Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Development

In the startup world, the wisdom of Paul Graham’s mantra, “Do things that don’t scale,” often resonates. Yet, a pressing question emerges: how do we apply this principle effectively to software development? After eight months dedicated to building my AI podcast platform, I’ve cultivated a pragmatic framework anchored in the idea of giving each unscalable solution a lifecycle of three months. After this period, we assess their value to determine whether they evolve into robust solutions or phase out.

Traditionally, engineers are conditioned to consider scalability from day one. We dive into intricate architectures involving design patterns, microservices, and distributed systems aimed at accommodating potentially millions of users. Unfortunately, this method often aligns more closely with the mindset of large enterprises rather than the nimble, fast-paced nature of startups.

In the realm of startups, emphasis on scalability can quickly become a costly form of procrastination. We often find ourselves optimizing for imagined future users and tackling nonexistent issues. With my 3-month rule, I am compelled to prioritize straightforward, albeit imperfect, code that enables quick deployment and real-time feedback on user needs.

My Practical Infrastructure Strategies: Uncovering Value in Simplicity

1. Single VM Architecture

Currently, my entire tech stack—from the database and web server to background jobs and Redis—operates on a single $40/month virtual machine, with no redundancy and manual backups.

Why is this approach strategic? In just a couple of months, I’ve gained invaluable insights into my actual resource requirements, far surpassing any theoretical capacity planning I could have performed. My platform, heavily focused on AI, peaks at 4GB of RAM, revealing that the ambitious Kubernetes infrastructure I nearly implemented would have involved managing idle containers.

Encountering crashes (which has happened a couple of times) provides tangible data regarding performance issues. Oddly enough, the breakdowns occur in areas I hadn’t anticipated.

2. Hardcoded Configurations Across the Board

Imagine configuration values like:

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

In my case, there are no configuration files or environmental variables—just constants peppered throughout the code. Any changes demand redeployment.

The real advantage? I can swiftly search my entire codebase