Embracing the Unscalable: A Fresh Technical Approach to Development

In the world of startups, the prevailing wisdom often echoes the advice of thought leaders like Paul Graham: “Do things that don’t scale.” Yet, the challenge remains: how can we effectively implement this in our development processes? After eight months of building my AI podcast platform, I’ve established a straightforward framework that I call the “3-Month Rule.” This framework allows me to experiment with unscalable hacks for just three months. At that point, each hack either earns the right to be developed into a solid solution or is discarded.

As engineers, we are conditioned to prioritize scalability from the outset. We focus on sophisticated design patterns, microservices, and robust architectures that can support millions of users. However, this approach often leads to over-engineering, especially in a startup environment where scaling is often hypothetical. The 3-Month Rule encourages me to create simple and direct code that is functional and insightful, enabling real learning about user needs.

My Current Strategies and Their Advantages

1. Single Virtual Machine Setup

I’ve consolidated my database, web server, background jobs, and Redis into a single, cost-effective virtual machine. While this setup lacks redundancy and relies on manual backups to my local machine, it has provided unparalleled insights into my resource utilization. I learned that my platform, which I initially projected to be resource-intensive, only requires 4GB of RAM. Attempting to create a more complex Kubernetes architecture would have resulted in unnecessary maintenance without real data.

When this setup crashes – which it has a couple of times – I gain valuable insights into what truly fails. Surprising as it may be, the causes of these outages are rarely what I anticipated.

2. Simplified Configuration Management

Instead of complicated configuration files, I utilize hardcoded constants for important values, such as pricing tiers and user limits. While this may seem primitive, the simplicity allows me to quickly locate and change configurations. Every adjustment can be made with minimal redeployment time, and I can efficiently track changes in my Git history. Whereas creating a dedicated configuration service would require significant development time, I’ve found that my current method is sufficient for my needs.

3. Using SQLite in Production

In an unconventional move, I’ve opted for SQLite as the database for my multi-user application. Managing a database of just 47MB has proven more than capable of handling up to 50 users at once without any issues. This choice