Containers, large language models (LLMs), and GPUs provide a foundation for developers to build services for what Nvidia CEO Jensen Huang describes as an "AI Factory."

At QCon New York earlier this month, two OpenAI engineers demonstrated ChatGPT's newest feature, Functions, in one session. Another talk, however, pointed to the inherent limitations of LLMs.

Recently I’ve been chatting with a number of companies who are building out internal LLM labs/tools for their teams to make it easy to test LLMs against their internal usecases. I wanted to take a couple hours to see how far I could get using Streamlit to build out a personal LLM lab for a few usecases of my own. See code on lethain/llm-explorer. Altogether, I was impressed with how usable Streamlit is, and was able to build two useful tools in this timeframe:

What role does Hugging Face play in the generative AI developer ecosystem? We take a look at the company's savvy open source branding.

Last week I had to convert a table in a Google Doc to a JSON structure that will render as an HTML page. This is the sort of mundane task that burns staggering amounts of information workers’…

We’ll use the fast-rising LLM application framework for a practical example of how to use a GPT to help answer a question from a PDF document.

Curated collection of prompts for use with LLMs. Accessible interactively in Chat Notebooks and programmatically in functions like LLMFunction. Initial categories in the Prompt Repository cover personas, functions, modifiers.

Wolfram expands Notebooks, integrating LLM functionality into the new chat cell and Chat Notebook. Stephen Wolfram explains how chat-enabled and chat-driven versions work.

We look at what JavaScript developers need to know about LangChain, the fast-rising LLM application framework created by Harrison Chase.

In this post, Phillip talks through the challenges & pitfalls of LLMs we faced when building our Query Assistant - and that you too may face.

This article introduces prompt engineering to developers using large language models (LLMs) such as GPT-4 and PaLM. I will explain the types of LLMs, the importance of prompt engineering, various types of prompts with examples.

This week we’re talking about LLMs with Simon Willison. We can not avoid this topic. Last time it was Stable Diffusion breaking the internet. This time it’s LLMs breaking the internet. Large Language Models, ChatGPT, Bard, Claude, Bing, GitHub Copilot X, Cody…we cover it all.

Forget those huge hyped-up systems, that smaller one might be right for you. And here's why

Developers are diving into the open source LLaMA, but what benefits does it have over GPT? We spoke to Sebastian Raschka from Lightning AI.

List Of All The Websites Companies like Google, FB, OpenAI, Microsoft, etc, uses To Train Large Language Models like ChatGPT, Bard, Llama, etc.

IntroductionI feel that the world of coding is changing. In a couple of years, I expect a lot of new developer tools based on LLMs to come up. They will like...

WorLLMs. GitHub Gist: instantly share code, notes, and snippets.

I haven’t spent much time playing around with the latest LLMs, and decided to spend some time doing so. I was particularly curious about the usecase of using embeddings to supplement user prompts with additional, relevant data (e.g. supply the current status of their recent tickets into the prompt where they might inquire about progress on said tickets). This usecase is interesting because it’s very attainable for existing companies and products to take advantage of, and I imagine it’s roughly how e.

While there’s been a truly remarkable advance in large language models as they continue to scale up, facilitated by being trained and run on larger and larger GPU clusters, there is still a need to be able to run smaller models on devices that have constraints on memory and processing power. Being able to run models at the edge enables creating applications that may be more sensitive to user privacy or latency considerations - ensuring that user data does not leave the device.