A para from from [0] makes it seem that students understand that LLM use doesn't lead to learning, but still do so. Do they not see effort put into learning worthwhile?

  A few months ago, I overheard some college students talking about their classes.
  One was complaining about an assignment they needed to do that night, and
  another incredulously asked why they wouldn’t just have ChatGPT do it. The first
  replied, “This is my major, I actually need to learn stuff in this class. I use
  AI for my other classes.”

From what I understand tho, most of LLM use for learning is just LLM used as a tool for cheating. Even tfa mentions something of the sort:

  few of Musall’s most advanced students have taken advantage of AI to learn new
  topics. But, as far as she can tell, more students are using it to just find
  answers

[0] https://arstechnica.com/science/2026/04/to-teach-in-the-time...

[1] https://www.reddit.com/r/Teachers/

For instance the current high score model (311 params [0]), when given 12345678900 + 1, responds with 96913456789.

An interesting experiment would be: what's the minimum number of parameters required to handle unbounded addition (without offloading it to tool calls).

Of course memory constraints would preclude such an experiment. And so a sensible proxy would be: what kind of neural-net architecture and training would allow a model to handle numbers lengths it hasn't been trained on. I suspect, this may be not be possible.

[0] https://github.com/rezabyt/digit-addition-311p

Comments URL: https://news.ycombinator.com/item?id=46771364

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=42461108

Points: 2

# Comments: 0

The rear derailleur on my cycle wasn't shifting as expected, and so I spent an afternoon following YouTube and adjusting its limit screw and barrel adjuster to no avail.

Finally gave up and took it to a shop, the mechanic took the cable out of the housing, wiped it down, greased and put it back in; the derailleur starts shifting normally.

Agreed, I've tried solving it by setting an attribute `sb-mark` which allows syncing just the branch of DOM elements that maps to that particular key in the reactive object.

This removes the need for VDOM diffing, but unless I use a `MutationObserver` external updates to marked branches will probably mess it up.

Haven't yet tested it for recursive components, it should work for nested loops.

> and it is simple but sometimes confusing

I understand what you mean, my approach has the aforementioned `sb-mark` attribute/directive which syncs primitives, lists, and objects.

I've started feeling that the convenience of having just one attribute to remember is supplanted by the confusion of its implications not being immediately apparent from context.

Comments URL: https://news.ycombinator.com/item?id=35937464

Points: 171

# Comments: 78

A lot of the new frontend codebases involve a build step before running. For such codebases, TypeScript's build hurdle has already been overcome.

[0] https://github.com/tc39/proposal-type-annotations

Going to the gym and losing weight did help a lot with self esteem (and posture) issues though.

After observing my interactions, I found that if I'm unfamiliar with the person, I'll miss out on social cues or there'll be a delay before I perceive them. Also my brain goes into some kind of _fight or flight_ causing slightly impaired speech and memory.

What I do to _fix_ this is watch how others interact with this person and try to mimic them while adjusting for unfamiliarity. Assuming familiarity could be perceived as rude.

For me building familiarity allows me to interact with decreasing awkwardness, so I just try and find the fastest way to do that.

It's more of an information retention problem rather than an information loss one. IE not committing to long term memory as the author states.

Not very unlike consuming food without chewing.

[0] https://github.com/asvetliakov/vscode-neovim