Embracing the 3-Month Rule: An Innovative Approach to Non-Scalable Development

In the world of startups and technology, there’s a well-known piece of advice from Paul Graham: “Do things that don’t scale.” While many are aware of this philosophy, few delve into how to effectively apply it within the realm of code development. After eight months of creating my AI-driven podcast platform, I’ve formulated a straightforward framework that guides my work: every non-scalable solution is given three months to demonstrate its worth. Following this period, it either evolves into a robust solution or is discarded.

As engineers, we’re often conditioned to design scalable systems from the outset—think design patterns, microservices, and distributed architectures that can accommodate millions. However, this approach often aligns more closely with the needs of established companies rather than startups. In the early stages, investing time in scaling architecture can lead to unnecessary delays. My three-month rule compels me to prioritize simplicity and efficiency, allowing for rapid deployment and genuine user feedback on what truly matters.

A Glimpse into My Current Infrastructure and Why It Works

1. Centralized Operations on a Single VM

I host my database, web server, background jobs, and caching tool, Redis, all on one virtual machine costing just $40 a month. While this setup lacks redundancy and relies on manual backups, it has taught me invaluable lessons about my actual resource needs. Within two months, I’ve learned that my AI-centric platform peaks at just 4GB of RAM. The complex Kubernetes architecture I initially considered would have resulted in managing dormant containers. Each crash (yes, there have been two) provides insights into the unexpected weak points of my system.

2. Hardcoded Configurations Throughout

My codebase features constants like:

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

There are no configuration files or environment variables—just these hardcoded values. While this might seem primitive, it has its advantages. I can swiftly search the entire codebase for configuration values without any hassle. Every price change is neatly logged in the git history, and each modification undergoes a review process—albeit by me. In instances where I needed to alter these constants, it only took 15 minutes of redeployment instead of the anticipated 40 hours of engineering to create a dedicated configuration service.