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 While this may sound outdated, it offers a surprising advantage: I can quickly search through the entire codebase for any configuration value using a simple grep command.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
One Comment
This post offers a compelling perspective on balancing speed and learning with engineering rigor, especially in early-stage startups. I appreciate how the 3-month rule provides a pragmatic approach: embracing unscalable solutions temporarily to validate assumptions before investing in infrastructure. It reminds me that the goal isn’t to build perfect systems from day one but to accelerate learning and adapt quickly.
Your example of hosting everything on a single VM resonates—I’ve seen similar setups that, despite seeming “hacky,” offer invaluable insights into real-world resource needs and failure points. Sometimes, simplicity accelerates understanding more than elaborate architectures. The hardcoded configurations, while traditionally frowned upon, can indeed streamline rapid iteration and troubleshooting, especially when changes are frequent and testing environments are evolving.
Ultimately, this approach aligns well with the principle of “build, measure, learn.” It’s a vital reminder that in the startup realm, the ability to deploy quickly and iterate based on actual data often outweighs the pursuit of perfect scalability from the outset. Thanks for sharing this practical framework—it’s an inspiring reminder to prioritize agility and learning over premature optimization.