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Average Ratings 0 Ratings
Description
Emotion is an efficient and adaptable CSS-in-JS library tailored for crafting CSS styles via JavaScript, accommodating both string and object styles while ensuring an excellent developer experience with features like source maps, labels, and testing tools. It presents two robust usage patterns; one is a framework-agnostic method that requires no special setup yet facilitates vendor-prefixing, nested selectors, media queries, and class composition through its CSS and CX functions. The second pattern is specifically optimized for React, offering advanced functionalities such as the CSS prop for direct styling, akin to the style prop, yet with enhanced support for nested selectors, media queries, and theming features. This variant also allows for seamless server-side rendering without configuration, native theming options, and full compatibility with ESLint tools. Additionally, Emotion provides styled-component-like APIs that allow for both tag-based and component-based styled elements, promoting dynamic styling driven by props. Furthermore, these capabilities make Emotion an appealing choice for developers seeking to streamline their styling processes across various frameworks.
Description
Selector’s software-as-a-service leverages machine learning and natural language processing to deliver self-service analytics that facilitate immediate access to actionable insights, significantly decreasing mean time to resolution (MTTR) by as much as 90%. This innovative Selector Analytics platform harnesses artificial intelligence and machine learning to perform three critical functions, equipping network, cloud, and application operators with valuable insights. It gathers a wide array of data—including configurations, alerts, metrics, events, and logs—from diverse and disparate data sources. For instance, Selector Analytics can extract data from router logs, device performance metrics, or configurations of devices within the network. Upon gathering this information, the system normalizes, filters, clusters, and correlates the data using predefined workflows to generate actionable insights. Subsequently, Selector Analytics employs machine learning-driven data analytics to evaluate metrics and events, enabling automated detection of anomalies. In doing so, it ensures that operators can swiftly identify and address issues, enhancing overall operational efficiency. This comprehensive approach not only streamlines data processing but also empowers organizations to make informed decisions based on real-time analytics.
API Access
Has API
API Access
Has API
Integrations
Amazon CloudWatch
Apache Kafka
CSS
Elastic Cloud
GitHub
GitLab
Google Cloud Platform
Humio
JavaScript
Jira
Integrations
Amazon CloudWatch
Apache Kafka
CSS
Elastic Cloud
GitHub
GitLab
Google Cloud Platform
Humio
JavaScript
Jira
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Emotion
Country
United States
Website
emotion.sh/docs/introduction
Vendor Details
Company Name
Selector
Country
United States
Website
www.selector.ai/product/
Product Features
Product Features
Network Monitoring
Bandwidth Monitoring
Baseline Manager
Diagnostic Tools
IP Address Monitoring
Internet Usage Monitoring
Real Time Analytics
Resource Management
SLA Monitoring
Server Monitoring
Uptime Monitoring
Web Traffic Reporting