Embracing Unscalable Solutions: My 3-Month Experimentation Framework

In the startup world, the adage from Paul Graham, “Do things that don’t scale,” often resonates with those trying to innovate swiftly. Yet, how does one effectively apply this wisdom in the realm of coding? After dedicating the last eight months to developing my AI podcast platform, I’ve evolved a straightforward yet powerful framework: every unscalable hack is granted a lifespan of three months. Post that period, it must demonstrate its worth or be abandoned.

As engineers, we frequently find ourselves conditioned to prioritize scalability from the outset. We dive into design patterns, microservices, and distributed architectures aimed at accommodating millions of users. However, this perspective is often better suited for larger organizations rather than agile startups. At this stage of development, chasing after scalability can feel like a costly dalliance—an effort to cater to hypothetical users and concerns. My three-month guideline compels me to produce straightforward, even “imperfect,” code that can be swiftly deployed and reveals genuine user needs.

Insights from My Current Low-Tech Solutions:

1. Consolidated Operations on a Single Virtual Machine

Currently, all components of my platform, including the database, web server, background jobs, and caching layer, operate from a single $40/month virtual machine. While this setup lacks redundancy and necessitates manual backups, it has provided invaluable insights into my actual resource usage. Within just two months, I discovered that my “AI-heavy” platform only requires about 4GB of RAM. The complex Kubernetes system I once contemplated would have meant managing a lot of empty containers.

When my server has crashed—and it has happened twice—I gained real-world data on what issues occurred, often revealing unexpected pain points.

2. Simplified Hardcoded Configurations

I’ve opted for hardcoded constants scattered throughout my codebase:

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

This choice means no separate config files or environment variables, which in turn requires a quick redeploy for any changes. However, this setup possesses a hidden advantage: I can swiftly search my entire codebase for specific configuration values, and every price adjustment is meticulously documented in the git history. Over the past three months, I’ve changed these values only three times, which has saved immense development time