diff --git a/training/dashboard/static/dashboard.js b/training/dashboard/static/dashboard.js
index af7ee09..96f5935 100644
--- a/training/dashboard/static/dashboard.js
+++ b/training/dashboard/static/dashboard.js
@@ -2272,11 +2272,10 @@ def train_nn(*, model, X_train, y_train, X_val, y_val,
       const PROFILES = ['cpu-saturate', 'ransomware-lite', 'bursty-c2', 'fork-bomb', 'crypto-miner'];
       const PHASES = ['clean', 'armed', 'infecting', 'infected_running', 'dormant'];
       let counter = 0;
-      // Inference rate is bounded by how often new 10-second windows
-      // close on the fleet, not by A100 forward-pass speed. With ~3
-      // hosts × one window every 10 s × 5 models = ~1.5 events/sec
-      // total — so the demo ticks every 2.5 s to stay honest. Going
-      // any faster is misleading about real deployment cadence.
+      // ~1 event/sec — slow enough that the audience can read each
+      // prediction as it lands, fast enough that the lanes feel
+      // alive. Real ceiling is ~1.5/sec (3 hosts × 5 models × one
+      // window every 10 s) so we're under it but not by a lot.
       demoTimer = setInterval(() => {
         const m = MODELS[counter % MODELS.length];
         counter++;
@@ -2297,7 +2296,7 @@ def train_nn(*, model, X_train, y_train, X_val, y_val,
           window_idx: counter,
           t_wall: Date.now() / 1000,
         });
-      }, 2500);
+      }, 1000);
     }
     function demoStop() {
       demoActive = false;
diff --git a/training/dashboard/static/index.html b/training/dashboard/static/index.html
index 7e97501..4f3c5a4 100644
--- a/training/dashboard/static/index.html
+++ b/training/dashboard/static/index.html
@@ -1313,6 +1313,6 @@
     </article>
   </div>
 
-  <script src="/static/dashboard.js?v=aa585e5d"></script>
+  <script src="/static/dashboard.js?v=44e715a3"></script>
 </body>
 </html>