Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Development

When it comes to the startup world, the common mantra often echoes Paul Graham’s advice: “Do things that don’t scale.” While this is valuable guidance, the practical application in development is rarely discussed. After eight months of scaling my AI podcast platform, I’ve come to embrace a straightforward framework: each non-scalable hack is granted a lifespan of three months. At the end of this period, we either reinforce its value with a solid build or retire it entirely.

The typical engineering mindset gears us toward crafting scalable solutions from the outset. We obsess over design patterns, microservices, and robust architectures that can ostensibly handle millions of users. However, in a startup environment, prioritizing scalability can inadvertently turn into an expensive form of procrastination, wasting valuable resources while targeting hypothetical users. The 3-month rule compels me to produce simple, sometimes imperfect code that is functional and educational, enabling me to discern real user needs.

Current Infrastructure Hacks: Smart Solutions for Rapid Learning

1. Consolidating Operations on a Single Virtual Machine

Everything, from the database and web server to background tasks, runs on one $40/month virtual machine. This setup lacks redundancy, and backups are performed manually.

Why is this a smart strategy? After just two months, I’ve gained a clearer understanding of my actual resource requirements than traditional planning documents could provide. My “AI-driven” platform peaks at 4GB of RAM— a revelation that saved me from a complex Kubernetes setup that would have overcomplicated empty container management. Each crash (two so far) has delivered unexpected insights into what truly fails.

2. Hardcoded Configuration Values

Considerations for everything from pricing tiers to session limits are hardcoded directly into the code. There are no external configuration files—simply constants scattered throughout.

The hidden benefit here? It allows for swift searches through my entire codebase. Any price modification is easily tracked through git history, with each adjustment subject to a self-directed review. The time invested in creating a configuration service would vastly outweigh the few minutes needed for a redeployment whenever changes arise.

3. Utilizing SQLite in Production

Yes, I’m running SQLite for a web application that serves multiple users. With a database size of just 47MB, it efficiently accommodates 50 concurrent users.

The greatest discovery was realizing my usage patterns were skewed: 95% reads and only