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I live on a homestead in southern Oregon, surrounded by the vastness of the Umpqua National Forest in every direction. It’s the kind of place where nature dictates the rhythm of life. We rely on a natural mountain spring for water most days, but when that fails (as nature sometimes does), we turn to a small stream or a mile-long ditch. According to local lore, some intrepid homesteader dug that ditch in the early 1900s to water a single cow.

These water sources connect to a network of pipes, backup pumps, and an unoptimized system that could generously be described as "inventive." When our pump failed recently, we faced an immediate and critical question: how powerful does a replacement pump need to be?

From Problem to Solution

To answer that question, I did what any coder-lawyer-homesteader would do—I wrote a script. Specifically, a pump power calculator that factors in pipe diameter, distance, flow rate, pipe material, and other inputs to calculate the horsepower needed for a given setup. It factors in key considerations like friction head loss, flow velocity, and static head, ultimately providing recommendations with a built-in safety margin. For example, in our setup, with a 4000-foot run of 1" pipe that rises up around 120 feet and delivers around 7.5 gallons per minute, it calculated we needed at least 0.75 HP—but 1 HP if we want a 30% safety margin.

You can try it out for yourself at pumpcalc.sij.ai or embedded at the bottom of this post, and if you’re curious about the code, it’s open source at sij.ai/sij/pumpcalc. I built the calculator in Python using FastAPI for the backend and Jinja2 for templating—simple, reliable tools that get the job done without unnecessary complexity.

This wasn’t a solo endeavor. I leaned on the open-source AI tool Ollama and specifically QwQ, a powerful 32 billion parameter research model that rivals leading commercial AI systems like ChatGPT o1 in reasoning capabilities. QwQ particularly excels at technical problem-solving and mathematical tasks, making it perfect for engineering calculations like this.

The Iterative Process of Coding

Developing this script wasn’t a one-and-done affair. It took five back-and-forth sessions with the AI to:

1. Factor in relevant variables like pipe roughness and flow rate.
2. Exclude unnecessary inputs that made the interface clunky.
3. Add some polish, like the Gruvbox Dark color scheme that now graces the app.

Each iteration made the calculator more useful and user-friendly. By the end, I had something functional, simple, and—dare I say—elegant.

Why Share This?

I’m sharing this as the first in a series of "Thinking Like a Developer" stories, because I believe coding isn’t as mystifying as it might seem. If a lawyer on a homestead with a temperamental water system can write a pump calculator, anyone can. The key is thinking like a developer: break the problem into smaller, solvable pieces, and don’t be afraid to consult tools or collaborators along the way.

This approach to problem-solving—breaking down complex challenges and leveraging coding tools—mirrors how I approach legal technology challenges. I frequently rely on Python and AI libraries to streamline legal work, from document analysis to case law research. Whether it's calculating pump requirements or processing legal documents, the fundamental thinking process remains the same. Who knows? You might find your next project hidden in a problem you didn’t even know you wanted to solve.