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Git hooks are an accommodating solution for organizations that seek to implement consistent policies and procedures across their teams. First and foremost, they enable teams to run custom scripts according to certain Git operations. Building automation into your Git client helps remove the possibility of human error and improves workflows with little effort. Here we will explore how Husky works with Git hooks in JS projects. First, let’s start with the basics.

Process improvement is the continuous refinement of workflows within an organization. To achieve operational excellence, you need to relentlessly work on improving your processes. By doing so, you will minimize waste, optimize resources, and improve quality and productivity. Without any hesitation, let's explore the best process improvement examples.

PostgreSQL is a popular open source object-relational database system designed to work for a wide range of workloads from single machines to data warehouses to web services with many concurrent users. PostgreSQL runs on all major operating systems and is used by teams and organizations across the world, including Netdata. If you are using PostgreSQL in production, it is crucial that you monitor it for potential issues. And the more comprehensive the monitoring the better!

Rated as the top order tracking and revenue generation app on Shopify, Rush lets businesses build and personalize their own dashboards to manage the post-sale process with real-time data, custom product recommendations, and user feedback. Their business model focuses on low touch and user-centered design (UCD), which leaves little room for issues impacting how people interact with the platform.

Both Artificial Intelligence and Machine Learning are complex things. There are so many things to know. These days human life has changed because of AI. So, before understanding the differences, let’s know about different factors. If I have to say the difference in simple words. AI helps us solve various tasks; on the other hand, Machine Learning is the subset of AI’s specific tasks. So, you can say that all Machine Learning is AI, but all AI is not machine learning.