Embracing the 3-Month Rule: A Pragmatic Approach to Scaling in Software Development
In the realm of software engineering, one piece of wisdom often floats around: “Do things that don’t scale.” While this advice, famously espoused by Paul Graham, is widely recognized, the implementation of such a strategy—especially in coding—is often overlooked. After eight months of developing my AI podcast platform, I have devised a straightforward framework around this principle: every non-scalable hack gets a lifespan of just three months. If it proves its worth within this timeframe, it gets a robust build; if not, it is phased out.
A Reality Check for Startups
As engineers, we have been conditioned to prioritize “scalable” solutions from the outset. We immerse ourselves in design patterns, microservices architecture, and distributed systems to cater to potential millions of users. However, this big-corporation mindset can lead to unnecessary complexities.
In the startup environment, overengineering can often equate to procrastination, as it forces us to optimize for hypothetical users and tackle issues that may never materialize. My 3-month rule compels me to focus on straightforward, albeit crude, code that can be deployed quickly, providing invaluable insights into what users genuinely require.
Practical Infrastructure Hacks
Here are some strategic decisions I’ve made that defy conventional wisdom but have proven to be effective:
1. Everything Operates on a Single Virtual Machine
My database, web server, background jobs, and Redis functionality all run on a single $40/month virtual machine. This setup may seem risky with zero redundancy and manual backups, but it has taught me critical lessons. Within just two months, I have gained unparalleled insight into my resource needs—discovering that my platform peaks at only 4GB of RAM. The elaborate Kubernetes infrastructure I once considered would have merely created empty containers to manage. Each crash has provided data on failures I never anticipated.
2. Hardcoded Configurations Across the Board
Instead of employing configuration files or environment variables, I use constants distributed throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Changing any value requires a simple redeployment. This approach, while unconventional, allows me to quickly locate specific configurations and track changes through Git. Instead of investing a week to develop a configuration service
