The 3-Month Rule: A Pragmatic Approach to Rapid Development in Startups
In the tech universe, the mantra “Do things that don’t scale,” famously articulated by Paul Graham, is widely acknowledged yet often misunderstood when it comes to practical application in software development. As someone who has spent the last eight months cultivating my AI podcast platform, I’ve devised a straightforward framework: every unscalable approach is given a lifespan of just three months. After this period, we assess whether it has proven its worth and demands further investment, or if it should be phased out entirely.
As engineers, our instinct is to focus on scalable solutions right from the outset—a tendency shaped by our experiences with design patterns, microservices, and distributed systems. This approach is often aligned with the big company mindset. However, in the startup realm, striving for scalability too soon can lead to wasted resources and procrastination. We end up devising intricate systems to accommodate users who are not even onboard yet, tackling hypothetical concerns rather than real user needs. My three-month rule compels me to crank out straightforward, even “imperfect,” code that actually delivers results and reveals essential insights about user behavior.
Unconventional Infrastructure Hacks That Actually Make Sense
1. All-in-One Virtual Machine
My entire infrastructure—database, web server, background jobs, Redis—operates on a single, budget-friendly $40/month virtual machine. There’s zero redundancy and backups are done manually to my local system.
Why is this strategy effective? It has provided me with invaluable insights about my resource requirements in just two months, far beyond what any capacity planning document could offer. Interestingly, my AI-driven platform has yet to exceed 4GB of RAM. The complex Kubernetes setup I almost implemented would have only served to manage dormant containers.
Each time the system crashes (which has happened twice), I gather real-time data about the root causes—often surprising me with their unpredictability.
2. Hardcoded Settings Throughout the Codebase
Consider the following constants in my code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
No intricate configurations—just hardcoded values spread throughout the files. Any change necessitates a redeployment. This method may seem cumbersome, but its efficiency becomes apparent: I can quickly search my entire codebase for any configuration value and track changes
