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MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Transform your applications and workflows into powerful agentic systems at global scale.
Gemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
ZML, the Zeitung Markup Language, is a simple CMS for small newspapers. It was specifically designed to publish a student newspaper in print and on the Web. It uses LaTeX and XHTML. So far, it is documented in German only.
PyML server (Python Markup Language) is a content management framework. Page contents are written in pure Python, Python objects can be created and used to render HTML for remote browsers. PyML has been developped to be very simple and easy to use.
KML is a knowledge base with support of logical modeling. Advanced model is used to represent knowledge as a set of statements similar to natural language sentences. This project hosts a set of model storage library and server (vrb-ols) and clients.
YAML (tm) is a straight-forward data serilization language, offering an alternative to XML where markup (named lists and mixed content) are not needed. YAML borrows ideas from rfc822, SAX, C, HTML, Perl, and Python.
Auth0 Token Vault handles secure token storage, exchange, and refresh for external providers so you don't have to build it yourself.
Rolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
SchemaDoc is a XML-based markup language for documenting XML schemas. The work products include both the vocabulary and a set of tools for combining it with the schema source (e.g. a DTD) to produce documentation in HTML, XML DocBook, LaTeX, etc.