Libraries tagged by user managment

Filament StateFusion is a powerful FilamentPHP plugin that seamlessly integrates Spatie Laravel Model States into the Filament admin panel. This package provides an intuitive way to manage model states, transitions, and filtering within Filament, enhancing the user experience and developer productivity.

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An user management system

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This extension will help you to quickly password protected TYPO3 pages without frontend users management, Check manual for more details. Live-Demo: https://demo.t3terminal.com/t3t-extensions/general-utility/protect-site?inavlid=1&type=88889 You can download PRO version for more-features & free-support at https://t3terminal.com/password-protect-site-typo3-plugin/

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A Neos CMS package that handles the frontend login and user management. The forms are created with custom finishers and validators in the Neos Form Builder.

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Authentication and user management library

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Laravel 7 Admin Panel for lazy developers. Contains User Management, Roles and Permission Management, Activity Logging

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Extends TYPO3 for the management of frontend users and groups

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Gleez CMS, user-friendly website Content Management System

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qTranslate-XT (eXTended): Adds a user-friendly multilingual dynamic content management.

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Magento 2 extension to inform users about the use and the management of your website cookies

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InspireCMS is a flexible, un-opinionated content engine for Laravel, built on Filament. Visually model any content structure, empower editors with a modern UI, and render everything using standard Blade templates. Features include live preview, granular user roles, SEO tools, effortless media management, and a powerful Blueprint Builder.

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RedKiteCms is an Open Source Content Management System Application, built on top of Symfony2 components and TwitterBootstrap frameworks, providing an easy an intuitive user-interface, to improve the website's content management experience

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Modifies Authority-L4 to use Roles (instead of users) as the central unit of previlege management

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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[1MSG.IO](https://1msg.io/) is the perfect WhatsApp management tool for your business. With us you get full access to the official Whatsapp API/webhooks. Every API request must contain an Authorize HTTP header with a token. This is your channel token, which can be found in your channel project on your profile page. Please do not give the token to anyone or post it publicly. The authorization token must be added to each request in the GET parameter 'token' and always passed to query string (?token={your_token}). Parameters in GET queries pass query string. Parameters in POST requests — through the JSON-encoded request body. All 'send' methods (except /sendTemplate) will only work when the dialog session with the user is open. Some of our solutions simplify and avoid such limitations, but we urge you to pay more attention to this detail

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