I'd always thought Seattle's water was pretty good. The general consensus is that it's on the softer side, ideal for brewing coffee. So, for everyday use, I typically just used water straight from the tap, maybe running it through our Berkey filter at most. Honestly, I never gave water quality much thought beyond "good enough." That changed recently when a few minor challenges popped up with my hydroponics setup and making bubble solution for my daughter. With Agentic AIs like o3 becoming more common and capable, I decided to enlist AI's help to investigate water quality. This little journey not only upended many of my old beliefs but also gave me a fresh perspective on how AI can transform the way we learn and explore.
Sprout Speed of Hydroponic Bok Choy
My journey into water quality started with hydroponics. I'd noticed that my hydroponic bok choy seedlings were slow to sprout and their leaves looked a bit yellowish. I enjoy experimenting and had some hydroponics experience, so I already owned TDS and pH meters. My initial tests showed the tap water's pH was indeed a bit high. I tried switching to purified water and neutralizing the pH, but disappointingly, their growth didn't really improve.
Facing this puzzle, and with Agentic AIs like o3 becoming so much better, I decided to ask for AI's help. I described the problem and my existing measurements (like the poor growth despite pH adjustments). The AI quickly highlighted key factors I'd likely missed: the nutrient solution's buffering capacity (KH – carbonate hardness/alkalinity) and its general hardness (GH). It explained how these metrics influence pH stability and nutrient availability, and recommended I measure these in my solution. It even suggested specific testing kit brands and models, complete with purchase links and prices (around $10).
Prompted by the AI's thorough advice, I quickly bought a titration kit. My measurements revealed a KH of about 27 ppm and a surprisingly high GH of 770 ppm. The AI elaborated that very high GH could cause nutrient imbalances or antagonisms, while low KH could lead to wild pH swings in the nutrient solution. Armed with this knowledge, I learned to use distilled water as a base, mix my nutrient solutions more scientifically, and actively manage pH and KH levels for optimal plant growth.
The changes to the nutrient solution made a clear difference. Using the same bok choy seeds, the seedlings sprouted faster and had richer green leaves after I adjusted the water quality based on the AI's suggestions. It dawned on me then that the slow germination I'd accepted as normal was probably due to poor water quality. This realization deepened my understanding of how water quality affects plant growth and was my first real experience of AI's power in tackling these kinds of issues.
The Supercharged Bubble Solution
The success with hydroponics fueled my curiosity about water quality even more. Around that time, my daughter was completely obsessed with her bubble gun. I'd tried making bubble solution before using online recipes—basically dish soap and tap water. It made bubbles, and she had fun, but I always felt the results were just so-so. I hadn't really thought much more about it.
Recalling what I learned from hydroponics about how calcium and magnesium ions in hard water can affect things, I wondered if this also impacted bubble solution (specifically, surfactant performance). Our tap water's GH value wasn't terribly high (36 ppm), but it wasn't zero. I posed this thought, along with my goal of creating better bubbles, to the AI. Not only did the AI confirm my suspicion, but it also quickly researched more scientific bubble solution recipes. At first, it suggested a homemade recipe with guar gum and cornstarch. When I playfully asked for something "more heavy-duty, please!", it reviewed several research papers (like Pasquet, M., et al. "An optimized recipe for making giant bubbles." Eur. Phys. J. E 45, 101 (2022). https://doi.org/10.1140/epje/s10189-022-00255-6) and concocted a rather "extreme" recipe using glycerin, cocamidopropyl betaine, and carboxymethyl cellulose.
After spending a hefty $20 on the ingredients and mixing them with distilled water, I was absolutely astonished. This new solution didn't just create more bubbles; they were also incredibly durable. It felt like there were at least two or three times as many bubbles as before. Using the same bubble gun, I could easily fill our entire balcony with a dreamy shower of colorful bubbles. It was amazing to see how, even with a simple toy, paying attention to the details and applying a bit of science (guided by AI) could make such a world of difference.
Water Wisdom for Espresso
Seattle's reputation for excellent coffee water is even more common. I'd been using local tap water for my espresso for ages. The coffee tasted fine, and my machine didn't have any scaling problems, so I'd never given it a second thought.
