Go offers most of these, or has very similar functionality. Many are also in common with other languages.

As for Go, the differences from what you listed above are:

1. Go implements optional types using pointers (and, as with most languages, implements pointers in a much safer way than C).

2. Go's error handling is just as explicit, but does require you explicitly return the error (Go natively supports multiple return values). I'm not sure whether this is good or bad.

3. Go doesn't have tuples explicitly, but the ability to have multiple return values makes them less necessary. It's also possible to define structs inline (It's a little verbose, as you have to specify the type, but it's not too bad).

4. Named parameters are one nice thing that Go lacks.

5. Guard is just syntactical sugar around an if statement. It's nice, I guess, in that it helps to enforce better programming practices, but it's not anything particularly interesting.

6. Swift isn't a particularly fast language, except perhaps compared with Objective-C, which has some truly horrible performance characteristics in places.

They are different languages, with their own benefits and drawbacks, but I just don't see anything here that Swift offers that is all that special (and yes, Go has a playground as well).

Go's big differentiator, for instance, is its support of channels. Channels offer a paradigm for multithreaded processing that is quite different from what other languages offer. Most languages, for example, make use of mutexes to keep concurrent processes safe. Go, on the other hand, implements multithreading as communication: if thread A writes to a channel, and thread B reads from it, thread B will wait until thread A has its value ready. This is the main feature that makes Go so useful for server applications.

Some also appreciate the language's simplicity (the main design philosophy of Go was to make the base language as small as possible while supporting all of the main programming paradigms). One result of this decision is that generic programming can be extremely verbose (you can use interfaces to allow a function to accept anything that implements that interface, and there's a reflection API that lets you do different things with different types passed...it's rather clunky, but functional). Overall, this makes programming in the language rather weird.

Pretty much the only thing that sets Swift apart is the fact that Apple is pushing the language. It does appear to be quite a bit better than Objective-C (which has some truly horrific functionality, such as no type safety for containers and runtime errors for invalid message calls). But I really doubt it will make all that much headway into projects which aren't targeting Apple products. It's good, but I doubt many teams will see a reason to switch.

Eh. As near as I can tell, Swift is very similar to Go, except without the channels but with a more traditional generics system. I have yet to see anything particularly different about it except for a few slick string manipulation operations. But those really aren't of that much value.

There's a fairly good chance that Apple is going to push to have their OS transitioned over to Swift.

Just as with Objective-C, I doubt that hardly anybody else will make use of the language.

The problem is it just doesn't have all that much to offer for projects that are already making use of other languages. It's got a few slick features, but it can't really stand out all that much and the library support is going to be very far behind more mature languages for a long time (if not forever).

I was actually pretty impressed at how close it came to creating sensible cards, and pretty funny when it made small errors that made the cards absurd (such as the card with, "At the beginning of each player's upkeep, sacrifice a white Zombie creature").

Getting a computer to generate understandable language is an extremely difficult problem, and all neural networks have an issue with long-tail errors (that is, a small fraction of the results are always ridiculously inaccurate, no matter how good your neural net is). Getting a computer to generate meaningful language is an order of magnitude more difficult than that.

It looks like this only applies to older versions of the Ask toolbar. According to the link in the post, "The latest version of this application is not detected by our objective criteria, and is not considered unwanted software."

I do hope that their "objective criteria" will help to keep the Ask toolbar from being quite as annoying as it was, however.

It's not the language nearly as much as it's more general software development skills such as algorithms, data structures, algorithmic complexity, and design patterns. It's really easy to transition between languages and shore up your own holes in knowledge by keeping links to reference resources (or books).

The general practice of knowing how to translate an idea into a workable piece of code is far, far more important. The individual language is just the medium through which you're working. Different languages have somewhat different toolboxes (with a lot of overlap), but overall the general concepts are the same. Focus on the software design fundamentals. You can pick up a new computer language within a few weeks whenever you need to.

And there are things we can do to replace fossil fuels that will make most peoples' lives better due to the lower environmental damage of nearly every other energy source (whether or not you include CO2 emissions as "environmental damage").

And in deciding that there "was enough wrong with how climate research is currently conducted," did you actually ever speak to an actual climate scientist? Did you ever learn how they do their work? I doubt it. I bet you've just read a bunch of bullshit misinformation repeated from conservative think tanks.

Here's a hint: if you want to learn about science, listen to scientists. Not political hacks.

There's lots of evidence of global warming right now, and many peoples' lives have been directly impacted. But I can't help you if you don't bother to learn about the world around you.

It's really hard to know, unfortunately. The rate of melt of Greenland appears to have nearly doubled in just the last ten years. At the current rate, it would take something like 7500 years for Greenland to melt entirely. But the rate is likely to increase. By how much? It's hard to say.

Nah, they did that for the same reason that they screwed up on the sea level rise: they're far too conservative. If you look at the estimates of climate sensitivity, they cluster pretty closely around 3C per doubling of CO2 as a best-fit. A few of them permit a 1.5C sensitivity as possible, but there's no reason to believe that's remotely likely as those are in the minority. Other studies permit 5C of warming per doubling of CO2, but again, that's not very likely.

The biggest errors with the IPCC come from their habit of understating just how bad the warming is. To be fair, this may make political sense: the organization is, after all, intended to provide advice to policy makers, and overestimating global warming could give fuel to the denialists who want to claim that it isn't happening. But they have a tendency to go too far sometimes (the sea level issue was particularly egregious).

Then please explain to me why the 2007 IPCC report manipulated the data (and used old, bad data) in a rather dishonest way that halved the estimated future sea level rise?

They used a low estimate of future temperature increases (lower than the best-fit temperature estimates they reported elsewhere).

They extrapolated out to only 2095 instead of 2100.

They used a model of sea level rise that predicts a past sea level rise that is only 2/3rds the measurements.

See here for a detailed discussion of the IPCC's screwup with sea level rise.

Given that sea level rise estimates have so far significantly underestimated the rate of sea level rise, and the fact that nobody (to my knowledge) has successfully built a model of the melting rate of glaciers that is anywhere near as fast as observations, the smart money is on the upper part of that range (possibly even higher, if we're very unlucky).

Sure, if you pick some really short periods (say, 5 years), there may not appear to be any warming. Or there may appear to be a huge amount of warming.

But that's just because the Earth's temperature varies quite a bit from year to year, largely due to the El Nino Southern Oscillation (which exchanges heat between the atmosphere and the ocean). So it's foolish to take such short periods of time and claim they mean something for the overall trend.

There are also ways to lie with graphs that make it look as if certain longer periods show no warming, such as using monthly data to increase the variation in the graphs, and picking out a specific year that was warmer than the years before and after as a starting point. But if you look at the full data set, especially if you smooth it with a 3-year or 5-year window, the warming trend is clear as day, and hasn't changed all that much for the last three decades.

2-3 feet is a low estimate by 2100. Recent sea level rise has exceeded the model used to calculate that estimate by about 50%.