Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development
In the world of software development, we often hear the advice from tech visionary Paul Graham: “Do things that don’t scale.” While this principle resonates, translating it into actionable strategies can be challenging, especially in coding. After eight months of building my AI podcast platform, I’ve devised a straightforward framework that I call the “3-Month Rule.” This guideline stipulates that any unscalable workaround is given a lifespan of three months, during which its value is tested; after this period, it either evolves into a well-constructed solution or is discarded.
The Engineer’s Dilemma
As engineers, we are conditioned to focus on creating “scalable” systems right from the start. Think microservices, distributed architectures, and all the elegant structure that can handle a vast number of users. However, this mindset tends to align more with established enterprises than with startups, where the pursuit of scalable solutions can often lead to costly delays.
My approach—rooted in the 3-month timeframe—encourages me to embrace straightforward, even “messy,” code that is quickly deployable. This methodology not only gets my product out into the world but also helps me gain valuable insights into user needs.
My Current Infrastructure Hacks and Their Strategic Value
1. Centralized Operations on a Single VM
All components of my platform—including the database, web server, and background jobs—operate on a solitary $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has provided critical insights into my actual resource requirements in just two months. For instance, I discovered that my platform peaks at 4GB of RAM. Any complexities I considered, like a Kubernetes configuration, would have led to managing idle resources instead of real user demands.
When failures occur—it’s happened twice so far—I’m presented with concrete data on what truly breaks, which often surprises me.
2. Hardcoded Configuration Values
Currently, my configuration settings are hardcoded throughout my application. This means adjustments necessitate a full redeployment, resulting in quick access to configuration changes through a simple grep command. Each modification is tracked in Git history and subjected to my code review process—albeit a self-review. The time saved here is immense; implementing a dedicated configuration service would have consumed a week for changes I’ve made only thrice in three months.
**3. Utilizing
