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Wiko, the wiki compiler, compiles wiki like files into html and LaTeX, combining easy wiki syntax, your preferred non-web text editor and svn/cvs control to write static webs, cientific articles or even blogs.
A Trac plugin to allow pasting screenshots or images with one click
A Trac plugin to allow pasting screenshots or other images captured or copied in the clipboard directly as attachements to tickets, Wiki pages, etc., without the need to first saving as images and then uploading them.
Once the plugin is installed in Trac, you can easily attach a screenshot or any image you have in the clipboard to a Ticket or Wiki page, with one click.
Extension of Trac from Edgewall. This plugin adds possibility to use semantic features in wiki. It is targeted to support software development and presentation. Uses standalone semantic repository Sesame 2. Download plugin or try virtual machine.
Full access to Enterprise features. No credit card required.
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A Python library and collection of tools that automate work on MediaWiki sites. Originally designed for Wikipedia, it is now used throughout the Wikimedia Foundation's projects and on many other MediaWiki wikis including wikidata service.
We do not use sourceforge.net anymore, but are very grateful for their support in the past. Please see our website at https://www.mediawiki.org/wiki/Pywikibot
Our pypi package could be found at https://pypi.org/project/pywikibot/
Wikind is a wiki that heavily uses kind information for every aspect of pages, objects or files management, storing and operating. Wikind tries also to be kind to users; editors and readers of wiki. Wikind targets to have *real* file based backend.
Cloud data warehouse to power your data-driven innovation
BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.
BigQuery Studio provides a single, unified interface for all data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. It also allows you to use simple SQL to access Vertex AI foundational models directly inside BigQuery for text processing tasks, such as sentiment analysis, entity extraction, and many more without having to deal with specialized models.