The Three-Month Rule: A Practical Approach to Unscalable Solutions in Tech Development
In the tech landscape, the mantra “Do things that don’t scale” popularized by Paul Graham resonates widely. However, practical applications of this advice, particularly in coding, are often overlooked. As I embarked on building my AI podcast platform over the last eight months, I adopted a straightforward methodology: any non-scalable workaround is given a finite lifespan of three months. After this period, the solution must either validate its worth and be developed into a robust system or it will be discarded.
As engineers, we are conditioned to focus on creating scalable technologies right from the outset. We become enamored with elegant design patterns, microservices, and distributed architectures — the kind of frameworks anticipated to accommodate millions of users. Yet, this approach often dominates larger enterprises and can stifle innovation in a startup context.
In the early stages of a startup, striving for scalability can become an exercise in futility, often leading to delays and resource allocation for problems that may not even arise. My three-month rule compels me to prioritize rapid deployment of straightforward, albeit “imperfect,” code that allows me to gather real user feedback and insights.
Current Infrastructure Strategies: Embracing Simplicity for Efficiency
1. All-in-One Virtual Machine (VM)
My entire infrastructure operates on a single $40/month VM, which hosts the database, web server, background jobs, and Redis services. This setup lacks redundancy and relies on manual backups to my local device.
You might wonder how this could be beneficial. In just two months, I’ve gained a deeper understanding of my resource requirements than any traditional capacity planning document could provide. The platform’s peak usage only demands 4GB of RAM, rendering my plans for a complex Kubernetes architecture unnecessary. Every time the system crashes—an occurrence that has happened twice—I receive invaluable insights into actual malfunctions, dispelling my initial assumptions about what would fail.
2. Simplified Configuration Management
In my codebase, configuration values are hardcoded as constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no external config files or environment variables to manage, which can be seen as a drawback. However, this method offers a streamlined ability to locate and modify values quickly. I can search my
One Comment
Thank you for sharing this practical and insightful approach to balancing speed and sustainability in early-stage tech development. The “Three-Month Rule” effectively emphasizes the importance of rapid iteration and real-world validation over premature investments in scalability—something often overlooked when teams get caught up in building “future-proof” systems from day one.
Your example of a single VM setup demonstrates how close monitoring and understanding actual usage patterns can inform smarter infrastructure decisions, avoiding over-engineering. The mindset of giving unscalable solutions a clear time window encourages experimentation and learning without the fear of immediate failure, fostering innovation at the startup stage.
Additionally, your streamlined configuration management approach highlights a valuable trade-off: simplicity for speed. While hardcoded values can be risky in production, in early prototypes, they enable quick adjustments and faster development cycles.
Overall, your framework champions a pragmatic, feedback-driven process—one that recognizes when to prioritize immediate progress and when to refactor for scalability as the platform grows. It’s a compelling reminder that sometimes, less complexity upfront can lead to more informed and scalable growth down the line.