The 3-Month Rule: A Practical Approach for Non-Scalable Solutions in Coding

In the entrepreneurial realm, there’s a well-known piece of wisdom from Paul Graham: “Do things that don’t scale.” While the concept is widely acknowledged, there’s often little guidance on effectively applying it within the realm of coding.

After dedicating eight months to developing my AI podcast platform, I’ve stumbled upon a straightforward yet effective framework: any unscalable workaround is allocated a three-month trial period. At the end of this duration, it must either demonstrate its worth and undergo proper development or be phased out.

Many of us, especially in engineering roles, are conditioned to prioritize “scalable” solutions from the outset. We’re drawn to the allure of sophisticated design patterns, microservices, and distributed systems that promise to accommodate millions of users. However, this perspective often caters more to established corporations than to start-ups. In the early stages of a startup, investing in scalable code can sometimes amount to costly procrastination—optimizing for hypothetical users and potentially unnecessary challenges. My three-month rule compels me to develop straightforward, if imperfect, code that actually gets launched and reveals genuine user needs.

My Current Infrastructure Innovations and Their Rationale

1. Unified Virtual Machine Hosting

Currently, my entire operation runs on a single $40/month virtual machine, housing my database, web server, background jobs, and caching system—all without redundancy and relying on manual backups.

This strategy, often misunderstood, has proven invaluable. In just two months, I’ve gained clarity on my actual resource needs—information that typical capacity planning documents might not reveal. Interestingly, my “AI-heavy” platform peaks at just 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have resulted in managing idle containers.

When my system crashes (which has happened twice, so far), it provides genuine insights into what fails—not always what I anticipated.

2. Simplified Hardcoded Configuration

Here’s a sneak peek into my configuration method:

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

No external configuration files or environment variables—just constants distributed throughout my source code. Updating these values requires a redeployment.

The beauty of this approach is that I can swiftly search my codebase for any configuration setting. Any price adjustments are documented in my git history and