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

In the tech world, Paul Graham’s popular maxim, “Do things that don’t scale,” is frequently cited, yet the intricacies of applying this advice, especially in software development, are often overlooked. After eight months of developing my AI podcast platform, I have developed a straightforward framework that has become instrumental in my journey: each non-scalable solution is granted a lifespan of three months. Following this period, if a hack demonstrates its value, it is fully developed; if not, it is discarded.

As software engineers, we are systematically trained to engineer for scalability from the outset, employing elegant design patterns, microservices, and intricate distributed systems suited for handling vast user bases. However, this large-scale thought process can lead startups to miss essential learning opportunities. In fact, optimizing for potential users who may not exist yet can transform into an expensive form of procrastination. My 3-month rule compels me to write straightforward, although imperfect, code that not only gets deployed but also provides invaluable insights into user needs.

Current Infrastructure Hacks: Why They Are Clever Choices

1. Consolidating Everything on One VM

My database, web server, background jobs, and Redis are all hosted on a single virtual machine costing just $40/month, without any redundancy and manual backups to my local setup.

This seemingly basic strategy has provided tremendous insights; within just two months, I’ve learned more about my actual resource needs than any comprehensive planning document could offer. I discovered that my platform peaks at roughly 4GB of RAM, which would have rendered an intricate Kubernetes setup unnecessary. The crashes I’ve experienced, though unfortunate, have offered real-time feedback on failures I never anticipated.

2. Hardcoded Configuration: A Simple Solution

Instead of utilizing configuration files or environment variables, I have implemented hardcoded constants throughout my codebase:

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

This approach comes with its advantages. I can swiftly search for any configuration value, track changes through git history, and ensure every update undergoes code review. While building a dedicated configuration service would have required a considerable time investment, I’ve only altered these hardcoded values three times in three months—saving me significant engineering hours.

**3. Leveraging