The 3-Month Rule: A Pragmatic Approach to Imperfect Solutions in Software Development
In the world of software engineering, you often hear the advice from Paul Graham to “do things that don’t scale.” This principle is straightforward, but many developers struggle with its practical application, especially in situations where technical solutions are vital. After eight months of crafting my AI podcast platform, I have devised a framework that has helped me navigate this challenge: the 3-Month Rule. This method revolves around allowing each unscalable solution to exist for three months, after which it must either demonstrate its effectiveness and be fully developed or be discarded altogether.
As engineers, we are typically trained to prioritize scalable solutions from the outset, focusing on sophisticated systems, design patterns, and architectures that can accommodate millions of users. While this is essential for large companies, for startups, such forward-thinking can often be a costly form of procrastination—investing time and resources in optimization for scenarios that might never materialize. My 3-Month Rule encourages me to produce straightforward, albeit “imperfect,” code that can be deployed swiftly, thereby unveiling the genuine needs of my users.
Current Infrastructure Strategies: Simplifying for startup Efficiency
1. Consolidated Single-VM Architecture
I’ve housed my web server, database, background jobs, and Redis on a single virtual machine, costing only $40 a month. This setup may lack redundancy and relies on manual backups, but it has provided invaluable insights into my operational needs. In just two months, I’ve gathered more data about my resource requirements than any theoretical planning document could ever offer. Surprisingly, my AI-centric platform peaks at just 4GB of RAM. The complex Kubernetes configuration I was considering would have left me managing empty containers instead of focusing on real-time data when issues arise.
2. Hardcoded Configurations
Instead of using configuration files or environment variables, I’ve employed constants throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this approach might seem inefficient, it allows me to search for any configuration value swiftly. By keeping track of changes in my git history and conducting code reviews (even if it’s just me reviewing my own contributions), I have streamlined the process of making modifications. In three months, I’ve changed these values only three times, saving me
