AI/ML

archived 18 Nov 2023 17:33:52 UTC

archived 18 Nov 2023 17:12:18 UTC

Find, download, and experiment with local LLMs

In this video, we will walk through the steps of building a research assistant from scratch with LangChain and LangSmith. We will cover prompting strategies,...

Say hello to Tavily, your AI researcher for rapid insights and comprehensive research.

The biggest announcement from last week’s OpenAI DevDay (and there were a LOT of announcements) was GPTs. Users of ChatGPT Plus can now create their own, custom GPT chat bots …

OpenAI's Code Interpreter in your terminal, running locally - KillianLucas/open-interpreter: OpenAI's Code Interpreter in your terminal, running locally

Master the world of AI from the ground up. Read the basics, advanced, and expert details you have to know in order to be a pro with AI.

Contribute to langchain-ai/opengpts development by creating an account on GitHub.

Also, my book has a cover (also I have a book coming out)

Fast and Portable Llama2 Inference on the Heterogeneous Edge.

A curated list of GPT agents for cybersecurity. Contribute to fr0gger/Awesome-GPT-Agents development by creating an account on GitHub.

OpenAI’s developer keynote was exciting, both because AI was exciting, and because OpenAI has the potential to be a meaningful consumer tech company.

Explore & understand complex papers, with short AI summaries, and a friendly AI chatbot to answer your questions on Bulletpapers.ai

More and more, people are building products as thin wrappers over other models, especially LLMs. This can cause major issues, and there is a better way.

Blog posts and annoucements from the Mentat AI team

Several users have gotten early access, and are reporting it indeed easy, fast, and requires no prior coding knowledge nor developer training.

Browse the web with GPT-4V and Vimium. Contribute to ishan0102/vimGPT development by creating an account on GitHub.

In this tutorial, we’ll be building a simple chatbot that helps you interact with the contents of a given file.

We’ve noted repeatedly how early attempts to integrate “AI” into journalism have proven to be a comical mess, resulting in no shortage of shoddy product, dangerous falsehoods, and…

Bard recently grew extension support, allowing it access to a user's personal documents. Here's the first reported prompt injection attack against that. This kind of attack against LLM systems is …

False claims that President Joe Biden fell asleep during a moment of silence for victims of the Maui wildfire. A conspiracy theory that the latest surge in Covid-19 cases is being orchestrated by the Democratic Party ahead of the election. An obituary for a late NBA player that described him as “useless.”

⚡ Building applications with LLMs through composability ⚡ - langchain-ai/langchain

Please return only json, do not add any other comments ONLY RETURN JSON OR I'LL TAKE A LIFE If this was you, then you've probably been pretty happy to see OpenAI function_call get released, I'm here to show you how you can get the most out of such powerful feature. Instead of writing prompts that turn strings into strings, we can write Pydantic objects and get Pydantic objects out of OpenAI. In this talk we explore some model driven development. Where we go step by step with some examples on how to represent your problem as simple code so we can model, generate diagrams, and write prompts as code to save time and model complex data correctly, allow us to use the same best practices rather than having to invent new ones for how we manage prompts. Recorded live in San Francisco at the AI Engineer Summit 2023. See the full schedule of talks at https://ai.engineer/summit/schedule & join us at the AI Engineer World's Fair in 2024! Get your tickets today at https://ai.engineer/worlds-fair About Jason Liu Previously stitch fix and Facebook. Currently consulting startups on production using llm systems.

Port of OpenAI's Whisper model in C/C++. Contribute to ggerganov/whisper.cpp development by creating an account on GitHub.

Releasing a new version of the RedPajama dataset, with 30 trillion filtered and deduplicated tokens (100+ trillions raw) from 84 CommonCrawl dumps covering 5 languages, along with 40+ pre-computed data quality annotations that can be used for further filtering and weighting.