The 3-Month Experiment: A Pragmatic Approach to Developing Unscalable Solutions
In the startup world, advice like “do things that don’t scale,” attributed to Paul Graham, is often echoed but rarely unpacked, especially regarding technical implementation. After eight months of developing my AI podcast platform, I’ve crafted a practical framework that I call the “3-Month Rule.” This guideline advocates for giving each unscalable tactic a trial period of three months. At the end of this timeframe, the solution either proves its worth and is rebuilt, or it gets discarded.
As engineers, our instinct is often to construct scalable architectures right from the start, envisioning systems capable of servicing millions. While robust design patterns, microservices, and distributed systems have their merits, this mindset can become a hindrance in a startup environment. In many cases, rushing towards scalability translates to delaying the actual growth and insights that come from direct user engagement. By adhering to my 3-month strategy, I focus on crafting straightforward solutions that are ready for deployment, allowing me to better understand genuine user needs.
Current Infrastructure Strategies: Learning Through Simplicity
Here’s a glimpse into my current infrastructure approaches, which may initially seem unconventional but have their own logic and benefit:
1. Single VM for Everything
I operate my database, web server, background jobs, and caching all on a single virtual machine costing $40 a month, without the safety net of redundancy or automated backups. This might sound reckless, yet it’s been remarkably insightful. In just two months, I’ve gained a clearer understanding of my resource requirements than any capacity planning document could have provided. The realization that my AI platform peaks at 4GB of RAM has spared me from over-engineering solutions like Kubernetes, which would have only added needless complexity.
When issues arise—twice so far—I’m presented with actionable insights about what actually fails, which is often not what I anticipated.
2. Hardcoded Configuration
I maintain simplicity with hardcoded constants, such as pricing tiers and user caps, that are embedded directly into the codebase without the use of separate configuration files. Redeploying the application is straightforward, needing only a few minutes compared to the significant investment of time it would take to develop a configuration management service. The ease of searching through my codebase for specific configuration values has proven to be a hidden advantage, providing a clear audit trail for changes and ensuring accountability.
