “Do things that don’t scale,” articulated first by Paul Graham. While this concept is widely acknowledged, there’s often a lack of guidance on how to effectively apply it, particularly in coding practices.

Over the past eight months, as I’ve worked on building my AI podcast platform, I’ve adopted a practical framework that I call the 3-Month Rule. The essence of this rule is that any non-scalable approach I implement is granted a lifespan of just three months. At the end of that period, I evaluate its effectiveness—if it’s proven beneficial, I invest in a more robust solution; if not, it’s time to move on.

The Problem with Scalable Solutions

As engineers, we are often ingrained with the ideology of crafting scalable solutions from the outset. We dive into design patterns, microservices, and robust architectures capable of supporting millions of users. However, this is often a mindset better suited for large enterprises rather than startups. In reality, pursuing scalability too early can lead to unnecessary complexity and expensive delays as we optimize for users who may never materialize.

The essence of my 3-Month Rule is to encourage the creation of straightforward, even “imperfect,” code that can be released quickly. This approach helps clarify what users truly need, allowing me to gather real-world insights that inform future development.

Current Infrastructure Hacks: Insights Gained

Let’s delve into some of my current non-scalable hacks and why they represent strategic choices rather than oversights:

1. Operating on a Single Virtual Machine

I’ve consolidated my database, web server, background jobs, and caching all onto a single, cost-effective Virtual Machine. While lacking redundancy and relying on manual backups, this approach has provided invaluable insights into my actual resource utilization. Within two months, I identified that my platform operates comfortably on 4GB of RAM. Instead of pursuing a complex Kubernetes setup that would require managing a multitude of idle containers, I’ve gathered critical data on system failures—insights that challenge my initial assumptions.

2. Hardcoded Configuration

My configuration is set directly in the code, with constants defining user limits and pricing. While this method may seem limiting, it offers efficiency. Any changes necessitate a straightforward redeployment, and it allows for rapid searches through