The 3-Month Rule: My Practical Approach to Non-Scalable Development
In the world of startups and software development, we’re often reminded of Paul Graham’s famous mantra: “Do things that don’t scale.” While this advice is well known, the actual implementation in coding practices is frequently overlooked. After dedicating eight months to building my AI podcast platform, I devised a straightforward framework that embraces this principle: each non-scalable hack is given a trial period of three months. If it demonstrates its worth during this time, it will be reconstructed for scalability; if not, it will be discontinued.
As engineers, our training often emphasizes creating solutions that can handle extensive user bases from the outset. We default to employing design patterns, microservices, and other complex architectures—ideal for large organizations. However, in a startup environment, this pursuit of scalability can lead to unnecessary delays, as we find ourselves optimizing for users that aren’t yet on our platform and addressing problems that may never arise. My three-month rule encourages the creation of straightforward and functional code that can be deployed quickly, allowing me to genuinely assess user needs.
My Current Infrastructure Practices and Their Surprising Benefits
1. Single VM Setup
My entire infrastructure runs on a single virtual machine (VM), encompassing the database, web server, background jobs, and more—all for an economical $40 per month. While this setup lacks redundancy and relies on manual backups, the insights gained over just a couple of months have been invaluable. I’ve learned the true resource demands of my platform, which peaks at a mere 4GB of RAM. Rather than getting bogged down in complex Kubernetes configurations, I can see firsthand what causes issues when the system crashes—and it’s rarely what I anticipate.
2. Hardcoded Configuration
Configuration values such as pricing tiers and user limits are hardcoded directly into my files. This method might seem outdated, but it has its advantages. Searching through the codebase for any configuration is instantaneous, and I can track any changes through Git history. The need for a configuration service would initially consume a week of development time, but with my current approach, any configuration change requires no more than 15 minutes of redeployment.
3. Using SQLite for Production
While many might shy away from using SQLite for a multi-user application, my database is only 47MB and can effortlessly handle 50 concurrent users. This choice allowed me to recognize that my usage patterns heavily favor reads over writes—essential
