The 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech Development

In the landscape of startup development, the advice from Paul Graham to “do things that don’t scale” is widely recognized, yet rarely explored from a coding perspective. After journeying through the process of developing my AI podcast platform for the past eight months, I’ve designed a straightforward yet effective framework: each unscalable approach I implement is granted a lifespan of just three months. At the end of this period, it’s either validated as beneficial and refined into a robust solution, or it’s phased out.

As engineers, we often fall into the trap of prioritizing scalable solutions from the outset. We become enamored with concepts like design patterns, microservices, and distributed systems, envisioning architectures capable of accommodating millions of users. However, this mindset is often more suited to established enterprises than to agile startups. At this stage of development, focusing on scalability can become an expensive form of procrastination, as it diverts our attention from immediate user needs toward hypothetical future demands. My three-month framework compels me to write straightforward, sometimes “imperfect,” code that actually delivers usable solutions, thus allowing me to gather valuable insights into what my users genuinely require.

Current Infrastructure Strategies: A Case for Simplicity

1. Consolidated Operations on a Single VM

I operate my entire setup — including the database, web server, and background jobs — on a single $40/month virtual machine. While this lacks redundancy and relies on manual backups, here’s the hidden advantage: I’ve gained a clearer understanding of my resource needs in just two months than any capacity planning document could provide. It turns out the resource demands of my “AI-heavy” platform only hit 4GB of RAM during peak usage. The elaborate Kubernetes architecture I nearly implemented would have resulted in wasted resources.

Furthermore, when the server experiences outages, I gain concrete data on system failures, which have consistently surprised me in their unpredictability.

2. Hardcoded Configuration for Ease of Management

Rather than utilizing configuration files or environment variables, I have hardcoded values scattered throughout my code, like so:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

While this might seem counterproductive, it offers an effortless way to track changes through version control. In the last three months