Your reference for #1, The Center Square, is a conservative rag and not a neutral source. Also, the source cited in its article, is from the Reason Foundation, a libertarian advocacy organization. Can you provide an actual source that is not some political advocacy organization? This is no better than if someone used an article from Mother Jones to support the assertion of how awesome CalPERS is. Do better.

I suspect that helicopter parenting is a much larger contributor than physical isolation. We have had sparse suburbs in the US since at least the 1950s, and generations of kids grew up in that environment and did just fine.

It's definitely this. I live in one of the safest cities in the country, in the Midwest. The other day on some local Facebook group, I saw a mom trying to find someone to pick up her kids from middle school and elementary school every day. It was a 10 and 5 minute walk respectively that EVERYONE in that neighborhood took 30 years ago. No busy streets, nothing. Sidewalks and everything. Absolutely insane.

I also live in an incredibly safe area and had a friend from outside the US say it is like a horror movie after walking around a few times on a beautiful summer night when they visited.

Perfect summer night and there is not a person to be seen. They felt like they were walking around in some kind of zombie horror movie that all the people had vanished.

It's hard to tell. You have to wait a generation or two to see the long term effects, as people will still have their earlier social habits.

It feels as though people slowly learned that they could get away with not introducing themselves to their neighbors, invite them over for dinner, and other activities that were once assumed.

1950s was the baby boom so is not a great example. Children per adult has been falling ever since and suburban areas growing.

I wrote starting in the 1950s. It was certainly true for other generations like gen X (me) and also for older millennials.

What makes Python a great language for data science, is that so many people are familiar with it, and that it is an easy language to read. If you use a more obscure language like Clojure, Common Lisp, Julia, etc., many people will not be familiar with the language and unable to read or review your code. Peer review is fundamental to the scientific endeavor. If you only optimize on what is the best language for the task, there are clearly better languages than Python. If you optimize on what is best for science then I think it is hard not to argue that Python (and R) are the best choices. In science, just getting things done is not enough. Other people need to be able to read and understand what you are doing.

BTW AI is not helping and in fact is leading to a generation of scientists who know how to write prompts, but do not understand the code those prompts generate or have the ability to peer review it.

I can't speak for Julia - never used it; never used Common Lisp for analyzing data (I don't think it's very "data-oriented" for the modern age and the shape of data), but Clojure is really not "obscure" - it only looks weird for the first fifteen minutes or so; once you start using it - it is one of the most straightforward and reasonable languages out there - it is in fact simpler than Python and Javascript. Immutable-by-default makes it far much easier to reason about the code. And OMG, it is so much more data-oriented - it's crazy that more people don't use it. Most never even heard about it.

Common Lisp fan here, but not a data scientist. Why do you say to avoid CL for data analysis? Not trying to flame or anything, just curious about your experience with it.

I don't have great experience of using CL for analyzing data, because of "why?", if I already have another Lisp that is simply amazing for data.

Clojure, unlike lists in traditional Lisps, based on composable, unified abstraction for its collections, they are lazy by default and literal readable data structures, they are far easier to introspect and not so "opaque" compared to anything - not just CL (even Python), they are superb for dealing with heterogeneous data. Clojure's cohesive data manipulation story is where Common Lisp's lists-and-symbols just can't match.

Homework assignments notwithstanding, very few serious Common Lisp programs use lists and symbols as their primary data structures. This has been true since around 1985.

Common Lisp has O[1] vectors, multidimensional arrays, hash-tables (what Clojure calls maps), structs, and objects. It has set operations too but it doesn't enforce membership uniqueness. It also has bignums, several sizes of floats, infinite-precision rationals, and complex numbers. Not to mention characters, strings, and logical operations on individual bits. The main difference from Clojure is that CL data structures are not immutable. But that's an orthogonal issue to the suggestion that CL doesn't contain a rich library of modern data structures.

Common Lisp has never been limited to "List Processing."

I wasn't trying to denigrate Common Lisp, I'm sorry if I hurt your feelings. It does have comprehensive support for all kinds of data structures. I wasn't talking it being limited to "list processing". SBCL is great for many things, but from many practical points Clojure actually much better suited for data analysis.

You're saying: "hash-tables (what Clojure calls maps)" not only inaccurate, you're hand-waving Clojure's core design philosophy (immutability, structural sharing, lazy sequences) as orthogonal. But those aren't cosmetic differences - they're the reason why Clojure's data structures are fundamentally better for data analysis. I think you're confusing "having equivalent data types" with "solving the same problem the same way"

I tried to get into Clojure, but a lot of the JVM hosted languages require some Java experience. Same thing with Scala and Kotlin or F# on .NET.

