The number they used for annualized revenue for these rapidly growing companies is taking the most recent month’s revenue and multiplying it by 12. It’s not a guarantee about the future, but it is misleading.
The extreme version is taking a good week’s revenue and multiplying it by 52, or even worse, multiplying a day by 365. Those are outright dishonest. But not accounting fraud, because it’s not accounting at all.
The tablet one eventually came true. Desktops and laptops aren’t anywhere near as popular for personal use by non-technical people.
In the U.S., copyright protections don’t extend to functionality, though. So copying even copyrighted code in a manner to replicate functionality is fair use when there’s no other way to accomplish the same thing.
For example, if you have a piece of equipment that checks the software on a cartridge for a string of text that says “Produced by or under license from Sega Enterprises Ltd.” before running that software, then it’s fair use to copy that exact text so that your software can run on that equipment. And it’s fair use to reverse engineer and decompile licensed cartridges to see what the bare minimum necessary to make it work.
One way to prove that you didn’t copy the software any more than is strictly necessary for functionality is to fully document the functionality, and then have a skilled programmer take the documentation and write new software from scratch, without ever having seen the original software whose function is being copied. That’s a “cleanroom implementation.” Compaq and other IBM clones built their own BIOS software to implement the exact same functionality of copyrighted IBM code, and created an entire industry of IBM compatible PCs that weren’t actually licensed from IBM. Similarly, Google moved Android off of Sun-licensed Java using a cleanroom implementation (when Oracle bought Sun and Google wanted to get away from Larry Ellison’s abusive pricing practices).
Ok, so if it’s permissible to reverse engineer the code to create documentation of how it works, and then have someone else take that documentation and implement the functionality using new code, how do AI/LLMs fit into this? Can it be said that it’s truly a “cleanroom” when the reverse engineering and decompilation functions are done by the same software that converts the decompiled code into documentation in human language, and then is the same software that converts the documentation into newly implemented code? Doesn’t quite hit the same way, and I’m not sure the courts would see it the same way.
All of this is a gray area, and people shouldn’t confidently predict what the courts will decide in specific nuanced examples. A lot will depend on the specific details, so there isn’t going to be much room for sweeping generalizations.
It is believed that pi is “absolutely normal” in the strict mathematical definition: that in any base numbering system, any finite sequence of digits appears as frequently as any other sequence of that length. If that’s true, then yes, a particular binary string, including one that corresponds to a particular digital file (like a particular mp4 file), can be found in pi.
That said, it hasn’t actually been proven that pi is normal. If it’s not normal, then the number can be infinitely non-repeating and still never hit that particular sequence of digits.
Yeah, you’re probably right. Still, if big tech’s ad behavior (ads for stuff you’ve already bought, ads for things you’d never buy but might have looked up information about for non-consumer reasons) is any indication, I’m not actually sure they’re using really sophisticated techniques here that can’t be thrown off by one-off bored sessions.
When I’m bored, I get price quotes from Lyft and Uber to the airport. I’ll click around and look at the different tiers of service. Then I choose not to actually request a fare, and close the app.
I fly enough to where both apps probably have me as a regular airport customer, but that I’m price sensitive enough where I just won’t use their service sometimes.
I do the same with Doordash and other food apps, where I just put together potential orders to get a sense of how much something would cost if I ordered it, and then just never order it, letting that cart expire. I’m hoping this poisons their data or adds something to the price sensitivity metrics.
I also look at airfares, hotel prices, etc., a lot. It’s not for the purpose of manipulating their algorithms, as I just like to get a sense for how prices move over time. But if there’s a side effect that these variable pricing algorithms take into account people who window shop and walk away, maybe I’m helping push prices down.
If they’re gonna create a panopticon, we should fuck with their data.
I’m also waiting to see how Linux support for the A-series chips develops, now that the MacBook Neo has A18 Pros running MacOS. There are people working on tools to allow Asahi’s m1n1 bootloader to work on the Neos. Unclear how much of that effort will ultimately be upstreamed to Asahi itself, but I’m hopeful.
Most CPUs implement a strategy called “race-to-sleep,” where it throws all the resources it has at a computing task, to finish it as fast as possible, so that it can hurry back to a low power sleep mode.
For that reason, optimizing speed often improves battery life.
Technically these are all System in Package (SiP) rather than systems on a chip (SoCs). It’s not cost effective to waste silicon area on things like memory and storage in the latest and greatest TSMC nodes, so they have several different silicon dies that they connect together using advanced packaging techniques, but where each silicon die in that package might come from a different foundry/process.
Not exactly, the bootpicker just won’t show partitions that are not flagged as bootable. This is a departure from previous versions, that would check to see whether partitions were bootable without looking at the flag, and took a little bit of investigating to figure out this undocumented behavior, but it is an easy fix:
What if Claude was one of us?
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.
Financial Times smells blood in the water. They’ve been reporting on leaked OpenAI documents for a bit the last few weeks.
If the major financial outlets start reporting on the negative, there may be a sentiment shift that does actually destroy the companies propped up by hype rather than fundamentals.