Andrew october blogs (#1800)

* wednesday blog post

* test for todays date

* executorch paragraph removal and addition of button + new blog post for Tuesday

* change date to Friday

* Wednesday blog fix

* removal

* Wedneday blog fix

* better images

* Thursday preview

* spacing

* remove by

* remove "by" since code is already adding it

* removal of old blog

* removing old executorch page

* remove TLDR

* clear cache

* new performance image

* image swap

Author: LinuxAzzamean

_posts/2024-10-25-intel-gpu-support-pytorch-2-5.md

@@ -56,11 +56,11 @@ The performance of Intel GPU on PyTorch was continuously optimized to achieve de
 
 The latest performance data measured on top of PyTorch Dynamo Benchmarking Suite using Intel® Data Center GPU Max Series 1100 single card showcase the FP16/BF16 significant speedup ratio over FP32 on eager mode in Figure 1, and Torch.compile mode speedup ratio over eager mode in Figure 2\. Both inference and training reached the similar significant improvements. 
 
-![Figure 2: FP16/BF16 Performance Gains Over FP32 Eager](/assets/images/performance-gains-over-fp32-eager.png){:style="width:100%"}
+![Figure 2: FP16/BF16 Performance Gains Over FP32 Eager](/assets/images/performance-gains-over-fp32-eager-2.png){:style="width:100%"}
 
 Figure 2: FP16/BF16 Performance Gains Over FP32 Eager
 
-![Figure 3: Torch.compile Performance Gains Over Eager Mode](/assets/images/performance-gains-over-fp32-eager-2.png){:style="width:100%"}
+![Figure 3: Torch.compile Performance Gains Over Eager Mode](/assets/images/performance-gains-over-fp32-eager.png){:style="width:100%"}
 
 Figure 3: Torch.compile Performance Gains Over Eager Mode