Inside the AI Stack
Key Takeaways
The video discusses AI-native tooling, open-source models, and systems thinking in the context of ML research papers, highlighting the importance of base models, reasoning capabilities, and multimodal interactions.
Full Transcript
like you get this like ultimately the base model and this is why I think like not at least today like we don't have a separate series of thinking models like it's not Gemini you know whatever some other name it's like truly the Gemini 2.0 know flash models with thinking built into them. So you benefit from all the like base capabilities of the model and that ability for the model to reason over the tokens. You get like both ends of the scaling curve which is like as the base model capabilities improve you get that value and then you also get the added sort of RL thinking chain of thought stuff that's happening which is just super cool and then the capabilities like multimodal and long context start to really matter a lot in those in those examples as well. I mean, Google's long context like a million, two million. Crazy. Like, it's it's uh it's wild. Yeah. Yeah. And like it feels like again like back to this the thread around these capabilities like it feels like long context with reasoning is like finally going to be that thing where like it actually just like blows the lid off of it and like it it makes the use case really come to life because like the challenges historically has been long context. X
Original Description
Why AI-native tooling, open-source models, and systems thinking are reshaping the ML landscape—season kickoff with Swyx & Alessio
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![[LLM Paper Club] Llama 3.1 Paper: The Llama Family of Models](https://i.ytimg.com/vi/TgLSYIBoX5U/mqdefault.jpg)
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![[Paper Club] 🍓 On Reasoning: Q-STaR and Friends!](https://i.ytimg.com/vi/Y5-FeaFOEFM/mqdefault.jpg)
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![[Paper Club] Who Validates the Validators? Aligning LLM-Judges with Humans (w/ Eugene Yan)](https://i.ytimg.com/vi/4o_ic83U1Kw/mqdefault.jpg)
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