The early tooling was also pretty dependent on Vim or Emacs. Maybe it's all easier now with VSCode or something like that.

It doesn't require any Java but the docs do at times sort of assume you understand the JVM to some extent - which was a bit frustrating when first learning the language. It'll use terms like "classpath" without explaining what that is. However nowadays with LLMs these are insignificant speedbumps.

If you want to use Java you also don't really need to know Java beyond "you create instances of classes and call methods on them". I really don't want to learn a dinosaur like Java, but having access to the universe of Java libs has saved me many times. It's super fun and nice to use and poke around mature Java libs interactively with a REPL :)

All that said I'd have no idea how to write even a helloworld in Java

PS: Agreed on Emacs. I love Emacs.. but it's for turbo nerds. Having to learn Emacs and Clojure in parallel was a crazy barrier. (and no, Emacs is not as easy people make it out to be)

None of this even remotely true. I've gotten into Clojure without knowing jackshit about Java, almost ten years later, after tons of things successfully built and deployed, still don't know jackshit about Java. Mia, co-host of 'Clojure apropos' podcast was my colleague, we've worked together on multiple teams, she learned Clojure as her very first PL. Later she tried learning some Java and she was shocked how impossibly weird it looked compared to Clojure. Besides, you can use Clojure without any JVM - e.g., with nbb. I use it for things like browser automation with Playwright.

The tooling story is also very solid - I use Emacs, but many of my friends and colleagues use IntelliJ, Vim, Sublime and VSCode, and some of them migrated to it from Atom.

It might not be a problem for you, but it has been for many. I did start by reading through 3 Clojure books. The repl and the basic stuff like using lists is all easy of course, but the tooling was pretty poor compared to what I was used to (I like lisp, but Emacs is a commitment). Also, a lot of tutorials at the time definitely assumed java familiarity, especially with debugging java stack traces.

I've had this discussion here on HN several times over the years. Lots of comments from others have pointed out similar experiences. I'm guessing your experience was more positive and that's great to hear.

I did point out that maybe things had changed a good bit (literally said maybe VSCode made that easier now as it has for other tools) and tried to make it clear that my experience was a bit dated.

As far as excuses go, I don't see how that's relevant. I just pointed out I had issues with a steep learning curve when I was seriously considering it many years ago along with other languages that are hosted on the JVM (Scala, Kotlin) or .NET (F#). Nothing against those languages, but all the tutorials and even many of the books at the time would frequently borrow from the host language in weird ways. Like I'd have to use some random Java library and when it didn't work, had no idea how to troubleshoot why it wasn't there and I didn't want to have to go learn Java first.

I own at least two books on F# and talked with some prominent authors personally and they admitted it was really geared towards intermediate or greater C# users who wanted to move over to functional programming. I could have stuck with it, but decided to stick with other tools.

Clojure certainly is nice and I wanted to take advantage of it...it just ended up not being as ergonomic for my needs as I had hoped.

> What makes Python a great language for data science, is that so many people are familiar with it

While I agree with you in principal this also leads to what I call the "VB Effect". Back in the day VB was taught at every school as part of the standard curriculum. This made every kid a 'computer wizz'. I have had to fix many a legacy codebase that was started by someone's nephew the whizz kid.

Peer review is fundamental to scientific endeavor but... in ML fields, reviewers almost never check the code and Python package management is hardly reproducible. So clearly we are not there, Python or not.

This is great news! I know the new Pebble has a small team and is focused on being a long term sustainable enterprise. This should help with that goal quite a bit.

I received my Pebble 2 Duo about a month ago, and it is awesome. Nice job and thank you! I feel 10 years younger :)

All I can say is I am very happy with my brand new Pebble. Thank you Eric and your team for providing new hardware. This is awesome! Thank you Rebble, for maintaining the legacy software.

Are they still open source? If so, why does it matter who updated them?

I think that’s the crux of the issue is rebble isn’t under any obligation distribute them open source, unless say the original app had a “copyleft” policy?

AFAIK the original apps were individually licensed by the creators... so Rebble would need to have permission before claiming anything for their own except in the case of explicit permissive licenses (like MIT). In some cases (copyleft licenses) Rebble would be required to make their maintenance also open source.

This whole topic is about politics and I am leery of steering even more in that direction, but based on recent polls, I’m not sure that the passengers currently do support the hijackers.

He was a grandmaster at age 13. In 2003.

I lived on an island in the middle of the Panama Canal for a few years, one of the hottest and most humid places imaginable. We had no AC. Everyone used a top sheet instead of a comforter or blanket, and had a ceiling fan going at night. It worked surprisingly well.

Ceiling fans are really underrated. Even if you have AC, you can probably save money/energy at night by running the AC at a higher temp with a ceiling fan.

I would have gone with 0