group: research-practice-sensemaking

Large Language Model

Large language models (LLMs) are advanced AI systems designed to understand and generate human-like text based on vast amounts of training data. They have revolutionized fields such as natural language processing, conversational agents, content creation, and more.

Versatility: Capable of handling a wide range of tasks from summarization to translation.
Contextual Understanding: Ability to comprehend context in long-form text due to their deep understanding of language patterns.
Scalability: Can be fine-tuned for specific applications or scaled up for broader use cases.
Retrieval Augmented Generation (RAG): A technique that augments an LLM’s generation process by providing it with relevant, up-to-date information from external sources, enhancing accuracy and context.
Data Visualization Backend: Utilized as the core engine in applications like Microsoft’s Data Formulator to automate the creation of rich, complex data visualizations.

[[conc

Backlinks:

Source Notes

2026-04-14: # Difference between RAG and Agents for workflow --- --- https://www.youtube.com/watch?v=WYqhc802nqk Here is a detailed breakdown of the video “RAG vs Agents” by Dr. Anil Variyar. Video Summary Dr. Anil Variyar provides a vi (Difference between RAG and Agents for workflow)
2026-04-09: # Project Glasswing: Mitigating Anthropic Mythos AI’s Zero-Day Vulnerability Capabilities Clip title: Anthropic just revealed ‘Project Glasswing’ (MYTHOS) Author / channel: Matthew Berman URL: https://www.youtube.com/watch?v=SQhfkWdxVvE Summary The video discusses (Project Glasswing: Mitigating Anthropic Mythos AI’s Zero-Day Vulnerability Capabilities)
2026-04-12: MiniMax M2.7 Open Source LLM: Technical Overview and Deployment Summary Clip title: MiniMax M2.7 is Now Open Source - Full Deep Dive and Local Deployment Steps Author / channel: Fahd Mirza URL: https://www.youtu (MiniMax M27 Open Source LLM Technical Overview and Deployment Summary)