The Three-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development

When diving into the world of startups, one often encounters the acclaimed advice by Paul Graham: “Do things that don’t scale.” However, the challenge lies in translating this principle into actionable practices within tech development. After dedicating eight months to creating my AI podcast platform, I’ve devised a straightforward framework for implementing this philosophy: each non-scalable hack receives a three-month trial period. At the conclusion of this period, the solution either proves its worth and is properly developed, or it’s phased out.

In the tech industry, especially as engineers, we often gravitate toward the allure of scalable solutions from the outset. We become enamored with sophisticated architectures like microservices and distributed systems designed to support thousands of users. However, this mindset can become a hindrance in a startup environment. Focusing solely on scalability may lead to valuable time wasted on problems that don’t yet exist, or worse yet, direct us away from the actual needs of our users. My three-month rule encourages me to prioritize straightforward and pragmatic coding that facilitates immediate learning about user requirements.

Current Infrastructure Hacks: Taking the Path Less Traveled

1. Consolidated Operations on a Single VM

My entire setup—spanning the database, web server, background jobs, and caching—operates on a single, affordable $40/month virtual machine. While this approach lacks redundancy and relies on manual local backups, it has provided unparalleled insights. Within two months, I’ve gained a deeper understanding of my resource allocation than any extensive planning document could offer. My platform, which I initially predicted to have heavy AI demands, only requires a peak of 4GB RAM. The intricate Kubernetes setup I almost implemented would have only managed unused resources.

When issues arose—and they have, twice—I received concrete data about actual failures rather than assumptions, enlightening me about where improvements are genuinely needed.

2. Embracing Hardcoded Values

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

My configuration is hardcoded across various files. Although it lacks the elegance of config files or environment variables, this straightforward method enables me to identify any configuration value across my code repository in seconds. Each adjustment is committed to my version control history, and I personally review every change.