The 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech Development
In the realm of startups and tech development, a common piece of wisdom from Paul Graham resonates: “Do things that don’t scale.” While many recognize this principle, few delve into how to effectively apply it in coding practices and product development.
After eight months of building my AI podcast platform, I’ve adopted a straightforward framework that I’ve dubbed the 3-Month Rule. This approach allows every unscalable solution a trial period of three months. After this time, we either assess its value and invest in proper development or decide to phase it out completely.
Why Unscalable Approaches Matter
As engineers, we’re often taught to craft solutions designed for scalability: from microservices to distributed systems, we’re trained to architect for the potential of millions of users. However, in the startup environment, this can lead to unnecessary complexity and expensive delays. My 3-month guideline has allowed me to prioritize writing straightforward, even “imperfect” code that can be implemented quickly and provide invaluable insights about user needs.
A Look at My Current Infrastructure Strategies
Here are some of my ongoing infrastructure decisions that, at first glance, might seem unconventional—but actually harbor underlying wisdom:
1. Consolidated Services on a Single VM
Currently, I operate my entire application—database, web server, background tasks, and Redis—on a single $40 monthly virtual machine. There’s little redundancy, and I’ve opted for manual backups to my local machine.
What makes this decision strategic? In just two months, I’ve garnered insights into my actual resource demands that would take an extensive capacity planning document to replicate. For instance, I discovered that despite the platform being “AI-heavy,” it peaks at only 4GB of RAM. The Kubernetes infrastructure I nearly implemented would have required maintenance for empty containers instead of addressing real-time needs.
When downtime occurs (twice so far), I collect tangible data on breakdowns—often revealing surprising insights about system failures.
2. Hardcoded Configuration Values
Configurations such as pricing tiers and user limits are hardcoded directly into my codebase:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This means adjustments require a redeployment. But the advantage? I can quickly search my entire codebase for any
One Comment
This framework offers a compelling perspective on balancing agility with long-term scalability. Embracing unscalable solutions through the 3-Month Rule allows startups and developers to iterate rapidly, gain real-world insights, and avoid premature overengineering. Your example of consolidating services on a single VM highlights the importance of resource validation before scaling infrastructure—often, simple setups reveal critical operational data that inform more informed decisions later on.
Similarly, hardcoded configurations can expedite iteration, but it’s worth considering strategies for gradual abstraction as your product grows to balance speed with flexibility. Perhaps integrating environment variables or lightweight config management tools once core assumptions stabilize could offer a middle ground.
Overall, your approach emphasizes the value of pragmatic experimentation—prioritizing learning and validation over perfection in the early stages. It’s a great reminder that in startup development, sometimes the best way to scale is to first figure out what truly makes sense at your current stage.