Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions
In the world of startups, advice from experts like Paul Graham—who famously encourages us to “do things that don’t scale”—is well-known. However, what often goes unaddressed is how to effectively apply this principle in the realm of coding and development.
Over the past eight months of building my AI podcast platform, I’ve pioneered a straightforward yet effective framework: every unscalable solution receives a lifespan of three months. After this period, each approach is evaluated to determine if it has proven its worth—subsequently warranting a structured implementation—or if it simply becomes obsolete.
As engineers, we are often conditioned to immediately pursue scalability. This leads us toward design patterns, microservices, and complex architectures well-suited for serving millions of users. However, this mindset can be counterproductive in a startup context, where aiming for scalable solutions often results in unnecessary delays. By applying the 3-month rule, I consciously prioritize the creation of simple, direct code that is not necessarily polished, but effective and informative, allowing real user needs to emerge.
My Practical Infrastructure Hacks That Work
1. Consolidated Operations on a Single Virtual Machine
All key components—database, web server, background processes, and caching—are housed on a single, $40/month virtual machine. Although this lacks redundancy and relies on manual backups, the insight I’ve gained in just a couple of months about my actual resource requirements far surpasses what any capacity planning document could offer. I discovered that my platform’s peak demand is a modest 4GB of RAM—a fact that saves me from engaging in complex setups like Kubernetes for a purpose that doesn’t yet exist.
Moreover, each crash (which has occurred twice thus far) provides valuable data on the real causes of failure—often surprising and entirely different from my predictions.
2. Simplified Hardcoded Configurations
In my code, configurations are hardcoded—prices, user limits, and model specifications are directly inscribed within files instead of being stored in separate config files or environment variables. This corrects any assumptions about needing intricate configuration management systems. Adjusting any value requires a redeployment, but with this method, I can track every change easily, ensuring clarity and simplicity.
I’ve made price modifications three times in three months, which translates to a mere 15 minutes of redeployment work, as opposed to writing code for
