Libraries tagged by tave

Pharen is a compiler that takes a Lisp-like language and turns it into PHP code.

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Laravel Taxify provides a set of helper functions and classes to simplify tax (VAT) calculations within Laravel applications. that allow developers to easily integrate tax calculation functionalities into their projects. it's offers a straightforward and efficient solution Designed to streamline the process of handling taxes.

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Yet another take on ORM. This time with both hookers AND blackjack.

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Simple method to take an html table and export to CSV/XLS

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A package that provides a multitude of tools to traverse, sort and modify arrays in your daily routine

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A controller that can take standard PHP classes and convert them to controller methods with auto validation.

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Provides full-featured collections for primitive types, and generic classes to build your own strongly-typed collections. Each collection has chainable methods to perform traversal, filter and projection operations (similar to Microsoft's LINQ-like features).

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Includes Klaro!-Consent-Manager into TYPO3. Completely file-based configuration for versioning without having to take care of database-entries.

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LUYA Payment allows you to integrate payments in a safe and fast way. The module take care of all the provider required steps (call, create, success, abort, etc.) and provides all the informations for your store.

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This extension provides a fully automated workflow and a graphical user interface for the well known Localization Manager (l10nmgr). While the L10nmgr still handles exports and imports of records and files, the Localizer will take care of all the necessary steps in between. Editors responsible for translations won't have to deal with any L10nmgr configurations anymore and as an administrator you create just one configuration per Localizer Project.

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This behavior makes it easy to save and to take date in correct format.

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A full-stack framework for Laravel that takes the hassle out of building dynamic pages.

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Another take on device-aware adaptive images and server side CSS3 media queries, made by Joschi Kuphal (@jkphl), licensed under the terms of the MIT license

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Check for common mistakes and enforce best practices before take off.

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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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