The 3-Month Experimentation Rule: A Practical Approach to Non-Scalable Solutions in Tech
In the realm of technology startups, the advice from industry expert Paul Graham to “do things that don’t scale” is often echoed, yet the practical application of this wisdom is rarely discussed. Over the past eight months, while developing my AI podcast platform, I’ve established a straightforward yet effective framework: each unscalable tactic resides for exactly three months. At the end of this period, it must either demonstrate its value and evolve into a more robust solution or be discarded.
As engineers, we are conditioned to prioritize scalable solutions right from the outset. The buzzwords—design patterns, microservices, distributed systems—all signify a mindset intended for enterprises handling thousands or even millions of users. However, in the context of startups, pursuing scalability can often turn into costly delays, where energy is expended on optimizing for hypothetical users and non-existent problems. My three-month rule compels me to write straightforward, even flawed code that is deployable and, more importantly, reveals the genuine needs of my users.
My Current Infrastructure Hacks: Smart Choices for a Growing Platform
1. Consolidated Resources on a Single VM
I’m running my database, web server, background jobs, and caching all on a single $40 per month virtual machine. Yes, there’s no redundancy and manual backups are taken at my local machine.
Here’s the bottom line: I’ve gained invaluable insights into my actual resource requirements far quicker than any lengthy capacity planning document could provide. Surprisingly, my “AI-heavy” platform operates well within a peak usage of 4GB of RAM. That complex Kubernetes setup I almost implemented? A time-consuming maintenance headache for an almost-empty container fleet. The few crashes I’ve experienced (two so far) have given me concrete data on where vulnerabilities lie—far from my initial assumptions.
2. Hardcoded Configuration Everywhere
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = “gpt-4”
No configuration files or environmental variables; just constants scattered through my code. Making any change necessitates a redeployment.
The advantage of this approach? Immediate access to configuration values across the codebase via a quick grep. Every pricing tweak is documented in my Git history, and each configuration update is reviewed—albeit by myself. Crafting a
One Comment
This is a fantastic approach that underscores the importance of experiment-driven development, especially in the early stages of a startup. I appreciate how you’ve structured the 3-month cycle as a clear feedback loop—allowing you to quickly validate assumptions and iterate without getting bogged down in premature scalability optimizations.
Your emphasis on deploying simple, straightforward solutions aligns well with the Lean Startup mentality—prioritizing learnings over perfection. The decision to consolidate resources on a single VM and use hardcoded configurations shows a pragmatic understanding that your primary goal at this stage is gaining real user insights.
As your platform matures and your user base grows, you’ll naturally need to revisit these choices, but your framework ensures that scalability is a deliberate future step rather than a premature constraint. It would be interesting to see how you balance the need for flexibility with your current approach—perhaps integrating lightweight environment variables or config files as your needs evolve, while preserving the rapid iteration cycle.
Thanks for sharing this practical methodology; it’s a valuable reminder that in startup engineering, speed and real-world feedback often trump waiting for the “perfect” scalable setup.