The 3-Month Approach: My Technical Framework for Embracing Non-Scalable Solutions
In the realm of startups and technology entrepreneurship, one piece of advice echoes prominently: “Do things that don’t scale,” as proclaimed by Paul Graham. However, the practical implementation of this principle, especially in coding, remains largely overlooked. After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward method: every unscalable solution I innovate is given a lifespan of just three months. Post this period, it either demonstrates its worth and is refined, or it is discarded.
As software engineers, we are often trained to prioritize scalability right from the onset. We talk about design patterns, microservices, and distributed systems—all essential for accommodating millions of users. However, such “big company thinking” doesn’t necessarily align with the agile mindset required in a startup environment. In fact, focusing on scalable solutions too early can lead to costly delays, as we optimize for potential users who may never materialize, while neglecting immediate needs. The three-month rule compels me to create simplified, sometimes “subpar,” code that gets deployed quickly, providing invaluable insights into user requirements.
My Current Infrastructure Hacks: Smart Decisions
1. Consolidating Resources on a Single VM
Within a single $40/month virtual machine (VM), all components—database, web server, background jobs, and Redis—operate together. This setup is devoid of redundancy, and backups are executed manually on my local machine.
Why is this strategy effective? In just two months, I’ve gained clarity about my resource requirements far superior to any planning document could provide. It turns out that my AI-driven platform only peaks at 4GB of RAM. The extensive Kubernetes architecture I nearly implemented would have resulted in overseeing empty containers.
When the system crashes (which has happened twice), I receive genuine insights into what truly fails. Spoiler alert: it’s never what I anticipated.
2. Utilizing Hardcoded Configurations
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
My approach involves no configuration files or environment variables; I operate with hardcoded constants in various files. Any necessary change requires a complete redeployment.
The advantage? I can swiftly search my entire codebase for any configuration value. Each pricing adjustment is documented in git history, and every configuration
