Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the startup world, it’s a well-known mantra: “Do things that don’t scale.” While this advice, often attributed to Paul Graham, resonates with many, the challenge lies in translating it into practical actions, particularly in coding. Over the past eight months, as I’ve been developing my AI podcast platform, I’ve adopted a straightforward strategy that I call the 3-Month Rule. This policy dictates that any non-scalable solution I implement has a lifespan of three months; during this period, it must either prove its effectiveness or be discarded.
As engineers, we often default to designing scalable architectures from the outset, investing time in sophisticated solutions like microservices and distributed systems. However, in a startup environment, such approaches can lead to expensive delays—often optimizing for potential users who don’t yet exist and addressing issues we may never encounter. My 3-Month Rule compels me to prioritize speed and simplicity over perfection, enabling me to deliver functional solutions quickly and gather invaluable insights about user needs.
My Current Infrastructure Strategies: Why They Make Sense
1. Unified Virtual Machine Environment
My platform operates on a single $40/month virtual machine that handles the database, web server, background jobs, and more—all without redundancy and requiring manual backups.
Why is this a smart choice? In just two months, I’ve gained a clearer understanding of my actual resource requirements than any theoretical capacity planning document could offer. With RAM usage peaking at only 4GB, the complex Kubernetes setup I nearly pursued would have been wasted on managing idle resources. Whenever the system crashes, which has happened twice, I obtain real insights into failure points—often contrary to my assumptions.
2. Directly Hardcoded Configuration Values
I use hardcoded constants across my codebase, such as:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach eliminates configuration files and environment variables. While changing any values does require a redeployment, I can quickly search the entire codebase for configuration values. Each change is documented in the git history and subject to code review. Given that I’ve only modified these values three times in three months, this method has saved countless hours that would have gone to developing a configuration service.
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