The 3-Month Rule: A Pragmatic Approach to Learning Through Unscalable Solutions
In the world of startups, the advice to “do things that don’t scale,” as emphasized by Paul Graham, is widely recognized but often underexplored when it comes to practical application, particularly in the realm of coding. After dedicating eight months to developing my AI podcast platform, I’ve created a straightforward framework that I call the “3-Month Rule,” where every temporary hack is granted a lifespan of three months. At the end of this period, the hack either validates its worth and is transformed into a robust solution or it’s obsolete.
As engineers, our training typically leans toward crafting scalable solutions from the outset, incorporating design patterns, microservices, and distributed systems that are capable of catering to millions of users. Yet, this type of thinking can be a pitfall for startups. Focusing on scalable infrastructure too early often leads to expensive delays in addressing real user needs. With my 3-Month Rule, I embrace the necessity of writing simplified code that can be deployed quickly, ensuring I learn what my users genuinely require.
Ingenious Infrastructure Hacks That Distill Experience
1. Consolidated Operations on a Single VM
Currently, everything from the database to the web server and background tasks runs on a single $40/month virtual machine. While this setup may seem precarious due to its lack of redundancy and reliance on manual backups, it has provided invaluable insights into my resource requirements. Within just two months, I discovered that my “AI-heavy” platform only peaks at 4GB of RAM—information that would have eluded me had I opted for a complex Kubernetes setup to manage empty containers. Crashes have been revealing, offering unexpected data on system vulnerabilities.
2. Hardcoded Configurations Throughout
Operating with hardcoded values like:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
means no configuration files or environment variables; simply constants embedded throughout the code. While some may view this as a drawback, it allows for rapid changes and easy tracking through version control history. In the last three months, I’ve found that modifying these values has required minimal redeployment time, sidestepping the need for a cumbersome configuration service that could take a week to set up.
3. Utilizing SQLite in a Multi-User Environment
One Comment
Thank you for sharing this practical and insightful approach with the 3-Month Rule. I appreciate how it encourages embracing unscalable, quick-and-dirty solutions as a learning tool rather than a permanent state—it’s a mindset that can save startups a lot of time and resources in the early stages. Your example of consolidating operations on a single VM echoes the value of simplicity and iterative validation before investing in complex infrastructure.
I’d also add that this approach fosters a culture of experimentation and rapid feedback, which is critical for understanding user needs deeply. One thing to consider as your platform grows is gradually introducing more scalable practices—like moving from SQLite to a more robust database—once the data and user base justify it.
Overall, your framework highlights an important balance: prioritizing speed, learning, and flexibility upfront, then transferring what works into more scalable, production-ready solutions as your understanding deepens. It’s a powerful reminder that sometimes, doing less at first can lead to smarter scaling later.