Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the realm of startups, the advice from tech luminaries like Paul Graham to “do things that don’t scale” is often cited but rarely put into actionable practice, especially in coding. After eight months of developing my AI podcast platform, I’ve formulated a straightforward framework that every unscalable approach I consider is granted three months to prove its worth. If it fails to deliver tangible results in that timeframe, it gets the axe.
Let’s face it: as engineers, we are often trained to think in terms of growth and scalability from the outset. Concepts like microservices, extensive architectural designs, and sophisticated systems to cater to millions of users dominate our thinking. However, at the startup level, pursuing scalability can often come across as an exercise in avoidance—focusing on hypothetical future users while neglecting the needs of current ones. My 3-month rule allows me to create pragmatic and somewhat “imperfect” solutions that can actually ship and provide insights into real user needs.
Current Infrastructure Hacks: Why They’re Strategically Sound
1. Consolidating Resources on a Single VM
Currently, my entire system runs on a single $40/month virtual machine, hosting the database, web server, background jobs, and even Redis, with no redundancy and manual local backups. This decision may appear reckless, but it has enabled me to grasp my actual resource requirements remarkably quickly. In just two months, I learned that my AI-centric platform peaks at 4GB of RAM—an elaborate Kubernetes setup would have led to endless management of largely unused resources.
In moments of failure (twice, to be precise), I gleaned invaluable information about the specific issues at hand, none of which were what I initially anticipated.
2. Utilizing Hardcoded Configuration
My approach to configuration is refreshingly straightforward:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of relying on complex configuration files or environment variables, I distribute constants throughout my codebase. With this strategy, I can swiftly search for any config value and track all changes via Git history. While creating a dedicated configuration service might take a week, in three months, I’ve changed the values three times—that’s a mere 15 minutes of deployment rather than a
One Comment
This is a compelling perspective on balancing pragmatism with experimentation, especially for early-stage startups. I appreciate how the 3-month rule provides a structured yet flexible framework to validate non-scalable solutions without getting bogged down in over-engineering. Your emphasis on rapid learning through simple, resource-conscious setups reminds me of the “build fast, iterate faster” philosophy—focusing on real-world feedback rather than perfect architecture from the start.
It’s interesting to see how strategic shortcuts—like consolidating resources on a single VM or using hardcoded configurations—can accelerate insights and decision-making. These approaches mitigate early development friction, helping teams prioritize learning and market fit. The key, of course, is knowing when and how to pivot or scale once validated, ensuring these pragmatic solutions don’t become technical debt down the road. Thanks for sharing these practical, experience-driven strategies—valuable lessons for founders and engineers alike who aim to balance immediate delivery with future growth.