In computing, gettext is an internationalization and localization (i18n and l10n) system commonly used for writing multilingual programs on Unix-like computer operating systems. One of the main benefits of gettext is that it separates programming from translating. The most commonly used implementation of gettext is GNU gettext, released by the GNU Project in 1995. The runtime library is libintl. gettext provides an option to use different strings for any number of plural forms of nouns, but this feature has no support for grammatical gender. The main filename extensions used by this system are
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In computing, gettext is an internationalization and localization (i18n and l10n) system commonly used for writing multilingual programs on Unix-like computer operating systems. One of the main benefits of gettext is that it separates programming from translating. The most commonly used implementation of gettext is GNU gettext, released by the GNU Project in 1995. The runtime library is libintl. gettext provides an option to use different strings for any number of plural forms of nouns, but this feature has no support for grammatical gender. The main filename extensions used by this system are .POT (Portable Object Template), .PO (Portable Object) and .MO (Machine Object).
==History== Initially, POSIX provided no means of localizing messages. Two proposals were raised in the late 1980s, the 1988 Uniforum gettext and the 1989 X/Open catgets (XPG-3 § 5). Sun Microsystems implemented the first gettext in 1993. The Unix and POSIX developers never really agreed on what kind of interface to use (the other option is the X/Open catgets), so many C libraries, including glibc, implemented both. , whether gettext should be part of POSIX was still a point of debate in the Austin Group, despite the fact that its old foe has already fallen out of use. Concerns cited included its dependence on the system-set locale (a global variable subject to multithreading problems) and its support for newer C-language extensions involving wide strings.
Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).