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

In the entrepreneurial landscape, the well-known mantra from Paul Graham, “Do things that don’t scale,” often reverberates through the halls of startups. However, there’s a lack of substantial discourse on how to practically apply this philosophy in the realm of coding. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework to guide me: every unscalable hack has a lifespan of precisely three months. Post this period, it must either validate its usefulness and evolve into a structured solution or be discarded altogether.

Rethinking the Engineer’s Mindset

As developers, we are frequently conditioned to engineer “scalable” solutions from the outset. We become engrossed in intricate design patterns, microservices, and expansive systems capable of accommodating millions of users. Yet, this is typically the mindset of larger corporations.

In a startup environment, pursuing scalable code can often transform into costly procrastination, as it risks catering to non-existent users and addressing hypothetical challenges. My 3-month rule encourages me to produce straightforward, albeit imperfect, code that is deployable. This approach not only facilitates immediate learning about user needs but also drives tangible progress.

Exploring My Current Non-Scaling Infrastructure Hacks

Here’s a glimpse into my current infrastructure choices, which may seem unorthodox but have proven to provide significant insights:

1. Consolidated on a Single Virtual Machine (VM)

My entire infrastructure—including the database, web server, and background jobs—operates on one $40/month VM. There’s no redundancy; I rely on manual backups to my local machine.

This approach has turned out to be beneficial. In just two months, I’ve gained deeper insights into my actual resource requirements than any in-depth capacity planning could reveal. My AI-centric platform peaks at just 4GB of RAM. The complex Kubernetes setup I contemplated would have only led to managing idle containers.

Every time the system crashes (which has happened twice), I receive invaluable data about real failure points—often surprising insights that were not anticipated.

2. Hardcoded Configurations

In my codebase, I’ve opted for hardcoded constants such as pricing tiers and maximum users, eliminating the need for configuration files or environment variables. Each update necessitates a redeployment.

While it might seem less efficient, the benefit is significant. I can quickly search my entire codebase for configuration variables, and changes are documented in my git