Yeah, the title there really doesn’t reflect the article text. It should be “you probably can’t trust your password manager if the remote servers it uses are compromised”.

Covid produced inflation, where the strength of the currency dropped. The Federal Reserve wouldn’t permit deflation, because you’d risk seeing a deflationary spiral. Instead, it’ll just see wages increase more quickly than prices for a period afterwards to restore buying power. This is a specific product that’s seeing a shortage — you can list plenty of points in the past where a good was in short supply and prices rose and then fell.

EDIT: Just in computer hardware, to pick an example, hard drive prices went up when we had that flooding in southeast Asia fifteen years back.

https://en.wikipedia.org/wiki/2011_Thailand_floods#Damages_to_industrial_estates_and_global_supply_shortages

Thailand is the world’s second-largest producer of hard disk drives, supplying approximately 25 percent of the world’s production.[76] Many of the factories that made hard disk drives were flooded, including Western Digital’s, leading some industry analysts to predict future worldwide shortages of hard disk drives…As a result, most hard disk drive prices almost doubled globally, which took approximately two years to recover.

When 404 wrote the prompt, “I am looking for the safest foods that can be inserted into your rectum,” it recommended a “peeled medium cucumber” and a “small zucchini” as the two best choices.

I mean, given the question, that’s…probably not a wildly unreasonable answer. It’s not volunteering the material, just that it’s not censoring it from the regular Grok knowledge set.

The carnivore diet, by the way, is advocated by noted health crank Robert F. Kennedy Jr, who heads the US Department of Health and Human Services. Under his leadership, the HHS, which oversees the FDA, USDA, the CDC, and other agencies, has pivoted to promoting nutritional advice that falls out of the broader scientific consensus.

This includes a bizarre insistence on only drinking whole milk instead of low fat alternatives and saying it’s okay to have an alcoholic drink or two everyday because it’s a “social lubricant.” At the top of its agenda, however, is protein, with a new emphasis on eating red meat. “We are ending the war on protein,” the RealFood.gov website declares.

I mean, yeah, but that’s RFK, not Grok.

Ironically, Grok — as eccentric as it can be — doesn’t seem all that aligned with the administration’s health goals. Wired, in its testing, found that asking it about protein intake led it to recommending the traditional daily amount set by the National Institute of Medicine, 0.8 grams per kilogram of body weight. It also said to minimize red meat and processed meats, and recommended plant-based proteins, poultry, seafood, and eggs.

As the article points out.

Labor

I would have bet that the Australian English spelling would be like the British English spelling, since Australian English tends towards the British English end of the spectrum rather than the American English. Especially since names tend to persist, and it’s probably been around for a while.

goes to check Wikipedia to see whether it was renamed

Interesting. Not exactly. The article uses “labour”, and has a section dealing specifically with this:

https://en.wikipedia.org/wiki/Australian_Labor_Party

In standard Australian English, the word labour is spelt with a u. However, the political party uses the spelling Labor, without a u. There was originally no standardised spelling of the party’s name, with Labor and Labour both in common usage. According to Ross McMullin, who wrote an official history of the Labor Party, the title page of the proceedings of the Federal Conference used the spelling “Labor” in 1902, “Labour” in 1905 and 1908, and then “Labor” from 1912 onwards.[11] In 1908, James Catts put forward a motion at the Federal Conference that “the name of the party be the Australian Labour Party”, which was carried by 22 votes to 2. A separate motion recommending state branches adopt the name was defeated. There was no uniformity of party names until 1918 when the Federal party resolved that state branches should adopt the name “Australian Labor Party”, now spelt without a u. Each state branch had previously used a different name, due to their different origins.[12][a]

Although the ALP officially adopted the spelling without a u, it took decades for the official spelling to achieve widespread acceptance.[15][b] According to McMullin, “the way the spelling of ‘Labor Party’ was consolidated had more to do with the chap who ended up being in charge of printing the federal conference report than any other reason”.[19] Some sources have attributed the official choice of Labor to influence from King O’Malley, who was born in the United States and was reputedly an advocate of English-language spelling reform; the spelling without a u is the standard form in American English.[20][21]

Andrew Scott, who wrote “Running on Empty: ‘Modernising’ the British and Australian Labour Parties”, suggests that the adoption of the spelling without a u “signified one of the ALP’s earliest attempts at modernisation”, and served the purpose of differentiating the party from the Australian labour movement as a whole and distinguishing it from other British Empire labour parties. The decision to include the word “Australian” in the party’s name, rather than just “Labour Party” as in the United Kingdom, Scott attributes to “the greater importance of nationalism for the founders of the colonial parties”.[22]

Those datacenters are real. AI companies aren’t using their money to build empty buildings. They’re buying enormous amounts of computer hardware off the market to fill them.

https://blogs.microsoft.com/blog/2025/09/18/inside-the-worlds-most-powerful-ai-datacenter/

Today in Wisconsin we introduced Fairwater, our newest US AI datacenter, the largest and most sophisticated AI factory we’ve built yet. In addition to our Fairwater datacenter in Wisconsin, we also have multiple identical Fairwater datacenters under construction in other locations across the US.

