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Latest AI Verify Foundation AI Videos

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... 11:18
I used AI to write tests for my .NET application... Here's what happened

Milan Jovanović

1+ months ago 3K views

This video demonstrates how to utilize AI, specifically Cursor, to generate unit tests, integration tests, and functional tests for a .NET application. The presenter showcases a project called 'Run Tracker' based on clean architecture, outlining its endpoints and use cases. The video emphasizes the importance of having a well-structured test project to maximize AI capabilities in test case generation while also detailing how the Cursor IDE enables efficient integration with language models. By running existing test cases, the presenter confirms their functionality before leveraging AI for further test generation.
06:16 - 06:46

AI occasionally makes mistakes, requiring manual prompting for corrections.

Machine Learning Education
... 56:4
API Testing Using Generative AI - End To End API Automation In Minutes With TestRigor

Mukesh otwani

1+ months ago 1K views

API testing can be automated using generative AI without writing code, allowing users to automate end-to-end API scenarios and integrate various functionalities efficiently. Test Trigger facilitates the automation of mobile, web, and mainframe applications, enabling users to create comprehensive test cases that leverage dynamic data in requests. The session demonstrates creating, updating, and deleting API calls using various AI-driven features, while addressing user queries related to automation challenges, best practices, and the capabilities of test trigger software in significant detail.
04:11 - 04:20

Showcasing automated token generation and validation in test cases.

TestRigor 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
... 8:9
6 AI Tools for US College Applications to get into Ivy Leagues!

Crazy Medusa

1+ months ago 1K views

AI tools have surged in popularity, becoming essential for college applications and student profiles. This video discusses six key AI tools that students can leverage to work smarter and meet their academic goals. Tools like Perplexity AI help organize information, while Claude AI facilitates code generation for portfolio websites. Noi acts as an exam prep aid, providing explanations for answers from screenshots. Notebook LM allows for efficient summarization and exploration of documents, while Winston AI improves plagiarism detection. Futurepedia serves as a comprehensive platform for discovering new AI tools, emphasizing responsible use in academic settings.
02:59 - 03:15

Noi explains exam problems by analyzing screenshot questions in-depth.

Machine Learning Education
... 26:38
Hacking LLMs Demo and Tutorial (Explore AI Security Vulnerabilities)

David Bombal Tech

1+ months ago 8K views

AI vulnerabilities pose significant risks, intertwining traditional network security threats with unique issues inherent to AI systems. Ensuring data integrity and secure interactions with APIs is crucial, particularly as organizations integrate large language models (LLMs) and other AI technologies into their operations. The OWASP Foundation now addresses threats specific to LLMs, such as prompt injection and data poisoning. Resources like Cisco's free tutorial on AI security are essential for educating professionals about potential vulnerabilities and mitigation techniques. As AI continues to evolve rapidly, staying informed and proactive in security measures is vital for safeguarding organizations.
02:06 - 02:10

Rapid AI development increases risks and potential attack vectors.

Cisco Ethical AI frameworks Cybersecurity
... 24:24
I asked AI to find FAKE Pentiums in my CPU collection!

Bits und Bolts

1+ months ago 5K views

Experimenting with AI tools, the speaker analyzed two potentially fake Pentium processors by providing AI seven different models with details like model number and frequency. Initially using Meta AI, which identified one CPU as fake, the speaker noted inaccuracies in its analysis regarding Intel's historical data. Subsequent AI tools like Deep Seek and ChatGPT yielded various results, often misidentifying genuine CPUs as fake. The experiment concluded with a points-based scoring system to determine AI effectiveness, revealing discrepancies in how each model interpreted the data, with varying success in identifying real versus counterfeit processors.
07:48 - 08:32

Grock AI conducted web searches yielding inaccurate results about Pentium authenticity.

Intel Machine Learning Tech & Hardware
... 26:56
Can Cline AI with Claude 3.7 Sonnet Fix Trae AI's Website Build? Unbelievable Results!

Michael Fricker

1+ months ago 2K views

AI technologies were utilized to enhance code generation and optimization. This involved using Klein and Sonet to automate development, significantly improving the speed and quality compared to prior methods, like those offered by Trey. The process led to rapid error identification and fixes, demonstrating the efficiency of these AI tools. Despite significant costs associated with API calls, the results yielded a more polished website with numerous functional components in a fraction of the time previously required. Overall, the experience showcased how AI can streamline software development and improve web application performance.
10:10 - 10:45

The AI identifies and corrects multiple coding issues in real-time.

Text generation AI Startups
... 30:3
LLM + Knowledge Graph + GNN = TRUTH by AI?

Discover AI

1+ months ago 2K views

AI advancements are reshaping fact-checking and verification processes through the integration of Knowledge Graphs and Graph Neural Networks. Emphasizing the complexities of verifying long, nuanced statements, the discussion engages with AI's hallucination challenges, and the need for models to dissect factual information's origins. The uptake of AI-driven analysis in journalism, research, and public discourse is highlighted, underlining the importance of enriching AI with contextual data to enhance its decision-making capabilities. Overall, the potential for AI to transform understanding and verification practices in various domains is underscored, pointing towards a future where AI tools are pivotal in information validation.
06:15 - 06:50

AI models struggle with contextual verification, revealing new challenges.

Graph Neural Networks Research & Innovations
... 30:3
LLM + Knowledge Graph + GNN = TRUTH by AI?

Discover AI

1+ months ago 2K views

AI advancements are reshaping fact-checking and verification processes through the integration of Knowledge Graphs and Graph Neural Networks. Emphasizing the complexities of verifying long, nuanced statements, the discussion engages with AI's hallucination challenges, and the need for models to dissect factual information's origins. The uptake of AI-driven analysis in journalism, research, and public discourse is highlighted, underlining the importance of enriching AI with contextual data to enhance its decision-making capabilities. Overall, the potential for AI to transform understanding and verification practices in various domains is underscored, pointing towards a future where AI tools are pivotal in information validation.
06:15 - 06:50

AI models struggle with contextual verification, revealing new challenges.

Graph Neural Networks Research & Innovations