Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions
In the startup world, the adage “Do things that don’t scale,” famously shared by Paul Graham, often resonates with entrepreneurs and developers alike. However, there’s a distinct lack of dialogue on how this philosophy applies to software development and coding practices.
After eight months of developing my AI podcast platform, I’ve established a unique framework: every unscalable hack is allocated a three-month lifespan. At the end of this timeframe, each solution must either demonstrate its value and evolve into a robust implementation, or it gets discarded.
As technical professionals, we are frequently conditioned to pursue scalable solutions from the very outset. We delve into design patterns, microservices, and distributed systems—all of which are designed to support vast user bases. However, in the startup environment, this emphasis on scalability can lead to unnecessary complexity and wasted resources.
More often than not, the pursuit of scalability becomes a form of procrastination, as we attempt to preemptively solve problems that may never arise. My self-imposed three-month rule encourages me to create straightforward, sometimes “messy,” code that allows me to ship quickly and understand my users’ needs.
My Tactical Hacks and Their Strategic Justification
1. Consolidation on a Single Virtual Machine
Currently, my entire infrastructure—database, web server, background jobs, and caching—is housed within one $40-per-month virtual machine. This setup lacks redundancy and utilizes manual backups, but it has illuminated my resource needs far more effectively than any capacity planning document ever could.
For instance, my “AI-focused” application consumes a maximum of 4GB RAM during peak usage. Had I opted for a complex Kubernetes architecture, I would have wasted time managing empty containers. The two system crashes I’ve experienced gave me invaluable insights into the actual issues at play—far removed from my initial expectations.
2. Hardcoded Configuration
My configuration strategy? Hardcoding constants directly into the code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This means I avoid using configuration files or environment variables. While redeployments are required for any changes, I can effortlessly search my codebase for specific variables, tracking every configuration alteration through version control. The time spent on deploying these changes is
One Comment
This is a compelling and pragmatic approach to balancing speed and scalability in startup development. I appreciate how you highlight the value of embracing quick, unpolished solutions for learning and validation—especially in the early stages—while setting clear boundaries with the three-month rule.
Your emphasis on hardware simplicity and direct code modifications reminds me of the importance of avoiding over-engineering before truly understanding user needs and system behavior. It’s often tempting to aim for perfection from the start, but as you demonstrate, iterative, unscalable hacks can offer critical insights that inform more robust solutions down the line.
Additionally, your framework aligns well with lean and MVP principles—building just enough to learn, then refining based on real-world data. How do you balance the potential technical debt that can accrue from quick hacks with the need to maintain a maintainable codebase as your project scales? Would love to hear your thoughts on transitioning from these unscalable hacks to more scalable architectures once certain growth benchmarks are achieved.