The 3-Month Rule: A Practical Approach to Implementing Non-Scalable Solutions
In the world of startups and software development, many of us have encountered Paul Graham’s famous mantra: “Do things that don’t scale.” However, the challenge often lies in translating this philosophy into actionable steps, especially in coding practices.
After eight months of developing my AI podcast platform, I’ve crafted a straightforward framework to navigate this concept: each unscalable solution I implement is granted precisely three months to demonstrate its worth. By the end of this period, it must either prove its utility and receive appropriate enhancements, or it will be discarded.
The Engineering Dilemma
As engineers, we’re often conditioned to create scalable solutions from the outset. We immerse ourselves in sophisticated architectures—microservices, distributed systems, and other scalable design patterns—without considering whether these solutions are relevant at our current stage. In the startup ecosystem, striving for scalability too soon can result in inefficient use of time and resources, focusing on theoretical users rather than real ones.
Thus, my 3-month rule compels me to adopt a different approach. I focus on crafting straightforward, albeit “imperfect,” code that delivers results and reveals what users genuinely need.
Practical Hacks and Their Merits
1. All-in-One VM Deployment
Currently, I’m running my entire infrastructure—database, web server, background jobs, and Redis—on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has yielded invaluable insights.
In just two months, I’ve gained a clear understanding of my resource requirements, debunking the need for an elaborate Kubernetes setup that would have been managing empty resources. The crashes I’ve experienced have provided real-world data that highlight unexpected failure points—lessons I couldn’t have gleaned through theoretical planning.
2. Directly Hardcoded Configurations
Many developers advocate for configuration files and environment variables, but my approach involves directly hardcoding important values throughout my codebase. This may seem suboptimal; however, the benefits are considerable: I can swiftly search for any configuration using simple tools, track changes easily through version history, and quickly redeploy as needed.
Creating a dedicated configuration service would have consumed a week’s worth of development time. Instead, I’ve made minimal configuration changes—just three over three months—allowing me to streamline my efforts significantly.
3. Utilizing SQLite in a Multi-User Environment
It may seem unconventional
One Comment
This framework offers a refreshing perspective on balancing practicality and scalability—especially in the early stages of a startup or project. I particularly appreciate the emphasis on rapid iteration and learning through direct experimentation, rather than over-engineering from the outset. Your 3-month rule acts as an effective guardrail, ensuring resources aren’t wasted on ideas that don’t prove their value quickly.
The examples you shared—such as running everything on a single VM and hardcoding configurations—highlight how simplicity and speed can drive real insights. It reminds me of the concept that some of the most scalable systems start small and evolve naturally as understanding deepens.
One thought to consider moving forward is how to balance this approach with eventual scalability needs—perhaps by designing a gradual transition plan once a solution proves its worth within the 3-month window. Overall, your approach underscores the importance of actionable experimentation, especially in the unpredictable and fast-paced environment of startups. Thanks for sharing these practical hacks!