Embracing the 3-Month Rule: A Pragmatic Approach to Rapid Learning in Development

In the tech world, advice often echoes from thought leaders like Paul Graham, who famously says, “Do things that don’t scale.” Yet, practical implementation of this principle in software development is rarely discussed. Drawing on eight months of experience constructing my AI podcast platform, I’ve cultivated a straightforward framework: each unscalable hack is given a three-month lifespan. After this period, we evaluate its effectiveness—either it gets refined into a robust solution, or it gets phased out.

Rethinking Scalability

As engineers, we’re often conditioned to prioritize scalability right from the outset. We delve into design patterns, microservices, and robust architectures capable of accommodating millions of users. However, this mindset can hinder innovation at startups, where anticipating the needs of future users may simply be a costly form of procrastination. By enforcing this 3-month rule, I’m prompted to create straightforward, albeit less elegant, code that allows for rapid deployment and learning about user requirements directly.

My Current Infrastructure Implementations: Smart Simplicity

1. Unified Operations on a Single VM

Currently, all components—database, web server, background tasks, and Redis—run on a single, cost-effective virtual machine. While this may seem reckless, the reality is that I’ve gained invaluable insights into my resource consumption. My platform peaks at just 4GB of RAM, debunking the need for a complex Kubernetes architecture that would have been managing idle resources. Each crash offers real feedback, highlighting unexpected failure points.

2. Hardcoded Configurations

In my codebase, critical parameters such as pricing tiers and user limits are hardcoded. There are no elaborate config files or environment variables; rather, constants are directly embedded within the code. This might seem cumbersome, but it has enabled me to track changes easily and redeploy with minimal fuss. Adjusting configuration parameters has occurred only a few times in the last three months—saving countless hours that would have otherwise gone into developing a complex configuration management system.

3. SQLite as a Production Database

Opting for SQLite in a multi-user application may raise eyebrows, but it’s proven effective; my database size is modest at 47MB, effortlessly supporting 50 concurrent users. This choice has illuminated my database usage patterns, which favor read operations over writes. If I had adopted a more robust database solution from the get-go,