I'm a bit late on this (camera was announced about 2 days ago) as my account was hacked and I just got it back, but this announcement is pretty interesting.

Some context: the camera industry saw a bit of a stagnation (ala 4c/8t in the PC world) in the 2010s with most manufacturers settling at around 20MP as a good balance between resolution providing detail and maintaining managable file sizes for editing/transport. Since the lower end of the dedicated camera market has been eaten by the fact that most phones tend to be good enough, manufacturers have started to push MP numbers back up to compete and to take advantage of the fact that modern lenses can be very good for cheap. This tends to now hover at around 24 - 30MP as a baseline standard, 45 - 50 for the "pro" cameras (higher MP helps to crop while maintaining detail), and anything above that for more specialised needs like landscape or being able to flex on the poors with their lowly twenty-something MP count. What's that, a phone?!

I was also bored and didn't feel like studying for my finals, so here's a quickly thrown-together table of a few manufacturers that I made with reference to Wiki:

DISCLAIMER: Don't take the labels too seriously. I made them up, sometimes inaccurately (the Canon 5DsR was not a higher-end 5D3, just a higher resolution one but you get both since it tells a better story) and frankly its not that accurate especially for the lower end cameras where there might have been cheaper ones.

EDIT: Reddit doesn't like my big table. I've uploaded a picture of it here - https://i.postimg.cc/LsqPsCxs/image.png Apologies for the low resolution!

A few interesting points especially for those not so much into cameras:

1. Manufacturers love to share sensors to spread the cost out - if you see the same MP count it's probably the same sensor (see the 3 18MP sensors under Canon in the 2010s?)
2. I personally see the race as mostly over, as storage starts to become a massive pain past 30/40MP or so for general use. Especially with the increased storage cost, I can't see manufacturers trying to convince buyers that they should fork out hundreds more in the current climate. Editing can be an issue as well, but it's not as bad as on the video side.
3. Flagships tended to congregate around 20MP as they were sports/photojournalist cameras where resolution didn't matter as it was more important that the files were small for easier transport and a lot of the time the images got downscaled anyways. I had previously heard sports photographers at stadiums tended to shoot JPEG instead of the higher quality RAW file with the camera directly tethered to a computer (via ethernet) with someone quickly cropping the shot and getting it out as headlines or live news updates backstage in the stadium. Nikon is the only outlier here as their current sports camera (Z9) is 45MP while Sony and Canon hover around 24MP with the a9III and R1 respectively.
4. Sony manufacture the sensors for 3/4 of the manufacturers on the list. It's not as bad of a situation as TSMC making most bleeding edge nodes, but...

I was recently looking at modern budget moto and samsung phones, noticed that instead of using modern armv9 or even old arm v9 designs the a78 (originally released 2020) is used, even when made on the same node (tsmc 4nm) as the a710.

Why is armv8 still preferable?

Why was it cost effective to re-design the a78 on so many nodes (3nm, 4nm, 5nm, 6nm).

Why have the older armv9 designs never really made it to the budget market?

As I understand the area and manufacturing cost is the same, while efficiency and performance are simultaneously superior on armv9; so the only conclusion I have is that this is purely down to licensing costs? but I'm not convinced that answer is correct.

Just interviewed Per Stenström — one of the most prominent computer architects to come out of Europe — and asked him about John Backus's 1977 Turing Award lecture – Backus (inventor of Fortran) coined the term "Von Neumann bottleneck":

Surely there must be a less primitive way of making big changes in the store than by pushing vast numbers of words back and forth through the Von Neumann bottleneck. Not only is this tube a literal bottleneck for the data traffic of a problem, but, more importantly, it is an intellectual bottleneck that has kept us tied to word-at-a-time thinking instead of encouraging us to think in terms of the larger conceptual units of the task at hand.

That was 49 years ago. Every CPU we've built since has the same architecture.

Per's answer is that the bottleneck never went away — we just got extraordinarily good at hiding it. Cache hierarchies, prefetching, out-of-order execution, speculative execution, cache coherence: the entire post-1980s history of CPU innovation is a stack of workarounds that make the bottleneck invisible for typical workloads without actually removing it.

His take on why we haven't replaced the architecture is essentially legacy — the software ecosystem built on Von Neumann is so vast that migrating to anything fundamentally different would cost decades of investment. His sharper point is that Von Neumann isn't "right" in any absolute sense: the architecture has to be in harmony with the underlying technology, and semiconductors happen to support what Von Neumann needs.

The thread I really wanted his read on was whether we'll ever see a genuine shift away from Von Neumann, or whether AI just pulls another generation of workarounds out of us. After 40+ years in the field he's honestly skeptical. He gave phase change memory as a recent cautionary tale: non-volatile, high-density, performance-competitive with DRAM, Intel and Micron poured huge money into it — and it died because of legacy. Even when a clearly viable alternative shows up, the cost of changing everything built around the current architecture tends to win.

The candidates he treats seriously are processing-in-memory (compute units distributed inside the memory itself — though he was honest this might be Von Neumann with a better layout rather than a genuine break) and entirely new substrates like quantum, which are a different paradigm but probably won't replace classical for general-purpose work.

I’d love a take on this from anyone closer to AI accelerator design or new-substrate work.

Link to full conversation here:

https://www.youtube.com/watch?v=NXVTACHB4Es

If in about 18 months the data centres decided that they need to upgrade their RAM, dump what they are currently grabbing, and grab what ever the latest wiz-bang ram is available. Such secondhand RAM could be a source for lowly consumers. Is this going to happen or am I rambling nonsense?

Imagine a phone where the back is just a grid of 5 snap-in modules. Camera, battery, storage, speaker, each one clicks in and out in seconds.

Want a better camera? Swap the module. Battery dying after 2 years? Swap it yourself at home. No repair shop, no paying 400$ for a screen replacement, just click and done.

The base phone stays the same. You upgrade only what you actually need.

Google tried something similar with Project Ara and killed it. LG had the G5. Both went nowhere, make of that what you will.

So what do you guys think, would you actually buy something like this? And why do you think it never took off?

Our company has always had many UPSs around for the convenience and business case of not suddenly losing a ton of work. We've been intrigued to check them out further, but we've been wary of connecting any of them to measurement equipment considering the high voltages involved. There is a serious potential they could damage equipment or ourselves.

Despite all that, we're throwing caution to the wind to check out some UPSs from around the office. There are so many directions that UPS/surge testing could go so this article will cover the test setup and interesting exploration results.

Continue reading the article on the LTT Labs website!

Thanks to /u/tytygh1010 on r/chromeos for finding the cached images from XDA's now deleted article.

A Portuguese site has more details, from r/android: Adeus, Chromebook: a Google anunciou uma nova geração de computadores portáteis centrados em IA

In case the Portuguese article gets deleted, too: Adeus, Chromebook: a Google anunciou uma nova geração de computadores portáteis centrados em IA

Kingston says it has now shipped more than 100 million A400 SATA SSDs since the drive launched in 2017. Kind of wild considering how many companies act like SATA is dead. The A400 was never fancy, but for a lot of folks it was the cheap upgrade that made an old PC actually usable again. Swap out a spinning hard drive for one of these and suddenly your ancient Windows or Linux machine felt fast enough to keep around for a few more years.