Back in January, I wrote a blog post about adding JSON-LD to your web pages to make it easier for Google to understand what they were about. The example I used was my ReadABooker site, which encourages people to read more Booker Prize shortlisted novels (and to do so by buying them using my Amazon

Today, we dive into the revolutionary Graph RAG from Microsoft, an advanced retrieval-augmented generation system that enhances AI responses by providing relevant context. GraphRAG: The Most Incredible RAG Strategy Revealed

📌 In this video, you will learn:

What is RAG (Retrieval-Augmented Generation)?
Differences between Basic RAG and Graph RAG
How to implement Graph RAG in your application
Step-by-step guide on setting up Graph RAG
Advantages of using Graph RAG over traditional methods

In the near future, transforming text over an entire document will become as commonplace as filtering images.

Up until now, text editors have been focused on input. The next evolution of text editors will make it easy to alter, summarize and lengthen text. You’ll be able to do this for entire documents, not just individual sentences or paragraphs. The filters will be instantaneous and as good as if you wrote the text yourself. You will also be able to do this with local files, on your device, without relying on remote servers.

In a “A camera for ideas”, I coined the term synthography to describe synthetic images created with generative models.

Text generator is a handy plugin for Obsidian that helps you generate text content using GPT-3 (OpenAI). - GitHub - nhaouari/obsidian-textgenerator-plugin: Text generator is a handy plugin for Obsidian that helps you generate text content using GPT-3 (OpenAI).

Use Text Generator to generate ideas, attractive titles, summaries, outlines, and whole paragraphs based on your knowledge database.

Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue) - GitHub - abi/screenshot-to-code: Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)

I've watched the 4 videos from 3blue1brown on neural nets. The web and youtube are awash with mediocre videos on Large Language Models. I'm looking for a good one.

This is part of a longer series but is maybe the single best video I know of on the topic:
https://youtu.be/kCc8FmEb1nY?si=zmBleKwlpV06O3Mw

I thought this video from Steven Wolfram was also quite good:
https://www.youtube.com/live/flXrLGPY3SU?si=SrP1EJFMPJqVCFPL

Evals, RAG, fine-tuning, caching, guardrails, defensive UX, and collecting user feedback.

In May 2023, Eric Hartford, a machine learning engineer authored a popular blog post “Uncensored Models” providing his viewpoints to the merits of uncensored models, and how they are created. It’s a great read!

This post will give some example comparisons running Llama 2 uncensored model vs its censored model.

https://erichartford.com/uncensored-models

People often ask me how to use AI. Here's an overview with lots of links.

• The Six Large Language Models
• Write stuff
• Make Images
• Come up with ideas
• Make videos
• Coding

Yes, there has been much hype over the imagined powers and flaws of the new Large Language Models (e.g., Chat GPT-4), but the recent advances (that is, as of today in April 2023) indicate that ther…

https://arxiv.org/pdf/2303.12712.pdf

A talk by the lead author, Sebastian Bubeck, at MIT on March 22, 2023: Sparks of AGI: Early experiments with GPT-4.
https://www.youtube.com/watch?v=qbIk7-JPB2c

Stephen Wolfram explores the broader picture of what's going on inside ChatGPT and why it produces meaningful text. Discusses models, training neural nets, embeddings, tokens, transformers, language syntax.

The short answer is that they convert documents that are over 100 or even 1,000 pages long into a numeric representation of data and related context (vector embedding) and store them in a vector search engine.

https://betterprogramming.pub/how-to-build-your-own-custom-chatgpt-with-custom-knowledge-base-4e61ad82427e

2. Extending ChatGPT With LlamaIndex (GPT Index)
   LlamaIndex, also known as the GPT Index, is a project that provides a central interface to connect your LLMs with external data. Yeah, you read that correctly. With LlamaIndex, we can build something that looks like the illustration below:

https://levelup.gitconnected.com/its-time-to-create-a-private-chatgpt-for-yourself-today-6503649e7bb6

🧠 Memory Bot 🤖 — An easy up-to-date implementation of ChatGPT API, the GPT-3.5-Turbo model, with LangChain AI's 🦜 — ConversationChain memory module with Streamlit front-end. With the emergence of…

https://github.com/avrabyt

Wouldn't it be nice if you could selectively choose your data sources and feed that information into ChatGPT conversation with your data with ease?

Prompt: Let's create an icon set for design patterns. Here are the parameters for building individual icons in the icon set

• the icon will be used on a website and even in an app
• the icon needs to be square in shape
• the icon needs to represent the design pattern in context
• Use a minimalist design style
• use stroke size as 1 for all lines
• use complementary colours; probably pastel colour shades
• use colours in a way that they can be adapted to the entire set
• define a consistent and uniform style that we can propagate to other icons in the set
  I need you to create this icon in SVG format and provide the SVG code for the icon

https://github.com/aruva-xyz/svg-icons-by-chatgpt.git