Embracing the 3-Month Rule: A Practical Approach to Rapid Development in Startups

In the startup ecosystem, the conventional wisdom often echoes Paul Graham’s well-known mantra: “Do things that don’t scale.” However, the implementation of this advice, especially within the realm of coding, is seldom discussed. After spending eight months building my AI podcast platform, I’ve crafted a straightforward framework that I call the “3-Month Rule.” This principle allows every unscalable hack a lifespan of three months, after which we assess its worthiness. If it proves valuable, it will receive a proper architectural overhaul; if not, it is phased out.

Rethinking Scalability in startup Development

As engineers, we tend to prioritize scalable solutions from the get-go. We immerse ourselves in design patterns, microservices, and distributed systems—creating sophisticated architectures capable of supporting millions of users. However, this approach often caters to the needs of large companies, leaving startups in a precarious position. In reality, crafting scalable code at this stage can equate to costly procrastination—optimizing for hypothetical users while ignoring the actual needs of those we currently serve.

My 3-Month Rule compels me to generate simple, direct, and often suboptimal code that can be quickly deployed, thus revealing real user needs in a timely manner.

Insights from My Current Development Choices

1. Consolidation on a Single VM

I opted to run every aspect of my platform—database, web server, background jobs, and caching—on a single virtual machine costing only $40 a month. While this approach lacks redundancy and relies on manual backups, it has yielded invaluable insights into my resource requirements. Over the last two months, I learned that my “AI-heavy” platform peaks at 4GB RAM, averting the need for an elaborate Kubernetes setup that would have managed mostly idle resources. The two crashes I’ve experienced informed me where the real weaknesses lie—providing data I would have otherwise never uncovered.

2. Direct Configuration in Code

Instead of employing external configuration files or environment variables, I’ve hardcoded key values directly into the code, such as pricing tiers and user limits. While this might seem unorthodox, it allows for swift searches across my codebase, with every change meticulously documented in version control. Rather than investing significant engineering time in building a configuration service, I’ve streamlined the process of making updates—saving substantial resources in the long run.

**3. Utilizing