fixing readme

Author: GioeleB00

README.md

@@ -27,20 +27,6 @@ With the new **event injection engine**, you can explore *what-if* dynamics: net
 
 ---
 
-### What Problem Does It Solve?
-
-Predicting how an async system will behave under real-world load is notoriously hard. Teams often rely on rough guesses, over-provisioning, or painful production incidents. **AsyncFlow replaces guesswork with scenario-driven simulations**: you declare the conditions (network RTT, jitter, resource limits, injected failures) and observe the consequences on latency, throughput, and resource pressure.
-
----
-
-### Why Scenario-Driven? *Design Before You Code*
-
-AsyncFlow doesn’t need your backend to exist.
-You can model your architecture with YAML or Python, run simulations, and explore bottlenecks **before writing production code**.
-This scenario-driven approach lets you stress-test scaling strategies, network assumptions, and failure modes safely and repeatably.
-
----
-
 ### How Does It Work?
 
 AsyncFlow represents your system as a **directed graph of components**, for example: clients, load balancers, servers—connected by network edges with configurable latency models. Each server is **event-loop aware**: CPU work blocks, RAM stays allocated, and I/O yields the loop, just like real async frameworks. You can define topologies via **YAML** or a **Pythonic builder**.
@@ -49,19 +35,43 @@ AsyncFlow represents your system as a **directed graph of components**, for exam
 
 Run the simulation and inspect the outputs:
 
-<p>
+<!-- Hero full-width -->
+<p align="center">
   <a href="https://raw.githubusercontent.com/AsyncFlow-Sim/AsyncFlow/main/readme_img/lb_dashboard.png">
-    <img src="https://raw.githubusercontent.com/AsyncFlow-Sim/AsyncFlow/main/readme_img/lb_dashboard.png" alt="Latency + Throughput Dashboard" width="300">
+    <img src="https://raw.githubusercontent.com/AsyncFlow-Sim/AsyncFlow/main/readme_img/lb_dashboard.png"
+         alt="Latency + Throughput Dashboard"
+         style="max-width:100%;height:auto;" loading="lazy">
   </a>
+</p>
+
+<!-- Due immagini affiancate -->
+<p align="center">
   <a href="https://raw.githubusercontent.com/AsyncFlow-Sim/AsyncFlow/main/readme_img/lb_server_srv-1_metrics.png">
-    <img src="https://raw.githubusercontent.com/AsyncFlow-Sim/AsyncFlow/main/readme_img/lb_server_srv-1_metrics.png" alt="Server 1 Metrics" width="300">
+    <img src="https://raw.githubusercontent.com/AsyncFlow-Sim/AsyncFlow/main/readme_img/lb_server_srv-1_metrics.png"
+         alt="Server 1 Metrics" width="49%" loading="lazy">
   </a>
   <a href="https://raw.githubusercontent.com/AsyncFlow-Sim/AsyncFlow/main/readme_img/lb_server_srv-2_metrics.png">
-    <img src="https://raw.githubusercontent.com/AsyncFlow-Sim/AsyncFlow/main/readme_img/lb_server_srv-2_metrics.png" alt="Server 2 Metrics" width="300">
+    <img src="https://raw.githubusercontent.com/AsyncFlow-Sim/AsyncFlow/main/readme_img/lb_server_srv-2_metrics.png"
+         alt="Server 2 Metrics" width="49%" loading="lazy">
   </a>
 </p>
 
 
+
+---
+
+### What Problem Does It Solve?
+
+Predicting how an async system will behave under real-world load is notoriously hard. Teams often rely on rough guesses, over-provisioning, or painful production incidents. **AsyncFlow replaces guesswork with scenario-driven simulations**: you declare the conditions (network RTT, jitter, resource limits, injected failures) and observe the consequences on latency, throughput, and resource pressure.
+
+---
+
+### Why Scenario-Driven? *Design Before You Code*
+
+AsyncFlow doesn’t need your backend to exist.
+You can model your architecture with YAML or Python, run simulations, and explore bottlenecks **before writing production code**.
+This scenario-driven approach lets you stress-test scaling strategies, network assumptions, and failure modes safely and repeatably.
+
 ---
 
 ### What Questions Can It Answer?