Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Development

In the realm of software development, especially within startups, the adage “do things that don’t scale” has gained substantial traction, particularly from tech luminary Paul Graham. While it’s a popular sentiment, the actual implementation of this advice—especially when it comes to coding—remains largely uncharted territory.

Over the past eight months, as I’ve been developing my AI podcast platform, I’ve formulated a straightforward yet impactful framework: any unscalable approach gets a dedicated lifespan of three months. After this period, it either proves its worth and is redeveloped into a more robust solution, or it gets the boot.

As engineers, we often fall into the trap of crafting “scalable” architectures right from the get-go. We gravitate towards sophisticated design patterns, microservices, and distributed systems—all ideal for managing immense user bases. This mindset can be more reflective of large enterprises than the agile world of startups. In many cases, attempting to create scalable solutions prematurely leads to unnecessary expenses and delays, as it focuses on hypothesized users and challenges rather than immediate, tangible needs.

The Framework in Action: My Unconventional Infrastructure Strategies

1. Unifying Everything on One VM

I’ve consolidated my web server, database, background jobs, and caching into a single $40/month virtual machine. There’s no redundancy, and I handle backups manually.

Why is this a smart move? Over the course of two months, I’ve gleaned crucial insights about my resource requirements that no capacity planning document could provide. For instance, I learned that my “AI-intensive” platform rarely exceeds 4GB of RAM usage. The intricate Kubernetes architecture I once considered would have resulted in wasted efforts managing empty resources.

When the server crashes—which it has on two occasions—I’m greeted with invaluable information about what truly fails. Spoiler alert: it’s never what I anticipated.

2. Utilizing Hardcoded Configurations

My configuration management consists of hardcoded constants spanning my codebase, such as:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

This approach may seem archaic, but its simplicity allows for rapid tracking and changes. I can quickly search for configuration values, and any alteration is logged in Git history. Considering