Embracing the 3-Month Rule: A Practical Approach to Building Unscalable Solutions
In the tech startup ecosystem, it’s common to encounter the advice from Paul Graham: “Do things that don’t scale.” While this concept is widely acknowledged, practical implementation, particularly in coding, remains less discussed. After dedicating eight months to developing my AI podcast platform, I’ve formulated a straightforward strategy that I call the 3-Month Rule. Essentially, any non-scalable workaround I implement is given a lifespan of three months. At the end of this period, the approach is either validated as useful and warrants further development, or it is discarded.
As engineers, we often find ourselves trained to create scalable solutions from day one. We immerse ourselves in architectural patterns, microservices, and distributed systems—developing complex systems designed to accommodate vast user bases. However, this mindset often aligns more closely with large corporations than with the agile environment of startups.
In reality, pursuing scalable solutions too early can transform into an expensive form of procrastination. We risk optimizing for hypothetical users and preemptively solving challenges that might never arise. By adhering to the 3-Month Rule, I prioritize crafting straightforward, albeit imperfect, code that actually reaches users, allowing me to glean insights into their true needs.
Innovative Infrastructure Hacks: Why They Work
1. Consolidated Operations on One Virtual Machine
My entire setup—the database, web server, background jobs, and Redis—is currently operational on a single $40 per month VM. This approach lacks redundancy and relies on manual backups.
Why is this an intelligent choice? Within two months, I’ve gained a deeper understanding of my resource requirements than any capacity planning document could offer. My AI-centric platform peaks at 4GB of RAM. The intricate Kubernetes system I contemplated would have merely managed idle containers. Each time the system crashes (and it has twice), I gather valuable data about actual failure points, which often surprise me.
2. Embracing Hardcoded Configurations
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I eschew configuration files and environment variables in favor of hardcoded constants spread throughout my files. Any changes necessitate a redeployment.
The hidden advantage? I can search my entire codebase for configuration values in seconds. Tracking price revisions is seamless with Git
