Libraries tagged by Page title
davide-casiraghi/laravel-jumbotron-images
73 Downloads
Add a jumbotron images with title and description to your pages. The titles and descriptions support multilanguage.
exadium/latestblogs
0 Downloads
Simple Silverstripe widget to show the 10 (default) latest blog posts on for example your homepage. After installing simply drop the widget onto a page, set the title and number of blog posts to display and you are up and running.
spartz/text-formatter
5822 Downloads
A simple to use text formatter, primarily used for page/blog titles
weberlei/wp-weberlei-notitle
57 Downloads
Wordpress plugin that adds a dropdown admin option in every page to hide the title from rendering on a page.
lch/seo-bundle
871 Downloads
Provides minimal SEO items on front-page-having entities : slug, title, description
zerosonesfun/flarum-featured-card
513 Downloads
A BBCode which creates a card to feature a link, page, content, etc. [featured url= title=optional theme=optional][/featured]
nsenkevich/seo
6 Downloads
Seo ZF2 Module help to add titles, description and keywords for different page types
heimseiten/contao-page-teaser-bundle
456 Downloads
Die Erweiterung 'Seitenteaser' stellt Inhaltselemente bereit, die automatisch aus bestehenden Seiten Seitenteaser generieren. Hierfür wird auf das Seitenbild (terminal42/contao-pageimage) zurückgegriffen, falls vorhanden, ansonsten auf das erste verwendete Bild in der Seite verwendet. Außerdem wird der Seitentitel und die Seitenbeschreibung verwendet. Dies kann überschrieben werden, wenn in der Seite Extraeingaben für den Teaser Titel und die Teaserbeschreibung gemacht werden. Im Startpunkt der Webseite kann eingestellt werden, welche Bildgröße verwendet und, ob ein Text angezeigt werden soll.
quarterloop/laravel-dashboard-page-thumb-tile
12 Downloads
A Page Thumbnail tile for the Laravel Dashboard
quarterloop/laravel-dashboard-google-page-speed-tile
27 Downloads
A google page speed tile for the Laravel Dashboard
inda-hr/php_sdk
454 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.