The 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the domain of startups, the conventional wisdom from Paul Graham rings true—embrace the philosophy of “doing things that don’t scale.” However, a significant gap remains in translating this advice into actionable technical practices. After eight months of developing my AI podcast platform, I’ve established a simple yet effective framework: every unscalable approach has a lifespan of just three months. If it proves its worth, it gets refined; otherwise, it’s time to move on.
As engineers, we often gravitate toward building scalable architectures from the get-go, envisioning systems that can accommodate millions of users through elegant design patterns, microservices, and distributed systems. While these are essential concepts for larger enterprises, they can represent costly delays for startups. In reality, focusing on scalable solutions prematurely can lead to optimizing for hypothetical users and addressing non-issues.
What I’ve learned through my 3-month rule is the value of straightforward, albeit “imperfect,” code. This approach allows me to ship products faster and gain genuine insights into user needs.
Current Hacks: A Deep Dive into My Strategies
1. Consolidated Operations on a Single VM
For just $40 a month, my entire setup—including the database, web server, background jobs, and Redis—operates on a single virtual machine. While some may see this as reckless, it has provided unparalleled insight into my resource needs. After two months, I’ve found that my AI-based platform only requires 4GB of RAM during peak times. An ambitious Kubernetes setup would have led to unnecessary management of idle containers. Each time my system encounters a crash (yes, it has happened), I gather practical data about real issues as opposed to hypothetical ones.
2. Hardcoded Configuration Management
My configuration strategy is simple: constants scattered throughout the codebase represent critical settings. I have things like PRICE_TIER_1 = 9.99 and MAX_USERS = 100 directly in the code, which means any adjustment requires a redeployment. While this may seem archaic, it enables me to quickly search through the entire codebase for values and keeps my deployment history neat and trackable through Git. Over three months, I’ve changed my configurations a mere three times, saving approximately 40 hours of development time that would have been spent building a configuration service.
3. Utilizing SQLite for Production
I currently use SQLite to power my multi
