Embracing the Unscalable: A 3-Month Framework for Effective Learning in Development
In the entrepreneurial tech landscape, advice from industry veterans is invaluable, and Paul Graham’s mantra to “do things that don’t scale” is no exception. However, while many nod along, the practical application of this wisdom in the realm of coding often goes unexplored.
Over the past eight months, as I designed my AI podcast platform, I’ve established a straightforward yet effective approach: each unscalable solution is given a trial period of three months. After this window, the solution must either demonstrate its worth and evolve into a scalable design or face termination.
The Challenge of Scalability in Startups
It’s common for engineers to zero in on scalable solutions right from the start. We become engrossed in building intricate architectures like microservices and distributed systems meant to accommodate millions of users. Yet, this mindset is more aligned with the ethos of established corporations than startups, where focusing on scalability can often be a form of costly procrastination.
My three-month rule has prompted me to embrace simplicity, crafting straightforward and even “imperfect” code that can be deployed quickly. This approach has taught me crucial insights about the actual needs of my users, rather than the theoretical problems I might encounter down the road.
Exploring My Infrastructure Hacks: Insights and Wisdom
1. One VM to Rule Them All
Everything—database, web server, background tasks, and caching—is housed on a single $40/month virtual machine. While this setup is lacking redundancy and relies on manual backups, the insights gained have far outweighed any concerns about stability. Within two months, I’ve gained a comprehensive understanding of my resource needs, determining that my AI platform peaks at just 4GB of RAM. The complex Kubernetes configuration I had envisioned would have involved managing unnecessary resources.
In the event of crashes—yes, there have been a couple—I receive invaluable data that highlights the actual points of failure, which have consistently surprised me.
2. Configuration Made Simple
Instead of relying on configuration files or environment variables, I’ve opted for hardcoded constants:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach, although simple, has its advantages. It allows me to locate and track configuration changes quickly through version control, enhancing efficiency. The effort required to create
