diff --git a/content/posts/2024-11-12-v0.1.0-release.md b/content/posts/2024-11-12-v0.1.0-release.md
index cfdd277..975c720 100644
--- a/content/posts/2024-11-12-v0.1.0-release.md
+++ b/content/posts/2024-11-12-v0.1.0-release.md
@@ -18,7 +18,7 @@ tocopen: true
 
 In recent years, large language models (LLMs) have revolutionized AI applications, powering solutions in areas like chatbots, automated content generation, and advanced recommendation engines. Services like OpenAI’s have gained significant traction; however, many enterprises seek alternatives due to data security concerns, customizability needs, or the financial impact of proprietary solutions. Yet, transforming LLMs into cost-effective, scalable APIs poses substantial technical challenges.
 
-### Key Challenges in AI Infrastructure
+## Key Challenges in AI Infrastructure
 
 1. **Efficient Heterogeneous Resource Management**: Managing GPU resources across clouds is crucial for balancing cost and performance. This involves autoscaling, high-density deployments, and efficiently handling mixed GPU types to reduce expenses and support peak loads without over-provisioning.
 2. **Next-Gen Disaggregation Architectures**: Cutting-edge architectures, like prefill and decoding disaggregating or employing a remote KV cache, enable more granular resource control and reduce processing costs. However, they demand significant R&D investment to develop reliable, scalable implementations.