But if you had read the article and attached links then you would have learned that the particular issue under discussion and source of other issues is from Project Big Sleep, which focuses on using generative tooling to confabulate issues. You would also see for yourself that the reported issues are in C.

No, this is an explanation of dataflow programming. Functional programming is only connected to dataflow programming by the fact that function application necessarily forces data to flow. Quoting myself on the esolang page for “functional paradigm”:

The functional paradigm of language design is the oldest syntactic and semantic tradition in computer science, originating in the study of formal logic. Features of languages in the functional paradigm are not consistent, but often include:

• The syntactic traditions of combinatory logic and lambda calculus, carried through the Lisp, ML, and APL families
• Applicative trees and combining forms
• A single unified syntax for expressions, statements, declarations, and other parts of programs
• Domain-theoretic semantics which admit an algebra of programs
• Deprecation or removal of variables, points, parameters, and other binders in favor of point-free/tacit approaches

This definition comes from a famous 1970s lecture. The author is a Scala specialist and likely doesn’t realize that Scala is only in the functional paradigm to the extent that it inherits from Lisps and MLs; from that perspective, functional programming might appear to be a style of writing code rather than a school of programming-language design.

You have no idea what an abstraction is. You’re describing the technological sophistication that comes with maturing science and completely missing out on the details. C was a hack because UNIX’s authors couldn’t fit a Fortran compiler onto their target machine. Automatic memory management predates C. Natural-language processing has been tried every AI summer; it was big in the 60s and big in the 80s (and big in the 90s in Japan) and will continue to be big until AI winter starts again.

Natural-language utterances do not have an intended or canonical semantics, and pretending otherwise is merely delaying the painful lesson. If one wants to program a computer — a machine which deals only in details — then one must be prepared to specify those details. There is no alternative to specification and English is a shitty medium for it.

Haskell isn’t the best venue for learning currying, monads, or other category-theoretic concepts because Hask is not a category. Additionally, the community carries lots of incorrect and harmful memes. OCaml is a better choice; its types don’t yield a category, but ML-style modules certainly do!

@[email protected] and @[email protected] are oversimplifying; a monad is a kind of algebra carried by some endofunctor. All endofunctors are chainable and have return values; what distinguishes a monad is a particular signature along with some algebraic laws that allow for refactoring inside of monad operations. Languages like Haskell don’t have algebraic laws; for a Haskell-like example of such laws, check out 1lab’s Cat.Diagram.Monad in Agda.

I’m most familiar with the now-defunct Oregon University System in the USA. The topics I listed off are all covered under extras that aren’t included in a standard four-year degree; some of them are taught at an honors-only level and others are only available for graduate students. Every class in the core was either teaching a language, applying a language, or discrete maths; and the selections were industry-driven: C, Java, Python, and Haskell were all standard teaching languages, and I also recall courses in x86 assembly, C++, and Scheme.

The typical holder of a four-year degree from a decent university, whether it’s in “computer science”, “datalogy”, “data science”, or “informatics”, learns about 3-5 programming languages at an introductory level and knows about programs, algorithms, data structures, and software engineering. Degrees usually require a bit of discrete maths too: sets, graphs, groups, and basic number theory. They do not necessarily know about computability theory: models & limits of computation; information theory: thresholds, tolerances, entropy, compression, machine learning; foundations for graphics, parsing, cryptography, or other essentials for the modern desktop.

For a taste of the difference, consider English WP’s take on computability vs my recent rewrite of the esoteric-languages page, computable. Or compare WP’s page on Conway’s law to the nLab page which I wrote on Conway’s law; it’s kind of jaw-dropping that WP has the wrong quote for the law itself and gets the consequences wrong.

My $HOME is recreated on boot and lives in RAM. I don’t care what gets written there; if I didn’t know about it and intend to save it to disk, then it won’t be saved. It would be nice if tools were not offenders here, but that doesn’t mean that we can’t defend ourselves somewhat.

Also, the author has a standalone blog post on the topic from 2011, Expression Parsing Made Easy.

