Embracing the Non-Scalable: My 3-Month Framework for Agile Development

In the tech startup world, there’s a well-known piece of wisdom from Paul Graham that advocates for “doing things that don’t scale.” However, when it comes to practical implementation—particularly in coding—this advice often remains vague. Having spent the past eight months developing my AI podcast platform, I’ve crafted a straightforward approach: every unscalable hack has a life span of three months. After this period, it’s evaluated for its effectiveness and either developed into a more robust solution or phased out.

Traditionally, engineers are trained to create scalable designs from the outset. We’re often immersed in creating sophisticated architectures—think microservices and distributed systems—geared to handle an influx of users. But let’s face it; in the startup space, this kind of thinking can translate to expensive delay tactics.

What I’ve found is that writing code that prioritizes immediate deployment instead of scalability has proven invaluable. My 3-month rule pushes me to create simple, straightforward, and sometimes “imperfect” code that directly addresses user needs and provides crucial insights.

Practical Examples of My Infrastructure Choices

1. Single VM Infrastructure

Instead of spreading resources thin across multiple servers, I’ve consolidated my entire application—database, web server, background jobs, and caching—onto one $40/month virtual machine without redundancy. This choice may seem shortsighted, but it has provided tremendous insights about my actual resource needs. I’ve learned that my platform, which I assumed would be resource-hungry, peaks at merely 4GB of RAM. Had I implemented a complex Kubernetes configuration, I would have been managing idle containers, missing opportunities to gather real usage data when outages did occur.

2. Hardcoded Configurations

Instead of utilizing config files or environment variables, I rely on hardcoded constants throughout my code. While it may seem primitive, this method allows me to quickly search and track changes across my codebase using Git history. With just a few updates in three months, I’ve saved considerable engineering hours typically spent on creating a configuration service.

3. SQLite in Production

Yes, I opted for SQLite to manage my multi-user application, and it’s been quite effective. With a manageable database size of just 47MB, it accommodates 50 simultaneous users effortlessly. The data trends revealed that my access patterns lean heavily toward reads rather than writes, making SQLite a perfect fit. If I had chosen a more elaborate database