The 3-Month Experiment: A Practical Approach to Non-Scalable Development
In the startup world, conventional wisdom often encourages us to embrace the idea of “doing things that don’t scale,” a concept popularized by Paul Graham. However, the conversation rarely delves into the practicalities of applying this philosophy in the realm of coding. Over the past eight months, while developing my AI podcast platform, I’ve established a straightforward framework that I believe can help aspiring developers and entrepreneurs: every unscalable solution I implement has a shelf life of three months. After this period, I evaluate its performance. If it proves its worth, I will refine it; if not, I will discard it.
As engineers, we are conditioned to think big. We often chase highly scalable architectures from the outset—emphasizing design patterns, microservices, and distributed systems capable of accommodating millions of users. However, this mindset can lead to delays and inefficiencies, especially in a startup environment where we may be positioning ourselves for an audience that doesn’t even exist yet. My three-month rule encourages me to craft straightforward, albeit imperfect, code that can be deployed quickly, allowing me to better understand user needs in real-time.
Current Infrastructure Innovations: Respecting the Process
Here are some of my current technical hacks, each of which has proven smarter than they may first appear.
1. Single VM Deployment
I run everything—including the database, web server, background processors, and caching—on a modest $40 per month virtual machine. There is no redundancy, and I perform manual backups to my local storage.
What I’ve gained from this approach is invaluable insight into my resource requirements. Within just two months, I’ve identified that my platform’s peak usage requires only 4GB of RAM. The complex Kubernetes architecture I initially considered would have resulted in unnecessary resource management. When my server has crashed (twice, so far), I received firsthand data about failure points, which often defied my expectations.
2. Hardcoded Configurations
In my code, you’ll encounter hardcoded values for key settings:
PRICE_TIER_1 = 9.99PRICE_TIER_2 = 19.99MAX_USERS = 100AI_MODEL = "gpt-4"
Rather than using external configuration files or environment variables, I’ve opted for constants scattered throughout my codebase. Although this might seem cumbersome, it allows me
One Comment
This is a compelling approach that emphasizes rapid learning and iteration—key elements for early-stage startups. Your 3-month rule aligns well with the concept of building Minimum Lovable Products (MLPs) rather than obsessing over perfect scalability from the start. It’s insightful how you leverage simple infrastructure like a single VM to gain real-world data, avoiding the trap of over-engineering prematurely. The practice of hardcoding configurations, while unconventional, allows for quick adjustments and clearer understanding of system sensitivities, especially during initial experiments.
One aspect worth considering as you progress is gradually introducing external configuration management and automated backups, even within this fast-paced environment. This could help transition smoothly from experimental to production scaling without significant rework, ensuring robustness when user demand increases. Overall, your framework champions a pragmatic, data-driven mindset—something many founders and engineers could benefit from adopting early on.