Embracing the 3-Month Rule: A Pragmatic Approach to Building Unscalable Solutions

In the tech community, the wisdom of Paul Graham’s phrase “do things that don’t scale” is widely acknowledged, yet few delve into its practical application within coding. After dedicating eight months to developing my AI podcast platform, I’ve established a fundamental guideline: each unscalable solution is granted a lifespan of three months. If it proves its worth, we refine it into a robust implementation; if not, it’s time to let it go.

As engineers, our instinct often drives us towards creating scalable solutions from the ground up. We envision design patterns, microservices, and distributed systems capable of accommodating millions of users. However, this mindset is typically more suited to larger, established companies.

In a startup environment, prioritizing scalability too early can lead to wasteful resource allocation. I learned that optimizing for prospective users can detract from solving immediate problems effectively. The 3-month rule encourages me to focus on straightforward, albeit imperfect, code that can be deployed quickly, allowing me to gather insights on actual user needs.

My Innovative Infrastructure Approaches: Lessons Learned

1. Consolidating Resources on a Single VM

I operate my database, web server, background jobs, and caching all from a single $40-per-month virtual machine. There’s no redundancy and I rely on manual backups to my local machine.

What seems like a risky strategy has been a revelation; I’ve gained more understanding of my actual resource demands in just two months than any planning document could provide. My platform, which I anticipated would be resource-intensive, peaks at a modest 4GB of RAM. The elaborate Kubernetes infrastructure I nearly implemented would’ve only served to maintain idle containers. Each crash event (and there have been two) has yielded valuable insights into real system vulnerabilities, often featuring surprises I hadn’t anticipated.

2. Utilizing Hardcoded Configuration

Instead of relying on configuration files or environment variables, I’ve embedded constants throughout the codebase, like:

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

While this approach might raise eyebrows, it offers a unique advantage. I can swiftly search for any configuration value within the entire codebase. Each price modification is documented in Git history and undergoes code review—even if