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

When it comes to launching a startup, especially in tech, there’s no shortage of advice on “doing things that don’t scale.” While many founders are familiar with Paul Graham’s famous maxim, the journey of translating that advice into practical coding strategies often goes unaddressed.

After eight months of developing my AI podcast platform, I’ve established a straightforward framework that revolves around a simple concept: every non-scalable solution has a lifespan of just three months. At the end of this period, it must either demonstrate its value by being efficiently built out or be discarded.

Redefining Scalable Solutions

As engineers, we are often conditioned to aim for scalable architectures from the outset. We aspire to implement complex design patterns and robust systems that can cater to millions of users. However, this approach tends to reflect a big-company mentality that can lead to expensive delays in a startup environment. By focusing on the future ideal rather than the current reality, we run the risk of overcomplicating our solutions and ignoring what truly matters: our current users.

The three-month rule compels me to prioritize simple, straightforward coding that delivers results. Here’s a look at how I’ve implemented this rule in my ongoing project.

My Ingenious Infrastructure Hacks

1. Single Virtual Machine (VM) Setup

All components of my platform, including the database, web server, background jobs, and caching, run on a single $40/month VM. There’s no redundancy, and backups are done manually.

This approach may seem reckless, yet it has provided significant insights into my actual resource requirements. In just two months, I’ve learned that my resource consumption peaks at 4GB RAM. Instead of building a complex Kubernetes system, I now know that managing lightweight configurations is sufficient.

Each crash (and there have been a couple) gave me actionable data about failure points, revealing unexpected vulnerabilities.

2. Hardcoded Configuration Values

My configuration is simple: constants are directly embedded in the code rather than housed in config files or environment variables. This may sound archaic, but the convenience is undeniable. Any change necessitates a redeployment, yet every adjustment has quickly been accounted for, tracked within version control, and reviewed by me.

Constructing a sophisticated configuration service would take substantial time and effort; instead, I’ve made three minor adjustments in three months, saving on both time