The Three-Month Experiment: A Pragmatic Approach to Non-Scalable Solutions
In the tech startup world, you might have heard Paul Graham’s famous directive: “Do things that don’t scale.” While this advice is widely acknowledged, the practical application of it, particularly in coding, often gets overlooked.
After spending the last eight months developing my AI podcast platform, I’ve crafted a straightforward yet effective framework: any unscalable hack implemented in my code receives a three-month trial period. At the end of this timeframe, it either demonstrates its utility and earns a place in my permanent architecture or is phased out.
The Engineering Mindset
As software engineers, we’re conditioned to favor scalable solutions from the outset, favoring complex architectures such as microservices, distributed systems, and elegant design patterns that are capable of accommodating millions of users. However, in a startup setting, pursuing scalable solutions can often lead to delays in actual delivery—essentially, it’s a form of procrastination on future challenges that may never materialize.
My three-month rule compels me to write straightforward, albeit imperfect, code that is deployable. This approach helps reveal the genuine needs of users without drowning in speculative designs.
Current Infrastructure: Pragmatic Hacks That Yield Insight
1. Consolidated Resources on a Single VM
I operate my entire application ecosystem—a database, web server, background jobs, and caching—on a single virtual machine that costs just $40 per month. While this strategy lacks redundancy and relies on manual backups, it has accelerated my understanding of actual resource requirements.
Over the past two months, I’ve realized that my so-called “AI-heavy” platform only peaks at 4GB of RAM. The intricate Kubernetes architecture I contemplated would have been managing empty containers instead. System crashes, which have occurred twice, provided crucial data about real points of failure—insights that I would not have anticipated.
2. Hardcoded Configuration
My configuration approach relies on constants sprinkled throughout the codebase, such as:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
There are no config files or environment variables. Each change necessitates a redeployment, which might seem limiting. However, the efficiency is undeniable; I can quickly search for any config value, and changes are meticulously tracked in version history. Over three months, I’ve made just three adjustments to pricing—taking 15 minutes versus the
One Comment
This post offers a compelling perspective on balancing rapid experimentation with pragmatic engineering. I particularly appreciate your emphasis on the value of **temporary, unscalable hacks** as a means to gain real-world insights quickly—something often overlooked in favor of elegant, long-term solutions. Your three-month trial period acts as an effective filter to determine what genuinely adds value versus what is simply an over-engineered layer before truly understanding user needs.
The approach of using a **minimal infrastructure setup**—like consolidating everything onto a single VM—resonates with the idea of reducing complexity upfront, allowing startups to focus on validated learnings before investing in scaling. Similarly, the choice of **hardcoded configurations** speeds up iteration, although I imagine it can become cumbersome as your platform evolves; perhaps integrating simple environment variables or lightweight config files might offer initial flexibility without sacrificing speed.
Overall, your methodology champions a **high-velocity, feedback-driven development process** that aligns well with startup dynamics. It’s a solid reminder that sometimes, **less is more** in the early stages—building just enough to learn, then iterating based on real data rather than premature optimization. Thanks for sharing—definitely food for thought on when to pivot from hacky solutions to scalable ones.