Embracing the Three-Month Rule: A Pragmatic Approach to Unscalable Solutions

In the tech world, one piece of advice stands out: “Do things that don’t scale,” famously advocated by Paul Graham. However, the discussion often falters when it comes to practical implementation, particularly within the realm of software development. After eight months of developing my AI podcast platform, I’ve adopted a straightforward yet effective framework: Each unscalable tactic is granted just three months. At the end of that period, we assess its value—either it evolves into a well-built solution, or it’s discarded.

As software engineers, our instinct is to craft scalable solutions from the outset—think of intricate architecture, microservices, and distributed systems designed to cater to potentially millions of users. Yet, this approach is often more aligned with large corporations rather than agile startups. Focusing on scalability too soon can lead to delays in tackling current user needs, ultimately resulting in wasted resource allocation. My three-month rule compels me to create straightforward, sometimes “messy,” code that allows for prompt delivery and a clear understanding of user demands.

Current Infrastructure Strategies and Their Hidden Benefits

1. Consolidating Services on a Single VM

All aspects of my application—from the database to web server and background jobs—operate seamlessly on a single $40/month virtual machine. While this might seem risky due to the lack of redundancy, it has provided invaluable insights into my actual resource requirements. In just two months, I’ve learned that my platform, which I presumed required extensive resources, only peaks at 4GB of RAM. The formidable Kubernetes setup I once considered would have been occupied managing idle containers instead of addressing real needs. Moreover, the crashes I encountered offered genuine data on failure points, revealing unexpected vulnerabilities.

2. Simplifying Configuration Management

Constants are my game:

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

With no reliance on configuration files or environment variables, every change prompts a straightforward redeployment. This simplicity has a significant upside: I can swiftly search through the entire codebase for configuration values, and each change has documentation within the version control history. Constructing an elaborate configuration service could take up a week, but my occasional updates spanning only fifteen minutes showcase efficient time management.

3. Leveraging SQLite in Production

My