Embracing the 3-Month Rule: A Pragmatic Approach to Early-Stage Development
In the world of startups, the mantra “do things that don’t scale,” famously imparted by Paul Graham, is often easier said than done. While the advice is sound, few discuss the practical implementation of this philosophy, particularly from a technical standpoint. Over the past eight months of developing my AI podcast platform, I’ve devised a strategic framework: each unscalable hack is given a three-month trial period. After this duration, it must either demonstrate its value and be refined into a more robust solution or be discarded.
Why the 3-Month Rule Works
As developers, we are often conditioned to design scalable systems right from the outset—think microservices, distributed databases, and intricate architectures capable of supporting thousands of users. However, at a startup, focusing on scalability too early can lead to unnecessary complexities and expenses, as we tend to invest time and resources into problems that might not yet exist.
By enforcing my three-month rule, I am compelled to produce straightforward, albeit sometimes “flawed,” code that gets deployed quickly. This approach not only allows me to gather insights on actual user needs but also enables me to refine the platform based on real feedback instead of assumptions.
Current Infrastructure Hacks: Smart Decisions for Early Development
1. Consolidated Resources on a Single VM
I operate my entire platform—database, web server, background jobs, and cache—on a $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it’s been enlightening. Within just two months, I’ve gained more understanding of my resource requirements than any elaborate capacity planning could provide. For example, my “AI-heavy” application peaks at only 4GB of RAM, revealing that the sophisticated Kubernetes architecture I almost implemented would have only managed idle containers.
2. Hardcoded Configurations
Instead of intricate configuration management, I’ve employed hardcoded values throughout my code. This may seem primitive, but having constants like PRICE_TIER_1 = 9.99 scattered across files makes tracking changes easy. If I need to update something, I can quickly grep the entire repository, and the changes are documented in version control. Over three months, I’ve made only three updates, resulting in a time investment of about 15 minutes versus nearly 40 hours of setting up a configuration service.
3. Utilizing SQLite for Production
In a move that
