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

In the tech world, there’s a well-known piece of advice from Paul Graham: “Do things that don’t scale.” While many embrace this concept in theory, the real challenge lies in translating it into actionable steps, particularly in the realm of software development.

After dedicating nearly eight months to the development of my AI podcasting platform, I’ve created a straightforward framework to put this advice into practice: any unscalable workaround I implement is given a mere three-month lifespan. At the end of this period, the goal is clear: either the solution proves its worth and gets refined, or it’s discarded entirely.

Rethinking the Mindset of Scalable Solutions

As software engineers, we often get entrenched in the mindset of building scalable systems right from the start—complete with intricate design patterns and robust architectures that can accommodate millions of users. However, this perspective can be a trap, particularly for startups. In many cases, prioritizing scalable solutions translates to unnecessary spending and effort, focusing on hypothetical users and challenges that may never arise.

My three-month rule encourages me to embrace simplicity and to produce straightforward, albeit imperfect, code that directly meets the needs of my current user base.

Insights Gained from Current Infrastructure Hacks

1. Consolidation on a Single Virtual Machine (VM)

I’ve opted to host my entire application—database, web server, background jobs, and caching—on a single $40/month VM. While this might seem reckless due to the lack of redundancy and reliance on manual backups, the clarity I’ve gained about my resource requirements has been invaluable. Within just two months, I learned that my platform only reaches 4GB of RAM, revealing that the elaborate Kubernetes setup I initially considered would have been wasted effort managing idle resources.

2. Utilizing Hardcoded Configurations

Instead of employing sophisticated configuration files or environment variables, I’ve relied on hardcoded constants throughout my code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach might seem outdated, but it offers a significant advantage: I can swiftly search my entire codebase for any configuration value. The time saved on deploying changes—only three updates in three months—highlights the efficiency of this method, which prevents unnecessary complexity