ThursdAI - Sunday special deep dive, interviews with Joao, and Jon, AI agent Crews and Bagel Merges. Happy Sunday dear reader, As you know by now, ThursdAI pod is not a standard interview based podcast, we don't focus on a 1:1 guest/host conversation, but from time to time we do! And this week I was very lucky to have one invited guest and one surprise guest, and I'm very happy to bring you both these conversations today. Get your Crew together - interview with João Moura, creator of CrewAI We'll first hear from João Moura, the creator of Crew AI, the latest agent framework. João is a director of AI eng. at Clearbit (acquired by Hubspot recently) and created Crew AI for himself, to automate many of the things he didn't want to keep doing, for example, post more on Linkedin. Crew has been getting a lot of engagement lately, and we go into the conversation about it with João, it's been trending #1 on Github, and received #2 product of the day when Chris Messina hunted this (to João's complete surprise) on Product Hunt. CrewAI is built on top of Langchain, and is an agent framework, focusing on Orchestration or role-playing, autonomous agents. In our chat with João we go into the inspiration, the technical challenges and the success of CrewAI so far, how maintenance for crew is now partly a family effort and what's next for crew Merges and Bagels - chat with Jon Durbin about Bagel, DPO and merging The second part of today's pod was a conversation with Jon Durbin, a self described AI tinkerer and software engineer. Jon is a Sr. applied AI researcher at Convai, and is well known in our AI circles as a master finetuner and dataset curator. This interview was not scheduled, but I'm very happy it happened! If you've been following along with the AI / Finetuning space, Jon's Airoboros dataset and set of models have been often mentioned, and cited, and Jon's latest work on the Bagel models took the lead on HuggingFace open LLM leaderboard So when I mentioned on X (as I often do) that I'm going to mention this on ThursdAI, Jon came up to the space and we had a great conversation, in which he shared a LOT of deep insights into finetuning, DPO (Direct Preference Optimizations) and merging. The series of Bagel dataset and models, was inspired by the Everything Everywhere All at Once movie (which is a great movie, watch it if you haven't!) and is alluding to, Jon trying to throw as many datasets together as he could, but not only datasets! There has been a lot of interest in merging models recently, specifically many folks are using MergeKit to merge models with other models (and often a model with itself) to create larger/better models, without additional training or GPU requirements. This is solely an engineering thing, some call it frankensteining, some frankenmerging. If you want to learn about Merging, Maxime Labonne (the author of Phixtral) has co-authored a great deep-dive on Huggingface blog, it's a great resource to quickly get up to speed So given the merging excitement, Jon has set out to create a model that can be an incredible merge base, many models are using different prompt techniques, and Jon has tried to cover as many as possible. Jon also released a few versions of Bagel models, DPO and non DPO, that and we had a brief conversation about why the DPO versions are more factual and better at math, but not great for Role Playing (which is unsurprisingly what many agents are using these models for) or creative writing. The answer is, as always, dataset mix! I learned a TON from this brief conversation with Jon, and if you're interested in the incredible range of techniques in the Open Source LLM world, DPO and Merging are definitely at the forefront of this space right now, and Jon is just at the cross-roads of them, so definitely worth a listen and I hope to get Jon to say more and learn more in future episodes so stay tuned! So I'm in San Francisco, again... As I've mentioned on the previous newsletter,