The 3-Month Experiment: A Pragmatic Approach to Unscalable Solutions
In the world of entrepreneurship and software development, many of us are familiar with Paul Graham’s poignant advice: “Do things that don’t scale.” However, implementing this philosophy—especially within the context of coding—remains a lesser-discussed topic.
After eight months of tirelessly working on my AI podcast platform, I’ve crafted a straightforward methodology: every unscalable solution or quick hack is given a lifespan of three months. At the end of that period, it must either prove its worth and be transformed into a sustainable solution, or it will be discarded.
As developers, we often prime ourselves to create scalable designs from the outset—think meticulous design patterns, intricate microservices, and robust distributed systems—all tailored to serve millions of future users. However, this approach often represents a mindset suited to larger organizations, not startups.
In a fledgling business, overly complex coding schemes can devolve into costly delays. We end up optimizing for theoretical users rather than addressing the real needs of those who are currently engaged with our product. My three-month rule compels me to produce fast, straightforward, and often imperfect code that can be released quickly, allowing me to gain tangible insights into what users truly require.
Current Infrastructure Hacks: Learning Through Simplicity
1. Consolidation on a Single Virtual Machine
My entire setup, including the database, web server, background jobs, and Redis, operates on a single $40/month virtual machine with no redundancies and manual backups. This may sound risky, but in two months, I’ve gleaned invaluable insights into my resource needs that any planning document could not provide. For instance, I discovered that my “AI-heavy” platform’s peak resource requirement is only 4GB of RAM.
When this system fails (twice thus far), I gather genuine data about the causes—revealing unexpected breakdowns, which is far more enlightening than theoretical speculation.
2. Hardcoded Values Across the Codebase
My code includes constants like:
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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no external configuration files or environment variables; just clear constants. Updating these values necessitates a redeployment, but this design offers a distinct advantage: I can quickly search my entire codebase for any value.
