Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions

In the realm of startups, the advice from tech luminaries like Paul Graham to “do things that don’t scale” is often cited but rarely put into actionable practice, especially in coding. After eight months of developing my AI podcast platform, I’ve formulated a straightforward framework that every unscalable approach I consider is granted three months to prove its worth. If it fails to deliver tangible results in that timeframe, it gets the axe.

Let’s face it: as engineers, we are often trained to think in terms of growth and scalability from the outset. Concepts like microservices, extensive architectural designs, and sophisticated systems to cater to millions of users dominate our thinking. However, at the startup level, pursuing scalability can often come across as an exercise in avoidance—focusing on hypothetical future users while neglecting the needs of current ones. My 3-month rule allows me to create pragmatic and somewhat “imperfect” solutions that can actually ship and provide insights into real user needs.

Current Infrastructure Hacks: Why They’re Strategically Sound

1. Consolidating Resources on a Single VM

Currently, my entire system runs on a single $40/month virtual machine, hosting the database, web server, background jobs, and even Redis, with no redundancy and manual local backups. This decision may appear reckless, but it has enabled me to grasp my actual resource requirements remarkably quickly. In just two months, I learned that my AI-centric platform peaks at 4GB of RAM—an elaborate Kubernetes setup would have led to endless management of largely unused resources.

In moments of failure (twice, to be precise), I gleaned invaluable information about the specific issues at hand, none of which were what I initially anticipated.

2. Utilizing Hardcoded Configuration

My approach to configuration is refreshingly straightforward:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of relying on complex configuration files or environment variables, I distribute constants throughout my codebase. With this strategy, I can swiftly search for any config value and track all changes via Git history. While creating a dedicated configuration service might take a week, in three months, I’ve changed the values three times—that’s a mere 15 minutes of deployment rather than a