Embracing the 3-Month Rule: A Technical Approach to Non-Scalable Solutions
In the world of startups, the maxim “Do things that don’t scale,” attributed to Paul Graham, is often cited yet seldom put into practice, particularly within the realm of software development. After dedicating eight months to constructing my AI podcast platform, I’ve devised a pragmatic framework to incorporate this philosophy: every non-scalable workaround I implement is permitted to exist for only three months. At the end of this period, it’s make-or-break: either the solution demonstrates its worth and warrants a proper build-out, or it gets discarded.
As engineers, we are conditioned to pursue scalability from the outset—think sophisticated architectures like microservices and distributed systems designed for vast user bases. This is the mindset of large organizations. However, in a startup setting, focusing on scalable solutions can often lead to costly delays, as you’re investing energy into accommodating users who don’t yet exist and resolving issues that may never arise. My approach compels me to create straightforward, albeit “subpar,” coding practices that not only ship tangible products but also reveal genuine user needs.
My Current Infrastructure Strategies and Their Hidden Advantages:
1. Consolidation on a Single VM
Currently, my database, web server, background jobs, and caching system all operate on a single $40-per-month virtual machine, thriving without redundancy and relying on manual backups.
Why is this smart? The concentrated setup has provided more insights into my resource requirements in just two months than any extensive capacity planning document could offer. Interestingly, my presumptions about building an “AI-heavy” platform revealed that it peaks at a mere 4GB of RAM. The complex Kubernetes infrastructure I nearly implemented would have effectively managed empty containers. Each time it crashes (which has happened twice), I gain valuable insights about the actual flaws—insights I wouldn’t have anticipated.
2. Directly Hardcoded Configuration
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve opted for hardcoded constants throughout my files rather than using configuration files or environment variables. This lets me redeploy with any changes immediately.
The unexpected strength here lies in my ability to quickly search through my codebase for any configuration value. Each adjustment to pricing is neatly logged in Git history, and every modification undergoes a code
One Comment
This framework of testing non-scalable solutions within a strict three-month window is a refreshing approach that emphasizes agility and learning in the startup context. I particularly appreciate the emphasis on “building fast, learning fast” rather than over-engineering from the outset. Your consolidation of multiple components on a single VM, despite its simplicity, aligns well with the “test in production” mindset—allowing real-world insights to steer future infrastructure investments.
The use of hardcoded configurations for rapid iteration is another practical tactic, especially in early stages where speed trumps perfect practices. It’s a reminder that, in startups, the goal is to validate ideas quickly rather than to adhere rigidly to scalable architectures that may be unnecessary at this point.
One area to consider as you progress might be the eventual transition from these temporary, hardcoded setups to more scalable, maintainable solutions—perhaps using feature flags or environment-specific configs—to facilitate smoother scaling when your user base grows. Overall, your strategy underscores an essential truth: early-stage engineering should prioritize learning, validation, and adaptability over perfection. Thanks for sharing these valuable insights!