Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Development
When it comes to startup culture, one piece of wisdom stands out: “Do things that don’t scale.” While this advice is often associated with entrepreneurial success, the challenge lies in translating it into actionable steps, particularly within the realm of coding. Over the past eight months, as I have been developing my AI podcast platform, I stumbled upon a practical framework that I now swear by: the 3-Month Rule.
The Framework: Experimentation with a Deadline
Here’s the crux of my approach: any non-scalable solution I implement comes with a three-month trial period. After that, it must either demonstrate tangible value, warranting a transition to a fully-fledged version, or be discarded altogether. This rule allows me to maintain agility and focus on immediate user needs rather than getting lost in the complexities of scalable design that are often more applicable to established companies.
As software engineers, we are ingrained with the notion of building robust solutions from the outset. We’re drawn to complex architectures that can handle millions of users. However, in the startup environment, this line of thinking can lead to costly delays. That’s why my 3-Month Rule encourages me to write simpler, more straightforward code—code that actually gets deployed while simultaneously providing valuable insights into user behavior.
Current Hacks and Their Considerable Value
Let’s delve into some of my current infrastructure choices that may seem suboptimal but have proven to be effective teaching tools.
1. A Single VM for Everything
Everything from the database and web server to background jobs runs on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has provided invaluable insights into my resource needs. Within two months, I learned that my “AI-heavy” platform only requires 4GB of RAM. Had I opted for a more complex Kubernetes architecture, I would have ended up managing idle containers rather than addressing my actual needs.
When the system has crashed (which has happened twice), I received real, actionable data on what failed—something that surprised me each time.
2. Hardcoded Configurations
Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants throughout my codebase:
“`
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL =
