Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions
In the startup world, the well-known advice from Paul Graham to “do things that don’t scale” often raises eyebrows, especially in technical circles. However, the challenge lies not just in understanding this mantra but in implementing it effectively within the realm of software development.
Over the past eight months, as I’ve been crafting my AI podcast platform, I’ve adopted a straightforward yet powerful framework: each unscalable hack is granted a lifespan of just three months. At the end of that period, it must demonstrate its value through tangible results or face extinction.
As engineers, we tend to prioritize scalable solutions from the outset, diving straight into sophisticated architectures involving microservices, distributed systems, and design patterns that cater to millions of users. This mindset is more suitable for larger organizations, where efficient scaling is vital. In a startup environment, however, chasing after scalability can translate to costly delays—wasting time optimizing for users who haven’t yet arrived and addressing challenges that may never materialize.
My three-month framework compels me to prioritize simplicity and efficiency in my coding practices, allowing me to focus on delivering functional code that meets real user needs.
Current Hacks and Lessons Learned
1. Unified Operations on One Virtual Machine
Currently, my entire application—from the database and web server to background jobs and caching—is running on a single virtual machine at an affordable $40 per month, without redundancy and with manual backups.
This strategy has proven to be genius rather than shortsighted. In just two months, I gained insights about my actual resource utilization that I could never have gleaned from theoretical capacity planning documents—discovering that my AI-centric platform peaks at 4GB of RAM. My initial idea of a complex Kubernetes setup would have resulted in managing unnecessary resources.
When the server crashes (and yes, it has twice), I gather invaluable data on what specifically fails—often surprising me in the process.
2. Simplified Configuration Management
My application uses hardcoded configuration values, such as pricing tiers and user limits, embedded directly within the code.
This might sound archaic, but the key advantage is speed and simplicity. I can quickly search my entire codebase for any configuration value within seconds. Each change in pricing is easily tracked in Git history, and all modifications are subject to review (even if that reviewer is just me).
Creating a dedicated configuration service could take an entire week
