Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech
In the world of technology startups, Paul Graham’s famous mantra to “do things that don’t scale” is often quoted. However, the practical application of this philosophy—particularly in coding—remains largely unexplored. After spending the last eight months cultivating my AI podcast platform, I have developed my own framework: the 3-Month Rule. This approach dictates that every non-scalable solution I implement is given a lifespan of three months. At the end of this period, the solution is either refined into a robust feature based on its proven value or discarded entirely.
The startup Perspective
As engineers, we are frequently conditioned to pursue scalable solutions right from the outset. We dream in terms of design patterns, distributed systems, and microservices while picturing a platform that can accommodate millions of users. However, in a startup environment, focusing on scalability too early can lead to inefficient use of resources. It often becomes a form of expensive procrastination—optimizing for hypothetical users and resolving issues that may never arise. My 3-Month Rule encourages me to create straightforward, although seemingly inefficient, code that provides immediate insights into user needs.
Current Infrastructure Hacks: Valuable Lessons Learned
1. Consolidated Virtual Environment
My entire setup—database, web server, background jobs, and caching—is housed on a single virtual machine costing $40 per month. This means zero redundancy and manual backups to my local machine. The key lesson here: I’ve gained invaluable insights into my actual resource demands within just two months. For example, I learned that my platform’s peak usage requires only 4GB of RAM, making the complex Kubernetes configuration I initially considered unnecessary.
2. Hardcoded Configurations
Instead of relying on configuration files and environment variables, I use constants like:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
This approach means that changes necessitate redeploying the code but offers the hidden advantage of quick access and straightforward tracking of configuration changes through version control. The time saved here is substantial—15 minutes spent redeploying has been far more efficient than the potential 40 hours it would have taken to engineer a configuration management system.
3. SQLite for Production Use
I’m utilizing SQLite for a multi-user application with a total database size of just 47MB, easily handling 50 concurrent users
