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... 1:9:48
Python + AI: Vision models

Microsoft Reactor

1+ months ago 1K views

Multimodal LLMs, which can process both images and text simultaneously, are significantly enhancing AI applications. Specifically, today's presentation focused on how these models analyze visual data, exemplified through image classification and embedding visual and textual data into a unified format. Practical demonstrations illustrated using public URLs and data URIs for image transmission to models. The session also covered practical use cases, including tree loss analysis via charts and insurance fraud detection using images of vehicle damage, emphasizing the benefits and considerations involved in deploying AI for efficient decision-making. Future developments and challenges in multimodal approaches were hinted at through discussions of embeddings and RAG methodologies.
18:21 - 19:09

Showcased using AI for assessing the legitimacy of insurance claims.

Image Recognition Education
... 24:26
Claude MCP has Changed AI Forever - Here's What You NEED to Know

Cole Medin

1+ months ago 13K views

mCP, developed by Anthropic, standardizes how tools are provided to AI agents, providing unprecedented ease for developers. This protocol enables the integration of diverse services into AI applications, enhancing productivity significantly. mCP's persistent significance distinguishes it from fleeting AI trends, providing real value over time, and facilitating better-designed AI agents. The speaker emphasizes the importance of adopting mCP to harness its capabilities effectively while illustrating its practical application in real-time projects with examples and a walkthrough of integration with common tools and platforms, including Python and n8n workflows.
05:37 - 05:55

mCP servers standardize AI tool integration, improving compatibility across frameworks.

Natural Language Processing (NLP) AI Trends
... 1:28:27
MCP AI Agents: Automate ANYTHING…

Julian Goldie SEO

1+ months ago 2K views

Automating workflows using mCP AI agents simplifies complex tasks by integrating seamlessly with various applications, making setup easier than traditional methods. The mCP (Model Context Protocol) serves as a universal plug for connecting AI systems, allowing for the execution of API requests and real-time updates without extensive coding. This video outlines how to set up mCP tools, integrate them with Visual Studio Code, and use Google Gemini to enhance functionalities, thereby improving workflow efficiencies while reducing setup complexities. Key features and benefits of mCP servers are highlighted, emphasizing their role in automating processes across different platforms.
02:00 - 03:00

Demonstration of using API integrations with mCP to automate tasks.

Machine Learning AI Startups
... 12:22
Mistral Small 3.1: New Powerful MINI Opensource LLM Beats Gemma 3, Claude, & GPT-4o!

WorldofAI

1+ months ago 11K views

A new AI model, Mistol Small 3.1, is introduced as a lightweight alternative to existing models like Gemma 3, boasting 24 billion parameters. It outperforms GPT-4 Omni mini in various tasks, supports 21+ languages, and utilizes a 128k context window. Testing its capabilities through various prompts, the model demonstrated impressive results in programming, visual understanding, and logical reasoning, with a few limitations in generating SVG graphics. This model stands out due to its open-source nature and ease of local deployment, making it ideal for high-quality AI applications.
02:22 - 02:27

The model excels in reasoning and problem-solving compared to other models.

Mistral Natural Language Processing (NLP) Tech & Hardware
... 10:5
Model Context Protocol (MCP): The Key To Agentic AI

Jack Herrington

1+ months ago 2K views

mCP, or the model context protocol, enhances AI capabilities, particularly for large language models like OpenAI's GPT-4, by providing necessary context from various data sources. It allows for efficient task completion through specialized servers that furnish tools, resources, sampling methods, and parameterized prompts. The protocol's reflection capability distinguishes it from traditional APIs, enabling clients to discover available server functionalities. Integrating mCP with backend APIs streamlines AI interactions, allowing multiple servers to manage complex tasks efficiently while maximizing abstraction and minimizing direct access to underlying data structures.
02:25 - 02:36

mCP client-server interactions define how AI accesses tools and resources.

Machine Learning Research & Innovations
... 10:41
Install GPTme with Ollama and QWQ Locally - AI Agents in Terminal

Fahd Mirza

1+ months ago 1K views

Installation of GPT Me has improved significantly since its initial review, particularly with the addition of support for local models like Olama. This video demonstrates how to run GPT Me locally and integrate it with AMA-based models, highlighting the importance of using models that support tool use for effective functionality. Recent advancements in AI capabilities include running code, editing files, and browsing the web, although issues remain with AI's reasoning speed and Vision model performance. Overall, improvements are noted, but further development is required for enhanced capabilities.
01:26 - 01:42

GPT Me enables various AI agent functionalities through terminal interface.

Ollama Chatbots Tech & Hardware
... 26:3
What is MCP & How to Use It with AI Agents in n8n (Full Tutorial!)

AI Workshop

1+ months ago 3K views

mCP provides AI agents with a universal key to access diverse tools and data resources, streamlining interactions with AI systems. It allows seamless access to content repositories and APIs that previously required manual connections, thereby offering flexibility and scalability for AI agents. The video illustrates various demos of mCP functionality, including scraping data from websites and accessing tools dynamically. This integration marks a significant advancement in how AI agents interact with external resources, reducing time-consuming setups and enhancing operational efficiency in AI applications.
03:22 - 03:31

FIR Craw mCP simplifies access to web data tools.

n8n Machine Learning Education
... 26:3
What is MCP & How to Use It with AI Agents in n8n (Full Tutorial!)

AI Workshop

1+ months ago 3K views

mCP provides AI agents with a universal key to access diverse tools and data resources, streamlining interactions with AI systems. It allows seamless access to content repositories and APIs that previously required manual connections, thereby offering flexibility and scalability for AI agents. The video illustrates various demos of mCP functionality, including scraping data from websites and accessing tools dynamically. This integration marks a significant advancement in how AI agents interact with external resources, reducing time-consuming setups and enhancing operational efficiency in AI applications.
03:22 - 03:31

FIR Craw mCP simplifies access to web data tools.

n8n Machine Learning Education