RIG software replaces a deficient MS-DOS implementation by providing a better open-source version.
RIG is a software that serves as an improvement to a disreputable closed-source, unattributed public-domain implementation on MS-DOS. This software collects data from random sources like first and last names, location data, and street names to create a fake name and address containing a ZIP code, area code, state, and city name with proper geographic consistency.
The fake implementation of this software is GPL, but the database belongs to the initial creator. If the creator is unknown, it is assumed that the database lies in the public domain. If the user is not comfortable with this, they can always provide a new database.
The installation is straightforward, and typing "make install" should suffice. However, if the system doesn't possess /dev/urandom and would instead like to use /dev/random, do a "make install CFLAGS=-DDEVRANDOM".
The latest version contains several upgrades, including the correction of compilation problems on various platforms, correction of issues with -m and -f options, the addition of Coward, SC, and matching the Biloxi area and ZIP codes. These fixes were suggested by numerous users, while the credit goes to Norbert Veber, Casey Carter, Aaron Lehmann, and Chris Lawrence.
The fake implementation of this software is GPL, but the database belongs to the initial creator. If the creator is unknown, it is assumed that the database lies in the public domain. If the user is not comfortable with this, they can always provide a new database.
The installation is straightforward, and typing "make install" should suffice. However, if the system doesn't possess /dev/urandom and would instead like to use /dev/random, do a "make install CFLAGS=-DDEVRANDOM".
The latest version contains several upgrades, including the correction of compilation problems on various platforms, correction of issues with -m and -f options, the addition of Coward, SC, and matching the Biloxi area and ZIP codes. These fixes were suggested by numerous users, while the credit goes to Norbert Veber, Casey Carter, Aaron Lehmann, and Chris Lawrence.
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