Embracing Imperfection: The 3-Month Rule for Scalability in Tech Development

In the world of tech entrepreneurship, we often hear the advice from Paul Graham: “Do things that don’t scale.” However, the challenge lies in translating this wisdom into actionable tasks, particularly when it comes to programming.

For the past eight months, I’ve been developing an AI podcasting platform, and in this journey, I’ve created a straightforward framework I call the “3-Month Rule.” This principle maintains that every unscalable solution is allowed a trial period of three months; at the end of this timeframe, we assess whether it has demonstrated its value—or if it should be discarded.

As engineers, we are frequently inclined to focus on “scalable” designs right from the start, employing sophisticated architectures such as microservices and distributed systems meant to accommodate countless users. However, this mindset can be a hindrance at a startup, transforming scalable solutions into costly delays. By applying the 3-month rule, I compel myself to embrace simplicity. I prioritize practical, albeit rudimentary, code that can be quickly deployed, allowing me to understand user needs more effectively.

Current Infrastructure Strategies to Learn From

1. Consolidated Services on a Single VM

I’ve opted to run my database, web server, and background jobs all on a single virtual machine at $40 per month. While there’s no redundancy and I perform manual backups, this approach has yielded invaluable insights. In just two months, I’ve grasped my platform’s actual resource requirements far better than any capacity planning documentation could provide. I discovered that my “AI-heavy” application peaks at merely 4GB of RAM, making the elaborate Kubernetes setup I considered unnecessary.

When unexpected crashes occur (and they have), I gain authentic data about the failure points—insights that often surprise me.

2. Simple Hardcoded Configurations

My codebase features hard-coded configurations like this:

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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

I’ve done away with config files or environment variables, using scattered constants instead. Though it requires a redeployment to make any changes, this method allows easy tracking of every price alteration through version history and straightforward access to any configuration value.

Creating an elaborate configuration service might take a week, but I’ve modified these