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

In the world of startups, the advice from tech luminary Paul Graham to “do things that don’t scale” has become a mantra. However, as engineers, the transition from theory to practice is often overlooked, especially in the realm of coding. After dedicating eight months to developing my AI podcast platform, I’ve discovered an effective framework: every non-scalable solution will have a lifespan of just three months. If it proves its worth, I’ll refine it into a scalable solution; if not, it will be phased out.

The startup Mindset: Rethinking Scalability

As developers, we’re often conditioned to aim for scalability right from the outset. We think in terms of design patterns, microservices, and elaborate architectures intended to support millions of users. While this approach is sound for large companies, at a startup level, it can often lead to unnecessary complications and wasted resources. My three-month rule compels me to write straightforward, albeit imperfect, code. The goal is to deploy swiftly and understand my users’ needs genuinely.

My Current Strategies: Pragmatic Infrastructure Hacks

1. Consolidated Operations on a Single VM

I’m running my database, web server, background jobs, and caching system all on a single $40/month virtual machine. This setup sacrifices redundancy and relies on manual local backups, but it’s been illuminating.

In two months, I’ve gained insight into my actual resource usage that far exceeds what any capacity planning document could provide. My platform typically peaks at just 4GB of RAM. The extravagant Kubernetes system I contemplated would have resulted in managing idle containers. Every time the system crashes—which has occurred twice—I receive concrete feedback on what truly needs addressing.

2. Hardcoded Options for Simplified Configuration

Instead of the usual approach of creating configuration files or using environment variables, I utilize hardcoded constants throughout the codebase. This means any updates require redeployment, which initially seems inconvenient.

The benefit of this method is profound: I can quickly search for configuration values across my entire codebase. Each pricing change and configuration update is meticulously recorded in version history. In the past three months, I’ve changed those values just three times, saving significant time compared to the potential hours spent on developing a configuration service.

3. SQLite for a Multi-User Application

Surprisingly, my dynamic web application relies on SQLite