The 3-Month Experiment: A Technical Framework for Non-Scalable Solutions
In the landscape of startups and innovation, the phrase “do things that don’t scale” resonates profoundly, yet the mechanisms for integrating this philosophy into our coding practices remain under-discussed. Drawing inspiration from Paul Graham’s insightful advice, I have constructed a straightforward but effective framework during my eight-month journey of developing an AI podcast platform: every unscalable approach earns a provisional lifespan of three months. After that timeframe, it’s either elevated to a scalable solution or discarded entirely.
As engineers, we are typically conditioned to create scalable solutions from the outset, investing time in intricate design patterns, microservices, and robust distributed systems capable of accommodating vast user bases. However, this mentality is often characteristic of bigger enterprises, where such planning may not be practical in a startup environment. Here, overly complex code can lead to expensive delays and misguided priorities, focused on hypothetical user bases rather than actual demands. My three-month rule encourages me to embrace simplicity and produce “imperfect” code that delivers real insights into user needs.
Ingenious Infrastructure Hacks in Practice
1. Consolidating Everything on a Single Virtual Machine
My entire stack—database, web server, background jobs, and caching—operates on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, the benefits have been significant. In just two months, I have gained a clearer understanding of my resource requirements than any planning document could provide. Surprisingly, my “AI-heavy” platform only peaks at 4GB of RAM. The complicated Kubernetes architecture that I had almost deployed would have involved managing idle containers.
When system failures occur (and they have twice), I receive valuable data about the true causes, rather than the hypothetical scenarios I had anticipated.
2. Embracing Hardcoded Configurations
Instead of utilizing configuration files or environmental variables, I’ve opted for hardcoded constants throughout my codebase, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Although this method may seem outdated, it allows me to locate any configuration value swiftly and track changes comprehensively through git history. While building a configuration service might have taken a week, I’ve revised these values only three times in three months. The 15 minutes spent redeploying is negligible compared to the
One Comment
This post offers a compelling reminder that in the high-velocity world of startups, prioritizing agility and learning over perfection is often the most effective approach. Your 3-month rule elegantly captures the essence of iterative experimentation—allowing teams to test, learn, and pivot without getting bogged down by over-engineering upfront.
I particularly appreciate the practical infra hacks, such as consolidating into a single VM and using hardcoded configurations. These strategies promote speed and real-world insight, which are crucial during early product validation. That said, do you see any potential pitfalls in long-term maintainability, especially as the platform matures? For example, should there be a plan to transition from hardcoded configs to more flexible systems once the core hypotheses are validated?
Overall, your framework reinforces the importance of embracing imperfect, rapid iterations—saving mental cycles and resources for what truly matters: validating your ideas quickly and adapting accordingly. Thanks for sharing these valuable insights!