Embracing the Imperfect: A 3-Month Strategy for startup Development
In the world of startups, we often hear the sage advice from Paul Graham to “do things that don’t scale.” Yet, the question remains: how do we effectively apply this concept in our technical work? After eight months of developing an AI podcast platform, I’ve devised a straightforward strategy that I call the 3-Month Rule. This framework allows any unscalable approach to remain in play for just three months—long enough to assess its true value before deciding whether to refine it into a robust solution or to discard it altogether.
Traditionally, engineers are conditioned to construct scalable solutions right from the outset. We frequently immerse ourselves in intricate design patterns, microservices, and distributed systems, all aimed at accommodating potentially millions of users. However, this is often rooted in a corporate mindset rather than the agile approach crucial for a startup environment.
In the life of a startup, drafting scalable code too early can resemble expensive procrastination, as it addresses challenges that may never materialize and anticipates users that don’t exist yet. My 3-Month Rule compels me to create straightforward, albeit imperfect, code that delivers actual functionality while clarifying the needs of my users.
My Current Non-Scalable Hacks and Their Strategic Value:
1. Unified Virtual Machine
I host my entire set-up—including the database, web server, background jobs, and Redis—on a single, budget-friendly virtual machine costing only $40 per month. There’s no redundancy and backups are done manually to my local machine.
This approach may seem reckless, but it’s proving to be a smart strategy. Within just two months, I’ve gained a clearer understanding of my resource requirements than any capacity-planning document could provide. As it turns out, my AI-centric platform only peaks at 4GB of RAM, which would have rendered an elaborate Kubernetes setup unnecessary—resulting in the management of empty resources. Each crash (which has occurred twice so far) has offered real insights into the weak points of my setup; surprisingly, they weren’t what I had originally anticipated.
2. Hardcoded Values
My configuration files consist of constants directly embedded in the code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this might not conform to best practices,
