Embracing Unscalable Solutions: My 3-Month Framework for Growth in Tech
In the startup world, we often hear the sage advice from Paul Graham: “Do things that don’t scale.” While many discuss the philosophy behind this perspective, there is surprisingly little guidance on how to apply it practically, especially in the realm of coding.
After dedicating eight months to the development of my AI podcast platform, I’ve crafted a practical framework that I abide by: any unscalable solution I implement is given a lifespan of three months. At the end of that period, it must either demonstrate its worth and be developed further or be discarded entirely.
The reality for engineers is that we are trained to create scalable systems from the outset. We often find ourselves gravitating towards complex architectures like microservices and distributed systems, designed to handle vast user volumes. However, in the context of startups, such thinking can often lead to inefficiencies and wasted resources. My three-month rule compels me to focus on writing straightforward, even “imperfect” code that can be deployed quickly, allowing me to learn invaluable lessons about user needs without unnecessary delays.
My Current Infrastructure Strategies: Smart Hacks for a Growing Platform
1. Consolidated Operations on a Single VM
I run my entire stack—including the database, web server, background jobs, and caching—on a single virtual machine, costing me just $40 per month. This configuration eschews redundancy, and I rely on manual backups to my local storage.
Why is this a savvy move? It has enabled me to gain crucial insights into my actual resource requirements in a mere two months, insights that would have taken hours of planning otherwise. I’ve discovered that my AI-driven platform experiences peaks of just 4GB of RAM usage. The intricate Kubernetes architecture I nearly adopted would have led to managing idle containers.
When my system crashes (which has occurred twice thus far), I receive concrete data about the real failure points, which often differ from my initial expectations.
2. Streamlined Configuration Management
Instead of using configuration files or environment variables, I’ve opted for hardcoded constants throughout the codebase. For instance:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While it may sound counterproductive, this approach allows me to quickly search for any configuration value, ensuring that every price
One Comment
This is a fantastic and pragmatic approach that underscores the value of rapid experimentation and learning in startup environments. Your three-month rule effectively encourages iterative development—allowing you to test unscalable solutions, gain real user insights, and make data-driven decisions about scaling or discarding features. I particularly appreciate your emphasis on simplicity, as it reduces unnecessary complexity and associated overheads early on.
Your infrastructure choices, like consolidating everything on a single VM, highlight the importance of understanding actual resource needs without preemptively over-engineering. It’s a great reminder that “over-architecting” can hinder agility and slow down learning cycles. Additionally, using hardcoded constants for configurations, though unconventional, can accelerate development speed—provided you plan to refactor once features mature.
Overall, your framework exemplifies a disciplined balance between speed, resourcefulness, and strategic evaluation. It’s an inspiring model for founders and engineers aiming to build sustainable, learn-driven products without getting bogged down in unnecessary scalability considerations too early. Thanks for sharing such actionable insights!