Libraries tagged by Wise

Render Drupal 8 images through Imgix, a real-time image processing service and CDN

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Drupal 8/9 module providing developer-oriented improvements to the core image module

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When you implement YetiForce CRM, then you will find yourself wanting to communicate with your customers through a professional customer portal. That is why we created for you YetiForcePortal that integrates with CRM and has a wide range of functionalities.

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das etwas intelligentere Widerrufsformular für OXID eShop V6.0

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The Agent Detection library offers a wide variety of methods that allow you to explore and analyze connection agent data in your application.

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Stores system wide application settings in the default repository.

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Silex wired with radioactive caffeine.

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More than just a TYPO3 Slider! The TYPO3 Slider Revolution is developed in official partnership with Slider Revolution. It allows you to create effective sliders easily using the drag-and-drop feature. It comes with over 250+ modern slider templates and more than 2000+ pre-built slider elements, offering a wide range of transitions and effects.

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A codeable and flexible custom post type manager for Laravel with custom fields. Extendable and as dynamic as you wish.

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Quotes of wisdom from the failure coach

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Allows configuration injection through auto-wired constructor arguments

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Simple common, starter, empty, boilerplate-bundle to show best practice bundle development for Symfony2.1. This bundle has the right directory structure, coding standards, unittests and functional tests. It uses the Travis Continuous Integration buildbot, Composer for dependency management and Twig for templating. Read the documentation and fork/clone if you wish.

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Validation classes for a wide range of domains, and the ability to chain validators to create complex validation criteria

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Manage application-wide events for SOAs with Google Cloud PubSub

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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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