HTML / JS is still a buggy pile of slop.

In what world is JavaScript considered buggier than Flash, either in the actual content in the wild or the client-side software rendering/running that content? It wasn’t true when Flash died and certainly isn’t true today.

Flash was a security nightmare, with all sorts of kludges patched on to try to deal with fundamental flaws in how it handled privileges. If you want to go back to the days where zero click exploits can take over your machine just from a browser visiting the wrong URL, leave the rest of us out of that vision.

The current web experience is complete garbage in comparison.

Mm, and what makes you think that handing Adobe the keys to control everyone’s web experience would make it better?

As a browser-supported web image format, the only real runaway advantage of JXL is the ability to losslessly reencode existing JPEG files, when the vast majority of image files that people already have are stored as JPEG “originals.”

But as an overall image format, JXL has a much more ambitious scope: much higher limits in the spec to resolution, bit depth, layers, etc., showing an intent to be used as a raw image capture format and printing format, not limited to screen resolutions and bit depth like AVIF is.

So if the original file gets stored as JXL, the workflow and pipeline of a JXL native process the whole way may have an advantage over exporting to a screen-friendly AVIF at the end of the process, while the original still gets stored as another format.

Re-encoding from JPEG to AVIF will always drop quality. Lossless re-encoding from JPEG to JPEG XL makes a pixel perfect reproduction, while shrinking file size.

Apple’s Safari supports still images in JXL.

Perhaps more notably, Apple’s iPhone ProRaw supports JXL encoding for its raw sensor data. They’re laying the groundwork for JXL to be a supported format from the point of capture at the camera, through all the processing to make an image for web publishing or whatever.

The killer feature is that JXL is better in that it can losslessly encode JPEG further, and the overwhelming majority of the legacy image files that people have are JPEG. That alone should justify its support, because there are a lot of files out in the world where the highest quality, closest to “original” quality file is stored in JPEG format. A format that allows for the further compression with zero loss of quality from those originals is really important.

And the other thing this article (and a lot of the discussion around JXL) chooses not to cover is how JXL is a good format outside of just web images. It’s not just looking to replace JPG/PNG/webp. It’s also looking to replace raw photography formats like DNG, TIFF, and other formats that are used for full workflows from image capture from the imaging sensor itself, from cameras to scanners to medical imaging.

If JXL succeeds at becoming the dominant raw capture format, the entire workflow of processing those raw images into exported web-friendly images will favor JXL for photography.

No, you’ve got it all wrong. Flash was buggy and insecure proprietary software written for proprietary OSes. I’m glad it died, after several years of trying to fight its normalization on the web, where you’d need to install shitty plugins like gnash on Linux to try to see the same web that people were seeing on Mac or Windows.

The web should always be built on open standards, and Flash was the biggest barrier to that ideal at the time that iPhones came out. Flash deserved to die, and I celebrated when it did.

Steps that either would not work between architectures or that Rosetta would handle (thereby answering your own question)

What? They would compile the code that they control for the M-series architectures. The target architecture is M-series ARM chips running on MacOS.

Rosetta is for translating x86 to ARM. Which wouldn’t be necessary because there are no x86 binaries involved at all.

I don’t understand why Rosetta has to be involved in any way at all.

Apple provides APIs for programming low level GPU instructions, including AI workloads, on their chips, using the Metal API. Anyone interested in using Apple hardware to its full potential can just write Mac-native software, same as any other MacOS native software directly compiled as binaries to run on Mac M series chips. No Rosetta required for translating x86 instructions to ARM, when the whole thing compiles for Apple’s native instructions in the first place.

Yup. My main living data is on a NAS, my on-site backup is an external USB enclosure with a SATA hard drive hooked up to a Mac Mini, all so that I can leverage a Backblaze account to back up the external hard drive with unlimited data.

Reportedly, the app uses an on-device machine learning model to classify the content.

Using your shiny new phone to train AI…

I would think the models are already trained, and then deployed to phones to do on-device inference. The models should be fixed and static by the time they end up on a phone (in large part because phone hardware is just not technically suitable for training, whether pre training or post training).

aren’t just wrong — they’re a bridge too far

Haven’t the PR flacks learned by now to sound less obvious about using LLM generated text?

But it’s not the company itself putting those assets up for sale. It’s the court-approved or court-appointed bankruptcy trustee of that company, executing transactions approved by the court. That’s why bankruptcy has to go through court, but it also provides a level of immunity (commonly called “exculpation” in bankruptcy cases) to the individuals actually doing the things ordered by the court.

If it’s illegal to do, a court shouldn’t be ordering it, so the affected people should have to fight the court order before it happens. But it’s not illegal to sell anonymized user data like this, and not illegal to sell employee work-related data, either, in the U.S.

It needs to be a system and a contractual framework that complies with the medical privacy regulations of the place where it’s being used (in the US, HIPAA). I have some doubts about whether specific companies or people will actually comply, or the enforcement of those laws by certain governmental authorities, but I don’t think it’s conceptually impossible to have this kind of tech be compliant with those types of regulations.

on average peoples’ computers

Average people don’t have self-administered computers anymore.

People have work laptops administered by their work IT departments, or they have phones and tablets running iOS/Android. Some children have school-administered Chromebooks, and may never own a normal laptop running a traditional desktop OS.