But as I learned more about water chemistry, I became curious about the Specialty Coffee Association (SCA) standards for coffee brewing water. My titration tests showed our local tap water had a KH of 36 ppm and a GH of 54 ppm. Compared to the SCA's ideal ranges, the KH was a tad low, and the GH was just at the bottom end of the recommended spectrum. Unsure how this might be affecting the taste, I asked the AI for adjustment strategies (like adding specific minerals or blending with purified water). The AI promptly offered several solutions, detailing the pros and cons of each regarding flavor and, importantly, equipment maintenance—something I hadn't considered.
I first tested some bottled mineral water I had, which registered a KH of 54 ppm and a GH of 90 ppm – still not quite hitting the SCA targets. So, I purchased distilled water and mineral packets, adjusting my brewing water to approximately 54 ppm KH and 143 ppm GH. Then I pulled some shots. To my palate, the espresso made with this customized water seemed to have a better balance, enhanced sweetness and body, and a richer crema. Granted, this was purely subjective; with only one coffee machine, a proper blind test wasn't feasible.
This experiment taught me that even water generally considered excellent might not be optimal for specific uses like brewing specialty coffee. With AI's assistance, I could more systematically balance the quest for perfect flavor with practicalities like machine care.
Reflections: Curiosity, Quantification, Cross-Disciplinary Learning, and AI
Looking back on these water-related adventures, I've learned a lot. Initially, whether it was how bok choy typically grew, the usual quality of bubble solution, or common beliefs about coffee water, I was pretty much just accepting the status quo. My curiosity – that constant "why?" and "could this be better?" – is what pushed me to explore. And what turned that curiosity into real action and understanding was a combination of measurement, comparison, and the incredible support of AI. Simple tools and methods transformed fuzzy feelings into hard data, clearly showing the way to improvements and their effects.
What's even more valuable is how insights from one seemingly separate exploration could inform another. My understanding of KH's role in pH buffering from hydroponics helped me when I tackled coffee water. Realizing how hard water impacts surfactant performance led directly to a better bubble solution. This kind of cross-disciplinary learning creates a sort of compound interest for knowledge.
Thinking about it, I probably overlooked factors like water quality for so long because there's no immediate feedback loop. Unlike water temperature or the obvious sour, sweet, or bitter notes in coffee, the effects of subtle changes in water's KH, GH, or pH often show up slowly and indirectly – a plant's leaf color after a few days, limescale buildup in a kettle over months, or those elusive nuances in coffee flavor. When a variable's impact isn't instant, we're more likely to dismiss it as just part of the background, or even "voodoo," rather than something we can actively measure and manage. Finding an intermediate variable that does offer quick feedback for measurement and tracking can help us break through this and get a better handle on complex systems.
AI's role in all this was particularly interesting. In the past, diving deep into specialized topics and applying that knowledge to everyday situations meant a huge time investment: searching academic papers, reading articles, sifting through information, and then trying to synthesize it all. The barrier to entry was high, and the whole process was full of friction – countless small hurdles or distractions would chip away at my patience and willpower, often leading me to just give up. With AI, however, the entire journey became remarkably efficient and smooth. I could spend just a minute describing my problem and context using voice input, and the AI would handle the research, information synthesis, and personalized application behind the scenes, even providing direct purchase links and prices. For instance, if it hadn't mentioned that a KH/GH titration kit was only about $10, I probably would have just lived with yellowish leaves. But its response laid out the principles, improvement methods, and next steps so clearly, complete with a cheap purchase link, that the barrier to trying was incredibly low. Naturally, I took that next small step on the path of discovery.
So, while AI might not be creating new knowledge from scratch, its ability to match existing knowledge to individual needs with such low friction is a game-changer. It doesn't have to know everything, but it's more than capable of helping us quickly identify problems, start testing solutions, and effectively carry out those little explorations that enrich our daily lives.
Therefore, this series of little experiments with water quality gave me more than just faster-growing bok choy, super-powered bubble solution, and potentially more flavorful coffee. Far more importantly, it introduced me to a whole new way of learning and solving problems in this age of AI: stay curious, don't be afraid to ask questions, and harness the power of AI to understand and improve the world around us with greater ease and depth. This kind of exploration—fueled by curiosity, grounded in hands-on practice and data, and supercharged by AI—is, in itself, incredibly fun and brimming with endless possibilities.
Comments