Search Results for "thin client"
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Showing 9 open source projects for "thin client"
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Auth0 for AI Agents now in GAConnect your AI agents to apps and data more securely, give users control over the actions AI agents can perform and the data they can access, and enable human confirmation for critical agent actions.
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Payments you can rely on to run smarter.Accept payments at your counter or on the go. It’s easy to get started. Try the Square POS app on your phone or pick from a range of hardworking hardware.
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BlueWallet
Bitcoin thin client for iOS & Android. Built with React Native
Easily connect to your hardware wallet and manage your cold storage. The best security is available on the Bitcoin protocol. Create wallets with multiple keys. Unfairly cheap and blazing fast transactions on Bitcoin. Get notified of all transactions you are sending or receiving. download bitcoin wallet for iOS. Create multiple storage or fake ones in case of forced disclosure. Choose your fees when sending. Easily bump, cancel or batch transactions. BlueWallet was forged in the ashes of the... -
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Tonic
Easy to audit, zero-dependencies and build-tool-free
A low-profile component framework. 1 file. 1 class. ~350 lines of code. No build tools are required. Native web components. Ideal for JAM stacks. Identical on client & server. Composition oriented. Event delegation by default. Lots of examples. Building a component with Tonic starts by creating a function or a class. The class should have at least one method named render which returns a template literal of HTML. onic is a thin wrapper around web components. Web components require a name with two or more parts. ... -
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SiteFusion
https://github.com/sitefusion
See https://github.com/sitefusion SiteFusion is a server-based development environment. Applications are written in PHP and work through a thin XUL client. SiteFusion applications look and behave like native system applications but run on a server generating JavaScript commands. -
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SweetDEV RIA is a complete set of Java/J2EE world-class Ajax tags. It helps you to design Rich GUI in a thin client.
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Create and run cloud-based virtual machines.Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications.
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This project is a CRM/ERP program, based on OpenTaps. Instead of the web-based thin client, it is an XUL/Mozilla based thick client.
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Wi.Ser is a Java/XML server-side GUI-framework which enables an application to run as either a monolithic Swing application, a thin-client/server Swing application, or as a Web (AJAX) application without any change! The project includes a GUI builder.
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A three-tiered database application / framework for serious genealogy research, mainly using PostgreSQL and PHP. Based on Fat Server / Intelligent Middleware / Thin Client approach, with scalability and extendability as primary guidelines.
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A generic sports centre management tool implemented using PHP and MySQL. This web-based thin client solution will allow for the administration of a sports centre, including its resources, members, subscriptions and reservations.
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WABClient is a 100 % Pure Java implementation of a thin client application framework. It's very small, about 70K and therefore fast to install on a client machine. The content of the application is dynamic and depends on the XML input.
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Cloud data warehouse to power your data-driven innovationBigQuery 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.
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