diff --git a/docs/index.md b/docs/index.md
index 67eedd5..a4467b9 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -74,25 +74,25 @@ Use `sched_ext` to apply bounded runtime scheduling policy and protect critical
 
 <div class="grid cards" markdown>
 
--   :material-eye-outline:{ .lg .middle } **Scheduling observability**
+-   **Scheduling observability**
 
     ---
 
     Pod-level scheduling metrics with eBPF, plus Prometheus and Grafana integration.
 
--   :material-tune-variant:{ .lg .middle } **Fine-grained control**
+-   **Fine-grained control**
 
     ---
 
     Apply scheduling intent to specific workloads, processes, or non-leader worker threads with TID-aware matching.
 
--   :material-server-network:{ .lg .middle } **Cloud-native operation**
+-   **Cloud-native operation**
 
     ---
 
     Manager + per-node Decision Makers distribute scheduling intent across Kubernetes nodes.
 
--   :material-chart-line:{ .lg .middle } **SLO-oriented automation**
+-   **SLO-oriented automation**
 
     ---
 
@@ -125,18 +125,3 @@ The principle is simple:
 - Gthulhu turns that allocation into a verifiable runtime execution policy **without crossing the CPU/resource boundaries Kubernetes already established**.
 
 [Explore the Claim2Core roadmap](claim2core.md){: .md-button }
-[Follow roadmap issue #141](https://github.com/Gthulhu/Gthulhu/issues/141){: .md-button }
-
-## Start with a real workload
-
-<div class="gth-cta" markdown>
-
-### See what CPU scheduling is doing to your workload
-
-Deploy Gthulhu on Kubernetes, inspect scheduler behavior, then apply policy only where the data shows it matters.
-
-[Deploy Gthulhu](k8s.md){: .md-button .md-button--primary }
-[Read the free5GC case study](https://free5gc.org/blog/20251126/20251126/){: .md-button }
-[Contribute](contributing.md){: .md-button }
-
-</div>
diff --git a/docs/index.zh.md b/docs/index.zh.md
index 3d7c0f0..bbce959 100644
--- a/docs/index.zh.md
+++ b/docs/index.zh.md
@@ -74,25 +74,25 @@ Gthulhu 專注處理這個 execution gap。
 
 <div class="grid cards" markdown>
 
--   :material-eye-outline:{ .lg .middle } **Scheduling Observability**
+-   **Scheduling Observability**
 
     ---
 
     使用 eBPF 提供 Pod-level scheduling metrics，並整合 Prometheus 與 Grafana。
 
--   :material-tune-variant:{ .lg .middle } **Fine-grained Control**
+-   **Fine-grained Control**
 
     ---
 
     對特定 workload、process，甚至非 leader worker thread 套用 scheduling intent；支援 TID-aware matching。
 
--   :material-server-network:{ .lg .middle } **Cloud-native Operation**
+-   **Cloud-native Operation**
 
     ---
 
     透過 Manager 與每節點 Decision Maker，把 scheduling intent 分散到 Kubernetes nodes。
 
--   :material-chart-line:{ .lg .middle } **SLO-oriented Automation**
+-   **SLO-oriented Automation**
 
     ---
 
@@ -125,18 +125,3 @@ Delivered workload SLO
 - Gthulhu 把 allocation 轉成可驗證的 runtime execution policy，並且**不突破 Kubernetes 已建立的 CPU / resource boundary**。
 
 [查看 Claim2Core Roadmap](claim2core.md){: .md-button }
-[追蹤 Roadmap Issue #141](https://github.com/Gthulhu/Gthulhu/issues/141){: .md-button }
-
-## 從真實 workload 開始
-
-<div class="gth-cta" markdown>
-
-### 先看 CPU scheduling 到底怎麼影響你的服務
-
-在 Kubernetes 部署 Gthulhu、觀察 scheduler behavior，再只對數據證明有影響的 execution path 套用 policy。
-
-[部署 Gthulhu](k8s.md){: .md-button .md-button--primary }
-[閱讀 free5GC Case Study](https://free5gc.org/blog/20251126/20251126/){: .md-button }
-[參與貢獻](contributing.md){: .md-button }
-
-</div>
diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css
index 668e16b..704166e 100644
--- a/docs/stylesheets/extra.css
+++ b/docs/stylesheets/extra.css
@@ -218,20 +218,8 @@ body,
   background: rgba(59, 130, 246, 0.06);
 }
 
-.gth-cta {
-  margin: 1.2rem 0 2rem;
-  padding: 1.5rem 1.7rem;
-  border-radius: 0.9rem;
-  background: rgba(59, 130, 246, 0.08);
-}
-
-.gth-cta h3 {
-  margin-top: 0;
-}
-
 [data-md-color-scheme="slate"] .gth-hero,
-[data-md-color-scheme="slate"] .gth-flow > p,
-[data-md-color-scheme="slate"] .gth-cta {
+[data-md-color-scheme="slate"] .gth-flow > p {
   background-color: rgba(59, 130, 246, 0.08);
 }