Embracing the 3-Month Rule: A Technical Approach to Non-Scalability
In the tech world, a common piece of advice from thought leader Paul Graham is to “do things that don’t scale.” However, the discussion often stops there, leaving many developers unsure about how to integrate this concept into their coding practices. After eight months of building my AI podcast platform, I’ve crafted a practical framework that revolves around a pivotal timeline: every unscalable hack is given a lifespan of three months. At the end of this period, we assess whether it has proven its value or needs to be discarded.
Rethinking Scalability
In our training as engineers, we emphasize scalable solutions from the onset. Concepts like design patterns, microservices, and distributed systems are often celebrated for their capability to support vast user bases. Yet, this mindset can hinder startup progress, as obsessively pursuing scalability may simply delay your development. The reality is that creating scalable code too early can lead to wasted resources, as it addresses potential problems for users that may not even exist. My three-month rule encourages me to write simple, straightforward code that prioritizes immediate user needs over hypothetical future demands.
Key Infrastructure Hacks That Support My Framework
- Unified VM Architecture
I’ve consolidated my operations—database, web server, background jobs, and Redis—onto a single $40-a-month virtual machine. This minimalist setup may lack redundancy and rely on manual local backups, but it has allowed me to gain crucial insights into my resource requirements far more effectively than any capacity-planning document ever could. My platform’s peak memory usage turned out to be a mere 4GB. The complex Kubernetes infrastructure I nearly implemented would have simply managed excess capacity for a system that wasn’t being utilized.
- Hardcoded Configurations
My approach involves embedding constants directly in the code rather than utilizing configuration files or environment variables. This means updating values requires a simple redeployment but results in tangible efficiency. I can effortlessly search for any configuration across my codebase and track modifications in my version history. With only three changes in three months, I’ve saved countless hours that would have been spent creating a convoluted configuration system.
- Utilizing SQLite in Production
Yes, I chose SQLite for my multi-user web application, and it’s performing beautifully. By analyzing my access patterns, I discovered that 95% of requests are reads, making SQLite an ideal fit. Had I
One Comment
This post offers a refreshing perspective on balancing immediate product needs with scalable architecture, especially through the lens of the 3-month rule. I appreciate how you emphasize the importance of rapid iteration and avoiding premature optimization—it’s a principle often overlooked in favor of complex, “future-proof” solutions. Your practical examples, like consolidating infrastructure and using hardcoded configs, highlight the value of simplicity during early stages.
Additionally, your approach resonates with the concept of “learning fast and cheap”—by limiting the lifespan of unscalable hacks, you create a natural feedback loop that informs whether to invest further or pivot. It would be interesting to see how this framework might evolve as the platform scales, perhaps transitioning from SQLite to more scalable databases as needs grow. Overall, your methodology encourages a disciplined, outcome-oriented mindset that can help startups avoid getting bogged down in over-engineering during their crucial early phases.