diff --git a/blog/kubecon-eu-2026-recap/index.md b/blog/kubecon-eu-2026-recap/index.md
index bc0edca4..87e4280a 100644
--- a/blog/kubecon-eu-2026-recap/index.md
+++ b/blog/kubecon-eu-2026-recap/index.md
@@ -75,7 +75,7 @@ This is why what appears to be a low-level problem has become one of the core is
 
 HAMi Maintainer Xiao Zhang's talk started from a classic, long-standing problem in the Kubernetes community: **How can multiple containers share a GPU?**
 
-While this question seems specific, it actually points to a challenge the entire AI infrastructure ecosystem faces. Once you enter inference, batch processing, online serving, and multi-tenant mixed scenarios, GPUs can no longer be simply allocated in an "exclusive whole-card" manner.
+While this question seems specific, it actually points to a challenge the entire AI infrastructure ecosystem faces. Once you enter inference, batch processing, online serving, and multi-tenant mixed scenarios, GPUs can no longer be allocated in an "exclusive whole-card" manner.
 
 The significance of this talk lies in putting HAMi's solution back into the original context of the Kubernetes community: not building an isolated solution from scratch, but addressing a long-standing upstream problem that hasn't been fully resolved.