Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the world of technology startups, a familiar piece of advice often cited is Paul Graham’s mantra: “Do things that don’t scale.” While the concept is widely acknowledged, the execution—especially in the realm of coding—remains a topic that isn’t discussed as frequently.
Having spent the past eight months developing my AI podcast platform, I’ve established a straightforward framework: every unscalable hack I implement is given a lifespan of three months. After this period, I assess its value; if it demonstrates enough merit, it will be properly developed; otherwise, it gets retired.
Why Scalable Solutions Can Be a Trap
As engineers, we often emphasize building scalable solutions right from the outset. Concepts such as design patterns, microservices, and distributed systems serve as cornerstones in crafting architectures capable of handling millions of users. Yet, this “big company” mindset can lead to unnecessary complexity in the early stages of a startup.
In reality, striving for scale too soon can result in costly delays and wasted resources. My three-month rule encourages me to write straightforward, albeit imperfect, code that can be deployed quickly, allowing me to gain invaluable insights into the needs of my users.
Current Infrastructure Hacks: Simple Yet Effective
1. Utilizing a Single Virtual Machine
My entire stack—database, web server, background jobs, and caching—runs on a single $40/month virtual machine. While it may seem reckless, this approach taught me more about my resource requirements in two months than any complex capacity planning document could have revealed. As a result, I discovered that my platform’s “AI-heavy” operations peaked at just 4GB of RAM. The sophisticated Kubernetes setup I nearly implemented would have been wasted on idle containers.
Each crash (and there have been a couple) has provided me with real-time insights into failure points, exposing issues I never anticipated.
2. Hardcoded Configuration
Instead of relying on config files or environment variables, I’ve opted for hardcoded constants throughout my codebase, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this might seem inefficient at first glance, it allows for rapid changes—redeploying only takes 15 minutes versus the potential
One Comment
This approach exemplifies the value of rapid experimentation and learning in early-stage development. The 3-month rule acts as a disciplined method to balance the necessity of quick, unscalable solutions with the eventual goal of scalable architecture. It’s a reminder that, especially in startups, focusing on getting user feedback and understanding real usage patterns often trumps premature optimization.
Your point about starting with simple infrastructure—like a single VM—resonates strongly. It minimizes initial overhead and enables real-world insights that are often impossible to glean from theoretical models alone. I also appreciate the pragmatic choice of hardcoded configs; while not ideal long-term, it accelerates iteration and reduces friction during critical early phases.
This framework prompts a broader reflection: scaling efforts are valuable, but they shouldn’t overshadow the importance of validating core product-market fit first. The 3-month rule provides a tangible timeline to pivot or scale based on concrete evidence, which is a powerful mindset for sustainable growth. Thanks for sharing these practical insights—definitely a helpful perspective for founders and engineers alike!