data:image/png;base64,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

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere ca:_ sense la primera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra ca:_ sense la primera paraula

All but first of empty sentence.

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere ca:_ sense la darrera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra ca:_ sense la darrera paraula

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ ca:primera lletra de _ 0

pt:a última palavra de _ ca:darrera paraula de _

Last of empty sentence.

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia ca:paraula buida? _ 0

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia ca:frase buida? _

Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ firstbffirst1

1bf

Takes a sentence in list form and reports the sentence as a text string.

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _

pt:lança o erro _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

ca:notació textual de _ (

)

Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _ ca:primera lletra de _

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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==0g0000