Embracing Imperfection: The 3-Month Rule for startup Development
When diving into the world of startups, established wisdom dictates that we should “do things that don’t scale,” as Paul Graham famously advised. However, the implementation of this principle in software development remains an underexplored territory. After dedicating eight months to building an AI podcast platform, I’ve devised a straightforward yet effective framework: any unscalable workaround receives a trial period of three months. At the end of this timeframe, we determine its viability—either it gets refined into a robust solution or it gets the axe.
As engineers, we’re often trained to prioritize scalable solutions right from the onset. We are drawn toward elegant architectures, microservices, and systems designed to handle vast numbers of users. While such grand designs are fitting for major corporations, in the startup realm, they can frequently delay progress unnecessarily. The three-month rule encourages me to focus on simple, direct, and, yes, sometimes messy code that generates real outputs and reveals valuable insights about user needs.
Current Hacks in My Infrastructure: Smart Strategies Below the Surface
1. Utilizing a Single Virtual Machine
Currently, my database, web server, background processes, and Redis are all housed on one cost-effective $40/month VM. This setup lacks redundancy, and I perform manual backups locally.
Why is this a smart strategy? In just two months, I’ve gained more awareness of my actual resource requirements than I would have from extensive planning documents. I discovered that my “AI-heavy” platform peaks at a mere 4GB of RAM. The complex Kubernetes configuration I nearly deployed would have involved many empty containers.
When the system does crash (which has happened a couple of times), I gain invaluable insights into the actual breakdowns. It’s often from unexpected sources.
2. Hardcoded Configurations
Consider this simple code snippet:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
No separate configuration files or environment variables. All settings are constants embedded in the code itself. Changing a line necessitates a redeploy.
The strength of this method lies in its transparency: I can quickly search through my codebase for any setting in seconds. Historical price changes are documented within git history, with each modification reviewed—albeit by myself.
Building a dedicated configuration service would have taken a week.
