Embracing the 3-Month Rule: A Pragmatic Approach to Building Scalable Solutions
In the world of startups, the mantra “Do things that don’t scale,” as popularized by Paul Graham, often gets thrown around. While it’s essential advice, the challenge lies in understanding how to effectively apply this principle, especially in the realm of software development.
After dedicating the past eight months to building my AI podcast platform, I’ve created a straightforward framework that guides me in navigating the unscalable: each hack or workaround I implement has a lifespan of just three months. This timeframe ensures that, at the end of the period, each experiment must either validate its worth with tangible results or be phased out.
The start-up Mentality vs. The Big Company Approach
As engineers, we frequently prioritize constructing scalable solutions from the outset. We immerse ourselves in design patterns, microservices, and distributed systems tailored for enterprises managing millions of users. However, such architecture often suits larger organizations better than startups, where scalable code can become an expensive diversion. My three-month rule compels me to develop straightforward, albeit imperfect, code that focuses on real user needs rather than theoretical scenarios.
Current Strategies that Challenge Conventional Wisdom
1. Streamlined Operations with a Single Virtual Machine
Currently, my entire platform operates on a single $40/month virtual machine that hosts the database, web server, background jobs, and Redis altogether. This setup, devoid of redundancy, might seem reckless, but it has provided invaluable insights. Within just two months, I’ve gained a deeper understanding of my actual resource requirements compared to any rigorous capacity planning document. For instance, I discovered that my “AI-heavy” application peaks at a mere 4GB of RAM, rendering the complex Kubernetes framework I almost implemented unnecessary.
Remote crashes (which have occurred twice) offer authentic feedback, pinpointing failures in ways I hadn’t anticipated.
2. Utilizing Hardcoded Configurations
I’ve opted for hardcoded constants scattered throughout my codebase for crucial configurations:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Rather than developing configuration files or environment variables, this approach allows me to search my entire codebase for any values quickly. With just three changes over three months, I can redeploy in 15 minutes rather than spend 40 hours creating a complex configuration service.
