Embracing the Three-Month Rule: A Pragmatic Approach to Scaling in Development
In the world of startups and product development, conventional wisdom often champions the idea of creating scalable solutions. However, seasoned entrepreneur Paul Graham advocates for an alternative mindset: “Do things that don’t scale.” The challenge, though, lies in translating this principle into actionable strategies for coding and engineering.
After eight months of developing my AI podcast platform, I’ve adopted a unique framework: any unscalable hack in my codebase is granted a lifespan of just three months. At the end of this period, the hack must either demonstrate its value, leading to a well-crafted solution, or it gets discarded.
The Playbook for startups: Ditching “Big Company” Mentality
As developers, we are often conditioned to prioritize scalability from day one. We immerse ourselves in design patterns, microservices, and advanced architectures meant for handling vast user bases. However, for a startup, this mindset can lead to premature optimization—essentially, putting off urgent tasks by solving issues related to users who haven’t even arrived yet. My three-month rule obliges me to write straightforward, albeit imperfect, code that can quickly ship and provides vital insights into real user needs.
Current Infrastructure Hacks and Their Hidden Benefits
1. Consolidated Operations on a Single Virtual Machine
All essential components of my platform—database, web server, and background jobs—run on a single $40/month virtual machine without redundancy. While this setup seems risky, it’s been an invaluable learning experience. Within two months, I gained a clear understanding of my actual resource requirements, discovering that my “AI-heavy” platform only requires 4GB of RAM. Had I invested in a complex Kubernetes system, I would have wasted time managing idle containers. When the system crashes (which has happened twice), I receive real feedback about what needs fixing—insights that often surprise me.
2. Hardcoded Configuration Approach
By maintaining configuration directly in the code as constants—like PRICE_TIER_1 = 9.99 and MAX_USERS = 100—I can quickly search my entire codebase for any configuration values. Any changes require a quick redeploy, and I can track all modifications through git history. This direct method has proven efficient; changing values three times in three months cost me just 15 minutes, compared to potentially 40 hours spent engineering a configuration service.
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