Embracing Unscalable Solutions: The 3-Month Experimentation Framework
In the world of tech startups, the wisdom of Paul Graham often resonates: “Do things that don’t scale.” While many take this to heart, few discuss how to put it into practice, especially in the realm of software development.
After dedicating eight months to building my AI podcast platform, I established a straightforward methodology: every temporary, unscalable solution receives a lifespan of three months. At the end of this period, it either demonstrates its value and transitions into a robust solution or is discarded completely.
As software engineers, our instincts lead us toward creating scalable solutions from the outset. We often prioritize advanced techniques like microservices and distributed architectures, which are great for handling millions of users—ideal for large enterprises. However, in a startup environment, focusing on scalability can lead to unnecessary complexity and delay. My three-month rule compels me to produce straightforward, albeit imperfect, code that can be deployed quickly and truly informs me about user needs.
My Temporary Solutions: Why They Make Sense
1. Single Virtual Machine Architecture
I currently run my entire setup—including the database, web server, and background processes—on a single $40 per month virtual machine without redundancy. While this approach lacks the safety net of backups and failovers, it has provided me with invaluable insights into my system’s actual resource consumption over the past two months.
It turns out that my AI-intensive platform only requires 4GB of RAM at peak usage. Had I pursued a complex Kubernetes architecture, I would have been left managing empty resources. Each crash (which has occurred twice) has yielded real-world data about system vulnerabilities, often revealing surprises that were not on my radar.
2. Hardcoded Configurations
My configuration settings look like this:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of using configuration files or environment variables, I utilize hardcoded constants across my codebase. Should I need to make changes, redeploying takes only minutes due to the simplicity of the setup. I’ve tracked configuration changes through git history, making it easier to manage variations over time.
Creating a dedicated configuration service could take a week, but I’ve only altered these values three times in three months—a task that requires a mere fraction of the time
