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... 16:19
AgenticSeek - Manus-like AI with Deepseek R1 Agents - Install Locally with Ollama

Fahd Mirza

1+ months ago 2K views

The installation of the Agentic Seek tool locally demonstrates its functionality as an autonomous agent inspired by the Menace project. This software performs web searching, coding, and file manipulation without any API reliance, maintaining privacy and allowing local execution. Utilizing large language models, specifically the 70 billion parameter Deep C Car 1 model, enhances the performance of autonomous tasks. The video emphasizes optimizing Agentic Seek's configuration for effective operations and showcases interactions with the system that exemplify its capabilities in coding and web browsing.
02:01 - 02:11

Discussion on using the 70 billion Deep C Car 1 model for the task.

AgenticSeek Voice Recognition Tech & Hardware
... 23:2
SEDUCING AI NPCs... or at least trying to

BrainFrog

1+ months ago 3K views

The dialogue revolves around two characters, Brain Frog and Verando, who engage in a competitive banter while vying for the affection of a lady named Aris. They showcase contrasting approaches to romance, with Brain Frog emphasizing sincerity and directness, while Verando relies on poetic charm and sophistication. The interaction escalates into a playful challenge where both compete to woo Aris, ultimately exploring themes of passion, authenticity, and the differences in romantic tactics. Their conversation highlights the complexities of relationships and the dynamics between confidence and genuine connection.
07:40 - 08:40

Verando relies on poetic language while wooing Aris, creating a humorous contrast.

Chatbots Gaming
... 27:1
Agentic AI Tutorial One shot Using Agno(Phidata)

Krish Naik

1+ months ago 4K views

Using the Agno platform, AI agents can be created and managed efficiently, allowing for complex workflows and end-to-end projects. This video demonstrates how to utilize Agno for multi-agent systems, emphasizing performance and ease of use compared to other tools like Langra. Key features include model-agnostic capabilities and faster agent creation, enhancing the usability of AI in applications. The speaker also highlights the upcoming data science and generative AI courses available for learners, which will cover foundational knowledge for effective AI application development.
02:17 - 02:27

Integration of various models and tools such as OpenAI and DugDugGo.

Phidata Machine Learning Education
... 23:28
Is This AI Coding Assistant Better Than Cursor?

Conner Ardman

1+ months ago 2K views

This presentation evaluates augment, a new AI coding assistant designed for professional software engineers working with large codebases. The demonstration highlights augment's ability to synchronize with existing projects, provide code summaries, and assist in code modifications effectively. Noteworthy features include its handling of game state management in a Tetris game and a unique 'next edit' functionality that prompts users on subsequent changes needed after an edit. The speaker tests augment against an unfamiliar open-source repository to assess its capabilities, drawing attention to various useful features and the AI's adaptability to different environments.
03:12 - 03:19

Augment explains Tetris game's state management effectively.

Augment Machine Learning Education
... 12:42
Microsoft PIKE-RAG - Specialized Knowledge Meets AI Reasoning - Install and Test

Fahd Mirza

1+ months ago 1K views

Pike Rag offers a unique approach to retrieval-augmented generation (RAG) for specialized knowledge, addressing complexities within domains like healthcare or manufacturing. It improves accuracy and reliability by integrating structured logical reasoning with specialized domain knowledge directly into its generation pipeline. Despite its promising capability and potential for shaping AI projects, commitment from Microsoft seems lacking. Practical applications highlight its effectiveness, yet customization challenges remain a significant barrier for users looking to adapt it to their needs, suggesting an opportunity for innovation in the field.
03:23 - 03:34

Pike Rag efficiently breaks down complex problems with improved accuracy in reasoning.

Microsoft Machine Learning Research & Innovations
... 32:40
The ULTIMATE n8n RAG AI Agent Template - Local AI Edition

Cole Medin

1+ months ago 14K views

Retrieval augmented generation (RAG) empowers AI agents to effectively access knowledge bases, enhancing their expertise in document-based Q&A. However, traditional RAG often lacks dynamic reasoning capabilities. The video introduces an enhanced approach called agentic RAG, which improves flexibility and power in document handling. A local version of the n8n agentic RAG template is provided, enabling users to build offline AI systems, control data, and use various tools for accessing and querying knowledge bases. The video demonstrates the setup process and showcases the advantages of local AI implementation for efficient and secure operations.
06:00 - 08:00

Testing scenarios include retrieving basic information and handling complex queries.

Natural Language Processing (NLP) Tech & Hardware
... 34:1
I Built the ULTIMATE n8n RAG AI Agent Template

Cole Medin

1+ months ago 18K views

Retrieval Augmented Generation (RAG) enhances AI agents' access to knowledge bases, making them domain experts. Despite its popularity, RAG often struggles with context, misses data links, and inadequately analyzes trends in documents. The solution lies in an agentic RAG model that allows AI agents to reason about data exploration, adapt queries, and utilize diverse tools. This video introduces the construction of an agentic RAG agent in n8n, detailing the integration of various functional tools and databases, streamlining the retrieval process, and presenting a more flexible approach to data analysis and document summarization.
09:32 - 09:49

Unra offers a platform for structured data extraction from unstructured documents.

Natural Language Processing (NLP) Tech & Hardware
... 34:1
I Built the ULTIMATE n8n RAG AI Agent Template

Cole Medin

1+ months ago 18K views

Retrieval Augmented Generation (RAG) enhances AI agents' access to knowledge bases, making them domain experts. Despite its popularity, RAG often struggles with context, misses data links, and inadequately analyzes trends in documents. The solution lies in an agentic RAG model that allows AI agents to reason about data exploration, adapt queries, and utilize diverse tools. This video introduces the construction of an agentic RAG agent in n8n, detailing the integration of various functional tools and databases, streamlining the retrieval process, and presenting a more flexible approach to data analysis and document summarization.
09:32 - 09:49

Unra offers a platform for structured data extraction from unstructured documents.

Natural Language Processing (NLP) Tech & Hardware