One of the primary tasks of engineers is to minimize complexity. JSX changes such a fundamental part (syntax and semantics of the language) that the complexity bubbles up to everything it touches. Pretty much every pipeline tool I've had to work with has become far more complex than necessary because of JSX. It affects AST parsers, it affects linters, it affects code coverage, it affects build systems. That tons and tons of additional code that I now need to wade through and mentally parse and ignore whenever I need to debug or want to contribute to a library that adds JSX support.

The important goal here is that if we're going to introduce different semantics between classes and function components, we need to know before calling them which semantics we're going to apply.

We want to improve this while keeping both the API and the semantics and behavior as Reactish as possible.

The worst answer I can imagine is the one Pope Gregory VII gave for refusing to let the Holy Scripture be translated out of Latin: “... [I]f it were plainly apparent to all men, perchance it would be little esteemed and be subject to disrespect; or it might be falsely understood by those of mediocre learning, and lead to error.”

In general, I'm unpersuaded by these arguments (learning curve is determined not just by unfamiliar syntax, but by unfamiliar semantics and APIs as well, and the frameworks in question excel at adding complexity in those areas).

Note that the point of JSON-LD is that it's just JSON, in this case using the schema.org context to interpret the values.

Apache Stanbol's main features are: Content Enhancement Services that add semantic information to “non-semantic” pieces of content. Reasoning Services that are able to retrieve additional semantic information about the content based on the semantic information retrieved via content enhancement. Knowledge Models Services that are used to define and manipulate the data models (e.g. ontologies) that are used to store the semantic information. Persistence Services that store (or cache) semantic information, i.e. enhanced content, entities, facts, and make it searchable.

direct usage from web applications (e.g. for tag extraction/suggestion; or text completion in search fields), 'smart' content workflows or email routing based on extracted entities, topics, etc.

Apache Stanbol provides a set of reusable components for semantic content management.

This graph view is the easiest possible mental model for RDF and is often used in easy-to-understand visual explanations

AI relies upon a bet. It is the bet that if you get your syntax (mechanism) right the semantics (meaning) will take care of itself. It is the hope that if computer engineers get the learning feedback process right, a new transhuman intellect will emerge.

In “Getting Real,” Barad proposes that “reality is sedimented out of the process ofmaking the world intelligible through certain practices and not others ...” (1998: 105). If,as Barad and other feminist researchers suggest, we are responsible for what exists, what isthe reality that current discourses and practices regarding new technologies makeintelligible, and what is excluded? To answer this question Barad argues that we need asimultaneous account of the relations of humans and nonhumansandof their asymmetriesand differences. This requires remembering that boundaries between humans and machinesare not naturally given but constructed, in particular historical ways and with particularsocial and material consequences. As Barad points out, boundaries are necessary for thecreation of meaning, and, for that very reason, are never innocent. Because the cuts impliedin boundary making are always agentially positioned rather than naturally occurring, andbecause boundaries have real consequences, she argues, “accountability is mandatory”(187). :We are responsible for the world in which we live not because it is an arbitraryconstruction of our choosing, but because it is sedimented out of particular practicesthat we have a role in shaping (1998: 102).The accountability involved is not, however, a matter of identifying authorship in anysimple sense, but rather a problem of understanding the effects of particular assemblages,and assessing the distributions, for better and worse, that they engender.

Text is not going away, but if we really want it to be understood and remembered, we should integrate it better with physical and emotional experience. This convergence may happen with the “physicalization” of the digital world, where digital experiences become part of our physical life.

What is missing is a space between the $( and the following (, to avoid the arithmetic expression syntax. The section on command substitution in the shell command language specification actually warns for that:

I regret that the documentation does not focus on what is most relevant; by focusing on a largely irrelevant implementation detail, we enlarge the importance of that implementation detail and obscure the importance of what makes a value type semantically useful. I dearly wish that all those articles explaining what “the stack” is would instead spend time explaining what exactly “copied by value” means and how misunderstanding or misusing “copy by value” can cause bugs.

Surely the most relevant fact about value types is not the implementation detail of how they are allocated, but rather the by-design semantic meaning of “value type”, namely that they are always copied “by value”.

For there are who read and yet neglect them; they read to remember the words, but are careless about knowing the meaning. It is plain we must set far above these the men who are not so retentive of the words, but see with the eyes of the heart into the heart of Scripture. Better than either of these, however, is the man who, when he wishes, can repeat the words, and at the same time correctly apprehends their meaning.