Embracing the 3-Month Experiment: A Practical Approach to Unscalable Solutions in Tech Development
In the tech world, we often hear the mantra from Paul Graham: “Do things that don’t scale.” While it’s a popular concept, the process of integrating this philosophy into coding practices doesn’t receive much attention.
After dedicating eight months to developing my AI podcast platform, I stumbled upon a straightforward yet effective framework: I allow each unscalable approach a trial period of three months. At the end of this period, I assess whether it has demonstrated its utility and is worth formalizing, or if it should be discarded.
As developers, we’re usually conditioned to prioritize scalable solutions from the outset; we’re focused on intricate design patterns, microservices, and distributed systems tailored for vast user bases. However, this mindset is often more suited to established companies rather than startups. In early-stage ventures, aiming for those scalable solutions can lead to unnecessary delays, optimizing for hypothetical users while potentially overlooking immediate needs. My three-month rule, on the other hand, compels me to craft simpler, quicker solutions that lead to tangible user insights.
Current Strategies That Challenge the Norm
1. Unified Infrastructure on a Single Virtual Machine
My setup includes my database, web server, background jobs, and Redis, all running on a single $40/month virtual machine, with no redundancy and manual backups to my local device.
This approach, often criticized, has proven invaluable. I’ve gained a clearer understanding of my resource requirements within two months than I ever could have anticipated through formal planning. My platform’s peak usage only demands 4GB of RAM—what I nearly set up with Kubernetes would have merely managed empty resources. When failures occur (as they have twice), I gain essential insights into the true causes—usually not what I expected.
2. Hardcoded Configuration Settings
My configuration settings are hardcoded throughout the code without the use of configuration files or environment variables. Each change requires a redeployment.
While this might seem primitive, it offers unexpected advantages: I can swiftly search my entire codebase for configuration values. Each pricing adjustment is recorded in Git history, and any updates undergo a review process—despite it being my own scrutiny. In three months, I’ve only modified these parameters three times, resulting in merely 15 minutes of redeployment versus countless hours of development.
3. Utilizing SQLite for Production Needs
Surprisingly, I
