Embracing the 3-Month Experiment: A Pragmatic Approach to Non-Scalable Solutions
In the realm of startup culture, the adage “Do things that don’t scale,” popularized by Paul Graham, is widely acknowledged but seldom addressed in practical terms, particularly within the coding landscape. Having spent the last eight months developing my AI podcast platform, I have established a straightforward framework that allows unscalable hacks a lifespan of just three months. After this period, these temporary solutions must either demonstrate their efficacy and be transformed into robust systems, or they are discarded.
As engineers, we often feel pressured to create scalable solutions from the outset—think design patterns, microservices, and distributed systems—an approach that is well-suited for larger organizations. Yet, this mindset can lead to unnecessary complexities and deferred decision-making in the startup world. My three-month approach compels me to write straightforward, albeit imperfect, code that directly serves user needs and encourages me to learn from real-world application.
My Practical Hacks: Insights from My Current Infrastructure
1. Consolidation on a Single VM
My entire stack, including the database, web server, job queues, and Redis, operates on a single virtual machine costing $40 per month. Sure, it lacks redundancy and relies on manual backups to my local computer. However, this setup has revealed my actual resource demands more effectively than any formal planning documents could. For example, I discovered that my AI-driven platform only needs a peak of 4GB of RAM. The complex Kubernetes configuration I nearly implemented would have involved managing idle containers.
Every time the system crashes—twice so far—I gain valuable insights into its weaknesses, which often differ from my initial expectations.
2. Hardcoded Configuration
Throughout my codebase, I have constants like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve eliminated configuration files and environmental variables, opting instead for hardcoded values. Consequently, any changes necessitate a redeploy. While this may seem inefficient, it offers a hidden advantage: I can quickly search for and identify any configuration across the codebase. In the past three months, I have only changed these constants three times—a mere 15 minutes of redeployment versus 40 hours of engineering time for an adaptable configuration service.
