Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Development
In the ever-evolving landscape of startup culture, the advice from tech luminary Paul Graham resonates with founders and engineers alike: “Do things that don’t scale.” However, navigating this concept in the world of coding often remains uncharted territory. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework to make this principle a reality: I give every hack that defies scalability a three-month lifespan. After this period, I assess the hack’s impact—if it proves valuable, it gets a robust implementation; if not, it’s time to let it go.
As engineers, we often feel compelled to architect scalable solutions right from the get-go—envisioning grand designs like microservices and distributed systems that can handle millions of users. While this approach is typical in larger companies, it can lead to unnecessary complexity in a startup environment. I’ve learned that scalable solutions can sometimes be a means of avoiding immediate challenges and that my three-month rule encourages me to prioritize developing straightforward, albeit imperfect, code that provides real insights into user needs.
My Current Infrastructure Hacks: A Smart Approach
1. Consolidated Operations on a Single VM
With my entire stack—database, web server, background jobs, and Redis—all running on a single $40-per-month VM, I embrace operational simplicity. Though it may lack redundancy, this setup allowed me to gain invaluable insights regarding my resource needs within just two months, revelations that no elaborate planning document could offer. I discovered that my AI platform only peaks at 4GB of RAM, rendering my ambitious plans for a Kubernetes architecture unnecessary. Each crash teaches me what truly needs tweaking, often revealing unexpected factors at play.
2. Directly Hardcoded Values
Instead of employing configuration files or environment variables, I’ve hardcoded constants throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach may seem simplistic, but it holds a unique advantage. I can swiftly locate any configuration value through a quick search and track adjustments in version control. Over three months, I’ve made only three changes, which took 15 minutes to redeploy versus the extensive hours required for a formal configuration service.
One Comment
Great insights on embracing the 3-month rule—it’s a practical mindset that encourages experimentation without overengineering. I especially resonate with your point about starting with simple, unscaled solutions to gain real user feedback early on. Your approach to consolidating operations on a single VM and hardcoding values highlights the importance of agility and speed in the startup phase.
One thing to consider as you iterate is gradually introducing more flexibility—perhaps transitioning from hardcoded values to environment variables or configuration files once the core features prove their value. This phased approach ensures you don’t get bogged down prematurely but still move toward scalable practices when the time is right.
Your emphasis on learning from failures—crashing and tweaking—embodies a lean, feedback-driven development cycle. It’s a reminder that sometimes, immediate simplicity yields more valuable insights than complex, “future-proof” solutions. Thanks for sharing this pragmatic approach; it’s inspiring for startups navigating the tension between speed and scalability.