The Three-Month Rule: A Practical Framework for Developing Unscalable Solutions
When it comes to startup advice, one principle often stands out: “Do things that don’t scale,” a mantra popularized by Paul Graham. While many emphasize this approach, few delve into its practical application, particularly in the world of coding.
Having spent the last eight months developing my AI podcast platform, I’ve crafted a unique framework that I call the Three-Month Rule. This rule serves as a guideline for evaluating unscalable technical solutions: each hack gets a trial period of three months. After this time, it either proves its worth and is rebuilt for scalability, or it faces the chopping block.
As engineers, we’re often conditioned to prioritize scalable solutions from the very start, focusing on design patterns and distributed systems that can potentially serve millions of users. However, this perspective is more suited to larger corporations. In startup environments, seeking scalability too early can often result in unnecessary complexities and delays. My framework encourages a simpler approach: writing straightforward, if imperfect, code that delivers real value and insights regarding user needs.
Current Infrastructure Hacks: Why They Work
1. Single Virtual Machine for Everything
Currently, my entire infrastructure—database, web server, background jobs, and caching—operates on a single $40/month virtual machine. This design choice comes with zero redundancy and requires manual backups to my local system.
Why is this effective? In just two months, I’ve gained a clearer understanding of my actual resource requirements than I might have through elaborate capacity planning. My AI-driven platform’s maximum RAM usage peaks at just 4GB. The complex Kubernetes architecture I once considered would have led me to manage empty containers. Each time the system crashes (which has happened twice), I glean valuable insights about the true points of failure.
2. Hardcoded Configurations
Instead of relying on configuration files or environment variables, I utilize hardcoded constants throughout my code. These include key values like:
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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Although requiring redeployment to make changes may seem inefficient, the ability to quickly locate any config value through a grep search across the codebase proves invaluable. With this method, I’ve altered configuration parameters only three times in three months, saving significant development time.
