Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Software Development
In the world of startups and software development, the phrase “do things that don’t scale,” popularized by Paul Graham, is often repeated. Yet, there seems to be a lack of discussion on how to effectively implement this concept in coding practices. Having spent the past eight months building an AI podcast platform, I’ve adopted a straightforward strategy: every unscalable technique I employ is given a lifespan of three months. After this period, it either earns its place in the architecture through proven effectiveness or is discarded.
The Challenge of Building for Scale
As engineers, we are conditioned to prioritize scalable solutions from the very beginning. We immerse ourselves in practices like microservices, distributed systems, and complex architectural designs, all to accommodate possibly millions of users. However, in a startup environment, this mindset can lead to expensive delays, as we often focus on hypothetical scalability instead of addressing our actual needs.
My three-month framework compels me to prioritize straightforward coding practices that allow me to ship features quickly, gaining invaluable insights into what users truly require.
Current Infrastructure Hacks: Ingenious Solutions to Common Problems
1. Unified Server Setup
I operate my entire application—from the database and web server to background jobs—on a single virtual machine costing just $40 a month. While some may label this approach as risky, the reality is that it has provided me with critical insights into my resource requirements. Within just two months, I’ve discovered that my “AI-heavy” platform runs efficiently with only 4GB of RAM, helping me avoid the unnecessary complexity of an elaborate Kubernetes setup.
When my system experiences downtime, which it has a couple of times, I gather concrete data on what fails—often revealing surprises that guide my future decisions.
2. Simplified Configuration Management
Instead of utilizing configuration files or environment variables, I use hardcoded constants across my codebase, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Though this approach may seem outdated, it allows me to instantly search for any configuration value in my code. The process of updating configuration has only been necessary three times in three months, equating to a mere 15 minutes of redeployment. This stand-in for a configuration service saves me from
