Libraries tagged by upd

# 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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Update /etc/hosts to access running containers

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Yii2 logger for Sentry (updated)

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This Laravel package updates migration files during development without data loss, allowing seamless modification of table columns while preserving existing records. Ideal for maintaining data integrity during iterative development.

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定期更新，全国最新省市区划分数据，身份证号码解析地址，支持 Composer 安装与版本控制，适用于表单选项、数据校验、地址解析等场景 | Regularly updated dataset of China's administrative divisions with ID-card address parsing. Distributed via Composer and versioned for use in forms, validation, and address-related features

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Automates the process of updating assets version in Symfony 5 projects still using parameters.yml

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Add fingerprints to your assets on update

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Updates the Magento quote if it has a invalid state

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REST and CLI API for adding, updating, and deleting records in TYPO3. Tracks relations so records can be inserted in any order. Uses remote ID mapping so you don't have to keep track of what UID a record has gotten after import. Data is inserted using backend APIs as if a real human did it, so you can can inspect the record history and undo actions.

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Provides a class to decode mime messages. A updated fork of pear/mail_mime-decode.

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The StockProductApi module provides a REST API for simple product stock updates.

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Flashy's module for Magento 2+ adds tracking events View Content, Cart Updates, Purchases, Page Views and more and make sure everything is in sync with the Flashy platform.

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Update of the kopo kopo php sdk

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Ethereum web3 interface. Fork from sc0vu/web3.php because had to update dependencies

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Lock object against delete, update events

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