Embracing the 3-Month Rule: A Practical Framework for Implementing Unscalable Solutions
In the entrepreneurial ecosystem, one piece of advice resonates strongly: “Do things that don’t scale.” Yet, when it comes to coding, many find it challenging to operationalize this principle. After eight months of developing my AI podcast platform, I’ve established a straightforward framework for navigating unscalable hacks: every temporary solution is granted a lifespan of just three months. If it proves beneficial, it receives a proper infrastructure; if not, it’s retired.
As engineers, we often feel compelled to construct scalable systems from the outset—think sophisticated design patterns, microservices, and distributed architectures meant to accommodate a vast user base. However, this approach is frequently more suitable for large corporations than for startups. In an early-stage company, building for scale can lead to costly delays, optimizing for users who may never materialize and solving issues that might not even arise. My three-month rule encourages a focus on straightforward, perhaps even “subpar,” code that can be deployed swiftly, offering real insight into user needs.
Current Infrastructure Hacks: Practical Solutions with Learning Outcomes
1. Streamlined Operations on a Single VM
Currently, my database, web server, background jobs, and Redis are all hosted on a single $40/month virtual machine. Yes, there’s no redundancy and I’m handling backups manually. However, this simplicity has proven invaluable. Within just two months, I’ve gained a clearer understanding of my actual resource requirements than any theoretical capacity modeling might have provided. My “AI-intensive” platform only requires 4GB of RAM at peak usage. The complex Kubernetes configuration I almost implemented would have simply managed unused resources.
When the system crashes—an occurrence that has happened twice—I receive genuine insights into what fails, and it’s often not what I initially expected.
2. Configuration Hardcoded for Ease of Use
Consider the following hardcoded settings:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With constants embedded in my code instead of configuration files or environment variables, making any changes necessitates a redeployment. While this may sound outdated, it offers a surprising advantage: I can quickly search through the entire codebase for any configuration value using a simple grep command. Changes tracked in git give me a clear history
