Embracing the 3-Month Rule: A Strategic Approach to Non-Scalable Solutions in Software Development
In the realm of startups and software development, the mantra “Do things that don’t scale,” popularized by Paul Graham, resonates with many. Yet, the actual execution of this principle, especially in coding, is often overlooked. After eight months of developing my AI podcast platform, I’ve devised a straightforward methodology: every non-scalable hack is allotted a lifespan of three months. At the end of this period, the hack either proves its worth and evolves into a robust solution, or it is retired.
As software engineers, we are often conditioned to engineer scalable solutions right from the outset. We delve into design patterns, microservices, and distributed systems designed for the potential demands of millions of users. However, such big-company thinking can be counterproductive at a startup, where aiming for scale too early can lead to expensive delays. My three-month rule compels me to create straightforward, efficient, albeit “imperfect,” code that can be deployed quickly, ultimately illuminating the true needs of my users.
My Current Infrastructure Hacks: Insights Gained
1. Consolidated Infrastructure on a Single VM
Everything from the database to background jobs is hosted on a single, cost-effective $40/month virtual machine. This setup is devoid of redundancy and relies on manual backups to my local machine.
Why is this approach effective? Within just two months, I’ve gained invaluable insights into my actual resource requirements that no amount of capacity planning documentation could provide. My AI-centric platform peaks at 4GB of RAM, revealing that the elaborate Kubernetes architecture I contemplated would have been overkill. Each crash (I’ve experienced a couple) gives me immediate, actionable data on failures, often revealing unexpected issues.
2. Hardcoded Configuration for Simplicity
Configuration values are written directly into my codebase as constants rather than being housed in config files or environment variables:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This lack of abstraction does require a redeployment for any changes, but it streamlines my tracking. With the ability to quickly search my entire codebase, I can efficiently manage price alterations and configuration updates, demonstrating a huge time-saving compared to building a separate configuration service that would take a week to implement.
