Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development

When it comes to startup advice, few pieces are as frequently mentioned as Paul Graham’s mantra: “Do things that don’t scale.” However, implementing this principle, especially in the realm of software development, is rarely covered in depth.

Having spent the last eight months constructing my AI podcast platform, I’ve developed a highly effective framework: each unscalable workaround is given a lifespan of just three months. At the end of this period, the approach either validates its worth and is enhanced for scalability, or it is discarded.

Traditionally, engineers are conditioned to focus on creating scalable solutions right from the outset. Concepts like microservices and distributed systems dominate discussions, often leading to complex architectures designed to support millions of users right away. However, this big-picture thinking can be counterproductive in the startup environment.

At a young startup, investing in scalable code can often turn into a costly exercise in procrastination—you’re preparing for users who may never arrive and tackling challenges that may never materialize. My 3-month rule compels me to create straightforward, even “subpar,” code that is deployable and helps elucidate what users truly need.

Current Infrastructure Practices: Why They Make Sense

1. Everything Operates on a Single Virtual Machine
The entire operations—database, web server, background tasks, and caching—run on a single $40/month virtual machine. There’s no redundancy and backups are manually saved on my local machine.

This approach has proven to be quite insightful. Within just two months, I have garnered a better understanding of my resource needs than any detailed capacity planning document could provide. For example, I discovered that my “AI-heavy” platform peeks at only 4GB of RAM, which suggests that a complex Kubernetes infrastructure would have been overkill, managing empty containers instead of valuable resources. Each crash (yes, twice so far) has delivered real-time insights about actual failure points, often shocking me with their unpredictability.

2. Hardcoded Configuration
All parameters, like pricing tiers and user limits, are hardcoded into the application without using configuration files or environment variables. Making any modifications requires a redeployment.

This may seem counterproductive at first, but it empowers me to quickly search the codebase for any configuration value. Changes are tracked meticulously through Git history, and every update undergoes a