This was a problem at my last job. Boss kept suggesting shoving AI into features, and I kept pointing out we could make the features better with less effort using simple heuristics in a few lines of code, and skip adding AI altogether.

So much of it nowadays is like the blockchain craze, trying to use it as a solution for every problem until it sticks.

Which is fine! That's me making the explicit choice that yes, I want to close this box and yes, I want to lose this data. I don't need an AI evaluating how important it thinks I am and second guessing my judgement call.

I tell the computer what to do, not the other way around.

> You become accustomed to blindly hitting "Yes" every time you've accidentally typed something into the text box, and then that time when you actually put a lot of effort into something... Boom. Its gone.

Wouldn't you just hit undo? Yeah, it's a bit obnoxious that Chrome for example uses cmd-shift-T to undo in this case instead of the application-wide undo stack, but I feel like the focus for improving software resilience to user error should continue to be on increasing the power of the undo stack (like it's been for more than 30 years so far), not trying to optimize what gets put in the undo stack in the first place.

>You become accustomed to blindly hitting "Yes" every time you've accidentally typed something into the text box, and then that time when you actually put a lot of effort into something... Boom. Its gone.

I'm not sure we need even local AI's reading everything we do for what amounts to a skill issue.

I have the exact opposite muscle memory.

I think this is covered in the Bainbridge automation paper https://en.wikipedia.org/wiki/Ironies_of_Automation ... When the user doesn't have practiced context like you described, to be expected to suddenly have that practiced context to do the right thing in a surprise moment is untenable.

> It's wild how often I've heard people say "we should have an AI do X" when X is something that's very obviously either a terrible idea or best suited for traditional algorithms.

Likewise when smartphones were new, everyone and their mother was certain that random niche thing that made no sense as an app would be a perfect app and that if they could just get someone to make the app they’d be rich. (And of course ideally, the idea haver of the misguided idea would get the lions share of the riches, and the programmer would get a slice of pizza and perhaps a percentage or two of ownership if the idea haver was extra generous.)

sawstop is not AI

> subtle UX behaviors

I'd wager it's more likely to be the opposite.

Older UIs were built on solid research. They had a ton of subtle UX behaviors that users didn't notice were there, but helped in minor ways. Modern UIs have a tendency to throw out previous learning and to be fashion-first. I've seen this talked about on HN a fair bit lately.

Using an old-fashioned interface, with 3D buttons to make interactive elements clear, and with instant feedback, can be a nicer experience than having to work with the lack of clarity, and relative laggyness, of some of today's interfaces.

Serious question: what are those things from windows 95/98 I might miss?

Rose tinted glasses perhaps, but I remember it as a very straightforward and consistent UI that provided great feedback, was snappy and did everything I needed. Up to and including little hints for power users like underlining shortcut letters for the & key.

I remember seeing one of those "kids use old technology" videos, where kids are confused by rotary phones and the like.

One of the episodes had them using Windows 98. As I recall, the reaction was more or less "this is pretty ok, actually". A few WTFs about dialup modems and such, but I don't recall complaints about the UI.

> But... A lot of stuff you rely on now was probably once distracting and unpredictable.

And nobody relied on them when they were distracting and unpredictable. People only rely on them now because they are not.

LLMs won't ever be predictable. They are designed not to be. A predictable AI is something different from a LLM.

> There are a ton of subtle UX behaviors a modern computer is doing that you don't notice, but if they all disappeared and you had to use windows 95 for a week you would miss.

Like what? All those popups screaming that my PC is unprotected because I turned off windows firewall?

Magic in real life never works 100% of the time. It is all an illusion were some observers understand the trick and others do not. Those that understand it have the potential to break the magic. Even the magician has the ability to fault the trick.

I want reproducibility not magic.

Who said anything about regexp? I was literally talking about something as simple as "if(text.length > 100)". Also the example provided was distinguishing "a 2 page essay or 'asdfasdf'" which clearly can be accomplished with length much easier than either an LLM or even regexp.

We should keep in mind that we're trying to optimize for user's time. "So, she cheated on me" takes less than a second to type. It would probably take the user longer to respond to whatever pop up warning you give than just retyping that text again. So what actual value do you think the LLM is contributing here that justifies the added complexity and overhead?

