NameScout
Search domains, package registries, code hosts, and plugin marketplaces from one practical report. No hype. Just the collisions that matter.
Provider coverage
Live checks across the registries developers actually publish to. Two more on the way.
Report
lexin.com
domain · DNS records found
lexin.io
domain · DNS records found
GitHub org/user
code · GitHub namespace exists
Docker Hub
distribution · Rate limited
npm
package · Exact package name is clear
PyPI
package · Exact project name is clear
Recommended actions
Full report
lexin.com
high confidence · DNS records found
lexin.dev
medium confidence · No DNS record found
lexin.io
high confidence · DNS records found
lexin.app
high confidence · DNS records found
lexin.ai
high confidence · DNS records found
lexin.co
high confidence · DNS records found
npm
high confidence · Exact package name is clear
PyPI
high confidence · Exact project name is clear
crates.io
high confidence · No exact crate found
RubyGems
high confidence · Gem name is clear
Packagist
high confidence · No vendor packages found
GitHub org/user
high confidence · GitHub namespace exists
VS Code
medium confidence · No matching publisher or extension
Docker Hub
low confidence · Rate limited
Homebrew
high confidence · No formula or cask found
Near matches
Even when your exact name is free, these are close enough to cause confusion.
biuaxia/editStepForLexinAndXiaoMi
GitHub乐心&小米 运动,刷步数
ontolex/lexinfo
GitHubLexInfo - Data Category Ontology for OntoLex-Lemon
facebookresearch/bitext-lexind
GitHubBilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces. In this paper, we show it is possible to produce much higher quality lexicons with methods that combine (1) unsupervised bitext mining and (2) unsupervised word alignment. Directly applying a pipeline that uses recent algorithms for both subproblems significantly improves induced lexicon quality and further gains are possible by learning to filter the resulting lex-ical entries, with both unsupervised and semi-supervised schemes. Our final approach out-performs the state of the art on the BUCC 2020shared task by 14 F1 points averaged over 12 language pairs, while also providing a more interpretable approach that allows for rich reasoning of word meaning in context.
Mateuszd6/simd-lexing
GitHubA very fast SIMD (AVX2) accelerated lexer that does not rely on tables