Embracing the 3-Month Approach: A Framework for Innovating Without the Pressure of Scale
When it comes to growing a business, many of us have heard Paul Graham’s famous mantra, “Do things that don’t scale.” However, translating this wisdom into practical coding strategies is often overlooked. Over the past eight months of developing my AI podcast platform, I’ve devised a straightforward yet effective framework that prioritizes unscalable techniques: every non-scalable hack earns a three-month lifespan. After this period, it either demonstrates its worth and earns a polished redesign, or it is shelved for good.
As engineers, we are traditionally conditioned to construct solutions that are scalable from the outset, focusing on intricate architectures suited for handling millions of users. Yet, in a startup environment, this approach can often lead to costly delays. By adhering to my three-month rule, I’m encouraged to write straightforward and perhaps “imperfect” code that gets implemented quickly, allowing me to truly understand user needs.
My Ingenious Infrastructure Hacks and Their Strategic Benefits
1. Consolidated Operations on a Single Virtual Machine
Currently, my entire platform—comprising the database, web server, and background jobs—operates on just one virtual machine costing $40 per month. This means I forgo redundancy and depend on manual backups.
Why is this method so effective? In just two months, I’ve gained invaluable insights into my resource needs that any detailed capacity planning document would struggle to provide. My platform, which I initially thought would demand extensive resources, has proven to peak at only 4GB of RAM. That elaborate Kubernetes setup I nearly implemented? It would have meant managing containers without any real workload.
Each time it crashes (which has happened a couple of times), I glean concrete data about the real issues. Spoiler alert: they’re rarely what I anticipated.
2. Explicit Configuration in Code
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
In my codebase, configuration values are hardcoded rather than stored in environment files, which means any modification requires a redeployment.
The genius here lies in simplicity: I can quickly search my codebase for any configuration value. Each price update is meticulously recorded in Git history, and each adjustment undergoes my personal code review process.
The time saved is
One Comment
This is an insightful approach, and I really appreciate how you’ve distilled the “do things that don’t scale” principle into a practical, time-bound framework. The three-month rule provides a clear boundary that encourages rapid experimentation without getting bogged down by premature optimization.
Your example of consolidating operations onto a single VM is particularly compelling—it highlights how early-stage resource management and real-world testing can inform more scalable solutions down the line, rather than over-engineering from the start. Similarly, explicit configuration in code facilitates quick iteration but also underscores the importance of planning for transition strategies—such as eventual environment-based configs—as the platform matures.
My suggestion for further refinement might be to incorporate a lightweight monitoring or logging system during these initial phases. This can help quantify insights gained from unscalable hacks, making it easier to decide whether to refactor or retire a solution after the three-month period. Overall, your framework exemplifies a pragmatic balance between speed, learning, and long-term scalability—a valuable mindset for startups and engineers alike.