Libraries tagged by rig

Silverstripe Module that makes it easier for CMS Editors to upload the right size image. It allows the developer to set the size for each image (width, height or both) in one place and apply this throughout the project in the CMS and on the front-end.

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The hottest news right in your Statamic dashboard.

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Versioned, environment-based Seeders in Laravel. Seed your database with the right data for the right environment.

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Will the doorman let you in? Only if you've got the right set of permissions.

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Holidays in Laravel, the right way.

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Nova Blocks is a collection of distinctive Gutenberg blocks, committed to making your site shine like a newborn star. It is taking a design-driven approach to help you made the right decisions and showcase your content in the best shape.

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Using the All In One TYPO3 Gallery Extension, you can easily create beautiful lightbox image and video galleries with various grid layouts right from the TYPO3 dashboard in just a few clicks. Try the best TYPO3 gallery extension for adding custom, unique and responsive galleries to your website.

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Magento 2 Order Information Extension helps to Empower your Magento 2 store with the ability to provide comprehensive order details right on the order success page. Our Magento 2 Order Information Extension enhances the post-purchase experience by displaying crucial order information, streamlining the process for both merchants and customers.

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Awesome and simple way to filter Eloquent queries right from the API URL without the clutter.

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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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Fortress is a powerful Laravel package designed to streamline and enhance attribute-based authorization through middleware. It acts as the ultimate security gatekeeper for your application, ensuring that only the right users with the correct attributes gain access to specific resources.

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A Laravel Admin Package to make your life easier and steer your project in the right direction

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eMail checker and suggests a right domain

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Right-to-Left (rtl) direction support for "Flarum" core and extensions.

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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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