Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech
In the tech startup landscape, embracing non-scalable solutions can often feel counterintuitive. Renowned investor Paul Graham famously advised entrepreneurs to “do things that don’t scale,” yet many developers struggle to translate this into actionable steps within their coding practices. As someone who has spent the past eight months crafting an AI podcast platform, I’ve devised a straightforward framework that I’ve come to call the “3-Month Rule.” This method allocates a strict three-month period for testing out unscalable hacks. After this timeframe, each hack must either prove its worth and evolve into a robust feature or be phased out entirely.
When we embark on building software, there’s a natural inclination to design for scalability right from the get-go. We often find ourselves drawn to advanced design patterns, microservices, and intricate architectures capable of hosting millions of users. However, this mindset may be more suited to established companies than to startups, where premature optimization can lead to complications and delays in product launch.
Why the 3-Month Rule Works
This approach emphasizes simplicity in coding. By writing straightforward, albeit imperfect, code, I am able to learn what my users genuinely need, rather than wasting time optimizing for hypothetical challenges. Below, I share some of my current structural hacks, each accompanied by the rationale for their effectiveness.
1. Consolidated Infrastructure on a Single VM
My platform operates entirely on a single $40/month virtual machine, hosting the database, web server, background jobs, and Redis without redundancy. While this setup may seem rudimentary, it has provided invaluable insights into my resource requirements. Within just two months, I’ve gleaned more about my usage patterns than any detailed capacity planning could offer. This single-instance approach has revealed that my peak resource usage is a mere 4GB of RAM—an elaborate Kubernetes architecture would have been an elaborate solution to a problem that didn’t exist.
2. Hardcoded Configurations
In my codebase, configurations such as pricing tiers and limits are hardcoded into the application itself. Although this may appear primitive, it simplifies tracking changes via version control and streamlines updates. The hidden advantage is speed: adjusting configuration parameters takes mere minutes of redeployment rather than the comprehensive overhaul necessary for a dedicated configuration service. In just three months, I’ve modified these hardcoded values only three times.
3. Using SQLite for Production
In order to