The main people operating self-administered traditional computers are PC gamers (a dying breed during the current price crisis), freelance/independent workers in the fields who still benefit from a mouse and keyboard for productive work and aren’t provided a computer by an employer, and tech nerds.

Probably not. The article credits Shutterstock submitter Vladislav Noseek, whose online presence with photographs of this style (soft lighting, bright lighting, shallow depth of field, a focus on breakfast foods) long predates generative AI, much less of this quality. It’s stock photographers like this guy whose inoffensive visual style and identity have been most directly incorporated into the AI defaults.

It hasn’t felt t like there’s been much significant performance increases or development in RAM in the last… decade?

In memory? There’s been a ton of improvement, even if most of the coolest stuff isn’t making it into DIMMs that are installed in user laptops/desktops.

Advanced packaging technology has allowed chip manufacturers to put different silicon dies together with increasingly high performance (high bandwidth, low latency) connections in the same package, including with some three dimensional stacking. That way they can mix and match different silicon dies for greater cost effectiveness, yield, performance, etc.

This also means that in-package memory is now the standard in certain chips. Apple’s M-series silicon has its memory packaged right into the CPU/GPU package, as a system-in-a-package, so that the connection between the logic and memory is comparatively much higher performance, several times higher bandwidth than desktops or laptops that don’t follow that kind of architecture.

Similarly, in data centers, the AI boom has caused all the memory manufacturers to switch their production lines to high bandwidth memory, where they vertically stack a bunch of DRAM chips on each other, with ultra-fast, high bandwidth connections, so that they can shove terabytes of memory into these data center servers. These recent generations have been improving speed and bandwidth in ways that make consumer level DDR5 RAM look like child’s play.

So they’re improving things. Just not in ways that really show up in DIMM sticks.

Not strictly, there are usualy hurdles to overcome for home usage of datacentre tech, but it’s possible.

The hurdles are basically insurmountable with the hardware released after 2024.

The NVL72 for the Blackwell generation cost about $3 million and takes up a single server rack. The power consumption is about 130 kW, and most configurations require dedicated plumbing for the liquid cooling.

To put things in perspective, a residential electrical hookup is usually 50A or 100A for a house, with recommendations that anyone who is going to be charging electric cars should have 100A service. 100A at 240V is 24 kW.

So one server rack uses as much power as the maximum electrical capacity of 5 homes. You’ll never be able to pull that off in an actual residential environment.

Oh, and the newest 2026 generation, the Rubin NVL72s, use something like 230 kW of electrical power, almost twice as much as the previous 2024 generation.

There’s always going to be a robust used market for phones that were purchased outright, to be resold on a different cycle than every 2 years (plenty of rich people changing phones every year, and plenty of people replacing on a 3, 4, 5, or 6 year cycle). You can expect the market to basically settle on a curve where it depreciates along a predictable rate.

Leases don’t really change that, any more than leases changed the market for used cars, or even certified pre-owned by the same dealers and organized by the same manufacturers who sell new cars.

There will be times that the predefined lease terms will unexpectedly prove to be either beneficial or detrimental to the consumer. Sometimes external factors will affect the entire used market, like currency issues, or component pricing issues (imagine if RAM prices dramatically swing again for new devices in a way that affects the value of the already-sold devices out in the world), where the predefined lease prices turn into a windfall for someone. Like in 2021 or so when expiring car leases allows the lessee to buy out the car at the end of the lease for much cheaper than the car itself was worth.

It’s generally going to be a less than ideal financial decision to lease, but it also won’t collapse the used device market and it won’t be that far off the practice of selling your old phone when you buy a new one.

It’ll only make a difference during the times in which it is being hit by a beam that wouldn’t otherwise hit the earth, since whatever it reflects will be offset by some shadow behind it. I wonder if, as a matter of orbital mechanics, it would be possible to make the satellite orbit in such a way so that it never casts a shadow and is always in the sunlight.

the growth itself is hella juiced because the GPUs are only relevant for about 3 years till the new ones are out and make more AI for less power. And they depreciate them over 7 years. More than twice as long as they can or should use the GPUs for.

We don’t actually know this for sure, yet. I had expected the A100 generation (released in 2020) to no longer be profitable to run by now, but the backlog in new data centers being turned on and the high demand from Anthropic and OpenAI still leaves those chips useful for inference. You can rent those 2020 chips out today at some price above what they cost to continue running (300W, so electricity prices of USD $0.20 per kWh would translate into about 6 cents per hour. Prevailing spot prices appear to be about $2/hour right now.

But just because I was wrong on 2020 chips, originally sold for about $15,000 in a low interest rate environment, doesn’t mean that I’m wrong about 2024 chips, the B100s that use 1000W and were sold for $35,000, requiring a ton more specialized cooling, power, and network infrastructure. Or the 2026 R100s that use 2000W, and whose prices I can’t seem to find published anywhere, but were set after the memory companies basically locked in their record breaking prices for their HBM. That’s an unsustainable path and at some point, data centers start struggling to find users willing to pay the bare minimum necessary to continue turning a profit on GPU usage.

I doubt the 2024 chips stay in service to 2031. And I’m really, really skeptical that the 2026 chips stay in service to 2033, especially after NVIDIA switches to yearly release cycles next year.