The 3-Month Experiment: My Pragmatic Approach to Building Non-Scalable Solutions
In the world of tech, one piece of advice reigns supreme: “Do things that don’t scale,” as famously stated by Paul Graham. However, the implementation of this wisdom in the realm of software development doesn’t get the attention it deserves.
After dedicating eight months to building my AI-driven podcast platform, I’ve crafted a straightforward framework: each non-scalable approach receives a trial period of three months. At the end of this timeframe, it either demonstrates its value and gets a full-fledged build-out, or it is discarded.
As software engineers, we often find ourselves gravitating towards ‘scalable’ solutions right from the outset. We become enamored with intricate design patterns, microservices, and distributed systems that can accommodate millions of users. However, this big-company mindset can be counterproductive in a startup environment.
Focusing on scalability too early can lead to an expensive delay in development, as you may be optimizing for hypothetical users and addressing challenges that may never arise. My 3-month rule encourages me to embrace simpler, more straightforward coding practices that can be rapidly deployed. This approach reveals genuine insights into user needs and usage patterns.
Why My Current Infrastructure Hacks Make Sense
- Consolidated Resources on One VM
Everything—from the database to the web server and background jobs—resides on a single $40/month virtual machine. While there’s no redundancy and backups are manually managed to my local system, this has proven to be an ingenious choice.
The hands-on experience I’ve gained in just two months has illuminated my actual resource needs far more effectively than any meticulous capacity-planning document. It became clear that my “AI-heavy” platform only necessitates 4GB of RAM. The complex Kubernetes infrastructure I briefly considered would have been wasted on managing empty containers.
When the system crashes (which has happened twice so far), I gain invaluable insights into what truly fails—almost always unanticipated.
- Config Hardcoding for Simplicity
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of using configuration files or environment variables, I’ve opted for hardcoded constants spread throughout the codebase. Modifications necessitate a redeployment.
The hidden
One Comment
This post offers a refreshingly pragmatic take on the often-overlooked importance of quick experimentation and simplicity in early-stage development. I appreciate the emphasis on the “3-month rule” as a disciplined yet flexible way to validate ideas without over-investing upfront.
Focusing on rapid prototyping using straightforward infrastructure—like consolidating resources on a single VM and hardcoding configurations—allows you to gather real-world insights quickly. It reminds me that early-stage products benefit more from learning and iteration than from prematurely optimized architecture.
That said, it’s also vital to strike a balance: as your project matures and the product gains traction, gradually investing in scalability and more maintainable practices will be crucial. But your approach exemplifies how embracing non-scalable solutions temporarily can lead to sharper product-market fit understanding and ultimately inform smarter scaling strategies down the line. Thanks for sharing such a actionable framework!