Good to see that I'll be in the value-add stage of life soon where I can rescue things lol

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Cutting through the noise: which models work best for RAG and how does long context help? I think this is the most comprehensive analysis of model RAG performance. We also look at model failures! This is a great guide for users on what works for their application!

This is important for anyone trying to understand where we are in AI today. There are lots of amazing problems to solve…and it’s not *only * about scale. We need more fundamental breakthroughs. I’m here for that :)

Function calling significantly enhances the utility of LLMs in real-world applications; however, evaluating and improving this capability isn't easy — and no one benchmark tells the whole story. Learn more about our approach in the latest blog from Databricks:

Checks out 😂 I love his advice to Google

Demystifying function calling! As part of compound systems, calling funcs/APIs is super important. There's still a fair bit of work required to make these reliable. Learn about it here 👇 databricks.com/blog/unpacking…

Some great work by one of our undergrad interns! We are constantly looking for ways to improve quality of outputs leveraging existing artifacts like open LLMs. RLHF has been shown to work but is expensive. Here we show a technique to leverage the implicit capabilities of models

We've been working with Twelve Labs (twelvelabs.io) for more than a year now. These guys took a different strategy than most other AI startups and focused on gen AI and video. That bet is paying off now! Think "RAG but for video"

I posted this nearly 2 years about Tesla’s Dojo chip. Many Tesla fans argued that i was underestimating what they could do. Well, Elon Musk just disclosed his 100k H100 cluster built in 4 months.(!) As I said then, it’ll take years for a new hardware platform to actually work