Embracing the 3-Month Rule: A Practical Approach for Startups
In the world of startups, where agility and learning are paramount, Paul Graham’s famous advice to “do things that don’t scale” often gets overlooked—especially when it comes to the technical aspects of software development. As I build my AI podcast platform over the past eight months, I’ve developed a framework that has guided my decision-making: each unscalable hack is given a lifespan of just three months. At the end of that period, we either scale it up based on its proven value or let it go.
The startup Mindset: Rethinking Scalability
Traditional engineering encourages us to focus on scalable solutions from the outset. We often get caught up in elaborate design patterns, microservices, and distributed systems—essentially, frameworks suited for large enterprises. However, within a startup environment, striving for scalability too early can lead to unnecessary complexities and wasted resources. This is where my three-month rule comes into play, compelling me to create straightforward, albeit less-than-perfect code that gets delivered. This method has been invaluable in revealing genuine user needs.
Innovative Infrastructure Hacks That Spark Learning
1. Consolidated Resources on a Single VM
Currently, my entire setup resides on a single $40/month virtual machine, hosting the database, web server, background jobs, and Redis with no redundancy and manual local backups. Some might see this as a reckless approach, but it’s been tremendously enlightening. In just two months, I’ve gained insights into my real resource requirements, discovering that my “AI-heavy” application only peaks at 4GB of RAM. Had I pursued a larger setup with Kubernetes, I would have primarily spent time managing idle containers.
2. Simplified Hardcoded Configuration
Instead of employing complex configuration management, I’ve opted for hardcoding key values directly within my code. This includes parameters like pricing tiers and user limits. While this may seem inefficient, it enables me to quickly search and track changes across my codebase while minimizing deployment overhead. In three months, I’ve only modified these configurations a handful of times, making this simple setup far more efficient than building a complex configuration service.
3. Using SQLite for Production
I chose SQLite for its lightweight capabilities, even in a multi-user environment. With a database size of only 47MB, it comfortably supports 50 concurrent users. This choice allowed me to realize my application’s predominant access patterns—95% reads versus 5%
