Embracing the 3-Month Rule: A Tactical Approach to Scalable Development
In the realm of software development, the mantra “Do things that don’t scale,” famously articulated by Paul Graham, often sparks debate on implementation. While this advice is widely acknowledged, the methodology for putting it into practice—especially in coding—remains elusive.
After dedicating eight months to the development of my AI podcast platform, I’ve adopted a pragmatic framework: each unscalable approach is assigned a lifespan of three months. At the end of this period, we evaluate whether it merits further investment or is due for retirement.
The reality is that as engineers, we typically aim for scalable solutions from the onset. We often focus on advanced design patterns, microservices, and distributed systems, which are more suited for established enterprises. However, for startups, over-engineering can sometimes equate to unnecessary delays. By channeling my energy into simple, direct solutions, I swiftly learn what my users genuinely require.
Unconventional Infrastructure Hacks: Smart Choices That Foster Learning
1. Consolidated Operations on a Single VM
All my critical operations—database, web server, background jobs, and caching—run seamlessly on a single virtual machine that costs just $40 a month. While this setup lacks redundancy and relies on manual backups, it has allowed me to gauge my true resource requirements.
Within two months, I’ve discovered that my platform, which I initially deemed resource-intensive, only requires 4GB of RAM at peak usage. The complex Kubernetes configuration I nearly implemented would have been a misallocation of time and resources, resulting in empty containers. Each crash, of which I’ve experienced two so far, has provided real insights into failure points that I hadn’t anticipated.
2. Hardcoded Configurations for Ease of Change
I’ve opted for straightforward constants throughout my code instead of configuration files or environment variables:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach may appear crude, but it does enable rapid access to configuration values across the codebase. Tracking changes is straightforward since each update is logged in Git history. Why invest a week constructing a configuration service when I’ve only altered values three times in the past three months? What once could have required 40 hours of rigorous engineering has now been reduced to 15 minutes
