Embracing the 3-Month Rule: A Pragmatic Approach to Development
In the world of startups, navigating the delicate balance between innovation and practicality can be challenging, especially for budding developers and entrepreneurs. Renowned entrepreneur Paul Graham’s counsel to “do things that don’t scale” serves as a compelling reminder that sometimes, embracing imperfection can lead to profound insights. Yet, how does one effectively implement this philosophy within technical projects?
After spending eight months developing my AI podcast platform, I’ve devised a straightforward framework: any unscalable solution is given a lifespan of three months. Within that time frame, it is expected to demonstrate its value by either evolving into a robust solution or being phased out altogether.
The Dilemma of Scalability in Startups
As engineers, our instinct often leans toward creating scalable architectures from the outset. We envision grand solutions involving design patterns, microservices, and distributed systems—all geared to accommodate vast user bases. However, this large-scale thinking can be a trap for startups, where heavily optimized systems often represent costly delays in addressing immediate needs.
My three-month framework compels me to implement straightforward, minimally complex code that facilitates early user engagement, allowing me to glean invaluable insights into user requirements.
Insights from My Current Infrastructure Strategies
1. Centralizing Operations on a Single VM
Currently, my entire operation—from the database to the web server—is hosted on a single virtual machine for just $40 per month. While this may seem reckless due to the lack of redundancy, it has afforded me critical insights into my actual resource needs. After two months, I’ve discovered that my platform’s RAM needs peak at just 4GB. A complex Kubernetes system would have been a wasted effort managing idle resources. Each crash (which has occurred twice) provides real-time data on system vulnerabilities, unveiling surprises along the way.
2. Utilizing Hardcoded Configuration
Instead of separate configuration files or environment variables, I opt for constants distributed throughout my codebase. This choice facilitates rapid searches for any configuration parameter and enables easy tracking of changes in my git history. With only three adjustments made over three months, the simplicity of my approach has proven far more efficient than building a configuration service that would consume a week’s worth of development time.
3. Running SQLite in Production
Surprisingly, I’ve chosen to deploy SQLite for my multi-user application. At just 47MB, it seamlessly handles 50 concurrent users. This experience highlighted my