Plus that benefit needs to overcome the other undesired behavior that an LLM would introduce such as it will now present an unnecessary popup if people enter a little real data and intentionally navigate away from the page (and it should be noted, users will almost certainly be much more likely to intentionally navigate away than accidentally navigate away). LLMs also aren't deterministic. If 90% of the time you navigate away from the page with text entered, the LLM warns you, then 10% of the time it doesn't, those 10% times are going to be a lot more frustrating than if the length check just warned you every single time. And from a user satisfaction perspective, it seems like a mistake to swap frustration caused by user mistakes (accidentally navigating away) with frustration caused by your design decisions (inconsistent behavior). Even if all those numbers end up falling exactly the right way to slightly make the users less frustrated overall, you're still trading users who were previously frustrated at themselves for users being frustrated at you. That seems like a bad business decision.

Like I said, this all just seems like a solution in search of a problem.

Because in _what world_ do I want the computer making value judgements on what I do?

If I want to close the tab of unsubmitted comment text, I will. I most certainly don’t need a model going “uhmmm akshually, I think you might want that later!”

Because the computer behaving differently in different circumstances is annoying, especially when there's no clear cue to the user what the hidden knobs that control the circumstances are.

What about counting words based on user's current lang, and prompting off that?

Close enough for the issue to me and can't be more expensive than asking an LLM?

We went from the bullshit "internet of things" to "LLM of things", or as Sheldon from Big Bang Theory put it "everything is better with Bluetooth".

Literally "T-shirt with Bluetooth", that's what 99.98% of "AI" stickers today advertise.

Is there a way to tell if people are seeking out the same video or or if they are watch it because it was suggested? Especially when 90% of the recommendations are repeats?

There isn't even an "I've watched this" or "don't suggest this video anymore" option. You can only say "I'm not interested" which I don't want to do because it will seems like it will downrank the entire channel.

Even if that is the case, I rarely watch the same video, so the recommendation engine should be able to pick that up.

I work for YouTube. You’re hired.

As someone from 1969, but with an excellent circulatory system, I just roll my eyes and look forward to the sound of bubbles bursting whilst billionaires weep.

If memory shortages make existing products non-viable (e.g. 50% price increases on mini PCs, https://news.ycombinator.com/item?id=46514794), will consumers flock to new/AI products like OpenAI "pen" or reject those outright?

I think it does make sense if you're at a certain level of user hardware. If you make local computing infeasible because of the computational or hardware cost it makes it much easier to sell compute as a service. Since about 2014 almost every single change to paid software has been to make it a recurring fee rather than a single payment, and now they can do that with hardware as well. To the financially illiterate paying a $15 a month subscription to two LLMs from their phone they have a $40 monthly payment on for two years seems like a better deal than paying $1,200 for a desktop computer with free software that they'll use a tenth as much as the phone. This is why Nvidia is offering GForce Now the same way in one hundred hour increments, as they can get $20 a month that goes directly to them, with the chance of getting up to an additional $42 maximum if the person buys additional extensions of equal amount (another one hundred hours). That ends up with $744 a year directly to Nvidia without any board partners getting a cut, while a mid grade GPU with better performance and no network latency would cost that much and last the user five entire years. Most people won't realize that long before they reach the end of the useful lifetime of the service they'll have paid three to four times as much as if they had just bought the hardware outright.

With more of the compute being pushed off of local hardware they can cheapen out on said hardware with smaller batteries, fewer ports and features, and weaker CPUs. This lessens the pressure they feel from consumers who were taught by corporations in the 20th century that improvements will always come year over year. They can sell less complex hardware and make up for it with software.

For the hardware companies it's all rent seeking from the top down. And the push to put "AI" into everything is a blitz offensive to make this impossible to escape. They just need to normalize non-local computing and have it succeed this time, unlike when they tried it with the "cloud" craze a few years ago. But the companies didn't learn the intended lesson last time when users straight up said that they don't like others gatekeeping the devices they're holding right in their hands. Instead the companies learned they have to deny all other options so users are forced to acquiesce to the gatekeeping.

The customers are CEOs dreaming of a human-free work force.

A lot of consumer tech needs have been met for decades. The problem is that companies aren't able to extract rent from all that value.