Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the world of tech startups, instructional wisdom often circulates around the notion of “doing things that don’t scale,” a concept famously championed by Paul Graham. However, the conversation often neglects the practical implementation of this advice, particularly in software development. Over the past eight months spent constructing my AI podcast platform, I’ve devised a straightforward framework: any hack that lacks scalability is given a lifespan of three months. After this period, it’s either validated and developed into a more robust solution or it gets retired.
It’s essential to acknowledge that as engineers, we are conditioned to prioritize scalable solutions from the inception of our projects. We focus on sophisticated architectures—design patterns, microservices, and distributed systems—that can accommodate millions of users. This mindset is well-suited for larger companies but can be less effective for startups, where chasing scalability can serve as a form of costly procrastination. By adopting my three-month rule, I compel myself to create straightforward, albeit imperfect, code that is deployable. This strategy allows me to develop a clearer understanding of my users’ actual needs.
Key Infrastructure Innovations and Their Rational Underpinnings
1. Single VM Architecture
My entire setup runs on a single $40-per-month virtual machine that accommodates the database, web server, background jobs, and Redis without any redundancy. Yes, I’m taking a risk with manual backups, but this decision has yielded invaluable insights regarding my resource needs in a way that no capacity planning document could. For instance, it turns out that my AI-driven platform does not exceed 4GB of RAM during peak use. The intricate Kubernetes configuration I almost implemented would likely have involved managing empty containers rather than solving real problems. When the system crashes (and it has twice), I gain valuable data on what truly fails, which is often unexpected.
2. Hardcoded Configuration
Instead of using config files or environment variables, I integrate constants throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this approach may seem antiquated, it offers me the ability to quickly search for any configuration value. With every price change stored in my Git history and reviewed during pull requests, I can make updates in a matter of minutes rather than expending weeks
One Comment
This post does a fantastic job highlighting the balance between rapid experimentation and thoughtful scaling—something that’s often overlooked in software development discussions. Your three-month rule provides a pragmatic framework that encourages startups to avoid overcommitting to complex architectures prematurely, which can lead to unnecessary delays and resource drain.
The decision to use a single VM and hardcoded configs underscores the value of simplicity and immediate feedback over premature optimization. Sometimes, quick wins and empirical data—like observing peak RAM usage or crash points—offer far more actionable insights than elaborate plans that may never be tested in real-world conditions.
One insight I’d add is that this approach fosters a mindset of “validated learning”—building just enough to learn quickly, then iterating based on actual user behavior. It’s reminiscent of Lean Startup principles—build, measure, learn—applied through a practical lens.
Have you considered documenting these experiments and their outcomes to build a repository of “lessons learned”? This could serve as a valuable reference for other founders balancing agility with growth, and further reinforce the importance of adaptable, evidence-driven decision-making in early-stage projects.