I’ve read a lot of criticisms of ORMs, as I’m sure everyone else has. Some of them are certainly valid criticisms that are immovable and just inherent in what an ORM tries to do. Some of them just seem to be caused by not very many ORMs being good, and the writer not having used one of the better ones.

You can, at best, make them aware that there is such thing as “too much”, and “the right tool for the job”, and keep reminding them.

But nothing, nothing, comes remotely close to the real-world experience of needing to work with over-engineered spaghetti, and getting frustrated by it. Especially if it’s code that you wrote 6 months prior.

Juniors will always do this. It’ll always be the senior’s job to…let it happen, so the junior learns, but to still reduce the blast radius to a manageable amount, and, at the right moment, nudge the junior toward seeing the errors in their ways.

Our final assignment for this unit was to build a piece of software, following some provided specification, and to write some supplementary document justifying the patterns that we used. A mature-aged student that had a little bit of industry experience under his belt didn’t use a single pattern we learned about the entire semester. His code was much more simple as a result. He put less effort in, even when taking into account his prior experience. His justifying documentation simply said something to the effect of “when considering the overall complexity of this problem, and the circumstances under which this software is being written, I don’t see any net benefit to using any of the patterns we learned about”.

He got full marks. Not in a “I tricked the lecturer!” way. I was, and still am, a massive fan of the academic that ran the unit. The feedback the student received was very much “you are 100% correct, at the end of the day, I couldn’t come up with an assignment that didn’t involve an unreasonable amount of work and ALSO enough complexity to ever truly justify doing any of the stuff I’ve taught you”. All these years later, I still tell this story to my team. I think it’s such a compelling illustration of “everything in moderation”, and it’s fun enough to stick with people.

America loves to pretend that no other data points exist so they can attribute whatever good performance they’ve historically seen to whatever supposed cornerstone of American life is advantageous to make whatever point they want to make.

If all the people whinging on here took some of that time and actually ‘formally’ experimented with LLMs, measuring their reliability / correctness against a human in some task in their domain, they may be surprised by the results. And no, “I tried Copilot for an afternoon and hated it” doesn’t suffice.

At work, recently, I happened across an opportunity to do just this. There was a task that I thought that it was quite possible for an LLM to be good at. The task was such that we could run a bit of a ‘study’ to see how the LLM fared against a real-world meat-bag person. A skilled person at that. The person we would’ve had do the job in the first place. The LLM and the human agreed the vast majority of the time (>99.9%), and the LLM with its infinite ‘attention’ (heh) was on more than one occasion correct in cases where the human wasn’t, because it was a repetitive task that’d put someone to sleep.

It was a task that involved parsing language, but I’m sure one that the geniuses on HN would say requires “understanding semantics”, “intelligence” or whatever armchair philosophy nonsense they whip out in lieu of intelligent conversation. It wasn’t sentiment analysis, categorisation, or anything of that nature. Maybe it’s something I could’ve tackled without an LLM, with traditional ‘deep learning’, or whatever. I really don’t know. I couldn’t think of a way off the top of my head. It was beyond ‘throw linear regression at it’ anyway.

Software engineering isn’t engineering, but evidently computer science is increasingly not a science. This industry deserves all of the belittling pejoratives people throw at it. There’s a disappointingly large contingent of utterly unengaged, incurious, drones that let their entire professional skill set be guided by whatever some other incurious drone says on a social network.

In the metaphorical galaxy brain comic of this situation, “GATE programs fell out of fashion because of tall poppy syndrome” is the first or AT BEST the third frame.

It’s the line of thinking held by the ‘uninformed self-described smart guy’ contingent, that knows nothing about education, yet think that they can intuit their way through it. It’s unrealistic, naive, and utterly condescending. It’s no surprise that I see it so much in tech people.

I work with a lot of curricula. There are degrees of specificity. Common core is, notably, absurdly specific, and dense. It’s the US at its best, which is to say its worst. Including the fact that CC hasn’t ever really been touched since. This is seldom how curricula in actual developed jurisdictions are built.

There are also degrees to which a teacher can ‘play it by ear’ in their classroom. This is informed by their ability to do so competently, and the freedom allowed by their school, school system, etc. The existence of a curricula, even in a public education context, doesn’t inherently disallow a teacher from attempting different teaching styles to get through to a kid, or changing what they focus on in order to teach to their students’ zone of proximal development.

Schools “adapting to metrics” is very much moreso informed by the undeniably reality that, with any sizeable group of kids, and realistic constants on resources, you quickly need to start doing “formative assessment”, and doing assessment well is really hard.

Above all though, schools adapt to metrics because it’s what’s demanded of them by their bosses. By that, I don’t mean ‘educational bureaucrats’, I mean…parents, taxpayers at large, etc. ‘Hold hands under a rainbow and nothing bad ever happens’ individualised education is simply very hard to monitor, it’s very hard to hold anyone to account. Stakeholders hate this. They want measurement. They want numbers. And numbers invite systems, and putting people into boxes. We get exactly what we deserve here. Nothing more and nothing less.