The hot concept around the late 2000’s and early 2010’s was crowdsourcing: leveraging the expertise of volunteers to build consensus. Quora, Stack Overflow, Reddit, and similar sites came up in that time frame where people would freely lend their expertise on a platform because that platform had a pretty good rule set for encouraging that kind of collaboration and consensus building.

Monetizing that goodwill didn’t just ruin the look and feel of the sites: it permanently altered people’s willingness to participate in those communities. Some, of course, don’t mind contributing. But many do choose to sit things out when they see the whole arrangement as enriching an undeserving middleman.

Most Android phones with always on have a grayscale screen that is mostly black. But iPhones introduced always on with 1Hz screens and still show a less saturated, less bright version of the color wallpaper on the lock screen.

It’s actually pretty funny to think about other AI scrapers ingesting this nonsense into the training data for future models, too, where the last line isn’t enough to get the model to discard the earlier false text.

On phones and tablets, variable refresh rates make an “always on” display feasible in terms of battery budget, where you can have something like a lock screen turned on at all times without burning through too much power.

On laptops, this might open up some possibilities of the lock screen or some kind of static or slideshow screensaver staying on longer while idle, before turning off the display.

It’s a fancy marketing term for when AI confidently does something in error.

How can the AI be confident?

We anthropomorphize the behaviors of these technologies to analogize their outputs to other phenomena observed in humans. In many cases, the analogy helps people decide how to respond to the technology itself, and that class of error.

Describing things in terms of “hallucinations” tell users that the output shouldn’t always be trusted, regardless of how “confident” the technology seems.

Apple supports its devices for a lot longer than most OEMs after release (minimum 5 years since being available for sale from Apple, which might be 2 years of sales), but the impact of dropped support is much more pronounced, as you note. Apple usually announces obsolescence 2 years after support ends, too, and stop selling parts and repair manuals, except a few batteries supported to the 10 year mark. On the software/OS side, that usually means OS upgrades for 5-7 years, then 2 more years of security updates, for a total of 7-9 years of keeping a device reasonably up to date.

So if you’re holding onto a 5-year-old laptop, Apple support tends to be much better than a 5-year-old laptop from a Windows OEM (especially with Windows 11 upgrade requirements failing to support some devices that were on sale at the time of Windows 11’s release).

But if you’ve got a 10-year-old Apple laptop, it’s harder to use normally than a 10-year-old Windows laptop.

Also, don’t use the Apple store for software on your laptop. Use a reasonable package manager like homebrew that doesn’t have the problems you describe. Or go find a mirror that hosts old MacOS packages and install it yourself.

This write-up is really, really good. I think about these concepts whenever people discuss astrophotography or other computation-heavy photography as being fake software generated images, when the reality of translating the sensor data with a graphical representation for the human eye (and all the quirks of human vision, especially around brightness and color) needs conscious decisions on how those charges or voltages on a sensor should be translated into a pixel on digital file.