Libraries tagged by possible

This dataset contains all the countries in the world. They are translateable in different languages. It is also possible to get the flag from said countries. The model is extendable so you can extend and overide all functionality if needed.

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Json wrapper to provide a more robust Api (matching 7.3 where possible)

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Do things as late as possible in a request. Don’t let your users wait

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This bundle makes it possible to manage multiple websites from within KunstmaanCMS.

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Helpers to make it possible to preview posts on frontend.

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RTE-MIS is a distribution for implementing the RTE 12(1)(c) scheme as widely as possible.

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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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A Guzzle middleware that can throttle requests according to (multiple) defined rules. It is also possible to define a caching strategy, e.g. get response from cache when rate limit is exceeded or always get cached value to spare your rate limits.

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A fork hammerstonedev/airdrop to support Laravel 5.8. A Laravel package to deploy your application faster by skipping asset compilation when possible.

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This Package makes it possible for users to have their config files stored in a database table, making it easier to customize these values from UI.

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Easily migrate magento 1 orders to magento 2 and show orders in self contained area in customer account. Offer reorder functionality where possible.

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Maintains a history of balance movements in the eloquent models. This simple package will keep track of the balance of your models. You can increase, decrease, reset and set the balance. It is also possible to check if a model has a positive balance or no balance.

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Template compiler for PHP, Django-style (as much as possible). Pretty efficent by avoiding to have anything at run-time.

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PHP bindings for OpenAI Whisper made possible by whisper.cpp

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Php Sandbox in a child process which should make it possible to run user supplied code safely

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