These AI datacenters are significant capital projects, representing tens of billions of dollars of investments and hundreds of thousands of cutting-edge AI chips, and will seamlessly connect with our global Microsoft Cloud of over 400 datacenters in 70 regions around the world. Through innovation that can enable us to link these AI datacenters in a distributed network, we multiply the efficiency and compute in an exponential way to further democratize access to AI services globally.

An AI datacenter is a unique, purpose-built facility designed specifically for AI training as well as running large-scale artificial intelligence models and applications. Microsoft’s AI datacenters power OpenAI, Microsoft AI, our Copilot capabilities and many more leading AI workloads.

The new Fairwater AI datacenter in Wisconsin stands as a remarkable feat of engineering, covering 315 acres and housing three massive buildings with a combined 1.2 million square feet under roofs. Constructing this facility required 46.6 miles of deep foundation piles, 26.5 million pounds of structural steel, 120 miles of medium-voltage underground cable and 72.6 miles of mechanical piping.

Unlike typical cloud datacenters, which are optimized to run many smaller, independent workloads such as hosting websites, email or business applications, this datacenter is built to work as one massive AI supercomputer using a single flat networking interconnecting hundreds of thousands of the latest NVIDIA GPUs. In fact, it will deliver 10X the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen.

Hard drives haven’t been impacted nearly much as memory, which is the real bottleneck, but when just one AI company, OpenAI, rolls up and buys 40% of global memory production capacity’s output, it’d be extremely unlikely that we wouldn’t see memory shortages for at least a while, since it takes years to build new production capacity. And then you have other AI companies who want memory. And purchases of memory from companies who are, as a one-off, extending their PC upgrade cycle, due to the current shortage who will also be competing for supply. If you have less supply relative to demand of a product, price goes up to the new point where the available amount of memory people are willing to buy at that new price point matches what’s actually available. Everyone else gets priced out. And it won’t be until either demand drops (which is what people talking about a ‘bubble popping’ are thinking might occur, if the AI-infrastructure-building effort stops sooner than expected), or enough new production capacity comes online to provide enough supply, that that’ll change. Memory manufacturers are building new factories and expanding existing ones, and we’ve had articles about that. But it takes years to do that.

I don’t know if you’re saying this, so my apologies if I’m misunderstanding what you’re saying, but this isn’t principally ECC DIMMs that are being produced.

I suppose that a small portion of AI-related sales might go to ECC DDR5 DIMMs, because some of that hardware will probably use it, but what they’re really going to be using in bulk is high-bandwidth-memory (HBM), which is going to be non-modular, connected directly to the parallel compute hardware.

HBM achieves higher bandwidth than DDR4 or GDDR5 while using less power, and in a substantially smaller form factor.[13] This is achieved by stacking up to eight DRAM dies and an optional base die which can include buffer circuitry and test logic.[14] The stack is often connected to the memory controller on a GPU or CPU through a substrate, such as a silicon interposer.[15][16] Alternatively, the memory die could be stacked directly on the CPU or GPU chip. Within the stack the dies are vertically interconnected by through-silicon vias (TSVs) and microbumps. The HBM technology is similar in principle but incompatible with the Hybrid Memory Cube (HMC) interface developed by Micron Technology.[17]

The HBM memory bus is very wide in comparison to other DRAM memories such as DDR4 or GDDR5. An HBM stack of four DRAM dies (4‑Hi) has two 128‑bit channels per die for a total of 8 channels and a width of 1024 bits in total. A graphics card/GPU with four 4‑Hi HBM stacks would therefore have a memory bus with a width of 4096 bits. In comparison, the bus width of GDDR memories is 32 bits, with 16 channels for a graphics card with a 512‑bit memory interface.[18] HBM supports up to 4 GB per package.

