Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the entrepreneurial space, Paul Graham’s advice to “do things that don’t scale” is often shared but rarely unpackaged, particularly from a technical perspective. As I appraise my journey of developing an AI podcast platform over the last eight months, I’ve implemented a unique framework that I like to call the “3-Month Rule.”
This framework centers around allowing any unscalable solution to exist for only three months. After this period, each approach needs to either prove its effectiveness and warrant a robust build-out or face elimination. This strategy is vital, especially for engineers who tend to focus on crafting scalable solutions from the outset, often optimizing for future users who may never materialize.
The Problem with Traditional Tech Mindsets
In larger organizations, the emphasis naturally gravitates towards scalability: advanced architectures, microservices, and distributed systems designed to accommodate vast user bases. While this is significant for big companies, startups may find that investing effort in scalable code can serve as a form of costly procrastination. My 3-month rule compels me to create straightforward and often imperfect code that is operational. This provides vital insights into user needs rather than making guesses based on assumptions about future demands.
Current Infrastructure Insights: Practical Hacks That Work
1. Unified VM Deployment
With everything operating on a single $40/month virtual machine—including the database, web server, background jobs, and Redis—I have no redundancy and handle backups manually. This may seem counterproductive, but it has allowed me to understand my actual resource demands in a fraction of the time that traditional capacity planning would have taken. Contrary to my initial projections, my platform utilizes just 4GB of RAM at peak. In the events of crashes (which have occurred twice), I gained invaluable data on failure points that were completely unexpected.
2. Simplified Hardcoding of Configurations
By placing constants directly within my code, like:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
I can easily search and modify parameters across the codebase. Each change prompts a quick redeployment, taking mere minutes instead of the extensive man-hours required to build out a separate configuration service—something I only needed to change three times in three months.
3. Utilizing SQLite in Production
Running SQLite for a multi-user web application has proved highly efficient. With
