But also, you're right. Here's an article from 1994 describing Visicalc as a "Killer App" from the late 1970's, that prompted people to buy personal computers.
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"Killer rap" was pretty popular in the 90's too.
It's because Al Gore invented the internet, so they are known as Al Gore Rhythms.
Windows is the first thing I can think of that used the word "application" in that way, I think even back before Windows could be considered an OS (and had a dependency on MS-DOS). Back then, the Windows API referred to the Application Programming Interface.
Here's a Windows 3.1 programming guide from 1992 that freely refers to programs as applications:
Common dialog boxes make it easier for you to develop applications for the Microsoft Windows operating system. A common dialog box is a dialog box that an application displays by calling a single function rather than by creating a dialog box procedure and a resource file containing a dialog box template.
Um excuse me the preferred term is "AI agent" if you want outside investment
Some people actively desire this kind of algorithm because they find it easier to find content they like this way.
Raw chronological order tends to overweight the frequent posters. If you follow someone who posts 10 times a day, and 99 people who post once a week, your feed will be dominated by 1% of the users representing 40% of the posts you see.
One simple algorithm that is almost always better for user experiences is to retrieve the most recent X posts from each of the followed accounts and then sort that by chronological order. Once you're doing that, though, you're probably thinking about ways to optimize the experience in other ways. What should the value of X be? Do you want to hide posts the user has already seen, unless there's been a lot of comment/followup activity? Do you want to prioritize posts in which the user was specifically tagged in a comment? Or the post itself? If so, how much?
It's a non-trivial problem that would require thoughtful design, even for a zero advertising, zero profit motive service.
Instead, I actively avoided conversations with my peers, particularly because I had nothing in common with them.
Looking at your own social interactions with others, do you now consider yourself to be socially well adjusted? Was the "debating child in a coffee shop" method actually useful at developing the social skills that are useful in adulthood?
I have some doubts.
Just ask Mike Waltz!
That's why the best places to work tend to be the places where your CEO has had your job before.
Plus domains should've gone left to right in terms of root, tld, domain, subdomain, etc., instead of right to left.
Longer queries give better opportunities for error correction, like searching for synonyms and misspellings, or applying the right context clues.
In this specific example, "is Angelina Jolie in Heat" gives better results than "Angelina Jolie heat," because the words that make it a complete sentence question are also the words that give confirmation that the searcher is talking about the movie.
Especially with negative results, like when you ask a question where the answer is no, sometimes the semantic links in the kndex can get the search engine to make suggestions of a specific mistaken assumption you've made.
Why do people Google questions anyway?
Because it gives better responses.
Google and all the other major search engines have built in functionality to perform natural language processing on the user's query and the text in its index to perform a search more precisely aligned with the user's desired results, or to recommend related searches.
If the functionality is there, why wouldn't we use it?
Search engine algorithms are way better than in the 90s and early 2000s when it was naive keyword search completely unweighted by word order in the search string.
So the tricks we learned of doing the bare minimum for the most precise search behavior no longer apply the same way. Now a search for two words will add weight to results that have the two words as a phrase, and some weight for the two words close together in the same sentence, but still look for each individual word as a result, too.
More importantly, when a single word has multiple meanings, the search engines all use the rest of the search as an indicator of which meaning the searcher means. "Heat" is a really broad word with lots of meanings, and the rest of the search can help inform the algorithm of what the user intends.
I can travel forward in time at a rate of 60 seconds per minute, and I think the US government can, too.
Which is is such a high dollar count that this simply cannot be USD
So I haven't used Windows on my own machines in about 20 years, but back when I built my own PCs that seemed about right. So I looked up the price history, didn't realize that Microsoft reduced the license prices around Windows 8.
I remember 20 years ago, Windows XP Home was $199 and Professional was $299 for a new license on a new computer. Vista and 7 were similarly priced.
Since Windows 8, though, I just don't understand their pricing or licensing terms.
I think back to the late 90's investment in rolling out a shitload of telecom infrastructure, with a bunch of telecom companies building out lots and lots of fiber. And perhaps more important than the physical fiber, the poles and conduits and other physical infrastructure housing that fiber, so that it could be improved as each generation of tech was released.
Then, in the early 2000's, that industry crashed. Nobody could make their loan payments on the things they paid billions to build, and it wasn't profitable to charge people for the use of those assets while paying interest on the money borrowed to build them, especially after the dot com crash where all the internet startups no longer had unlimited budgets to throw at them.
So thousands of telecom companies went into bankruptcy and sold off their assets. Those fiber links and routes still existed, but nobody turned them on. Google quietly acquired a bunch of "dark fiber" in the 2000's.
When the cloud revolution happened in the late 2000's and early 2010's, the telecom infrastructure was ready for it. The companies that built that stuff weren't still around, but the stuff they built finally became useful. Not at the prices paid for it, but when purchased in a fire sale, those assets could be profitable again.
That might happen with AI. Early movers over invest and fail, leaving what they've developed to be used by whoever survives. Maybe the tech never becomes worth what was paid for it, but once it's made whoever buys it for cheap might be able to profit at that lower price, and it might prove to be useful in the more modest, realistic scope.
For example, as a coding assistant, a lot of people quite like them. But as a replacement for a human coder, they're a disaster.
New technology is best when it can meaningfully improve the productivity of a group of people so that the group can shrink. The technology doesn't take any one identifiable job, but now an organization of 10 people, properly organized in a way conscious of that technology's capabilities and limitations, can do what used to require 12.
A forklift and a bunch of pallets can make a warehouse more efficient, when everyone who works in that warehouse knows how the forklift is best used, even when not everyone is a forklift operator themselves.
Same with a white collar office where there's less need for people physically scheduling things and taking messages, because everyone knows how to use an electronic calendar and email system for coordinating those things. There might still be need for pooled assistants and secretaries, but maybe not as many in any given office as before.
So when we need an LLM to chip in and reduce the amount of time a group of programmers need in order to put out a product, the manager of that team, and all the members of that team, need to have a good sense of what that LLM is good at and what it isn't. Obviously autocomplete has always been a productivity enhancer for long before LLMs have been around, and extensions of that general concept may be helpful for the more tedious or repetitive tasks, but any team that uses it will need to use it with full knowledge of its limitations and where it best supplements the human's own tasks.
I have no doubt that some things will improve and people will find workflows that leverage the strengths while avoiding the weaknesses. But it remains to be seen whether it'll be worth the sheer amount of cost spent so far.
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You cant remove that double negative without making it incorrect
Sure you can: The IP Laws That Reinforce Enshittification.
It's not a movie, but the Fallout series had a great first season, and I'm looking forward to the second.