The 3-Month Strategy: A Practical Approach to Building Non-Scalable Solutions
In the tech start-up world, Paul Graham’s mantra resonates loudly: “Do things that don’t scale.” Yet, implementing this principle in coding practices can be challenging and often overlooked. Having spent eight months developing my AI podcast platform, I’ve devised a straightforward framework that has guided my approach: each unscalable solution receives an experimentation window of three months. After this period, the solution must demonstrate its value or face termination.
Why Non-Scalable Solutions Matter
As engineers, we are frequently conditioned to chase scalable solutions right from the outset. We immerse ourselves in sophisticated design patterns, microservices, and distributed systems aimed at accommodating a massive user base. However, this line of thinking can lead to unnecessary complexity, especially in a startup environment where optimizing for future users often delays progress and breeds inefficiency.
My three-month strategy compels me to embrace simplicity—crafting “bad” code that’s functional and capable of delivering real insights about user needs.
Exploring My Current Infrastructure Hacks
Here are the non-scalable yet surprisingly effective practices I’ve incorporated into my project:
1. Consolidated Infrastructure on a Single VM
I operate my database, web server, background jobs, and Redis on a single virtual machine costing $40 a month. It is a setup devoid of redundancy and relies on manual backups to my local device.
This arrangement provides significant learning: in just two months, I’ve gained a clearer understanding of my resource requirements than I would have from any planning documentation. My AI-driven platform typically peaks at merely 4GB of RAM, negating the need for the elaborate Kubernetes infrastructure I contemplated. Each crash has provided vital data on failure points—insights that starkly contrast with my initial expectations.
2. Hardcoded Configuration
Instead of using configuration files or environment variables, I have hardcoded all configuration values directly into the application. The current constants include parameters such as pricing tiers and maximum users.
The brilliance of this approach lies in its simplicity: I can quickly search through my entire codebase for any configuration, ensuring every change is recorded in Git history. The time spent on redeployments is minimal compared to the weeks I’d need to create an intricate configuration service. In three months, I’ve only altered these constants three times, making it considerably more efficient.
3. Utilizing SQLite for Production
Surprisingly, I’m leveraging SQLite for my multi-user
