Embracing the 3-Month Rule: A Pragmatic Approach to Prototype Development in Tech

In the world of tech startups, the mantra “do things that don’t scale” is often echoed, yet actionable strategies for execution are rarely discussed. As I delve into building my AI podcast platform, a key principle has emerged—if an unscalable solution exists, it will only remain for three months. After this period, we either assess its value and develop it further, or we choose to discontinue it.

The startup Dilemma: Scaling vs. Learning

As engineers, we are conditioned to seek out scalable solutions right from the outset. We gravitate toward established design patterns, microservices architectures, and distributed systems capable of accommodating millions of users. However, this mindset often aligns more with large companies rather than the agile, iterative processes of a startup.

In a lean startup environment, preemptively crafting scalable solutions can be tantamount to procrastination. It often leads to excessive focus on future users who may not materialize and problems that may never arise. By adhering to the three-month rule, I prioritize producing straightforward, albeit imperfect, code that can be quickly deployed, shedding light on the true needs of my users.

Current Infrastructure Hacks: Simplifying for Insight

1. Consolidation on a Single VM

Instead of distributing resources across multiple machines or services, my entire infrastructure runs on a single $40/month virtual machine. This includes the web server, database, background jobs, and caching—all without redundancy or automated backups.

This configuration has proven invaluable: I’ve quickly learned my actual resource requirements within two months. My platform, which I presumed would demand extensive resources, peaks at just 4GB of RAM. The elaborate setup I considered would have simply compounded complexity without providing real benefits.

2. Hardcoded Configurations

My approach to configuration is decidedly low-tech—I use hardcoded constants like PRICE_TIER_1 = 9.99 scattered throughout my code. While this might seem archaic, it allows for rapid changes with minimal overhead, all tracked through version control.

Building a full-fledged configuration service would require significant time investment, and given that I’ve only changed these values a few times over the past three months, this method has proven both efficient and effective.

3. Utilizing SQLite for Production

Contrary to conventional wisdom, I’m running SQLite for my multi-user web application. With a mere 47MB database, it seamlessly supports 50 concurrent