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RAG vs PEFT: Understanding the Difference and How They Work Together * A comprehensive guide to Retrieval Augmented Generation and Parameter Efficient Fine-Tuning with LoRA* The AI community has been buzzing with questions about two important concepts: ** RAG (Retrieval Augmented Generation )** and ** PEFT (Parameter Efficient Fine-Tuning) ** with LoRA (Low Rank Adaptation). Many developers are wondering: Can I use RAG to fine-tune an LLM? Should I apply RAG first, then LoRA, or vice versa? Can I teach my LLM a second language using just RAG? Let's dive deep into these technologies, understand how they work, and explore how they complement each other in the modern AI landscape.   Understanding RAG: The Information Retriever   What is RAG? RAG starts with a fundamental problem: your LLM has learned information up to its training cutoff, but you need current, specific, or domain-specific information that wasn't in the training data. Here's how RAG works: 1. ** Que...

Unlock Your Documents: How DocLing Transforms PDFs into AI-Ready Content The true power of AI and large language models lies in their ability to work with your personal and organizational data. While techniques like RAG (Retrieval Augmented Generation) have gained popularity, there's a significant challenge: much of our valuable information is trapped in complex document formats like PDFs and proprietary files such as DOCX. These documents present unique obstacles for AI workflows. They contain nested elements, lack standardized layouts, and feature varying formatting and table structures. Traditional parsing methods often fail to preserve the document's integrity and context—until now.   Introducing DocLing: IBM's Open Source Solution DocLing is an innovative open source project from IBM Research that addresses these challenges head-on. This powerful toolkit can parse popular document formats and export them into markdown and JSON while using context-aware tech...

The Future of AI: Combining Neural Networks with Symbolic Reasoning The artificial intelligence landscape is experiencing a paradigm shift. While we've been focused on coding and retrieval-augmented generation (RAG), groundbreaking research from Harvard, FISAR, and Hong Kong Polytechnic University is pointing toward a revolutionary approach: **neurosymbolic AI systems** that combine the pattern recognition power of large language models with the logical reasoning capabilities of symbolic AI.   Three Groundbreaking Research Papers Recent studies have emerged that share a common thread - the integration of neural and symbolic systems: 1. **" Empowering Domain Specific LLMs with Graph Oriented Databases"** - Explores combining neural models with graph databases to improve explainability, reduce latency, and boost system performance. 2. **" Neurosymbolic Entity Alignment"** (Hong Kong Polytechnic University) - Demonstrates how neurosymbolic approaches ...

TL;DR Link:  https://www.fastcompany.com/91396595/the-silent-killer-of-ai-success-ai-leadership-success

TL;DR Link:  https://www.forbes.com/sites/jodiecook/2025/09/11/5-chatgpt-prompts-for-content-that-removes-objections-and-boosts-sales/

TL;DR Link:  https://www.tomsguide.com/ai/chatgpt/clever-chatgpt-prompt-turns-the-ai-into-a-personal-expert-heres-how-to-use-it

Link:  https://levelup.gitconnected.com/detecting-potential-outliers-in-text-data-through-a-semantic-ai-pipeline-ba77cb5bf01e

TL;DR Link:  https://www.howtogeek.com/bsds-worth-trying-instead-of-linux/ Ghost BSD:  https://www.ghostbsd.org/ Nomad BSD:  https://nomadbsd.org/

TL;DR Link:  https://neurosciencenews.com/human-ai-learning-29669/

Link:  https://huggingface.co/papers/2509.07980 GitHub:  https://github.com/zhengkid/Parallel-R1 See also: Graph-R1: An Agentic GraphRAG Framework for Structured, Multi-Turn Reasoning with Reinforcement Learning https://www.marktechpost.com/2025/08/09/graph-r1-an-agentic-graphrag-framework-for-structured-multi-turn-reasoning-with-reinforcement-learning/ https://arxiv.org/pdf/2507.21892v1

TL;DR Link:  https://timesofindia.indiatimes.com/technology/tech-tips/15-free-chatgpt-prompts-that-make-homework-more-fun-and-learning-focused-for-kids/articleshow/123878857.cms

TL;DR Link:  https://www.nature.com/articles/s43588-025-00854-1

TL;DR Link:  https://www.zdnet.com/article/forget-windows-11-nobara-linux-is-the-os-for-everyone/

TL;DR Link:  https://cybersecuritynews.com/villager-ai-powered-pentesting-tool/

Link:  https://towardsdatascience.com/preventing-context-overload-controlled-neo4j-mcp-cypher-responses-for-llms/

