It’s about making APIs more flexible, permissive, and harder to misuse by clients. It’s a user-centric approach to API design. It’s not done to make it easier on backend. If anything, it can take extra effort by backend developers.

But you’d clearly prefer vitriol to civil discourse and have no interest in actually learning anything, so I think my time would be better spent elsewhere.

As I already said, it’s very simple with JSON Patch:

[
  { *op": "replace", "path": "/Name™, "value": "otherName"}
]

Good practice in API design is to permissively accept either undefined or null to represent optionality with same semantics (except when using JSON Merge Patch, but JSON Patch linked above should be preferred anyway).

The semantics of the API contract is distinct from its implementation details (lazy loading).

Treating null and undefined as distinct is never a requirement for general-purpose API design. That is, there is always an alternative design that doesn’t rely on that misfeature.

As for patches, while it might be true that JSON Merge Patch assigns different semantics to null and undefined values, JSON Merge Patch is a worse version of JSON Patch, which doesn’t have that problem, because like I originally described, the semantics are explicit in the data structure itself. This is a transformation that you can always apply.

Zalando explicitly forbids it in their RESTful API Guidelines, and I would say their argument is a very good one.

Basically, if you want to provide more fine-grained semantics, use dedicated types for that purpose, rather than hoping every API consumer is going to faithfully adhere to the subtle distinctions you’ve created.

Only if using JSON merge patch, and that’s the only time it’s acceptable. But JSON patch should be preferred over JSON merge patch anyway.

Servers should accept both null and undefined for normal request bodies, and clients should treat both as the same in responses. API designers should not give each bespoke semantics.

Confused what you mean. OpenAPI has nothing to do with JS.

Pretty dumb, honestly. If anything it just adds a Streisand effect to it as people try to figure out what’s censored.

Not that censoring it has any value whatsoever. Like if a child sees that, so fucking what?

Yeah it’s not all that uncommon in school, just increasingly uncommon in industry.

Visual… programming languages? Yikes.

Common Lisp isn’t a functional programming language. Guile being based on Scheme is closer, but I’d still argue that opting into OOP is diverging from the essence of FP.

Just remember that if you aren’t actually concatenating files, cat is always unnecessary.

If you mean for programming specifically, I… don’t, really. At most it would be for a quick sanity check on syntax in a language I don’t write often, for which Google is fine. But otherwise I rely on documentation and search features of the various language/tool-specific websites.

https://porkmail.org/era/unix/award#cat

jq < file.json

cat is for concatenating multiple files, not redirecting single files.

Meanwhile, I can open a 1GB file in (stock) vim without any trouble at all.

Formatting is what xmllint is for.

:syntax off and it works just fine.

I understand what you’re saying—I’m saying that data validation is precisely the purpose of parsers (or deserialization) in statically-typed languages. Type-checking is data validation, and parsing is the process of turning untyped, unvalidated data into typed, validated data. And, what’s more, is that you can often get this functionality for free without having to write any code other than your type (if the validation is simple enough, anyway). Pydantic exists to solve a problem of Python’s own making and to reproduce what’s standard in statically-typed languages.

In the case of config files, it’s even possible to do this at compile time, depending on the language. Or in other words, you can statically guarantee that a config file exists at a particular location and deserialize it/validate it into a native data structure all without ever running your actual program. At my day job, all of our app’s configuration lives in Dhall files which get imported and validated into our codebase as a compile-time step, meaning that misconfiguration is a compiler error.

You’re just describing parsing in statically-typed languages, to be honest. Adding all of this stuff to Python is just (poorly) reinventing the wheel.

Python’s a great language for writing small scripts (one of my favorite for the task, in fact), but it’s not really suitable for serious, large scale production usage.

No, you divide work so that the majority of it can be done in isolation and in parallel. Testing components together, if necessary, is done on integration branches as needed (which you don’t rebase, of course). Branches and MRs should be small and short-lived with merges into master happening frequently. Collaboration largely occurs through developers frequently branching off a shared main branch that gets continuously updated.

Trunk-based development is the industry-standard practice at this point, and for good reason. It’s friendlier for CI/CD and devops, allows changes to be tested in isolation before merging, and so on.

Sure… That"s what libraries are for. No one hand-rolls that stuff. You can do all of that just fine (and, actually, in a lot less code, mostly because Java is so fucking verbose) without using the nightmare that is Spring.

The tooling has improved dramatically since then. There’s now a full-fledged language server (https://haskell-language-server.readthedocs.io/en/stable/), ghcup (https://www.haskell.org/ghcup/) is now a thing for installing/managing different versions of GHC/cabal/HLS, there’s now formatters (https://github.com/tweag/ormolu) and cabal has modernized significantly and supports multi-package projects much more comfortably now. Nix-based Haskell infrastructure is also now pretty nice. There’s even stuff like https://github.com/srid/haskell-template/blob/master/flake.nix to very quickly get spun up on a new project using Haskell and nix, including vscode, formatter, HLS, and a full development shell with a bunch of useful commands.

Another great modern thing (which powers HLS) is that GHC can now emit .hie files for each file it compiled, which is basically a standardized representation of the AST for that module that can be consumed/manipulated programatically. Lots of tools can use this. One such tool that’s particularly useful is https://github.com/wz1000/HieDb, which constructs an sqlite database from the information in these files, so you basically can have an index of every symbol definition, reference, export, etc. all readily available to use however you want.