The 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech Development
In the world of software development, particularly in startups, the mantra of “doing things that don’t scale” is widely advocated, especially by visionaries like Paul Graham. However, the practical implementation of this advice within the technical realm is seldom discussed. Over the past eight months, while developing my AI podcast platform, I have crafted a pragmatic framework to tackle this conundrum—the “3-Month Rule.” This guideline mandates that every unscalable workaround is given a three-month lifespan; at the end of this period, it either evolves into a fully-fledged solution based on proven value or is discarded.
The Scalable Solution Dilemma
As engineers, we often gravitate towards creating “scalable” infrastructures from the outset, employing sophisticated design patterns and systems geared to handle vast user bases. This inclination, while rooted in sound logic, can lead to excessive complexity, especially within the dynamic environment of a startup. Frequently, investing time in scalability translates to procrastination—you’re optimizing for hypothetical users and addressing issues that may never arise.
By adhering to my 3-month rule, I am compelled to write straightforward and sometimes “imperfect” code that actually reaches production. This practice illuminates user needs more effectively than any upfront architectural course.
My Current Infrastructure Strategies: Smart Hacks for Effective Learning
1. Consolidation on a Single Virtual Machine
Currently, my entire stack—database, web server, background jobs, and even Redis—operates on a single $40-a-month virtual machine. There is no redundancy, and I perform manual backups to my local machine. This approach has proven to be insightful; in just two months, I gained a clearer understanding of my resource requirements than I could have obtained through extensive capacity planning. For example, my AI platform operates efficiently with a memory peak of just 4GB. The complex Kubernetes architecture I nearly established would have been overkill, managing empty containers instead.
When crashes occur—a total of twice thus far—I observe real-time data on the points of failure, which, intriguingly, are never what I originally anticipated.
2. Hardcoded Configuration Values
Instead of relying on configuration files or environment variables, I’m utilizing hardcoded constants throughout my codebase:
“`python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS =
