Embracing the 3-Month Rule: A Pragmatic Approach for Startups
In the world of startups, conventional wisdom often emphasizes the importance of scalability from the outset. Paul Graham famously advised, “Do things that don’t scale,” yet practical implementation of this principle in software development rarely receives the attention it deserves. Over the past eight months spent building my AI podcast platform, I’ve devised a straightforward framework centered around a three-month lifespan for unscalable solutions. During this period, each hack must either demonstrate its value and transition into a robust, sustainable solution or fade away into obscurity.
As engineers, we instinctively gravitate toward designing for scale, crafting intricate architectures like microservices and distributed systems tailored to accommodate millions of users. However, this is an approach that aligns more closely with established corporations than emerging startups. Often, striving for scalability early on can lead to unnecessary complexity and procrastination, as we may be optimizing for problems that don’t yet exist. My three-month rule compels me to embrace simplicity, allowing me to produce straightforward and functional code that can be deployed quickly and effectively, ultimately shedding light on genuine user needs.
Current Infrastructure Hacks That Prove Effective
1. Consolidated Operations on a Single VM
I run everything—from the database and web server to background jobs and cache—all on a single $40/month virtual machine, without redundancy and with manual backups to my local drive. While this may seem reckless, it has provided invaluable insights into my system’s actual resource requirements. In just two months, I’ve discerned that my AI-focused platform only peaks at 4GB of RAM. Rather than engaging in complex container orchestration with Kubernetes, I’ve gained a clear understanding of what truly breaks during crashes—information I would have overlooked otherwise.
2. Hardcoded Configuration for Agility
Configurations like pricing tiers, user limits, and AI model references are hardcoded directly into the application. This may seem backward in a world that advocates for flexible configuration management, but it allows me to swiftly search and track changes across my codebase with ease. In three months, I’ve updated configurations only three times, resulting in minimal deployment time compared to the extensive hours required for a dedicated configuration service.
3. Utilizing SQLite for a Multi-User Environment
Running SQLite to manage a multi-user web application may raise eyebrows, yet my data footprint is a modest 47MB, efficiently supporting 50 concurrent users. Through this setup, I’ve identified my access patterns—
