The 3-Month Rule: A Pragmatic Approach to Non-Scalable Development

In the fast-evolving world of startup culture, the axiom “Do things that don’t scale,” often echoed by tech luminary Paul Graham, rings particularly true. However, the challenge lies in translating that philosophy into actionable strategies, especially when it comes to coding.

After dedicating eight months to the development of my AI-driven podcast platform, I’ve crafted an innovative method for embracing non-scalable solutions. I call it the “3-Month Rule.” Any unscalable tactic I implement gets a three-month trial period. Within this timeframe, each approach must either demonstrate tangible value and evolve into a scalable solution or be discarded.

Why Embrace Non-Scalable Solutions?

As software engineers, we are often conditioned to prioritize scalable architectures right from the outset. We delve into design patterns, microservices, and distributed systems—all designed to accommodate millions of users. However, this mindset can be a pitfall for startups, as focusing on scalable solutions may equate to delaying necessary actions.

In reality, scalable development can sometimes feel like procrastination, creating solutions for users that may not exist yet and tackling potential problems that may never arise. My 3-Month Rule encourages me to create straightforward, perhaps imperfect code that’s deployable and, more importantly, helps me gain firsthand insights about my users’ needs.

Current Infrastructure Hacks: Smart Choices or Risks?

1. Running Everything on a Single Virtual Machine:
I host my database, web server, background jobs, and even Redis on a single $40/month virtual machine, with no redundancy and manual backups.

Why is this a strategic move? In just two months, I’ve acquired more knowledge about my resource requirements than any capacity planning document could provide. It turns out my platform, which I initially flagged as “AI-heavy,” peaks at just 4GB of RAM. Instead of becoming mired in an elaborate Kubernetes setup that would’ve forced me to manage idle resources, I glean real-time insights from every crash (which has occurred twice) that informs my future actions.

2. Hardcoded Configuration Values:
I’ve opted for hardcoded constants throughout my codebase (e.g., pricing tiers, maximum users, and AI model specifications) instead of relying on configuration files or environment variables, which require additional setup and management.

This approach has its merits. I can quickly search for any configuration value across my entire codebase, and all