UsernameGenerator

UsernameGenerator builds memorable usernames from curated word lists in 11 languages: English, Norsk, Português, Español, Deutsch, Français, Italiano, Nederlands, Polski, Română and Latina. Every list was reviewed by a native speaker of that language. It declares no Android permissions at all. Not "only the clipboard": genuinely none. Everything runs on word lists compiled into the APK, so there is no network access, no storage access and nothing to phone home with. Check it yourself before installing: aapt dump permissions <the APK you downloaded> The only line you should see is the package name. It also shows you what your settings are actually worth, as you change them: 44.6 bits of entropy 1 in 27 trillion combinations Even odds of a repeat after 6.1 million names Those are two different questions and the second is the one people guess wrong. Two words with no digits is 90,000 combinations but a coin-flip repeat after just 353 names. One word repeats after about twenty people. - 1 to 5 words: mixed, or adjectives / nouns / verbs only. - Digits: on by default, before or after each word, one to five per word. Each word gets its own independently drawn run. - Languages: pick one, or let it draw one at random for the whole name, or mix every word from the pooled vocabulary of all eleven. - Separators: none, hyphen, underscore, full stop, one drawn at random for the whole name, or a different one in every gap. - No word ever appears twice in the same name. - Accents are stripped after generation, so "condução" becomes "conducao" and the result is accepted everywhere. - Randomness comes from SecureRandom, not a predictable PRNG. People generate usernames to keep identities apart, and that only works if the next one is not guessable from the last. - Recent generations are kept in memory for the session and never written to disk. A saved list of the identities you generated is exactly the file you would not want on the device. A note on the entropy figures, since most apps quote the flattering number: the guessing figure uses min-entropy, the worst case, and the repeat figure uses collision entropy, because that is what actually governs how soon two people collide. Mixing languages is worth less than it looks, because the word lists overlap: "ninja" appears in nine of the eleven. The app measures what mixing is really worth rather than assuming. The same engine also runs on the web at username.stormberry.as and as a Python CLI in the repository. All three are pinned to a shared corpus of 4,684 expectations, so they cannot silently disagree.

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