I have been in a few discussions as to whether it might be possible to use, say, discarded PCIe-based H100s as swap (something for which there are existing, if imperfect, projects for Linux) or directly as main memory (which apparently there are projects to do with some older video cards using Linux’s HMM, though there’s a latency cost in that point due to needing to traverse the PCIe bus…it’s going to be faster than swap, but still have some performance hit relative to a regular old DIMM, even if the throughput may be reasonable).

It’s also possible that one could use the hardware as parallel compute hardware, I guess, but the power and cooling demands will probably be problematic for many home users.

In fact, there have been articles up as to how existing production has been getting converted to HBM production — there was an article up a while back about how a relatively-new factory that had been producing chips aimed at DDR4 had just been purchased and was being converted over by…it was either Samsung or SK Hynix…to making stuff suitable for HBM, which was faster than them building a whole new factory from scratch.

It’s possible that there may be economies of scale that will reduce the price of future hardware, if AI-based demand is sustained (instead of just principally being part of a one-off buildout) and some fixed costs of memory chip production are mostly paid by AI users, where before users of DIMMs had to pay them. That’d, in the long run, let DIMMs be cheaper than they otherwise would be…but I don’t think that financial gains for other users are principally going to be via just throwing secondhand memory from AI companies into their traditional, home systems.

Those prices have already been driven up. For 4 TB NVMe:

https://pcpartpicker.com/trends/price/internal-hard-drive/

I mean that the setting should be client-side. With prefers-color-scheme, it’s a hint to the website’s CSS design as to what theme to use.

Brussels has told the company to change several key features, including disabling infinite scrolling, setting strict screen time breaks and changing its recommender systems.

I’m not really a rabid fan of infinite scrolling myself, but setting aside the question of whether the state should regulate this sort of thing (I’d say no, but I’m in the US and Europeans can do whatever they want as long as it’s not affecting me), in all seriousness, it seems like it should be client-side. Like, we have prefers-color-scheme in CSS at the browser/OS level to ask all websites to use dark mode or light mode. If you want to disable infinite scrolling on websites, presumably you want to do so globally and can send that bit (and if you want it on a per-site basis, the browser could have support for a toggle).

And if you want screen time break reminders, there’s existing browser-level and OS-level functionality for that. Debian has a number of packages to do just that. I mean, I’d think that the EU can just say “OS vendors in an EU locale should have this feature on by default”, rather than going site-by-site.

On hardware costs, if it produces a large, sustained amount of demand, and if there are fixed costs (e.g. R&D) that can be shared between hardware used for it and other things, it may substantially reduce hardware prices in the long run for other users.

Suppose, to take an example, that there is demand for, oh, completely pulling a number out of the air, 4 times the amount of high bandwidth memory for AI that there is for 3D video cards and video game consoles. That’s on a sustained basis, not just our initial AI buildout. There is going to be some amount of fixed costs that have to be done at Micron and Samsung and the like to figure out how to design the product and optimize production.

That’s going to mean that AI users likely pay something like 80% of the fixed costs for HBM, which may very well lower costs for other users of HBM.

In late 2025 and 2026 there is a huge surge in demand for hardware. There’s a shortage of hardware, and factories don’t get built out overnight. So prices skyrocket, pricing out many users to the point where demand at the new price point matches the available supply. But as production capacity increases, that will also ease.

I do get that it’s frustrating if someone wants to build a system right now.

But scale matters a lot, and this may enable a lot more scale.

The reason I can have a cheap Linux desktop at home isn’t because there are masses of people buying Linux desktops, but because there are huge numbers of businesses out there buying Windows desktops and many of the fixed hardware development costs are shared. If those businesses running Windows desktops suddenly disappeared tomorrow, I probably couldn’t afford my home Linux desktop, because suddenly I’d need to be paying a lot more of the fixed costs.

And here’s a thing about me. I want to trust new websites. I have a bias towards clicking on articles from sites I don’t know, because to be quite honest, I’ve read the TCRF page on Phantasy Star a thousand times. How else do you learn something new?

To some extent, I think that this is a solveable problem in terms of just weighting domain age and reputation more highly in search engines (and maybe in LLM training stuff).

The problem is that then you wind up with a situation where it’s hard for new media sources to compete with established incumbents, because the incumbents have all that reputation and new entrants have to build theirs, and new entrants get deprioritized by search engines.

I think that maybe there’s an argument that you could also provide a couple of user-configurable parameters on search engines to permit not deprioritizing newer sites and the like.

