Embracing the 3-Month Rule: A Pragmatic Approach to startup Development

In the world of startups, the mantra “do things that don’t scale” champions the value of taking risks and experimenting during early development. However, the journey of applying this wisdom, especially in a technical context, often goes unaddressed. Having spent the past eight months building my AI podcast platform, I’ve cultivated a straightforward yet effective framework: every unscalable solution has a lifespan of just three months. After this period, it either proves its worth and evolves into a more robust implementation, or it gets phased out.

The startup Mindset: Prioritizing Learning Over Perfection

As engineers, we often feel the pressure to create scalable systems from the outset—think along the lines of intricate design patterns, microservices architectures, and distributed systems capable of accommodating millions of users. While this mindset is essential for large organizations, it can lead startups down a costly path of unnecessary complexity. In essence, focusing on scalability too soon can become an expensive form of procrastination, as it diverts resources towards optimizing for a hypothesized user base that may never materialize.

My three-month rule has encouraged me to prioritize simplicity and efficiency over elegance. It pushes me to produce straightforward code that gets shipped quickly, allowing for real user feedback that informs actual needs versus assumptions.

My Current Infrastructure Strategies: Lessons Learned

1. Consolidated Operations on a Single VM
   I’m running everything—database, web server, background jobs, Redis—on a single, cost-effective VM. There is zero redundancy, and I perform manual backups. Far from being a reckless decision, this setup has provided invaluable insights. Within just two months, I’ve learned about my actual resource consumption far beyond what any planning document could convey. My “AI-intensive” platform peaks at a mere 4GB of RAM, meaning a complex Kubernetes setup would have been overkill—managing empty containers, to be precise. When issues arise (and they have), I gain firsthand data on what truly fails, surprisingly often revealing unforeseen bottlenecks.

2. Directly Hardcoded Configuration
   Instead of using external configuration files, I keep constants scattered throughout my codebase. Making changes requires a redeployment, which ensures I maintain a clear history of every adjustment. This strategy might seem archaic, but it allows me to search for configuration values quickly. Changing my pricing structure, for instance, took 15 minutes—compared