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

In the world of startups, we’ve all heard Paul Graham’s popular phrase, “Do things that don’t scale.” However, the conversation often stops there, leaving many developers scratching their heads on how to apply this wisdom effectively, particularly in the realm of coding.

After dedicating eight months to developing my AI podcast platform, I’ve established a straightforward methodology: every unscalable approach gets a trial period of three months. At the end of this timeframe, I assess its value and decide whether to enhance it into a robust solution or discontinue it altogether.

The Challenge of Scalability in Startups

As engineers, we’re typically trained to opt for “scalable” solutions right from the start. This mentality leads us to design complicated architectures involving microservices and distributed systems—the kinds of frameworks geared toward accommodating millions of users. However, this represents the mindset of larger enterprises.

In a startup environment, building scalable solutions can often equate to needless delays, as it forces us to optimize for prospective users who may never arrive and solve issues we might never encounter. By adopting my 3-month rule, I focus on creating straightforward and sometimes suboptimal code that gets products shipped quickly and reveals what users genuinely require.

The Value of Unconventional Infrastructure Hacks

Here are some of my unorthodox yet effective infrastructure choices, paired with insights that prove their merit:

1. Consolidated Operations on a Single VM

Everything from the database to the web server and background jobs operates on a single, cost-effective $40/month Virtual Machine (VM) with no redundancy and manual backups to my local device.

Why is this approach beneficial? It has allowed me to gauge my actual resource needs more accurately than any theoretical capacity document would. For instance, my AI-centric platform has peak usage at just 4GB of RAM, rendering the elaborate Kubernetes setup I nearly implemented unnecessary since I would have been managing idle containers.

When crashes occur—yes, there have been a couple—I gather vital information on what actually fails, and it often surprises me.

2. Hardcoded Configuration Values

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

In my setup, there are no configuration files or environment variables—just constants spread throughout the codebase. Any changes necessitate a