Libraries tagged by variable

Drush is a command line shell and scripting interface for Drupal, a veritable Swiss Army knife designed to make life easier for those of us who spend some of our working hours hacking away at the command prompt.

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Flexible Searchable, Mappable, Metable, Validation and more extensions for Laravel Eloquent ORM.

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Class variance authority implementation in php

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Trait to activate validation when saving Eloquent Model

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Measure time between events, variability and compare results.

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Collect valuable traffic stats and insights.

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Winston is a AB/split/multivariate testing library utilizing redis and a basic machine learning algorithm for automatically displaying the most successful test variations.

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Drush is a command line shell and scripting interface for Drupal, a veritable Swiss Army knife designed to make life easier for those of us who spend some of our working hours hacking away at the command prompt.

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The TYPO3 Feedback Extension is a great tool for gathering feedback from visitors or customers on your website. With the All In One TYPO3 Feedback extension, website admin can easily add feedback forms in various styles to their website, allowing them to collect valuable insights from their visitors.

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A package that provides a data source with all countries in the world including translations and other valuable data.

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Class Variance Authority implementation in PHP

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This Laravel package seamlessly integrates Umami analytics into your Blade templates. Easily track website visits and user engagement directly within your Laravel application, providing valuable insights into your website's performance. This package simplifies the integration process, saving you time and effort. With minimal configuration, you can leverage Umami's powerful analytics features to gain a clearer understanding of your audience and website usage.

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Filament Pixel is a Laravel package that seamlessly integrates Meta Pixel analytics into your Blade templates. Using your Meta Pixel ID, it enables easy tracking of website visits and user interactions, providing valuable insights into your audience and website performance. With minimal setup, you can leverage Meta’s powerful analytics features directly within your application, helping you optimize your digital strategy and improve user engagement.

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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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Serviço de comunicação via api com a plataforma do GissOnline.

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