Add videos and tweak blog post

_posts/2024-10-15-performance-boost-windows.md

@@ -88,7 +88,7 @@ Install PyTorch CPU 2.4.1 or later on Windows from the [official repository](htt
 
 ## ACKNOWLEDGMENTS
 
-The results presented in this blog post was achieved through the collaborative effort of the Intel PyTorch team and Meta. We would like to express our sincere gratitude to [Xu Han](https://github.com/xuhancn), [Jiong Gong](https://github.com/jgong5), [Haozhe Zhu](https://github.com/zhuhaozhe), [Mingfei Ma](https://github.com/mingfeima), [Chuanqi Wang](https://github.com/chuanqi129), [Guobing Chen](https://github.com/Guobing-Chen) and [Eikan Wang](https://github.com/EikanWang). Their expertise and dedication have been instrumental in achieving the optimizations and performance improvements discussed here. Thanks to [Jiachen Pu](https://github.com/peterjc123) from community for his participation in the issue discussion and suggesting the use of [mimalloc](https://github.com/microsoft/mimalloc). We’d also like to express our gratitude to Microsoft for providing such an easily integrated and performant mallocation library. Thanks to [Pierre Blanchard](https://github.com/blapie) , [Nathan Sircombe](https://gitlab.com/Nathan.Sircombe) from ARM and [Alex Reinking](https://github.com/alexreinking) from Qualcomm for their contribution in overcome the compatibility issues with the [sleef](https://github.com/shibatch/sleef) integrated to PyTorch Windows. Finally we want to thank [Jing Xu](https://github.com/jingxu10), [Weizhuo Zhang](https://github.com/WeizhuoZhang-intel) and [Zhaoqiong Zheng](https://github.com/ZhaoqiongZ) for their contributions to this blog.
+The results presented in this blog post was achieved through the collaborative effort of the Intel PyTorch team and Meta. We would like to express our sincere gratitude to [Xu Han](https://github.com/xuhancn), [Jiong Gong](https://github.com/jgong5), [Haozhe Zhu](https://github.com/zhuhaozhe), [Mingfei Ma](https://github.com/mingfeima), [Chuanqi Wang](https://github.com/chuanqi129), [Guobing Chen](https://github.com/Guobing-Chen) and [Eikan Wang](https://github.com/EikanWang). Their expertise and dedication have been instrumental in achieving the optimizations and performance improvements discussed here. Thanks to [Jiachen Pu](https://github.com/peterjc123) from community for his participation in the issue discussion and suggesting the use of [mimalloc](https://github.com/microsoft/mimalloc). We’d also like to express our gratitude to Microsoft for providing such an easily integrated and performant mallocation library. Thanks to [Pierre Blanchard](https://github.com/blapie) , [Nathan Sircombe](https://github.com/nSircombe) from ARM and [Alex Reinking](https://github.com/alexreinking) from Adobe for their contribution in overcome the compatibility issues with the [sleef](https://github.com/shibatch/sleef) integrated to PyTorch Windows. Finally we want to thank [Jing Xu](https://github.com/jingxu10), [Weizhuo Zhang](https://github.com/WeizhuoZhang-intel) and [Zhaoqiong Zheng](https://github.com/ZhaoqiongZ) for their contributions to this blog.
 
 
 ### Product and Performance Information

_videos/vid1.md

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+---
+title: 'PyTorch 2.5 Live Q&A'
+youtube_id: B3IgXpl4xt4
+date: Oct 21, 2024
+---

_videos/vid2.md

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+---
+title: 'DistServe: disaggregating prefill and decoding for goodput-optimized LLM inference'
+youtube_id: Bh-jlh5vlF0
+date: Oct 16, 2024
+---

_videos/vid3.md

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+---
+title: 'Efficient Streaming Language Models with Attention Sinks'
+youtube_id: RnM84Sv9WpA
+date: Oct 11, 2024
+---

_videos/vid4.md

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+---
+title: 'PyTorch Expert Exchange: Adapting open source models with Open-Instruct and Tulu'
+youtube_id: e1qUJFAo10s
+date: Sep 11, 2024
+---

_videos/vid5.md

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+---
+title: 'PyTorch Expert Exchange: Efficient Generative Models: From Sparse to Distributed Inference'
+youtube_id: Eqg0VIiWrgM
+date: Aug 30, 2024
+---

_videos/vid6.md

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+---
+title: 'torch.compile: The Missing Manual'
+youtube_id: rew5CSUaIXg
+date: Aug 13, 2024
+---

_videos/vid7.md

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+---
+title: 'Using PyTorch for Monocular Depth Estimation Webinar'
+youtube_id: xf2QgioY370
+date: Sep 27, 2024
+---

_videos/vid8.md

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+---
+title: 'Accelerating LLM family of models on Arm Neoverse based Graviton AWS processors with KleidiAI'
+youtube_id: NeHIhQWewug
+date: Aug 22, 2024
+---

_videos/vid9.md

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+---
+title: 'PyTorch 2.4: Live Q&A'
+youtube_id: ry_QgUIYX1E
+date: Jul 25, 2024
+---