The Three-Month Rule: A Pragmatic Approach to Unscalable Solutions

In the tech world, Paul Graham’s advice to “do things that don’t scale” is widely acknowledged yet often underexplored, especially when it comes to practical implementation in software development. After dedicating eight months to the creation of my AI podcast platform, I’ve devised a straightforward approach: every unscalable workaround is given a trial period of three months. By the end of this timeframe, each solution must either demonstrate its value through tangible results or be phased out.

As engineers, we are usually conditioned to prioritize scalable solutions right from the start. We focus on elegant design patterns, microservices, and robust architecture suited for applications with millions of active users. This mindset, however, is more fitting for large corporations than for startups. In a startup environment, pursuing scalable architectures too early can serve as a costly delay, directing energies towards optimizing for hypothetical users and addressing challenges that may not truly exist.

My three-month rule compels me to focus on crafting straightforward, albeit less polished, code that delivers functionality and, more importantly, reveals genuine user needs.

Innovative Infrastructure Hack: A Detailed Look

1. Consolidated Operations on a Single VM

I’ve chosen to run my entire setup—database, web server, background jobs, and caching—on a single virtual machine, costing just $40 a month. This approach lacks redundancy and relies on manual backups, yet I consider it a strategic advantage.

Why? Within just two months, I’ve garnered insights about my actual resource consumption better than any comprehensive planning document could have provided. For instance, the peak demand for my platform has emerged as 4GB of RAM, highlighting that a complicated Kubernetes architecture would have resulted in unnecessary overhead management.

When system failures occur (and they have, twice), I gain invaluable data about the actual points of failure, which often differ from my expectations.

2. Simplified Hardcoded Configurations

My configuration management is straightforward: I use hardcoded constants throughout the application.

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

While it might seem like a lapse in best practices, I can quickly search and track these values across my codebase. Changes in pricing are documented in git history, and even my self-review process provides