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

In the world of startups and technology, there’s a well-known mantra from Paul Graham: “Do things that don’t scale.” However, what often goes unaddressed is how to effectively apply it in the realm of coding. Over the past eight months, while developing an AI podcast platform, I’ve cultivated a unique framework that I call the “3-Month Rule.” This principle states that every non-scalable solution is given a trial period of three months. If it demonstrates its worth during that time, it will be properly integrated into my architecture; if not, it’s time to discard it.

Many of us, especially engineers, are conditioned to design scalable solutions from the get-go—think intricate architectures, distributed systems, and the allure of microservices working seamlessly for millions of users. However, that mindset is often more suited for well-established companies rather than agile startups. In the early stages, focusing on scalability can sometimes lead to unnecessary delays, as you’re busy planning for hypothetical future users instead of addressing present needs. This is where my 3-month rule comes into play, pushing me to write straightforward, albeit imperfect, code that can be deployed quickly and, importantly, provides valuable insights into user behavior.

My Ingenious Infrastructure Hacks

Here are some of the unconventional choices I’ve made in my current infrastructure and why I consider them to be strategic moves:

1. Unified VM Deployment

All of my essential services, including the database, web server, and background jobs, run on a single virtual machine that costs just $40 per month. This setup lacks redundancy and relies on manual backups to local storage, yet I view it as a smart choice. In just a couple of months, I’ve gained more insight into my resource usage than any capacity planning documentation could offer. For instance, my platform, which I initially deemed heavy on AI, has only needed 4GB of RAM at peak times. The complex Kubernetes framework I was about to implement would have led to the needless management of inactive containers.

2. Simplistic Hardcoded Configurations

I opted for hardcoded configuration settings throughout my codebase—a practice considered suboptimal by many. Take, for example:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"