Embracing the 3-Month Rule: A Technical Approach to Non-Scalable Solutions
In the world of startups, the mantra of “doing things that don’t scale” often surfaces, thanks to the wisdom of industry leaders like Paul Graham. However, a gap exists when it comes to translating this advice into tangible coding practices. Over the past eight months, while developing my AI podcast platform, I’ve adopted a unique framework that I like to call the 3-Month Rule. This approach enables me to rigorously test unscalable ideas, allowing them three months to demonstrate their worth before deciding whether to enhance or eliminate them.
The Challenge of Scalability
As engineers, we are frequently programmed to prioritize scalability from the outset. We’re drawn to elegant architecture and advanced patterns, such as microservices and distributed systems, that are well-suited for large-scale applications. However, in the startup environment, the pursuit of scalable solutions can become an expensive form of procrastination—targeting problems and users that may never materialize. The 3-Month Rule compels me to create straightforward—and sometimes even suboptimal—code that can be deployed quickly, enabling me to uncover what users genuinely need.
My Current Infrastructure Hacks: Why They Work
1. Consolidated Operations on a Single VM
I’ve opted to run my entire infrastructure—database, web server, background jobs, and cache—on a single $40/month virtual machine. This setup lacks redundancy and relies on manual backups to my local system.
Why is this method effective? Within two months, I’ve gained insights into my resource requirements that I wouldn’t have dissected through traditional capacity planning. For instance, I discovered that my AI-heavy application only peaks at 4GB of RAM. The complex Kubernetes setup I initially considered would have been a burden, managing empty containers.
When the system does crash—twice so far—it reveals specific failures that I wouldn’t have anticipated, enabling me to learn and adapt.
2. Simplicity in Configuration Management
By using hardcoded constants for configurations like pricing tiers and maximum users, I eliminate the need for configuration files or environment variables. Each change requires a redeployment.
The benefit of this approach is clarity. I can quickly search my entire codebase for any variable, with complete changelogs available via Git history. In the last three months, I’ve adjusted these values only three times—saving significant engineering hours by forgoing complex configuration services.
One Comment
This is a fantastic and practical exploration of how embracing unscalable, fast-fail strategies can lead to deeper user understanding and more effective solutions. Your 3-Month Rule echoes the core startup principle of rapid iteration—highlighting that sometimes the best way to discover what truly works is to build simple, even rough, prototypes and give them real-world testing time.
Your infrastructure hacks, especially consolidating everything onto a single VM and simplifying configuration management, illustrate how reducing complexity can accelerate learning and cut costs—qualities that are invaluable during early-stage testing. It’s a reminder that flexibility and agility often trump perfection in the beginning.
I wonder, as you gather more insights over these three months, how do you plan to evolve or iterate on these initial solutions? Do you foresee replacing the simple VM setup with more scalable architecture once key learnings are solidified, or will some of these unscalable practices remain a core part of your process?
Overall, this approach champions the mindset that engineering decisions should serve learning first—an insightful strategy for any startup aiming to innovate rapidly without getting bogged down by premature optimization.