The 3-Month Rule: A Unique Approach to Unscalable Solutions in Development
In the realm of startup development, the adage from entrepreneur Paul Graham, “Do things that don’t scale,” is often echoed. However, the challenge lies in finding the practical steps to apply this wisdom in a coding environment. After dedicating eight months to building my AI podcast platform, I’ve implemented a straightforward yet effective framework: any unscalable solution I deploy is granted a lifespan of three months. At the end of this term, it either demonstrates its value and evolves into a robust feature or is phased out.
The Paradigm Shift in Engineering
As professionals in technology, we are ingrained with the ideal of constructing scalable solutions right from the outset. Our training promotes a preference for sophisticated architectures—design patterns, microservices, distributed systems—all tailored to manage a vast influx of users. While these concepts hold merit, they often reflect the mindset of larger enterprises.
In a startup context, pursuing scalable solutions can morph into costly procrastination. You might find yourself designing for users who haven’t arrived yet, tackling potential problems that may never surface. My 3-month rule compels me to write simpler, more direct code that is readily deployable and helps me identify actual user needs—flaws and all.
Current Infrastructure Hacks: Practical Innovations
1. Centralized Operations on One Virtual Machine
Currently, my database, web server, background jobs, and Redis all function on a single $40/month virtual machine. This setup lacks redundancy and relies on manual backups to my local system.
This approach, while seemingly simplistic, has proven invaluable. In just two months, I have gained insights into my real resource requirements—insights that meticulous capacity planning rarely yields. Surprisingly, my resource usage peaks at just 4GB of RAM. The complex Kubernetes system I nearly built would have merely been an exercise in managing idle containers. When crashes occur (which has happened twice), I receive concrete feedback about genuine failure points, which, interestingly, never align with my initial assumptions.
2. Simplified Hardcoded Configuration
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I have opted for hardcoded values across my codebase instead of using configuration files or environmental variables. Adjusting any setting necessitates a straightforward redeployment.
The beauty in
One Comment
Thank you for sharing your practical approach to unscalable solutions! The 3-month rule is a refreshing mindset shift—embracing quick implementation, real-world feedback, and iterative refinement rather than over-engineering from the start. I particularly appreciate your emphasis on simplicity and how it enables genuine insights into your resource needs and user behavior.
This approach aligns well with the lean startup philosophy—building minimum viable solutions, testing assumptions rapidly, and pivoting as needed. It also echoes the ‘build, measure, learn’ cycle, encouraging us to prioritize learning over perfecting the architecture upfront. Have you found that this methodology reduces technical debt over time, or does it sometimes lead to reworking components later? Would love to hear your thoughts on balancing initial simplicity with long-term scalability considerations.