Link:  https://levelup.gitconnected.com/building-an-advanced-agentic-rag-pipeline-that-mimics-a-human-thought-process-687e1fd79f61

TL;DR Link:  https://www.yahoo.com/lifestyle/articles/1970s-burger-king-sandwich-wish-183900012.html

Link -  https://research.google/blog/making-llms-more-accurate-by-using-all-of-their-layers/

How AI Models Learn: Exploring the Hidden Landscape of Meta's Llama 3.2 Imagine standing on a vast, mysterious landscape where every hill and valley represents different levels of performance for an AI model. This is the lost landscape of Meta's Llama 3.2 large language model—a visual metaphor that helps us understand one of the most fascinating aspects of modern artificial intelligence: how these systems actually learn. The Gradient Descent Puzzle Virtually all modern AI models learn through a process called gradient descent. Picture yourself dropped randomly onto this performance landscape, tasked with finding the lowest valley—the point where your model performs best. The intuitive approach would be to simply walk downhill, step by step, until you reach the bottom. But here's where it gets interesting: this seemingly simple approach initially stumped many AI pioneers. Jeff Hinton, who won the Nobel Prize in 2024 for his AI work, once entirely dismissed traini...

Why the Human Brain Must Be a Graph: Lessons for AI The human brain possesses remarkable advantages over today's artificial intelligence systems. It excels in energy efficiency, demonstrates genuine common sense, and masters one-shot learning – acquiring knowledge from single experiences rather than requiring millions of training examples. These capabilities raise a compelling question: can we understand and replicate the brain's design principles to create better AI? The Only Structure That Makes Sense With approximately 86 billion spiking neurons, each connected to thousands of others through synapses, the brain processes an overwhelming flood of signals. Yet somehow, this biological network organizes these signals into coherent thought, reasoning, and understanding. When we examine the constraints of neurobiology and observe human cognitive behavior, only one mathematical structure emerges as viable: the graph. A graph consists of nodes connected by edges. In the...

Building AI That Actually Understands: The Brain Simulator 3 Approach After four decades in artificial intelligence research, I've witnessed countless promising algorithms come and go. While I remain optimistic about experimentation and innovation in AI, I've become increasingly convinced that today's systems are missing something fundamental: the common sense and understanding that every human naturally possesses. Rather than chase the latest trends, I'm pursuing what I call "the sure thing." If we can decode the techniques used by the human brain and replicate them in software, we'll create AI systems that are not just more powerful, but genuinely smarter and more robust. We're not there yet, but we have a solid foundation to build upon. The Power of Unified Intelligence The human brain's remarkable capability stems from how it weaves together knowledge from multiple senses into a single, coherent internal model. This holistic approac...

How Researchers Used Physics to Solve AI's Biggest Hidden Problem Modern AI has achieved incredible breakthroughs, powering everything from ChatGPT to advanced image generators. But beneath these impressive capabilities lies a critical flaw that actually gets worse as models become more powerful. Today, we'll explore how a team of researchers turned to the fundamental laws of physics to solve this stubborn problem—and why their solution could revolutionize the future of artificial intelligence. The Hidden Weakness: Over-Smoothing Transformer models, the backbone of today's most advanced AI systems, suffer from a phenomenon called "over-smoothing." As information passes through layer after layer in these deep neural networks, the unique characteristics of each piece of data (called tokens) begin to blur together. Like colors bleeding into each other on wet paper, the sharp, distinctive features that make each token valuable gradually fade until everythi...

Robinhood's New Social Network and Financial Super App: A Game-Changer for Investors Robinhood, the popular investment platform, is making waves once again with its latest innovations. In a recent CNBC interview, CEO Vlad Tenev unveiled exciting new features that are set to transform the way people manage their finances and invest. Robinhood Social: A New Way to Connect and Invest One of the most anticipated features is Robinhood Social, a network that allows users to see real-time trading activity. Imagine a Reddit-like platform, but with real money at stake. This feature will provide valuable insights for traders looking to hedge risks or speculate on market trends. While not immediately available, Robinhood Social is expected to launch early next year, giving investors something to look forward to in 2024. Retirement Savings Made Easy Robinhood is also making a strong push into retirement savings. The platform now offers Individual Retirement Accounts (IRAs) with att...

Link to article -  https://www.xda-developers.com/docker-wine-weird-container-run-windows-programs-on-linux/