Embracing Unscalable Solutions: My 3-Month Experimentation Framework
In the startup world, the adage from Paul Graham, “Do things that don’t scale,” often resonates with those trying to innovate swiftly. Yet, how does one effectively apply this wisdom in the realm of coding? After dedicating the last eight months to developing my AI podcast platform, I’ve evolved a straightforward yet powerful framework: every unscalable hack is granted a lifespan of three months. Post that period, it must demonstrate its worth or be abandoned.
As engineers, we frequently find ourselves conditioned to prioritize scalability from the outset. We dive into design patterns, microservices, and distributed architectures aimed at accommodating millions of users. However, this perspective is often better suited for larger organizations rather than agile startups. At this stage of development, chasing after scalability can feel like a costly dalliance—an effort to cater to hypothetical users and concerns. My three-month guideline compels me to produce straightforward, even “imperfect,” code that can be swiftly deployed and reveals genuine user needs.
Insights from My Current Low-Tech Solutions:
1. Consolidated Operations on a Single Virtual Machine
Currently, all components of my platform, including the database, web server, background jobs, and caching layer, operate from a single $40/month virtual machine. While this setup lacks redundancy and necessitates manual backups, it has provided invaluable insights into my actual resource usage. Within just two months, I discovered that my “AI-heavy” platform only requires about 4GB of RAM. The complex Kubernetes system I once contemplated would have meant managing a lot of empty containers.
When my server has crashed—and it has happened twice—I gained real-world data on what issues occurred, often revealing unexpected pain points.
2. Simplified Hardcoded Configurations
I’ve opted for hardcoded constants scattered throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This choice means no separate config files or environment variables, which in turn requires a quick redeploy for any changes. However, this setup possesses a hidden advantage: I can swiftly search my entire codebase for specific configuration values, and every price adjustment is meticulously documented in the git history. Over the past three months, I’ve changed these values only three times, which has saved immense development time
One Comment
Thank you for sharing your insightful approach to embracing unscalable solutions during your startup journey. I really appreciate how you’ve operationalized the “do things that don’t scale” philosophy through your three-month experimentation framework—it’s a pragmatic method to stay lean and learn quickly.
Your emphasis on immediate, low-tech solutions like consolidating all components onto a single VM and using hardcoded configurations resonates with the concept of rapid iteration. It’s a reminder that in the early stages, the primary goal should be to validate assumptions and gather real user feedback rather than optimize for scale from day one.
Additionally, your practice of setting a clear timeframe (three months) for each hack ensures that technical debt is managed proactively, encouraging timely refactoring or abandonment. This disciplined approach can prevent the common trap of accumulating unmanageable technical debt while still capitalizing on the flexibility that unscalable hacks offer.
For others considering adopting a similar framework, I’d suggest complementing these tactics with robust documentation of decisions and the rationale behind each hack. It could facilitate smoother transitions when moving towards scalable solutions later on, once validated.
Looking forward to hearing how this approach evolves as your platform grows. Thanks again for sharing this thoughtful methodology!