Been reading a really long, dry book about the fundamentals of software architecture. In the book, the authors suggest there are 2 laws of Software Architecture: a. Every decision is a trade-off. b. If you haven’t found the trade-off, skill issue; keep looking. It’s about why, not how. I’m starting to think that they only re-discovered what economists consider opportunity cost and why repair costs factor into GDP but aren’t true economic advance.

So I’ve been on a kick to sort out how I might come close to self-reliant when it comes to: Purifying water for drinking Maintain communications Keep useful information around Exist without MDs or DMDs and the first three of those are hard enough without the fourth, which I have no solution for; at least nothing to speak of. I know ways to handle water, for biological threats, iodine. For chemical or mineral threats things get trickier, like maybe gathering materials and activating charcoal for a filter, using detergent or stockpiling filters, etc.

I’ve been meaning to write this down for a long time and the other day I looked for the phrase and couldn’t find it elucidated in a satisfying way. I want to declare a law (a useless truism but a law kinda): Mandatory-use software (i.e. CRM, ERP, etc.) is always as poor as it can be maintained; however optional use software is always the very best. Maybe not useless, this could be a guide when deciding where to work.

I remember watching my father type away at WordPerfect on his ‘95 vintage HP machine back when I was about 5 years old, same as it. I wondered how this machine could be controlled by so many small buttons, and how he could know what to type. I watched closely enough that it would become an anecdote he re-told throughout my childhood. I worked out how to print because when he pressed Ctrl+P and clicked around a bit the printer would start producing things.

Thanksgiving last year one of my cousins held a dinner. Right before we went home, her brother gifted me a fountain pen. A Pilot Japan Metropolitan Fountain Pen, in “Retro Pop Gray” I found out much later that this is the pen, a Pilot Metropolitan in “Retro Pop Gray”. It accepts ink cartridges and has what I would consider an almost-extra fine tip nib. I ran out of ink in a little over a month.

A few years ago now and again recently I was introduced to the effects of CO2 on cognitive abilities by a couple of youtubers and as a software engineer I found this especially worrying and interesting because I spend very long stretches of time in my home (previously school, work) in a single spot. I rely entirely on the central air systems in buildings or fans of all types to make sure I’m not actually losing the ability to do my job by just concentrating for long periods of time.

I don’t know how many people actually know about them. I learned only when I was invited to help at a industrial controls/robotics outfit. You know those little screw-type terminals you might find on a home theater receiver, a car audio amplifier, sprinkler control, thermostat or any other of the myriad things we use that connect directly via wire? One non-secret nobody may have ever told you: those screw terminals require a minimum gauge of solid core, ideally soft (pure copper) wire.

When my grandmother was still a child her family had electrical infrastructure run to their home for the first time. Her dad was so unsure of the new technology that he made it his job to stay up all night under the artificial light of the kitchen lamp to make sure they weren’t turning it off when nobody was looking. It’s been nearly 90 years since Great Grandpa put together his manually implemented check on grid stability, and here I am to take it a level or so deeper.

Attention-based LLMs re-train their learned patterns with attention to context distance, meaning they infer patterns, then collections between concepts as they train. As I understand it this is the thesis of the paper “Attention is All You Need” and it’s new machine learning pattern, transformers. ChatGPT is a generative (it writes things, makes content) pre-trained (they’re not training it on the fly, it has to fit context and is locked to inference) transformer (the attention thingy majig).

There’s something you should know about AI. Elon’s not telling the truth, but that’s not it that’s just a dependent fact. Whether he’s deceiving himself or not, I don’t have too much bandwidth to care. What is obvious to me is that people are not aware of the dimensions of complexity in AI. Dimensions are interesting. You can stack them until you’re dizzy and nothing inherent in the math tells you that they can’t keep overlapping, multiplying the information stored in a single point in the space.