The 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech Development
In the world of startups, the renowned advice from Paul Graham to “do things that don’t scale” is frequently echoed, yet the practical application of this principle in coding often remains uncharted territory. After spending eight months developing my AI podcast platform, I’ve adopted a straightforward framework that allows me to embrace this idea effectively: any unscalable solution I implement is given a three-month trial period. At the end of this period, it must either demonstrate its value and be fully developed or be discarded.
As software engineers, we often find ourselves focused on crafting scalable solutions right from the outset—think design patterns, microservices architectures, and distributed systems capable of handling vast user bases. However, this approach tends to align more with the mindset of large corporations rather than the agile, reactive pace of a startup.
In reality, what might seem like a scalable solution often becomes an expensive delay as we become preoccupied with optimizing for users who have yet to materialize. The three-month framework encourages the production of straightforward, direct, and yes, even “imperfect” code that allows me to rapidly deploy and understand my users’ actual requirements.
My Current Infrastructure Practices and Their Value
1. Consolidation on a Single Virtual Machine
All components—database, web server, background tasks, and caching—live on a single virtual machine (VM) costing $40 a month. This setup has no redundancy and relies on manual backups to my local system.
Why is this approach beneficial? Within two months, I’ve gleaned more insight into my resource usage than any capacity planning report could offer. My platform’s peak memory needs fall at around 4GB RAM. The overly complicated Kubernetes configuration I was considering would have only served to manage unused containers. Each time the system experiences a crash (which has happened twice), I gather real, actionable data about failures—often revealing surprises about the underlying issues.
2. Hardcoded Configurations
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of relying on separate configuration files or environment variables, I use hardcoded constants throughout the codebase. This method means any change requires a redeployment.
This seemingly rudimentary approach has its advantages: tracking changes in prices is
