The 3-Month Experiment: A Practical Approach to Non-Scalable Solutions in Tech
When it comes to startup environments, the advice to “do things that don’t scale” often gets tossed around, particularly in the context of growth and user acquisition. Yet, there’s a gap in the conversation regarding how to apply this philosophy in a technical landscape. After dedicating eight months to developing my AI podcast platform, I’ve established a straightforward framework: every non-scalable solution has a lifespan of three months. At the end of this period, it either proves its worth and undergoes substantial development or gets phased out.
In the world of software engineering, we’re frequently conditioned to aim for scalable solutions right from the outset. This often leads to a fixation on advanced design architectures like microservices or distributed systems—systems designed to accommodate large-scale user bases. However, this mindset might not be suitable for startups, where “scalable” can sometimes mean costly delay tactics. My three-month rule encourages the creation of straightforward, minimalistic code that allows for actual deployment and real user feedback.
Current Infrastructure Hacks: Why Simplicity Pays Off
1. Consolidated Virtual Machine Usage
All components of the platform—database, web server, and background tasks—run on a single $40/month virtual machine. Sure, there’s no redundancy and backups are manual, but this setup has provided invaluable insights. In just two months, I’ve learned about my authentic resource needs far beyond what any theoretical capacity planning document could provide. My AI-driven platform only pegs out at 4GB of RAM, indicating that the complex Kubernetes framework I nearly established would have been managing unnecessary resources.
When issues arise (and they have, twice), I receive immediate, actionable data regarding failures—most of which were entirely unexpected.
2. Hardcoded Configurations
Rather than relying on configuration files or environment variables, I’ve opted for hardcoded constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This might seem impractical, but it simplifies tracking and enables swift searches across the codebase for any configuration value. Since I’ve only altered these parameters three times in the last three months, the effort to redeploy has amounted to a mere 15 minutes as opposed to the potential 40 hours of engineering a full config service would
