Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Tech Development
In the world of technology startups, the advice from influential thought leaders like Paul Graham resonates strongly: “Do things that don’t scale.” However, when it comes to applying this concept in the realm of coding, the specifics often remain unspoken. As I’ve been developing my AI podcast platform over the past eight months, I’ve devised a straightforward framework that I’ve dubbed the “3-Month Rule.” This principle allows me to evaluate each unscalable strategy for a period of three months. After this timeframe, the hack either demonstrates its value and gets a proper build-out, or it is discarded.
Why focus on unscalable solutions initially? In our training as engineers, we’re conditioned to construct scalable systems from the get-go. We immerse ourselves in design patterns, microservices, and comprehensive architectures intended to support millions of users. While this approach is vital for large enterprises, startups often find that building scalable solutions too early can lead to costly procrastination. Instead of preparing for hypothetical users, I’ve learned to focus on delivering straightforward, albeit imperfect, code that encourages genuine user insights.
Key Unscalable Strategies: Lessons Learned
1. Consolidating Everything on a Single VM
Running my database, web server, background jobs, and caching system on one $40/month virtual machine might sound reckless, but it’s been enlightening. With zero redundancy and manual backups, I’ve uncovered my real resource needs in a couple of months—far more than any capacity planning document could reveal. My platform occasionally peaks at 4GB RAM, proving that an elaborate setup I nearly implemented would have been a futile effort in managing idle resources.
When the server crashes—an occurrence I’ve faced twice thus far—I gather valuable data on what genuinely fails. Surprisingly, it’s never what I anticipated.
2. Opting for Hardcoded Configurations
Instead of utilizing sophisticated configuration management, I’ve embraced hardcoded values throughout my code—think constants for pricing tiers and user limits. Although this may seem outdated, it offers the advantage of rapid searchability within my codebase and ease of tracking changes through version control.
In the past three months, I’ve made only three configuration changes. The time spent redeploying these adjustments? A mere 15 minutes compared to the potential 40 hours it would take to build an application configuration service.
