Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech
In the realm of startups and innovative technology, the well-known aphorism by Paul Graham—”Do things that don’t scale”—often resonates. However, the challenge lies in translating this wisdom into actionable strategies, particularly within the coding landscape. After eight months of developing my AI podcast platform, I’ve adopted an effective framework: any non-scalable hack is given a lifespan of just three months. At the end of that period, we evaluate whether it has demonstrated its value and deserves a proper build, or whether it’s time for it to fade away.
As developers, we often find ourselves trained to conceive scalable solutions from the very beginning. We dive into complex design patterns and build robust architectures, all aimed at accommodating millions of users. However, this approach can sometimes reflect a mindset that is more suited for large enterprises rather than agile startups. In the context of a burgeoning business, crafting scalable code can morph into an exercise in procrastination—devoting resources to problems that may not yet exist. My three-month rule has compelled me to produce straightforward, albeit less-than-ideal, code that can be deployed quickly and reveals the real needs of users.
Current Infrastructure Hacks: Smart Decisions in Simplicity
1. Everything on One Virtual Machine
All components—database, web server, background tasks, and Redis—operate on a single virtual machine costing just $40 per month. While this setup lacks redundancy and relies on manual backups to my local machine, it has proven to be incredibly revealing. Within just two months, I’ve gained deeper insights into my actual resource requirements. It turns out my resource-intensive platform only requires 4GB of RAM at peak. The intricate Kubernetes setup I nearly implemented would have merely managed idle containers. The occasional crashes serve as valuable teaching moments, illuminating breaking points that I never anticipated.
2. Hardcoded Configurations
With hardcoded constants like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve intentionally steered clear of config files or environment variables. Deploying changes is as simple as a redeployment, enabling me to swiftly access any configuration value across the codebase. Over the past three months, I’ve altered these values three times—requiring only a