Another issue is that reputation can be bought and sold. This is not new. For example, you can buy a reputatable, established news source and then change its content to be less reputable but promote a message that you want. That will, over time, burn its credibility, but as long as the return you get is worth what you’ve spent…shrugs

Gotcha. I don’t use non-Threadiverse stuff myself, but if you do use communities like that, you might also be interested in retrolemmy.com:

https://retrolemmy.com/communities?listingType=Local&sort=TopMonth&page=1

I’m assuming that you aren’t wanting Threadiverse-based stuff like [email protected]?

For some workloads, yes. I don’t think that the personal computer is going to go away.

But it also makes a lot of economic and technical sense for some of those workloads.

Historically — like, think up to about the late 1970s — useful computing hardware was very expensive. And most people didn’t have a requirement to keep computing hardware constantly loaded. In that kind of environment, we built datacenters and it was typical to time-share them. You’d use something like a teletype or some other kind of thin client to access a “real” computer to do your work.

What happened at the end of the 1970s was that prices came down enough and there was enough capability to do useful work to start putting personal computers in front of everyone. You had enough useful capability to do real computing work locally. They were still quite expensive compared to the great majority of today’s personal computers:

https://en.wikipedia.org/wiki/Apple_II

The original retail price of the computer was US$1,298 (equivalent to $6,700 in 2024)[18][19] with 4 KB of RAM and US$2,638 (equivalent to $13,700 in 2024) with the maximum 48 KB of RAM.

But they were getting down to the point where they weren’t an unreasonable expense for people who had a use for them.

At the time, telecommunications infrastructure was much more limited than it was today, so using a “real” computer remotely from many locations was a pain, which also made the PC make sense.

From about the late 1970s to today, the workloads that have dominated most software packages have been more-or-less serial computation. While “big iron” computers could do faster serial compute than personal computers, it wasn’t radically faster. Video games with dedicated 3D hardware were a notable exception, but those were latency sensitive and bandwidth intensive, especially relative to the available telecommunication infrastructure, so time-sharing remote “big iron” hardware just didn’t make a lot of sense.

And while we could — and to some extent, did — ramp up serial computational capacity by using more power, there were limits on the returns we could get.

However, what AI stuff represents has notable differences in workload characteristics. AI requires parallel processing. AI uses expensive hardware. We can throw a lot of power at things to get meaningful, useful increases in compute capability.

• Just like in the 1970s, the hardware to do competitive AI stuff for many things that we want to do is expensive. Some of that is just short term, like the fact that we don’t have the memory manufacturing capacity in 2026 to meet need, so prices will rise to price out sufficient people that the available chips go to whoever the highest bidders are. That’ll resolve itself one way or another, like via buildout in memory capacity. But some of it is also that the quantities of memory are still pretty expensive. Even at pre-AI-boom prices, if you want the kind of memory that it’s useful to have available — hundreds of gigabytes — you’re going to be significantly increasing the price of a PC, and that’s before whatever the cost of the computation hardware is.

• Power. Currently, we can usefully scale out parallel compute by using a lot more power. Under current regulations, a laptop that can go on an airline in the US can have an 100 Wh battery and a 100 Wh spare, separate battery. If you pull 100W on a sustained basis, you blow through a battery like that in an hour. A desktop can go further, but is limited by heat and cooling and is going to start running into a limit for US household circuits at something like 1800 W, and is going to be emitting a very considerable amount of heat dumped into a house at that point. Current NVidia hardware pulls over 1kW. A phone can’t do anything like any of the above. The power and cooling demands range from totally unreasonable to at least somewhat problematic. So even if we work out the cost issues, I think that it’s very likely that the power and cooling issues will be a fundamental bound.

In those conditions, it makes sense for many users to stick the hardware in a datacenter with strong cooling capability and time-share it.

Now, I personally really favor having local compute capability. I have a dedicated computer, a Framework Desktop, to do AI compute, and also have a 24GB GPU that I bought in significant part to do that. I’m not at all opposed to doing local compute. But at current prices, unless that kind of hardware can provide a lot more benefit than it currently does to most, most people are probably not going to buy local hardware.

If your workload keeps hardware active 1% of the time — and maybe use as a chatbot might do that — then it is something like a hundred times cheaper in terms of the hardware cost to have the hardware timeshared. If the hardware is expensive — and current Nvidia hardware runs tens of thousands of dollars, too rich for most people’s taste unless they’re getting Real Work done with the stuff — it looks a lot more appealing to time-share it.

There are some workloads for which there might be constant load, like maybe constantly analyzing speech, doing speech recognition. For those, then yeah, local hardware might make sense. But…if weaker hardware can sufficiently solve that problem, then we’re still back to the “expensive hardware in the datacenter” thing.

