Libraries tagged by User Presence

A nestable user interface building module for the Rhubarb PHP framework based upon the model-view-presenter pattern.

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Janolaw Service module is used for individual generation and automatic update of legal documents for online presence, blogs and online shops / online sale in Germany.

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Search.io offers a search and discovery service with Neuralsearch®, the world's first instant AI search technology. Businesses of all sizes use Search.io to build site search and discovery solutions that maximize e-commerce revenue, optimize on-site customer experience, and scale their online presence.

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A nestable user interface building module for the Rhubarb PHP framework based upon the model-view-presenter pattern.

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Provides a search panel presenter which allows users to save searches as remembered tabs

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Simple notification bundle to create flash and instant notifications to be presented to the user with javascript and css transitions

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Extension for Nette Framework: set presenters names to secure and they will be secured via http authentification. IAuthenticator service is used for validating user credentials.

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This package contains tools to describe and filter user inputs from an HTML form, including sub-forms/sub-fieldsets, fieldset collections, an interface for injecting custom filter systems, and CSRF protection. Note that this package does not include output functionality, although the "hints" provided by the Form object can be used with any presentation system to generate an HTML form.

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Checks for keywords (e.g. [email protected]) and block submissions of the strings are present

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collection of components to present your data in an elegant way

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Controlled profile updates and user presentation preferences.

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A lightweight static site generator, utilizing composable templates in pure PHP. This project uses a presenter -> view invokable component classes pattern to provide clean, composable, vanilla, php developer experience without a third-party templating engine.

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Preserve the authenticated user context when dispatching Laravel queued jobs.

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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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A package that helps to group methods that mostly use for the view presentation purpose.

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