Embracing the 3-Month Rule: A Unique Approach to Non-Scalable Coding
In the ever-evolving tech landscape, the advice “Do things that don’t scale” from Paul Graham resonates with many of us striving to innovate, especially in the startup environment. However, there’s a conspicuous gap in discussing how to effectively apply this mantra in the realm of software development. As a founder of an AI podcast platform with eight months of experience under my belt, I’ve crafted a straightforward yet powerful framework: every hack that lacks scalability is allowed a lifespan of three months. After this period, the hack either proves its worth and is developed into a sustainable solution, or it’s discarded.
The startup Mentality: Why Scaling Too Early is a Trap
In traditional engineering roles, there’s a strong emphasis on creating solutions that can accommodate growth from day one. We often find ourselves distracted by elaborate design patterns, microservices, and distributed systems—all the intricate architectures built to handle millions of users. However, in a startup context, focusing on scalability too early can turn into a costly form of procrastination.
By optimizing for future users who may never arrive, we might overlook the immediate needs of our existing, real users. My three-month rule encourages me to write straightforward, albeit “messy,” code that is deployable and reveals genuine user requirements.
The Hacks Revolutionizing My Infrastructure
1. Consolidating Resources on One VM
Currently, all my platform components—database, web server, background jobs, Redis—operate on a single $40/month virtual machine without redundancy and with manual local backups.
You might think this is impractical, but it’s been enlightening. In the past two months, I’ve gained far more insight into my actual resource requirements than any comprehensive capacity planning report could provide. For instance, my platform, which is designed to handle AI tasks, only peaks at 4GB of RAM. The elaborate Kubernetes infrastructure I had in mind would have simply been an unnecessary burden managing idle containers. Each crash (yes, there have been two) offers real data about failure points—often revealing surprises that contradict my initial assumptions.
2. Utilizing Hardcoded Configuration
In my setup, everything is hardcoded—price tiers, maximum users, AI model details scatter across my operational files.
This might seem counterproductive, but the efficiency here is undeniable. I can quickly search my entire codebase for configuration values and track changes in git history seamlessly. In