It’s not even an analogy; pointers and reference mechanics are the same concept in programming and linguistics. See the page on referents for an example blend of viewpoints.

The author would do well to look up SGML; Markdown is fundamentally about sugaring the syntax for tag-oriented markup and is defined as a superset of HTML, so mistaking it for something like TeX or Word really demonstrates a failure to engage with Markdown per se. I suppose that the author can be forgiven somewhat, considering that they are talking to writers, but it’s yet another example of how writers really only do research up to the point where they can emit a plausible article and get paid.

It’s worth noting that Microsoft bought PowerPoint, GitHub, LinkedIn, and many other things—but it did in fact create Word and Excel. Microsoft is, in essence, a sales company. It’s not too great at designing software.

So close to a real insight! The correct lesson is that Microsoft, like Blizzard, is skilled at imitating what’s popular in the market; like magpies, they don’t need to have a culture of software design as long as they have a culture of software sales. In particular, Microsoft didn’t create Word or Excel, but ripped off WordPerfect and Lotus 1-2-3.

Forget about your job for a moment. In general, why are you willing to set a threshold on how accessible your work is? I urge you to forget about how callous your employer wants you to be.

across the Internet … doesn’t require storing data twice

A flavor of the CAP theorem applies here; if you want your data to be available even during network partitions (and those are going to happen on the Internet!) then it has to be duplicated somehow. For example, I still have a soft spot for Tahoe-LAFS, which allows users to control how much duplication will be used and typically is configured to have some redundancy. Typical cloud providers build redundancy into their storage products; for example, it’s known that Google’s Colossus storage system uses Reed-Solomon to trade space for durability.

Nah, just use direnv instead, comrade.

For what it’s worth, most of your comments aren’t eligible for copyright; they aren’t sufficiently original or information-packed. Just like @[email protected] and their licensing efforts, it’s mostly a vanity to attach a license to unoriginal one-line throwaway jokes. I wouldn’t say that it’s arrogant so much as lacking in self-awareness; a one-liner must be deeply insightful, contain a pun or paraprosdokian, address the current zeitgeist, or otherwise be memorable above and beyond the time and place that contextualized it.

Like @[email protected] I use plain-text accounting (PTA) to manage a few bank accounts, including small businesses. In my case, I’m using hledger. Every time I get a receipt, invoice, or statement, I type it into a text file. My bank gives me CSVs which are easy to import. Reports are done with a few commands; repeatable reports are done by saving commands to a shell script. hledger comes with builtin tools for monthly reports, BSE, currencies, pending invoices, closing/opening new years, and those are merely what I recall using recently.

A claim which is mostly true is false. Programmers should pay attention to details, since that is all computers know.

Open your mind a little; collective action has an impact but individual action may not. Paraphrasing Cloud Atlas, certainly an ocean is nothing more than a vast collection of raindrops, but each individual raindrop collectively acts as a body of water. This dissolves your false dilemma.

There are a lot of programming languages. Also, features can often be hacked onto or off of a language. It’s therefore important to be able to quickly reject a language based on undesirable features. It’s also important to recall the big picture: to maintain a large amount of instructions or transformations which have been proven correct. Anything which gets in the way of that big picture should be quickly rejected.

Self’s descendants are not well-understood in our popular culture. The two most popular (Turing-complete) languages, ECMAScript and Python, are both Self grandchildren, and Java is also a child of Self; yet, the article’s author incorrectly believes them to be ALGOL descendants because of surface syntax as well as the Java/ECMAScript focus on performance. Note also that the author doesn’t mention E (WP, esolangs), which is akin to Erlang in making message-passing explicit but descends from Self, unlike Erlang which descends from Prolog. (I will give them partial credit for noting that Smalltalk is an ancestor of Java.)

So, the exemplar should be a message-passing everything-is-an-object language designed for JIT with no Prolog influence. The earliest such language in the family tree is Self!

INTERCAL is an ALGOL descendant. A holistic timeline of esoteric languages shows that esoteric languages don’t form a family; however, there are several families not mentioned in the article, notably cellular automata and string/wire diagrams.