Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the world of tech startups, there’s a well-known mantra from Paul Graham: “Do things that don’t scale.” However, the challenge lies in applying this philosophy to coding practices. Drawing from my experience of building an AI podcast platform over the past eight months, I have established a straightforward framework that revolves around the concept of giving each unscalable decision a lifespan of just three months. After this period, these temporary solutions either demonstrate their value and evolve into robust implementations, or they will be phased out entirely.
As developers, we’re often conditioned to focus on scalable solutions from the start—think design patterns, microservices, and distributed systems built to handle millions of users. While these concepts are certainly admirable, they often reflect a mindset more suited to larger companies than to nimble startups. In reality, crafting scalable architectures can sometimes hinder rather than help; it can become a form of procrastination, diverting our attention to algorithms for problems that may never manifest.
The three-month rule pushes me to write straightforward, albeit imperfect code that is deployable and provides genuine insights into user needs. Here are some of the unorthodox infrastructure strategies I’ve implemented, and the rationale behind them:
1. Consolidated Operations on a Single VM
All essential operations, from the database to background jobs and Redis, run on a single virtual machine costing $40 a month, with zero redundancy and manual backups. While this might seem reckless, it has allowed me to gauge my resource consumption far better than any theoretical planning document. Surprisingly, my AI-driven platform operates efficiently with just 4GB of RAM, revealing that a complex Kubernetes setup would have needlessly juggled empty containers. Each crash has given me invaluable insights into what truly fails, often in ways I hadn’t predicted.
2. Hardcoded Configuration
I utilize hardcoded constants throughout my codebase, such as pricing tiers and user limits. This means no environment variables or external configuration files—changes necessitate a simple redeployment. The advantage? I can quickly search my entire codebase for configuration values, ensuring that every modification is well-documented and easily trackable via Git history. This approach has saved me countless hours—estimated at 40 hours of work versus 15 minutes of redeployment.
3. SQLite as a Production Database
In a surprising move, I am leveraging SQLite for my multi-user web application.
