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Webinar Recording: https://www.panagenda.com/webinars/alles-neu-macht-der-mai-wir-durchleuchten-den-verbesserten-notes-eigenschaftendialog/ Haben Sie sich schon einmal über den zu kleinen Eigenschaftendialog in Notes geärgert? Mussten Sie einen Agenten oder eine Aktion erstellen, um schnell mal ein Feld zu ändern? Haben Sie jedes mal endlos nach dem zu vergleichenden Feld gesucht, nachdem Sie ein neues Dokument ausgewählt haben? Wollten Sie das verdammte Ding einfach nur größer machen? Zum Glück gibt es dafür eine Lösung – und sie ist wahrscheinlich bereits installiert! Mit dem kostenlosen panagenda Document Properties (Pro) erhalten Sie den Eigenschaftendialog, den Sie schon immer haben wollten. Größer, anpassbar, und im Volltext durchsuchbar. Sehen Sie mehrere Dokumente gleichzeitig oder vergleichen Sie mit einem Diff-Viewer. Ändern Sie beliebige Felder und haben Sie endlich eine einfache Möglichkeit, Profildokumente für alle Benutzer zu verwalten. Entdecken Sie mit HCL Ambassador Marc Thomas, wie Document Properties Ihre Arbeit vereinfachen und Sie bei der täglichen Verwendung von Domino-Anwendungen unterstützen kann – im Client oder im Designer. Sie werden es nicht bereuen! Für Sie in diesem Webinar - Was Document Properties ist, welche Editionen es gibt und wo es in Notes und Domino Designer zu finden ist - Wie Sie nach einem beliebigen Feld suchen und es bearbeiten, Dokumente vergleichen oder alle Daten per CSV exportieren können - Suchen, Bearbeiten und auch Löschen von Profildokumenten - Welche Konfigurationseinstellungen verfügbar sind, um Funktionen anzupassen - Wie Ihre Endbenutzer davon profitieren - Sehen Sie alles in einer Live-Demo

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Generative AI refers to a class of machine learning algorithms that are designed to generate new data samples that are similar to those in the training data. Unlike traditional AI models that are trained to recognize patterns and make predictions, generative AI models have the ability to create entirely new data based on the patterns they have learned. This is achieved through techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer architectures, among others.

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