Embracing the 3-Month Rule: A Practical Framework for Non-Scalable Innovations
In the startup world, advice often circles back to Paul Graham’s well-known mantra: “Do things that don’t scale.” Yet, actionable strategies for translating this concept into real code are surprisingly rare. After spending eight months developing my AI podcast platform, I’ve introduced a straightforward framework: every non-scalable tactic gets a three-month trial. If it proves its worth, it gets the upgrade it deserves; if not, it’s time to move on.
As developers, we’re conditioned to craft scalable solutions from the outset. We become infatuated with design patterns, microservices, and distributed systems—aiming for complex architectures that can manage millions of users. However, such approaches often represent a mindset typical of larger corporations, not innovative startups.
In the realm of a startup, obsessing over scalability can lead to costly delays, optimizing features that cater to users who don’t yet exist, and addressing challenges that may never arise. My 3-month framework encourages the creation of simplistic, straightforward (and often imperfect) code that actually gets deployed, allowing me to gain insights into user needs in a real-world setting.
Current Infrastructure Strategies: The Logic Behind My Choices
1. Consolidated Operations on a Single VM
Currently, my database, web server, background jobs, and Redis are all housed on a single $40/month virtual machine with no redundancy and manual backups to my local storage.
This approach might seem reckless, but it’s been enlightening. Within just two months, I’ve gained insights into my actual resource needs that no capacity-planning document could provide. My “AI-centric” platform only requires 4GB of RAM at peak times. The elaborate Kubernetes configuration I almost implemented would have only resulted in empty containers that demanded management.
When system crashes occurred—twice thus far—I gained invaluable insights into the actual issues at play. And here’s the kicker: the failures were never what I anticipated.
2. Hardcoded Configurations Across the Board
Instead of utilizing configuration files or environment variables, I’ve embedded constants directly into the codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While making changes involves redeploying the entire build, there’s a silver lining. I can swiftly search through
