The Smell of Molten Projects in the Morning
Ed Nisley's Blog: Shop notes, electronics, firmware, machinery, 3D printing, laser cuttery, and curiosities. Contents: 100% human thinking, 0% AI slop.
Who’d’a thunk it?
Not what you want to see when trying to extract a month’s worth of data from your blood pressure meter:
Perhaps the format string should be $1$s or %1%s, not a mixture?
It’s typical for Omron to issue two updates in quick succession after months of no activity, so programming by successive approximation may be a thing.
An inch-long Praying Mantis stood guard on a carton delivered to the front door:
We hope to see many more of its kind.
Two views of a one-turn snail found on a lettuce leaf from the garden:
Mary has developed a Zero-Tolerance Policy for snails & slugs, so this one must start over again from the bottom of the food chain.
With sparrows excluded, the House Wrens eventually began setting up their second nest of the season, with the male proclaiming both he and his lady’s chosen nest are the best in the land:
Wrens are on our Most Favored Critter list!
Previous encounters with their ancestors and relatives:
Just for fun, I handed the text of a recent post to one of the myriad AI detectors:
AI generation score: 12%
This text reads as highly authentic human writing, likely from a personal blog or DIY/home repair journal. The author demonstrates a genuine personal voice throughout, with specific technical details that feel lived-in rather than generic. Phrases like ‘sad bent wreckage on their stems’ and ‘yet another nonstandard standard size’ carry real personality and mild frustration that feels genuinely human. The writing has a casual, matter-of-fact tone typical of someone documenting their own project for an audience of fellow hobbyists.
Yeah, that’s about right.
Of course, an AI analyzed my text and generated the response, so I tossed its response back into the hopper:
AI generation score: 72%
This text has several hallmarks of AI-generated content, particularly in its analytical, evaluative framing. It reads like an AI detector’s own output — a meta-analysis of writing rather than original creative or personal content. The language is polished and structured in a way that feels templated: it moves systematically through observations (voice, tone, specific phrases, audience) in a formulaic progression typical of AI summarization tasks. Phrases like ‘feel lived-in rather than generic’ and ‘carry real personality and mild frustration’ are the kind of hedged, evaluative language AI systems use when describing human qualities they’re trying to identify or simulate.
However, we know real humans sometimes steal content from other authors, so I ran the same text through one of the myriad plagiarism detectors:
Guilty as charged: my text always exactly matches what you’ll find at softsolder.com. I have no idea where the “83% Match” for the first paragraph came from.
Remember: you read it here first …
I long ago learned to never trust Amazon’s unit pricing (or, for that matter, their recommendations), so this came as no surprise:
OK, you generally don’t buy hoses “by the ounce”, but “per fluid ounce” may not mean what you think it means:
Pricing items individually should be simple, if you know what a single item is:
Even knowing the number of items and the overall price isn’t enough for Amazon to get it right:
Amazon now has a “shopping assistant”, so I asked Alexa why the unit prices were incorrect. After some back-and-forth providing details Alexa should have known from the context, this seemingly plausible sequence of words emerged:
Amazon apparently stopped commingling knockoff crap with brand-name products under the same SKU earlier this year, a change driven by major brands refusing to have anything to do with Amazon’s “supply chain”, but the probability of my one-by-one reports producing any discernible improvement seems low.
Just over a month after mounting bamboo bee tunnel nest bundles here & there around the house & yard, they have occupants!
One of the bundles of small (a few mm ID) stalks on the kitchen window:
Another bundle of small stalks on a window a dozen feet away:
A bundle of medium (five to ten mm ID) stalks lashed to a downspout:
Unlike the mud dauber wasps decorating our previous house, these little bees dart in and out without announcing their presence: we’ve never seen them at work.
Assuming a single bee works on each bundle, she apparently starts with the lowest stalk and moves upward after filling & capping it.
Larger bees have yet to discover the bundles of larger stalks out on the trees, but … so far, so good!
Having been nerd-sniped again, I had to try this:
It turned out reasonably well:
That’s at 100 mm/s and 40% of a 60 W CO₂ laser. Although the exhaust fumes smelled pretty good, the bread tasted burned rather than toasted.
Undaunted, I tried another sandwich layer:
The patterns become more obvious in oblique light:
Settings, all with 0.3 mm line interval:
It’s good stinky provolone, so its taste remained undamaged by the experience.
Laser engraving apparently works really well on hot dogs and their buns, but I am not going there …
Under the plausible assumption the security / surveillance cameras along the Walkway Over the Hudson aren’t the cheapest junk available from a randomly named Amazon seller, this came as a surprise during a recent Walkway At Night stroll:
The IR LEDs emit just enough red light to be dimly visible to the human eye, but appear much brighter to a silicon detector. I think the long gap at the bottom right is a sensor of some sort, so the array of 18 LEDs has two deaders, one near death, and six more fading away.
The necklace lights on the Mid-Hudson Bridge are once again in good repair:
They were showing fixed white light, rather than a moving color display, but it’s still a nice effect.
The Smell of Molten Projects in the Morning 