XDA Developers on MSN

One tiny change made my local LLMs more useful than ChatGPT for real work

And it maintains my privacy, too ...
InfoWorld

Fully local retrieval-augmented generation, step by step

How to implement a local RAG system using LangChain, SQLite-vss, Ollama, and Meta’s Llama 2 large language model. In “Retrieval-augmented generation, step by step,” we walked through a very simple RAG ...
NextBigFuture

Looking at Hardware for Running Local Large Language Models

ChatRTX is a demo app that lets you personalize a GPT large language model (LLM) connected to your own content—docs, notes, images, or other data. Leveraging retrieval-augmented generation (RAG), ...
Geeky Gadgets

Llama 2 Retrieval Augmented Generation (RAG) tutorial

If you are interested in learning more about how to use Llama 2, a large language model (LLM), for a simplified version of retrieval augmented generation (RAG). This guide will help you utilize the ...
VentureBeat

Karpathy shares 'LLM Knowledge Base' architecture that bypasses RAG with an evolving markdown library maintained by AI

AI vibe coders have yet another reason to thank Andrej Karpathy, the coiner of the term. The former Director of AI at Tesla and co-founder of OpenAI, now running his own independent AI project, ...
Hosted on MSN

I started using my local LLM with Obsidian and should have done it sooner

I’ve been using Obsidian for several years now, and it’s been one of the most solid and consistently reliable options for storing my notes and building a knowledge base. This is where I keep long ...
InfoQ

The Devoxx Genie IntelliJ Plugin Provides Access to Local or Cloud Based LLM Models

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
VentureBeat

Most RAG systems don’t understand sophisticated documents — they shred them

But for industries dependent on heavy engineering, the reality has been underwhelming. Engineers ask specific questions about infrastructure, and the bot hallucinates. The failure isn't in the LLM.