Embracing the 3-Month Rule: A Pragmatic Approach to Building Scalable Solutions
In the realm of startup development, the philosophy of “doing things that don’t scale,” famously advocated by Paul Graham, is often heralded as essential advice. However, many overlook how to effectively put this principle into practice, particularly when it comes to coding.
After dedicating the past eight months to developing my AI podcast platform, I’ve created an innovative framework I like to call the “3-Month Rule.” This approach stipulates that any non-scalable hack is given a lifespan of just three months. After this period, it either demonstrates its value through tangible results and is transformed into a robust solution, or it is discarded entirely.
The Mindset Shift for Startup Engineers
Traditionally, engineers are trained to create scalable solutions, focusing on design patterns, microservices, and distributed systems — a methodology that works well for large corporations handling millions of users. However, in a startup environment, focusing on scalability too soon can lead to unnecessary, expensive procrastination, as you end up optimizing for users who may not even exist yet.
The 3-Month Rule compels me to write straightforward, direct code that facilitates shipping and, more importantly, delivers insights about what users truly need. Here are some of the unconventional strategies I’m utilizing that have proven to be valuable:
1. Centralized Operations on a Single VM
All essential components—database, web server, and background jobs—run seamlessly on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups to my local system, the simplicity has provided invaluable insights. Within just two months, I’ve gained a clearer understanding of my actual resource requirements. For instance, my AI-centric platform peaks at only 4GB of RAM, revealing that a complex Kubernetes infrastructure would’ve been excessively convoluted, managing empty containers instead of serving users.
2. Hardcoded Configurations
Instead of relying on external configuration files or variables, I’ve opted for hardcoded constants scattered throughout my codebase:
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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach enables rapid search capabilities through my entire codebase for any configuration changes in mere seconds. Although this tactic eliminates the flexibility of dynamic configurations, the reality is that I’ve made very few changes
One Comment
Thank you for sharing your thoughtful approach with the 3-Month Rule—it offers a practical mindset that’s often overlooked in the pursuit of scalability. Your emphasis on rapid iteration and learning aligns well with the Lean Startup principles: validate assumptions quickly, then iterate or pivot accordingly. I find the concrete examples of using a single VM and hardcoded configurations particularly compelling, as they highlight the value of simplicity in early-stage development.
One point worth considering as you refine your platform is to implement lightweight monitoring during those initial three months. Even simple metrics can help you assess whether your assumptions—such as resource requirements—hold true as user engagement grows. Additionally, while hardcoded configs accelerate development, maintaining some modularity might ease future transitions into more scalable solutions once your MVP demonstrates validated demand.
Overall, your framework embodies a healthy balance between pragmatism and agility—an approach that more startups should adopt to avoid the trap of over-engineering in the early phases. Looking forward to seeing how your “transform or discard” philosophy evolves!