Update README.md

README.md

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 short_description: The Deterministic AI Foundry
 ---
-🛡️ Mission
+# KHALM Labs 🛡️
 
-General AI is for brainstorming. Foundry AI is for infrastructure.
+> **General AI is for brainstorming. Foundry AI is for infrastructure.**
+
+**KHALM Labs** is an applied research foundry dedicated to solving the LLM hallucination problem in mission-critical environments. We do not build conversational chatbots. We forge **Expert Nodes**—mathematically verified, compiler-tested AI models designed exclusively for enterprise cloud infrastructure. 
 
-KHALM Labs is an applied research foundry dedicated to solving the LLM hallucination problem in mission-critical environments. We do not build conversational chatbots. We forge Expert Nodes—mathematically verified, compiler-tested AI models designed exclusively for enterprise cloud infrastructure.
 In environments where a 1% hallucination rate equals a 100% failure rate, we provide deterministic certainty.
 
-🏭 The Critic Loop Architecture
+---
+
+## 🏭 The Critic Loop Architecture
+
+We do not train our models on scraped, deprecated GitHub repositories. Our models are trained exclusively on synthetically generated, compiler-verified data through our proprietary **Critic Loop**. 
 
-We do not train our models on scraped, deprecated GitHub repositories. Our models are trained exclusively on synthetically generated, compiler-verified data through our proprietary Critic Loop.
-Generation: A base LLM generates complex cloud architecture (e.g., AWS Terraform).
-The Crucible: The output is routed directly into an isolated HashiCorp compiler.
-Reflection: If the compiler throws an error (duplicate resources, circular dependencies), the AI is forced to read the terminal error and rewrite the code.
-Verification: Only architecture that achieves a flawless Success: 0 exit code is added to our training data.
-Zero hallucinations. Zero syntax errors. Weapons-grade Infrastructure-as-Code.
+* **Generation:** A base LLM generates complex cloud architecture (e.g., multi-region AWS Terraform).
+* **The Crucible:** The output is routed directly into an isolated HashiCorp compiler and local mock cloud.
+* **Reflection:** If the compiler throws an error (duplicate resources, circular dependencies), the AI is forced to read the terminal error and rewrite the code.
+* **Verification:** Only architecture that achieves a flawless `Success: 0` exit code is added to our training data. 
+
+*Zero hallucinations. Zero syntax errors. Weapons-grade Infrastructure-as-Code.*
+
+---
 
-📦 Current Releases
+## 📦 Current Releases
 
-Datasets
-🟢 Aegis-Terraform-Phase1 - 1,000+ mathematically verified, syntax-perfect Terraform trajectories.
+### Datasets
+🟢 **[https://huggingface.co/datasets/KHALM-Labs/aegisnode-terraform-sample](#)**
+500 mathematically verified, syntax-perfect Terraform trajectories.
 
-Models
-🟡 AegisNode [TRAINING IN PROGRESS] - Our flagship deterministic Terraform deployment model. Trained on multi-phase compiler-verified data. Designed for zero-trust enterprise environments.
+### Models
+🟡 **AegisNode [TRAINING IN PROGRESS]** Our flagship deterministic Terraform deployment model. Trained on multi-phase compiler-verified data. Designed for zero-trust enterprise environments.