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
One Comment
Thank you for sharing such a practical and insightful approach to product development. The 3-Month Rule strikes me as a powerful mindset shift—encouraging founders and engineers to prioritize action and learning over premature optimization. It reminds me of the Lean Startup methodology, emphasizing validated learning through rapid experimentation.
Your emphasis on embracing simplicity—like deploying on a single VM, hardcoding configurations for quick iteration, and using SQLite—demonstrates that sometimes, “good enough” is the best way to gain real-world insights without over-engineering. I believe this approach can be especially valuable for early-stage startups that need to iterate quickly and avoid getting caught in the trap of building a “perfect” scalable system before validation.
It would be interesting to see how this framework might evolve as the product scales and the initial unscalable solutions start hitting limitations. Do you have a process in place for transitioning from the 3-month experimental phase to more robust, scalable infrastructure when needed?