Embracing Imperfection: The 3-Month Rule for Non-Scalable Solutions

In the startup world, the mantra “do things that don’t scale,” famously articulated by Paul Graham, often gets tossed around. However, applying this principle in the realm of coding is seldom discussed. After eight months of developing my AI podcast platform, I’ve formulated a straightforward approach: any unscalable hack is granted a lifespan of just three months. Following this period, it either proves its worth and is refined, or it’s discarded altogether.

As engineers, we’re conditioned to focus on developing scalable solutions from the outset—think sophisticated design patterns, microservices, and distributed systems capable of handling thousands of users. This mindset, while beneficial in larger corporations, can become detrimental in the startup arena where prioritizing scalability may lead to unnecessary delays.

The reality is, scalable code can often serve as an expensive form of procrastination. By optimizing for hypothetical users, we end up solving problems that may never materialize. My three-month framework compels me to create straightforward, albeit imperfect, code that is deployable, allowing me to gain genuine insights into my users’ needs.

My Pragmatic Infrastructure Hacks

1. Consolidated Architecture: Everything on One VM

I have chosen to host my database, web server, background jobs, and Redis all on a single $40/month virtual machine. This approach lacks redundancy and relies on manual backups to my local device.

However, this decision has proven to be strategically advantageous. In just two months, I’ve gained more clarity about my resource requirements than through any capacity planning documentation. For instance, I discovered my “AI-heavy” platform only requires 4GB of RAM at peak times. The complex Kubernetes architecture I almost implemented would have merely led to managing idle containers.

When the system crashes—an occurrence that has happened a couple of times—I gather valuable data about what actually fails, which often defies my expectations.

2. Hardcoded Configuration Values

In my code, configurations like pricing tiers and max user limits are hardcoded throughout various files, devoid of configuration files or environment variables.

This approach offers a surprising advantage: I can quickly search my entire codebase for specific values. Each pricing modification is logged in Git history, and every configuration adjustment undergoes a code review (even if it’s self-assigned). Developing a dedicated configuration service would have taken a week, while I’ve only changed these values three times over three months—resulting in