The 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the startup world, the adage from Paul Graham – “Do things that don’t scale” – often resonates, yet the practical implementation of this principle in coding remains a mystery for many. Over the past eight months, as I’ve been developing my AI podcast platform, I’ve crafted a straightforward framework: every non-scalable solution gets a limited lifespan of three months. After this period, it either proves its worth and gets a solid foundation built around it or it is discarded.
As technical professionals, we’re frequently conditioned to seek out scalable solutions from the outset. We obsess over design patterns, microservices, and distributed systems that boast the capacity to support millions of users. However, this approach often aligns more with the mindset of larger organizations rather than the innovative, agile nature of startups. In reality, aiming for scalability too soon can lead to expensive delays, where we optimize for hypothetical users while disregarding current needs.
My three-month rule encourages me to adopt simpler, more direct coding practices that facilitate rapid deployment and foster genuine learning about user requirements.
Innovative Infrastructure Strategies
Here are some of my current infrastructure tactics, which may seem unconventional but provide significant insights:
1. Single Virtual Machine Setup
My entire operation—database, web server, background jobs, and caching—runs on one $40/month virtual machine, without redundancy or intricate backups. This seemingly simplistic approach allows me to gauge my actual resource needs much more efficiently than any extensive planning document. In just two months, I realized that my platform’s data demands only peak at 4GB of RAM. The complex Kubernetes setup I nearly adopted would have mostly involved managing idle containers.
Interestingly, when the system crashes (which has happened twice), I gain valuable data about the true points of failure—information that often surprises me.
2. Hardcoded Values for Configuration
My configuration is straightforward: constants scattered throughout the codebase, rather than complicated configuration files or environment variables. This means that knowing any configuration value is just a quick search away. My git history retains a record of every price adjustment, and any modifications are subject to a brief review process.
Creating a comprehensive configuration service would demand a week of development; however, I’ve only needed to change these values three times in the past three months. This translates to a mere 15 minutes of redeployment instead of 40 hours of engineering labor.
