Libraries tagged by slugg
alexusmai/ruslug
8566 Downloads
Laravel 5 RuSlug, Transliteration RU -> ENG
alexbklnv/laravel-dadata
1126 Downloads
Laravel DaData API
akitogo/melissa-address-validator
7516 Downloads
Checkout shipping address validation and suggestion using Melissa API.
pauloamgomes/cockpitcms-betterslugs
33 Downloads
Better slugs Addon for Cockpit CMS, provides a new slug field type.
neurony/laravel-redirects
2410 Downloads
Nested URLs redirect logic for Laravel
wowworks/geocoder-php-dadata-provider
16952 Downloads
Integration with Dadata suggestions API.
sohibd/laravelslug
318 Downloads
make easy bangla and english slug with utf-8 standard format
silverstripe/recipe-kitchen-sink
1399 Downloads
Internal testing Silverstripe CMS recipe for all modules, optional and suggested
nglasl/silverstripe-extensible-search
29660 Downloads
This module allows user customisation and developer extension of a search page instance, including analytics and suggestions.
kadudutra/fpdi
749 Downloads
FPDI is a collection of PHP classes facilitating developers to read pages from existing PDF documents and use them as templates in FPDF. Because it is also possible to use FPDI with TCPDF, there are no fixed dependencies defined. Please see suggestions for packages which evaluates the dependencies automatically.
inda-hr/php_sdk
494 Downloads
# 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.
devnix/mailcheck
8436 Downloads
Provide email suggestions based on multiple dictionaries
customerparadigm/amazon-personalize-extension
1555 Downloads
Amazon Personalize Customer Suggestions by Customer Paradigm
betterflarum/nextadmintheme
1509 Downloads
The NEXT Flarum Admin theme by fanmade - Feel free to report bugs and suggest on Discuss link
b13/xray
871 Downloads
X-Rays your TYPO3 installation. Suggests and performs improvements.