Embracing the 3-Month Rule: A Practical Framework for Non-Scalable Solutions in Tech Development
In the startup landscape, the wisdom of Paul Graham rings true: “Do things that don’t scale.” But how do you translate this philosophy into practical coding methods? After eight months of developing my AI podcast platform, I’ve devised a straightforward approach: every non-scalable solution I implement has a shelf life of 90 days. At the end of this period, each solution must either demonstrate its effectiveness and evolve into a more refined approach or be discarded.
As engineers, we often feel the pressure to design systems capable of scaling from the outset. We indulge in intricate architectural patterns, distributed systems, and microservices, all tailored to support massive user bases. However, this is the mindset of larger corporations—not startups.
In the early stages of development, chasing scalability can be a costly distraction. It’s about anticipating the needs of potential users who may never materialize and resolving issues that don’t yet exist. My 3-month rule encourages me to craft simple, even “imperfect” code that is functional and teaches me invaluable insights about user needs.
Current Infrastructure Strategies: Why They Work
1. Unified Virtual Machine Setup
I operate my entire platform on a single $40/month virtual machine (VM) that houses everything: the database, web server, background jobs, and Redis. This setup may seem risky, but it has provided clarity about my resource consumption. After two months, I’ve realized that my platform only requires 4GB of RAM at peak usage—something I could never have determined during the theoretical planning stages. The complexities of a Kubernetes setup would have been overkill, potentially managing empty containers. When crashes do happen, I gather real-world data about failures, which are often surprising.
2. Hardcoded Configuration Values
I use constants directly in my code rather than environment variables or configuration files, which may sound counterintuitive. Here’s a snippet of how I set pricing and limits:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This structure allows me to quickly search for any configuration value across the codebase. Plus, changes to any value necessitate a redeployment, which keeps me organized and tracks every adjustment through git history. Building a configuration service would have taken me a week, yet in
