Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech

In the tech industry, there’s a saying from Paul Graham that resonates with many: “Do things that don’t scale.” While this advice is often shared, the intricacies of applying it in the realm of coding are less frequently discussed. Having spent the last eight months developing my AI podcast platform, I’ve formulated a straightforward framework that I call the 3-Month Rule. This principle dictates that every unscalable solution I implement is given a lifespan of three months. After this period, it must demonstrate its value and either transition into a robust solution or be eliminated.

From an engineering perspective, there’s a common inclination to focus on scalable solutions from the outset. We often get entangled in design patterns, microservices, and distributed systems—all hallmarks of a well-crafted architecture meant to support millions of users. While this approach is crucial for larger enterprises, in a startup environment, optimizing for nonexistent users can become a financial liability. My 3-Month Rule encourages me to create straightforward, even crude, code that I can deploy quickly. This way, I can gain firsthand insights into what my users truly need.

Smart Infrastructure Hacks that Foster Learning

1. Everything Consolidated on One Virtual Machine

Currently, my entire ecosystem—database, web server, background jobs, and caching—operates on a single $40/month virtual machine. While not redundant and requiring manual backups, this approach has been revelatory. In just two months, I’ve gained a clearer picture of my actual resource requirements, discovering that my so-called “AI-heavy” platform peaks at 4GB of RAM. The intricate Kubernetes architecture that I nearly implemented would have led me to manage idle containers instead of learning from real usage.

When the server crashes—an occurrence I’ve experienced twice—I gather valuable data about unexpected failures rather than anticipated ones.

2. Hardcoded Configurations

Instead of using external config files or environment variables, I’ve opted for hardcoded constants throughout my codebase:

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

While this seems counterintuitive, it offers the advantage of rapid searching across my entire codebase for configuration values. With git tracking these changes, I can review modifications even if I’m