Embracing the 3-Month Rule: A Unique Approach to Development in Startups
In the world of startups, the age-old wisdom from Paul Graham urging entrepreneurs to “do things that don’t scale” often resonates. Many entrepreneurs grasp the concept; however, the challenge lies in its execution, particularly when it comes to software development. After spending eight months building my AI podcast platform, I have devised a straightforward framework: all non-scalable solutions receive a lifespan of three months. At the end of this period, they either prove their worth and become robust features or are simply phased out.
Why Focus on Non-Scalable Solutions?
As software engineers, our instinct is to create scalable solutions from the get-go—utilizing design patterns, microservices, and sophisticated architectures that can handle enormous user bases. While these strategies are suitable for large companies, they may not serve startups effectively. In a small team setting, focusing on scalability can turn into an expensive form of procrastination. This is where my three-month rule comes into play. It encourages me to write straightforward, albeit “inefficient,” code that promptly delivers insights about user needs.
Practical Infrastructure Hacks and Their Hidden Benefits
1. Consolidating Resources on a Single VM
Currently, my entire setup—including the database, web server, background jobs, and Redis—operates on a single $40-per-month VM. There’s no redundancy, and backups are manually transferred to my local machine.
The brilliance of this approach is that I’ve gained a better understanding of my actual resource requirements in just two months than any capacity planning document could have provided. My AI-centric platform typically requires only 4GB of RAM. The complex Kubernetes framework I nearly implemented? It would have meant managing idle containers. Each system crash (yes, it has happened twice) gives me invaluable data regarding real failure points, which is never what I initially anticipated.
2. Simplicity Through Hardcoded Values
Gone are the days of extensive configuration files and endless environment variable adjustments. Instead, I employ straightforward constants across my codebase, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method allows for instantaneous searches of any configuration value across my codebase and tracks every pricing change through Git history. Despite the time and resources it would take to build a configuration service
