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... 21:49
Cursor vs. GitHub Copilot: AI Coding Showdown!

DevOps Toolkit

1+ months ago 2K views

AI tools like GitHub Copilot and Cursor significantly aid coding tasks, with Cursor emerging as a faster, more comprehensive alternative. The comparison highlights Cursor's ability to reference the entire codebase, offering more detailed responses and insights during coding tasks compared to GitHub Copilot, which focuses only on the currently open file. While both tools assist in writing, fixing bugs, and suggesting code improvements, Cursor's overall performance and context awareness yield better results. Ultimately, the analysis concludes that Cursor is a superior choice in the AI coding assistant landscape.
03:26 - 05:30

Cursor demonstrates superior speed and detail in responses compared to GitHub Copilot.

GitHub Machine Learning Tech & Hardware
... 36:25
Selenium WebDriver Java with Copilot AI - Super charge Productivity

Rahul Shetty Academy

1+ months ago 2K views

GitHub Copilot significantly enhances developer productivity when working with Selenium WebDriver in Java by integrating AI into the IDE, facilitating code suggestions based on project context. The intelligent plugin streamlines tasks like creating page object classes, generating Selenium methods, fixing compilation errors, and documenting code effectively. It provides accurate locator strategies and enables the conversion of manual test scenarios into automated code. Copilot's capabilities extend to optimizing existing code and generating step definitions for Cucumber, making it a versatile tool for test automation frameworks, thereby promoting efficiency in software development processes.
03:40 - 04:50

Copilot can generate methods, understand project structure, and optimize code.

GitHub Text generation Education
... 52:43
DAY-3 | AI Assisted DevOps | Gen-AI Project For DevOps Engineers

Abhishek.Veeramalla

1+ months ago 4K views

The video presents the third episode of the AI-assisted DevOps series, focusing on creating a generative AI project for generating Docker files. It explores how DevOps engineers can automate the creation of Docker files for various programming languages including Java, Rust, and Ruby on Rails by writing a Python script powered by large language models (LLMs). It compares local LLMs with hosted solutions, highlights the setup process, and discusses the implications of using generative AI in real-world development environments, such as considerations for security and cost-efficiency.
11:31 - 11:51

Covers potential security risks when using hosted LLMs.

Machine Learning Education
... 11:18
Cypress Studio + Copilot AI - Generate Scripts in Framework Standards with Manual clicks

Rahul Shetty Academy

1+ months ago 2K views

This lecture explains how to generate test automation scripts using Cypress Studio and enhance them with GitHub Copilot AI. It covers the manual recording of tests, the configuration of experimental features in Cypress, and the integration of AI to modify generated scripts according to specific framework standards. Following a demonstration of live coding, the integration of page object models for better code organization is addressed, emphasizing the usefulness of AI in streamlining automation processes and generating aligned code structures in testing frameworks.
10:23 - 10:28

Co-pilot AI successfully converts tests to align with existing framework structures.

Cypress Machine Learning Education
... 32:26
AI Coding DEVLOG: Claude Code has CHANGED Software Engineering

IndyDevDan

1+ months ago 19K views

CLI coding tools are evolving with advances in AI, enabling engineers to build applications faster and more efficiently. This video introduces CLA code and the Model Context Protocol (mCP) alongside tools like repo mix and pocket pick. By establishing a thorough planning phase, engineers can ensure their AI tools effectively utilize existing patterns and information. The speaker emphasizes the importance of structured documentation, collaboration, and thoughtful prompt design to allow AI systems to generate relevant outputs. Ultimately, better engineering practices lead to a substantial increase in productivity and impact.
01:16 - 01:25

Project structure is critical for enhancing AI coding efficiency.

Machine Learning Tech & Hardware
... 16:52
Write Your Unit Tests with Cursor AI

MFK in Tech

1+ months ago 1K views

Cursor AI enhances software development by providing advanced code assistance through features like natural language prompts, code refactoring, and unit testing. It incorporates a Model Context Protocol (MCP) to connect with various tools like GitHub and JIRA, facilitating seamless project integration. The AI editor understands the project's codebase, generates documentation, and manages coding standards through custom rules. The video showcases practical applications, such as creating unit tests in Go, emphasizing ease of use and efficiency gains for developers.
03:51 - 03:54

Model Context Protocol (MCP) integrates with tools like GitHub and JIRA.

Cursor Machine Learning Education
... 39:55
How Codeium Built A Billion-Dollar AI Company and a Winning Sales Machine

SaaStr

1+ months ago 2K views

Codium, a generative AI coding assistant, rapidly expanded its go-to-market team from 3 to 75 in under a year, focusing on strong recruitment strategies and building a robust sales culture. By emphasizing the importance of ownership over pipeline generation and leveraging a compelling compensation structure, the company has successfully cultivated an environment conducive to rapid growth and individual performance. Key insights include the necessity of establishing solid processes in sales and the critical role of targeted enablement and revitalization efforts within the team as it scales.
15:54 - 16:02

The go-to-market organization expanded from 3 to 75 in less than 12 months.

Codeium Machine Learning AI Startups
... 39:55
How Codeium Built A Billion-Dollar AI Company and a Winning Sales Machine

SaaStr

1+ months ago 2K views

Codium, a generative AI coding assistant, rapidly expanded its go-to-market team from 3 to 75 in under a year, focusing on strong recruitment strategies and building a robust sales culture. By emphasizing the importance of ownership over pipeline generation and leveraging a compelling compensation structure, the company has successfully cultivated an environment conducive to rapid growth and individual performance. Key insights include the necessity of establishing solid processes in sales and the critical role of targeted enablement and revitalization efforts within the team as it scales.
15:54 - 16:02

The go-to-market organization expanded from 3 to 75 in less than 12 months.

Codeium Machine Learning AI Startups