Embracing the 3-Month Rule: A Pragmatic Approach to Building Unscalable Solutions
In the ever-evolving landscape of startups, one piece of wisdom often stands out: “Do things that don’t scale,” a notion popularized by the esteemed Paul Graham. While the mantra is well-known, the challenge lies in effectively integrating it into our coding practices—especially in the dynamic environment of software development.
After an exhilarating eight months of crafting my AI podcast platform, I’ve established a guiding principle: every unscalable hack I implement must survive a trial period of three months. If it proves its worth, it gets refined into a robust solution; if not, it’s cast aside. This framework not only fuels innovation but also helps me stay grounded in what users genuinely need.
The start-up Dilemma: Scalable vs. Unscalable Solutions
As engineers, we’re often conditioned to seek out scalable solutions from the outset—think complex design patterns, microservices, and distributed systems capable of supporting millions of users. However, this mindset frequently aligns with the strategies of larger corporations, while startups require a different approach.
Focusing too heavily on scalability can sometimes lead to what I term “expensive procrastination.” In many cases, we’re optimizing for future users who may never materialize and addressing problems that might never arise. My three-month rule compels me to prioritize simplicity and clarity over theoretical architecture. By writing intentionally rudimentary code, I can rapidly deploy and learn from real user interaction.
Current Infrastructure Strategies: Smart Hacks for Real Learning
1. Centralized Operations on a Single VM
All core functionalities—including the database, web server, background jobs, and caching—operate on a single $40/month virtual machine. While this approach lacks redundancy and relies on manual backups, it’s been an enlightening experience. The limited setup allows me to grasp my resource usage far better than any detailed capacity report could provide. For instance, I discovered my platform’s peak usage only requires 4GB of RAM.
When system failures inevitably occur (twice so far), I gain valuable insights into the true points of failure—unexpectedly, none of the issues align with my original assumptions.
2. Hardcoded Configuration Values
Instead of utilizing separate configuration files or environment variables, I directly embed constants such as pricing tiers and user limits within my code:
“`plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