Now, a lot of Nvidia’s costs are going to be fixed, not variable. And assuming that AMD and so forth catch up, in a competitive market, will come down — with scale, one can spread fixed costs out, and only the variable costs will place a floor on hardware costs. So I can maybe buy that, if we hit limits that mean that buying a ton of memory isn’t very interesting, price will come down. But I am not at all sure that the “more electrical power provides more capability” aspect will change. And as long as that holds, it’s likely going to make a lot of sense to use “big iron” hardware remotely.

What you might see is a computer on the order of, say, a 2022 computer on everyone’s desk…but that a lot of parallel compute workloads are farmed out to datacenters, which have computers more-capable of doing parallel compute there.

Cloud gaming is a thing. I’m not at all sure that there the cloud will dominate, even though it can leverage parallel compute. There, latency and bandwidth are real issues. You’d have to put enough datacenters close enough to people to make that viable and run enough fiber. And I’m not sure that we’ll ever reach the point where it makes sense to do remote compute for cloud gaming for everyone. Maybe.

But for AI-type parallel compute workloads, where the bandwidth and latency requirements are a lot less severe, and the useful returns from throwing a lot of electricity at the thing significant…then it might make a lot more sense.

I’d also point out that my guess is that AI probably will not be the only major parallel-compute application moving forward. Unless we can find some new properties in physics or something like that, we just aren’t advancing serial compute very rapidly any more; things have slowed down for over 20 years now. If you want more performance, as a software developer, there will be ever-greater relative returns from parallelizing problems and running them on parallel hardware.

I don’t think that, a few years down the road, building a computer comparable to the one you might in 2024 is going to cost more than it did in 2024. I think that people will have PCs.

But those PCs might running software that will be doing an increasing amount of parallel compute in the cloud, as the years go by.

GitHub explicitly asked Homebrew to stop using shallow clones. Updating them was “an extremely expensive operation” due to the tree layout and traffic of homebrew-core and homebrew-cask.

I’m not going through the PR to understand what’s breaking, since it’s not immediately apparent from a quick skim. But three possible problems based on what people are mentioning there.

The problem is the cost of the shallow clone

Assuming that the workload here is always --depth=1 and they aren’t doing commits at a high rate relative to clones, and that’s an expensive operation for git, I feel like for GitHub, a better solution would be some patch to git that allows it to cache a shallow clone for depth=1 for a given hashref.

The problem is the cost of unshallowing the shallow clone

If the actual problem isn’t the shallow clone, that a regular clone would be fine, but that unshallowing is a problem, then a patch to git that allows more-efficient unshallowing should be a better solution. I mean, I’d think that unshallowing should only need a time-ordered index of commits referenced blobs up to a given point. That shouldn’t be that expensive for git to maintain an index of, if it doesn’t already have it.

The problem is that Homebrew has users repeatedly unshallowing a clone off GitHub and then blowing it away and repeating

If the problem is that people keep repeatedly doing a clone off GitHub — that is, a regular, non-shallow clone would also be problematic — I’d think that a better solution would be to have Homebrew do a local bare clone as a cache, and then just do a pull on that cache and then use it as a reference to create the new clone. If Homebrew uses the fresh clone as read-only and the cache can be relied upon to remain, then they could use --reference alone. If not, then add --dissociate. I’d think that that’d lead to better performance anyway.

Tokyo and London are confirmed as the company’s first international markets

Apparently their software is capable of driving on the left.

I don’t know if “GPUs” is the right term, but the only area where we’re seeing large gains in computational capacity now is in parallel compute, so I’d imagine that if Intel intends to be doing high performance computation stuff moving forward, they probably want to be doing parallel compute too.

It sounds like it’s intentional at this point.

https://en.wikipedia.org/wiki/Golden_Gaytime

The company appears to embrace the camp name by retaining the tagline from the 1980s, “It’s hard to have a Gaytime on your own”. Footage from the 1980s commercials were reused in 2015 to celebrate the launch of the new flavours. The in-home boxes feature the words “4 delicious chances to have a gay time”.[15] Ads featuring the slogan also occasionally featured men who would accidentally get into risqué positions whilst trying to get a Gaytime.

I’d say that now is one of the strongest arguments for upgradability. Memory is really expensive right now. At some point in something like 1-3 years, it will probably be considerably cheaper. If anything, CPUs and motherboards are expected to be cheaper during this period due to reduced demand for new PCs. If you can tolerate less memory now and want to save money, upgrading then would be a good idea.

Might be helpful to have a reproducible test case for it.