The 3-Month Approach: A Pragmatic Framework for Navigating Non-Scalable Solutions
In the entrepreneurial realm, Paul Graham’s advice to “do things that don’t scale” frequently echoes, yet the execution of this principle in coding often goes unexamined. After dedicating eight months to developing my AI podcast platform, I have cultivated a straightforward framework: each unscalable workaround is given a lifespan of just three months. At the end of this period, the solution must either demonstrate its worth and be formalized or be discarded.
As engineers, we are often conditioned to prioritize scalable solutions right from the onset. We are attracted to sophisticated design patterns, microservices, and distributed systems—architectures fit for handling millions of users. However, this mindset aligns more with established corporations and their needs rather than startups, where scalable coding can often become a costly delay. In fact, my three-month framework encourages me to embrace simpler, straightforward, and even “imperfect” code that delivers real results and helps me better understand the needs of my users.
Current Infrastructure Strategies That Prove Effective
1. Consolidated on a Single VM
My entire setup—database, web server, background jobs, and Redis—operates on a single $40-per-month virtual machine. This approach may lack redundancy and involves manual local backups, but its simplicity has provided invaluable insights. I’ve gained a deeper understanding of my actual resource needs in just two months compared to what extensive capacity planning documentation might have offered. My so-called “AI-heavy” platform has shown to peak at merely 4GB of RAM, and the elaborate Kubernetes configuration I nearly implemented would have led to managing empty containers.
When the system crashes (yes, that has happened a couple of times), I receive concrete data on the actual points of failure—revealing surprises each time.
2. Hardcoded Configuration
Instead of using configuration files or environmental variables, I rely on hard-coded constants throughout my codebase:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may seem crude, it offers a hidden advantage: I can quickly search my entire codebase for any configuration value in seconds. Each price adjustment is meticulously recorded in version history, and every update undergoes a code review process—albeit one I conduct myself.
Creating a dedicated configuration service would
