# Martin Casado recommends an MIT talk for an intuitive grasp of LLMs

*By Recommended Reading from Tech Founders • September 27, 2026*

Vishal Misra’s MIT talk explains how SFT, RLHF and RL reshape a distribution while next-token prediction remains underneath; Martin Casado recommends it for understanding in-context learning and LLMs.

### MIT talk on LLMs (video; descriptive title)

**Resource/creator:** Vishal Misra’s [MIT talk on LLMs](https://youtu.be/sLodkyHlQhY). [^1] **Recommended by:** Martin Casado, who calls it the best talk on in-context learning and says it builds an intuitive grasp of LLMs. [^2]

**Key takeaway:** Misra describes a first-principles account that skips attention and transformers: SFT/RLHF/R reshape the distribution, while the underlying LLM remains a next-token predictor. [^1] **Why it matters:** It connects post-training methods to the model’s underlying prediction process—the intuition Casado specifically recommends the talk for. [^1][^2]

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### Sources

[^1]: [𝕏 post by @vishalmisra](https://x.com/vishalmisra/status/2103954166372528308)
[^2]: [𝕏 post by @martin_casado](https://x.com/martin_casado/status/2103957860044648488)