The 3-Month Framework: A Practical Approach to Unscalable Solutions in Software Development

In the world of startups, the well-known advice from Paul Graham to “do things that don’t scale” often gets overlooked when it comes to implementation, especially in the realm of software development. After spending eight months developing my AI podcast platform, I’ve devised a straightforward approach: each unscalable solution is given exactly three months to prove its worth. If it demonstrates value, it evolves into a robust solution; if not, it’s time to phase it out.

As engineers, we tend to focus on crafting scalable solutions right from the start. We delve into design patterns, microservices, and distributed systems, tailormade for handling vast user bases. This big-picture thinking can be a disadvantage in a startup setting where the quest for scalability may divert our attention from immediate needs. My 3-month framework encourages me to embrace simple, straightforward code—code that actually executes and provides insights into what users genuinely require.

My Current Infrastructure Innovations—and Their Merits

1. Consolidated Operations on a Single VM

All essential components—database, web server, background jobs, and Redis—operate on a single $40/month virtual machine, lacking redundancy and relying on manual backups to my personal device.

This may seem unwise at first glance, but it has granted me valuable insights into my resource requirements faster than any elaborate planning could. For instance, I’ve discovered that my “AI-heavy” platform peaks at 4GB of RAM. The Kubernetes setup I almost initiated would have merely managed empty resources. During the instances of downtime (twice so far), I’ve gained firsthand information about what truly falters—insights I couldn’t have predicted.

2. Hardcoded Configurations for Simplicity

Consider the following snippet:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

Rather than employing configuration files or environment variables, I utilize hardcoded constants dispersed throughout my codebase. Modifying any element necessitates a redeployment.

This seemingly basic approach allows for rapid searches across my codebase for any configuration value. Every price adjustment is meticulously recorded in Git history, and I conduct thorough code reviews (albeit solo). Constructing a configuration service would have taken a week, yet I’ve made only three changes in three months