Embracing Unscalable Approaches: My 3-Month Experimentation Framework for Development
In the tech world, we often hear the sage advice from Paul Graham: “Do things that don’t scale.” While this wisdom is widely acknowledged, the practical implementation of such strategies—especially in coding—remains under-discussed.
Over the past eight months, while constructing my AI podcast platform, I crafted a straightforward yet effective framework: each unscalable approach is given a trial period of three months. At the end of this timeframe, I evaluate its worthiness; if it proves its value, I invest in building a more robust solution. Otherwise, it’s time to part ways.
As engineers, our instinct is often to rush towards scalable solutions. We dive headfirst into complex design patterns, microservices, and distributed systems, envisioning a future of thousands—if not millions—of users. While that is essential thinking for large corporations, in the startup environment, pursuing scalable code can frequently morph into costly procrastination. Rather than focusing on hypothetical users with non-existent problems, my three-month rule compels me to embrace straightforward, even “imperfect,” code that delivers quickly and helps uncover the genuine needs of users.
Insights from My Current Development Practices
1. Consolidated Infrastructure on a Single VM
My entire operation—database, web server, background jobs, and Redis—exists on a single $40/month virtual machine. With no redundancy and manual backups, this setup often raises eyebrows.
Yet, this approach has been enlightening. In just two months, I have gained invaluable insights into my actual resource requirements that capacity planning documents would never yield. My “AI-heavy” platform only peaks at 4GB RAM, revealing that the intricate Kubernetes architecture I nearly employed would merely have managed a multitude of empty containers. Each crash (which has happened twice) provides real data about failure points, which is often not what I anticipated.
2. Hardcoded Configuration
Here’s how I set my constants:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With no configuration files or environment variable complexities, I have an efficient system in place. Any change requires redeployment, but this also offers a distinct advantage: I can quickly search my entire codebase for any configuration value, and every price adjustment is
One Comment
Thank you for sharing this practical and insightful approach to early-stage development! The 3-month trial period for unscalable solutions is a valuable heuristic, especially for startups where speed and learning are paramount. I appreciate how you emphasize that embracing “imperfect” code can lead to a deeper understanding of user needs and resource requirements, which often get lost in over-engineering.
Your example of consolidating infrastructure on a single VM highlights an important principle: real-world experimentation often reveals more than theoretical planning. It’s a reminder that iterative testing—whether for infrastructure or code configuration—can prevent us from wasting time on premature optimization. Hardcoded configurations, while sometimes frowned upon in larger scalable systems, are perfectly suited for fast experimentation, as they provide immediate feedback and simplicity.
In future iterations, consider documenting not only what works but also what doesn’t—this can guide the transition from unscalable to scalable solutions when the time is right. Thanks again for this thoughtful perspective that truly champions the “learning by doing” ethos—valuable advice for developers navigating early-stage projects.