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

In the world of startups, the mantra “Do things that don’t scale,” as coined by Paul Graham, often takes center stage. Yet, many developers find it challenging to translate this philosophy into practical coding solutions. After spending the last eight months on my AI podcast platform, I’ve devised a straightforward strategy: any unscalable approach gets a lifespan of three months. Following this period, it either demonstrates its worth and gets refined, or it’s let go.

Rethinking Scalability in startup Development

As developers, we are frequently encouraged to construct scalable systems right from the start. Concepts like design patterns, microservices, and distributed architectures are showcased in impressive frameworks built to accommodate millions of users. However, this perspective often aligns more with established companies than with innovative startups.

In a startup environment, the push for scalability can lead to unnecessary delays and costs, often focusing on hypothetical future users and problems. By adopting my 3-month rule, I am able to prioritize swift, straightforward coding. This practice not only pushes out workable solutions but also reveals genuine insights into user needs.

My Current Impromptu Solutions: What I’ve Learned

1. Single VM Operations

Everything runs off one virtual machine – from the database and web server to background jobs and caching. All for just $40 a month, with manual backups to my local storage and no redundancy.

Despite it seeming reckless, this approach has offered invaluable insights into my resource needs. After just two months, I discovered that my AI-centric platform peaks at around 4GB of RAM. Had I opted for a complex Kubernetes setup instead, I would have been overwhelmed with managing idle containers. During the brief crashes I’ve experienced, the data collected has been eye-opening, revealing issues I never anticipated.

2. Simplistic Hardcoded Configurations

Consider this code snippet:

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

There are no configuration files or environment variables—just hardcoded values. Any change necessitates a redeployment.

The silver lining? With a few commands, I can quickly search for any configuration within my codebase. Tracking adjustments becomes a matter of checking git history, and every configuration tweak is subject to code review (even if