Does this suggest that for cryptographic purposes quantum computing can only go so far?
In other words, does this mean that a future-proof cryptographic algorithm is really possible, rather than a just a race with hardware progress?

Some time earlier I had a VHS of Shada using whatever footage they had plus Tom Baker narrating the missing parts and filling them in. This animated version might be fun. It's been long enough that I've pretty much forgotten everything except that the one scene in the boat made it into the Five Doctors special.

How long ago was that, and have they been tracked for cataracts? UV in general is helpful in developing cataracts.

It does exactly what I got it for, and I'm happy with it. Since installation there have been 42 "events" with a total usage of about a day, the longest 3 hours. Most of the events have been ten minutes or so, just a few longer ones. It's kind of nice to find out about a power outage on my phone, then look out and see the street dark. (numbers off the app on my phone)

I've been in this house 40 years now, serviced by GMP. When we first moved in the power reliability was rather iffy. I'd started looking into a generator and sizing things up - then the reliability got much better. It was that way for a long time, then probably 10 years or so ago the reliability started dropping again. So when the battery option popped back up I was an early adopter.

A few notes that others have hinted about. It's not just a battery, it's got an ethernet cable into my network, so it talks to the utility. It's also got an environmental system built in for both cooling and heating, though sitting in my basement it stays at a pretty constant temperature. Because it's got constant monitoring GMP will know if things start to go south, and I suspect I'd get a notification on my phone. So it's not like the thing is suddenly going to burst into flames with no warning at all - there will at least be prompt notification. More likely it's not completely out of the blue and there are warning signs.

I started a project at work on Python 1.5, and it was a bit disruptive when I had to move it to 2.0, and even 2.1 was some disruption from that. I continued supporting that project for between 10 and 15 years and kept it at Python 2.x even after Python 3 was out.

At the time I figured I would be retired before I needed to move to Python 3, but I was wrong. I ended up moving to 3.x several years before retiring this past spring. Part of the problem is that RedHat Enterprise last I knew shipped Python 2.7 by default. The company also had local installations of Python 3.x, and both versions had semi-overlapping additional libraries, which was quite annoying. I actually had some code that was split between the two versions and passed intermediate data between them.

FBP is the way to go -- I've used it for year. It blocks all sorts of Facebook intrusions while on the site. It also supports modifying the UI behavior (e.g. time line chronological or whatever), filtering posts by keywords, all kinds of bells and whistles. It's open source, the author accepts donations.

https://www.fbpurity.com/

Not affiliated with them in any way (in fact, I think Steeeve is a bit of a jerk some times). Only a happy user.

Cheers!

Or take the money and then run anyway.

Since people here presumably have experience with these things...

My wife and I will be in the market for a new car a few years down the road, and we've been thinking tentatively that it will be electric. One of my bigger concerns is that we live in Vermont. One of the things about internal combustion engines is the amount of waste heat, and that means that heating the interior in winter is pretty simple. Waste the heat inside instead of outside. With an electric there is less waste heat, and that means that heating the passengers means losing range.

I know with electrics they start with heated seats because that's a more efficient use of electricity. I'm wondering how comfortable are electric cars in deep winter from actual owners.

Or the other side of the solution is for Paramount to have a process for this kind of thing. They really should not be discouraging fans in any way, even as they need to protect their copyrights and trademarks legally. This isn't an either/or kind of thing, both can be done at the same time.

That's what's interesting. It's pretty common to use sodium hypochlorite, then a little hydrochloric acid to compensate for the pH rise. My pH doesn't rise, all summer. If anything it used to fall after rain, and I'd have to add borax or washing soda. This year the pH has been stable all summer after initial setup. I guess acid rain is getting cleaned up.

The semiconductor manufacturing industry practically lives on HF acid, it's utterly irreplaceable. They also know how to take proper precautions with it, and understand mitigations in case of accidents.

I regularly use 12% sodium hypochlorite in my swimming pool.

Almost all of us use a chemical that is cancerous, poisonous, and is so volatile that if you vaporize a 17 gallons of it, it's equivalent in explosive power to half a ton of TNT. By the way, 17 gallons is what it takes to fill my gas tank.

Lots of stuff is dangerous, but you just have to understand how to use it safely.

Extrapolating from the announcements, and knowing Anaconda is involved, they are locking a given tool set (package versions) that are known to work well together, much like Jupyter works. This guarantees that, for the versions available, everything will work well together. It may also hinder the ability to add new or updated packages (e.g. SARIMAX if it's not available in the standard distribution). The user may have little to no control over adding packages and importing them to Excel/Python code.

As for running in the cloud -- this is a very elegant solution to very large data problems. Excel has significant limitations re: data volume. It's fantastic at handle a few thousands of data rows, but there are Python-based tools that allow full parallelization and partitioning very large data sets for processing (e.g. Dask) that scale up and out. Large Python loads are often run in the cloud like this (or with Spark, or with...).

Cheers!