It’s kind of like in treesitter style editing, where you can “swap these two arguments,” “select this function,” “wrap this in a try block” with a single keyboard command… but way more standardized and granular. Plus with the ability to execute anything you highlight
All that and then you realize you can store code as data (since it’s just a data structure) and run data as code.
I think most programmers don’t realize how arbitrary the difference is between code and data until they get used to using LISP.
The concepts would be easier to grok up front if they just used normal function calls instead of "And now for this special syntax that only exists for this particular feature" which just adds more things to remember, instead of just the concepts themselves.
My point was, replacing n syntactic constructs by n functions or macros doesn’t reduce the cognitive load of having to know how each construct works. To the contrary, one can argue that everything having the same syntactic form makes it more difficult to distinguish different classes of features.
I just made a library with [query syntax](https://codeberg.org/veqq/declarative-dsls) over various data structures a la sql:
(import declarative-dsls/dataframes :as df)
(def people (df/dataframe :name :age :job))
(df/dataframe? people)
(df/insert! {:name "Bob" :age 30 :job "Developer"} :into people)
(df/insert! {:name "Alice" :age 27 :job "Sales"} :into people)
(df/update! :set {:job "Engineer"}
:where |(= ($ :job) "Developer")
:from people)
(df/save-csv people "people.csv" :sep "\\t")
(def people2 (df/load-csv "people.csv" :sep "\\t"))
(-> people2
df/dataframe->rows
df/rows->dataframe
df/print-as-table)
job age name
-------- --- -----
Engineer 30 Bob
Sales 27 Alice
(df/v + [1 2 3] 1 [1 2 3] 1) # returns: [4 6 8]
(df/v + 1 {:column [1 2 3] :key [1 2 3]}) # returns: {:column @[2 3 4] :key @[2 3 4]}
(df/v * [1 2 3] [[1 1 1]
[1 2 2]
[1 2 3]]) # returns: @[@[1 1 1] @[2 4 4] @[3 6 9]]
(jnj/j "3 4 $ i. 10") # returns: ((0 1 2 3) (4 5 6 7) (8 9 0 1))
(jnj/j "$" [3 4] (range 10)) # returns: ((0 1 2 3) (4 5 6 7) (8 9 0 1))
From my response, bigger than the article: https://lobste.rs/s/y0euno/why_janet_2023#c_lspe6n
Because industry lied to you, promising "simplicity and riches". The industry didn't just overcomplicate programming. It institutionalized the complication. Why? Because complexity is a moat.
Complex frameworks need certified experts. Certified experts charge more. Companies built around expertise need the complexity to persist. So the complexity gets marketed as sophistication.
They've promised: "Java/C# will get you hired anywhere", but you're hired to write xml (these days yaml). "OOP models the real world", they said. The real world doesn't have abstract factory visitors. "Design patterns make you senior", but you only learned workarounds for language deficiencies. "Learn the framework, get the job". Framework dies, you start over. "Specialization is valuable". you're now hostage to one ecosystem.
A programmer who understands fundamentals is dangerous to this system. The fundamentals:
- a function transforms input to output.
- composition builds complexity from simplicity.
- types describe what's possible.
- effects should be explicit.
And then you realize that Lisp is the skeleton key. All that above is Lisp, or came from Lisp. Every language is either: Lisp with different syntax, or C with different syntax, or arguing between the two.
If you learn Lisp, you don't learn a language. You learn what languages are. You're no longer a consumer of a programming language or two, or a few. You are native speaker in all of them.