The 3-Month Rule: A Practical Framework for Embracing Non-Scalable Solutions in Tech Development
In the tech world, one piece of advice stands out from the rest: “Do things that don’t scale,” as famously stated by Paul Graham. While this mantra is widely recognized, the intricacies of applying it in software development often remain unaddressed. After eight months of developing my AI podcast platform, I’ve crafted a straightforward approach: I give every non-scalable tactic precisely three months to demonstrate its utility. If it proves valuable, I’ll refine and scale it; if not, it’s time for it to go.
As developers, we are ingrained with the mentality of designing scalable systems from the outset. We immerse ourselves in concepts like design patterns, microservices, and distributed architectures, aiming to accommodate millions of users. However, this approach can often be misguided, particularly for startups, where building for scalability can lead to unnecessary delays.
In my experience, this three-month rule compels me to embrace simplicity and directness in my code. It encourages me to focus on shipping quickly and learning directly from user interactions rather than getting bogged down in theoretical optimizations.
Current Infrastructure Hacks: Why They Work
1. Consolidating Everything onto a Single VM
Right now, I’m running my database, web server, background jobs, and caching all on a single $40/month virtual machine. Yes, it lacks redundancy, and I perform manual backups to my local setup. But here’s the silver lining: in just two months, I’ve gained invaluable insights into my actual resource requirements. My platform, which I thought was “AI-heavy,” peaks at only 4GB of RAM. The complex Kubernetes architecture I contemplated would have been overkill, only forcing me to manage empty containers.
When system failures happen—twice so far—I gather real data on the causes rather than relying on assumptions. And predictably, the issues are rarely what I anticipated.
2. Hardcoded Configurations for Simplicity
Instead of employing configuration files or environment variables, I simply use hardcoded constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This streamlined approach may seem outdated, but it has its advantages. I can quickly search my entire codebase for any value, making change tracking straightforward. In three months, I’ve modified
One Comment
This post offers a compelling perspective on balancing experimentation with practicality, especially for startups and early-stage projects. The 3-month rule is a smart heuristic—allowing enough time to validate assumptions while preventing over-investment in solutions that may not align with user needs or real-world constraints.
I particularly appreciate the emphasis on simplicity and real-world data over premature optimization. Running everything on a single VM and hardcoding configurations may seem unorthodox, but these tactics prioritize learning, speed, and cost-effectiveness in the short term. This approach aligns well with the principle of rapid iteration—encouraging developers to focus on core features and user feedback before scaling complexity.
It’s worth noting, however, that as products mature, transitioning to more scalable and maintainable architectures becomes essential. Having a flexible plan to evolve from these initial hacks toward more robust systems ensures longevity and resilience.
This framework can serve as a valuable guide: periodize experimentation, prioritize real data, and remain adaptable. Thanks for sharing such practical insights rooted in actual development experience!