Embracing the 3-Month Rule: A Strategic Approach to Scaled Development

In the world of tech entrepreneurship, Paul Graham’s mantra of “doing things that don’t scale” resonates widely. Yet, as developers, the challenge often lies in translating this advice into actionable steps within our coding practices.

Over the past eight months, I’ve been working diligently on my AI podcast platform, and throughout this journey, I’ve formulated an informal yet effective framework: each temporary, unscalable trick is given a lifespan of three months. After this period, these mechanisms must either validate their worth and be further developed, or they must be discarded.

Here’s the crux: as engineers, we’re traditionally taught to prioritize “scalable” solutions right from the outset. Beautiful architectural concepts like microservices, distributed systems, and complex design patterns are often idealized—especially by larger corporations. However, in the startup realm, crafting solutions with scalability in mind can lead to costly delays. We might end up designing systems for a user base that hasn’t even materialized yet, addressing issues that may never arise. By adhering to my 3-month guideline, I can write straightforward, sometimes “imperfect” code that is deployable and reveals actual user needs.

Creative Hacks to Enhance My Infrastructure

1. Consolidated Operations on a Single Server

Running my database, web server, background jobs, and caching on a single $40/month virtual machine—without redundancy or automated backups—might seem reckless. However, this approach has granted me invaluable insights into my resource requirements in just two months. My initially underestimated “AI-heavy” platform peaked at 4GB of RAM. The elaborate Kubernetes architecture I almost constructed would have merely resulted in managing idle containers. Each crash (yes, there have been two) provided me with real data about failure points that defied my initial expectations.

2. Simplified Configuration Management

Instead of complicated config files or environment variables, I’ve opted for hardcoded constants throughout my codebase, like:

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

While some may criticize this approach, it offers unparalleled convenience for tracking changes. With a simple command, I can efficiently search for values across the code, and each change is meticulously recorded through Git. Reworking a configuration service would have