Embracing the 3-Month Rule: A Pragmatic Approach to Software Development
In the world of startups and tech innovation, the familiar mantra from Paul Graham rings true: “Do things that don’t scale.” However, applying this principle effectively, especially in software development, is often overlooked. After dedicating eight months to building my AI podcast platform, I have crafted a straightforward yet effective framework known as the 3-Month Rule. This approach allows each unscalable solution to exist for only three months, after which it must either demonstrate its value and evolve or be discarded.
The Challenge of Scalable Solutions
As developers, we are typically conditioned to prioritize scalable solutions right from the start. Concepts like design patterns, microservices, and distributed systems dominate our thinking—perfect for managing vast user bases, but not always ideal for early-stage startups. Often, focusing overly on scalability can result in misguided optimization for hypothetical users, postponing important decisions in the process.
Through my 3-Month Rule, I am encouraged to produce straightforward, albeit “bad,” code that can be deployed quickly. This method forces me to directly engage with users’ needs, refining my understanding of what truly matters.
Current Embraces of Unscalable Infrastructure
Here’s a breakdown of my current infrastructure decisions, which may raise eyebrows but have proven to be savvy choices:
1. Centralized on a Single VM
Hosting my database, web server, background jobs, and Redis on a singular $40/month virtual machine means zero redundancy and reliance on manual backups. While this setup may appear risky, it has provided crucial insights into my resource consumption. Within just two months, I discovered that my “AI-heavy” platform only spikes to 4GB of RAM usage, making elaborate Kubernetes configurations unnecessary.
When issues arise—which, admittedly, they have—I gain immediate, relevant information about the specific bottlenecks. This is never what I originally anticipated.
2. Hardcoded Configurations
Instead of managing configuration files or environment variables, I’ve opted for hardcoded constants throughout my code. For example:
plaintext
PRICE_TIER_1 = 9.99
MAX_USERS = 100
The simplicity of this approach allows me to search my codebase efficiently for any configuration value, ensuring that all changes are easily trackable through version history. This minimal setup has proven to be far less costly in terms of development time compared to building a dedicated configuration management service.
