Embracing the 3-Month Rule: A New Approach to Non-Scalable Solutions in Tech
In the realm of startups, the common wisdom often preached is Paul Graham’s notion to “do things that don’t scale.” Yet, there’s often a gap when it comes to translating this advice into practical coding strategies. Having spent the last eight months developing my AI podcast platform, I’ve devised a practical framework to navigate this challenge: every unscalable solution is given an experimental window of three months. After this period, I evaluate its effectiveness; if it proves valuable, it becomes a permanent fixture; if not, it’s time to let it go.
Why Does This Approach Work?
As engineers, we typically gravitate toward designing scalable systems right from the start. We immerse ourselves in intricate architecture like microservices, distributed systems, and all that wonderful technology meant to support millions of users. But in the dynamic environment of a startup, focusing too heavily on scalability can often be a counterproductive luxury. We end up optimizing for hypothetical users, addressing potential challenges that might never surface. Implementing my 3-month rule encourages me to create straightforward, though not perfect, code that delivers tangible results and helps me understand the actual needs of my users.
Current Infrastructure Insights and Their Wisdom
1. Consolidated Operations on a Single VM
Hosting my database, web server, and background jobs all on a single $40/month virtual machine might sound reckless, but here lies the wisdom. After two months, I have a crystal-clear understanding of my resource needs, which I could never have gleaned from extensive capacity planning reports. It turns out my AI platform operates comfortably under 4GB RAM. The complex Kubernetes environment I almost set up would have resulted in monitoring empty containers instead of focusing on actual user demands. When issues arise, which they have on two occasions, I gain real insights into the points of failure—insights that often defy my initial expectations.
2. Simplistic Hardcoded Configurations
Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants throughout my codebase. While this might seem primitive, it grants me the ability to quickly search for any configuration value across my entire project. Each price adjustment is meticulously tracked in my Git history, and I engage in code reviews for every update—even if it’s a review of my own work. A dedicated configuration service might take a week to implement, but given that I’ve modified these constants just
