Embracing the 3-Month Rule: A Pragmatic Approach to Scalable Development
In the world of startups, the mantra often heard is “do things that don’t scale,” a phrase popularized by Paul Graham. Yet, the challenge lies in how to effectively apply this concept to the realm of coding. After eight months dedicated to developing my AI podcast platform, I’ve devised a straightforward framework that has transformed my approach to coding: every unscalable solution is given a lifespan of just three months. At the end of that period, it either justifies its continued use with proven value or is discarded.
The Start-Up Mindset: Why Scalability Can Be Misleading
As engineers, we’re conditioned to focus on scalable architectures from the start—this includes utilizing design patterns, microservices, and distributed systems that are engineered to support millions of users. Such thinking is often indicative of larger organizations, whereas in a startup setting, building scalable solutions can frequently lead to unnecessary complications. Often, it feels like we’re procrastinating, developing functionalities and features for users who haven’t even arrived, all while missing out on the insights that come from simpler, more direct coding.
By implementing my three-month rule, I prioritize writing straightforward, sometimes “bad” code that can be deployed quickly. This not only facilitates faster learning but also provides real insights into user needs and platform performance.
My Unconventional Infrastructure Hacks
1. Consolidating to a Single Virtual Machine
Running everything—database, web server, background jobs, and caching—on a single, $40/month virtual machine may seem less than ideal, but this consolidation has proven invaluable. From this setup, I’ve gained insights about my resource needs in two months that no extensive capacity planning document could provide. The reality is that my “AI-heavy” platform only requires a mere 4GB of RAM during peak use. Had I opted for a complex Kubernetes setup, I would have been managing containers that weren’t even necessary.
2. Utilizing Hardcoded Configuration
Instead of extensive configuration files or environment variables, I’ve opted for hardcoded constants in my code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach allows me to quickly search for configuration values across my codebase, while any changes are meticulously tracked through git history. I’ve modified these constants
One Comment
This is a compelling approach that highlights the importance of agility and rapid iteration in early-stage development. The 3-month rule effectively balances the necessity of quick deployment with the discipline of evaluating real-world value—something that resonates deeply with the lean startup philosophy.
Your emphasis on simplifying infrastructure—like consolidating everything onto a single VM—reminds me that often, complexity can be a hindrance rather than an asset at early stages. It’s fascinating how such pragmatic choices, paired with disciplined review cycles, can accelerate learning and reduce unnecessary overhead.
Additionally, your use of hardcoded configurations exemplifies the importance of speed and flexibility during initial experimentation. While this might seem unorthodox compared to standard best practices, it underscores the value of prioritizing feedback and validation over premature optimization.
Looking ahead, I’d be interested in how you plan to transition from these unscalable, quick-and-dirty solutions to more robust, scalable architectures as your platform matures. The key will be maintaining the same iterative mindset—knowing when to scale up and refactor based on validated needs rather than assumptions. Thanks for sharing your practical insights; it’s a refreshing reminder that sometimes, simplicity paired with disciplined evaluation is the best path forward in early development.