Improve tutorial presentation and user experience

.claude/settings.local.json

@@ -0,0 +1,10 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(rg:*)",
+      "Bash(grep:*)",
+      "Bash(npm run compile:*)"
+    ],
+    "deny": []
+  }
+}

.github/workflows/build-extensions.yml

@@ -41,9 +41,9 @@ jobs:
       - name: Build theme extension
         run: |
           echo "Building theme extension..."
-          cd themes && npx @vscode/vsce package --out ../zenml-color-theme.vsix
+          cd themes && npx @vscode/vsce package --out ./zenml-color-theme-0.0.1.vsix
 
-      - name: Verify extensions were built
+      - name: Verify extensions were builts
         run: |
           echo "Checking built extensions..."
           if [ -d ".devcontainer/extensions" ]; then
@@ -54,11 +54,11 @@ jobs:
             exit 1
           fi
 
-          if [ -f "zenml-color-theme.vsix" ]; then
+          if [ -f "themes/zenml-color-theme-0.0.1.vsix" ]; then
             echo "Theme extension built successfully:"
-            ls -la zenml-color-theme.vsix
+            ls -la themes/zenml-color-theme-0.0.1.vsix
           else
-            echo "ERROR: zenml-color-theme.vsix not found"
+            echo "ERROR: zenml-color-theme-0.0.1.vsix not found"
             exit 1
           fi
 
@@ -72,7 +72,7 @@ jobs:
         uses: actions/upload-artifact@v4
         with:
           name: theme-extension
-          path: zenml-color-theme.vsix
+          path: themes/zenml-color-theme-0.0.1.vsix
 
       - name: Commit and push built extensions
         # Only commit on push to main/develop branches, not on PRs
@@ -83,7 +83,7 @@ jobs:
 
           # Add the built extensions to git
           git add .devcontainer/extensions/zenml-codespace-tutorial-*.vsix
-          git add zenml-color-theme.vsix
+          git add themes/zenml-color-theme-0.0.1.vsix
 
           # Check if there are any changes to commit
           if git diff --staged --quiet; then

assets/main.css

@@ -319,7 +319,7 @@ header {
 }
 
 .tutorial-title h2 {
-  font-size: 1rem;
+  font-size: 1.25rem;
   font-weight: 600;
   color: var(--zenml-dark);
   margin: 0;
@@ -448,13 +448,68 @@ footer .run-pipeline-button.running {
 footer .run-pipeline-button.completed {
   background: var(--zenml-green);
   color: white;
+  cursor: default;
+}
+
+footer .run-pipeline-button.completed:hover {
+  background: var(--zenml-green);
+  transform: none;
+  box-shadow: none;
 }
 
 footer .run-pipeline-button.failed {
   background: var(--zenml-red);
   color: white;
 }
 
+
+/* Pipeline button group */
+.pipeline-button-group {
+  display: flex;
+  gap: 8px;
+  align-items: center;
+}
+
+/* Dashboard button styling - smaller variant */
+.dashboard-button-small {
+  background: var(--vscode-button-secondaryBackground);
+  border: 1px solid var(--vscode-sideBar-border);
+  color: var(--vscode-button-secondaryForeground);
+  padding: 9px 16px;
+  border-radius: 6px;
+  font-size: 14px;
+  font-weight: 500;
+  cursor: pointer;
+  transition: all 0.3s ease;
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  justify-content: center;
+  font-family: "Inter", sans-serif;
+  margin: 0;
+  height: 38px;
+  text-decoration: none;
+  flex-shrink: 0;
+  white-space: nowrap;
+  box-sizing: border-box;
+}
+
+.dashboard-button-small:hover {
+  background: var(--vscode-button-secondaryHoverBackground);
+  transform: translateY(-1px);
+  box-shadow: 0 2px 6px rgba(0, 0, 0, 0.1);
+  color: var(--vscode-button-secondaryForeground);
+  text-decoration: none;
+}
+
+.dashboard-button-small:active {
+  transform: translateY(0);
+}
+
+.dashboard-button-small .codicon {
+  font-size: 14px;
+}
+
 #progress {
   height: 4px;
   background-color: var(--vscode-progressBar-background);

assets/main.js

@@ -476,7 +476,8 @@
       } else if (status === "completed" || status === "cached") {
         runButton.innerHTML = `<i class="checkmark">✓</i> ${buttonTexts[status]}`;
         //@ts-ignore
-        runButton.disabled = false;
+        runButton.disabled = true;
+        runButton.style.cursor = "default";
       } else if (status === "failed") {
         runButton.innerHTML = `<i class="codicon codicon-error"></i> ${buttonTexts[status]}`;
         //@ts-ignore
@@ -493,14 +494,18 @@
   }
 
   function showDashboardUrl(url) {
-    const dashboardLink = document.getElementById("dashboard-link");
-    const dashboardUrl = document.getElementById("dashboard-url");
+    const dashboardButton = document.getElementById("dashboard-button");
 
-    if (dashboardLink && dashboardUrl) {
+    if (dashboardButton) {
       //@ts-ignore
-      dashboardUrl.href = url;
-      dashboardUrl.textContent = "View Pipeline in Dashboard";
-      dashboardLink.style.display = "flex";
+      dashboardButton.href = url;
+      dashboardButton.style.display = "flex";
+
+      // Add click handler to open in external browser
+      dashboardButton.addEventListener('click', function(e) {
+        e.preventDefault();
+        vscode.postMessage({ type: "openDashboard", url: url });
+      });
     }
   }
 

pipelines/caching/cache_pipeline.py

@@ -11,9 +11,8 @@
 
 @step(enable_cache=True)
 def slow_step() -> Annotated[int, "answer"]:
-    logger.info("Starting slow computation (3 seconds)...")
+    logger.info("🔄 Actually computing result... (sleeping 3 seconds)")
     time.sleep(3)
-    logger.info("Computation completed!")
     return 42
 
 
@@ -23,10 +22,17 @@ def cache_pipeline():
 
 
 if __name__ == "__main__":
-    logger.info("First run - will take ~3 seconds")
-    cache_pipeline()  # first run ~3 s
-
-    logger.info("Second run - should be instant (cache hit)")
-    cache_pipeline()  # second run instant (cache hit)
+    logger.info("\n" + "="*60)
+    logger.info(">>> RUN 1: First execution (no cache available)")
+    logger.info("="*60)
+    cache_pipeline()
+
+    logger.info("\n" + "="*60)
+    logger.info(">>> RUN 2: Second execution (cache should be used)")
+    logger.info("="*60)
+    cache_pipeline()
+
+    logger.info("\n💡 Notice: The step's log message only appears in Run 1!")
+    logger.info("   In Run 2, the step was skipped entirely due to caching.")
 
     log_dashboard_urls("cache_pipeline")

pipelines/caching/caching.md

@@ -1,7 +1,3 @@
-# Caching - Smart Re-runs
-
-Learn how ZenML's caching system can save you time by avoiding redundant computations.
-
 ## What you'll learn
 
 - How to enable caching on steps
@@ -28,4 +24,5 @@ def slow_step() -> Annotated[int, "answer"]:
 
 ## Try it yourself
 
-Run this pipeline twice! The first run takes ~3 seconds, the second is instant thanks to caching.
+Run this pipeline twice! The first run takes ~3 seconds, the second is instant
+thanks to caching.

pipelines/completion/completion.md

@@ -14,7 +14,7 @@
 
 You Completed the Tutorial
 
-You've successfully completed the **ZenML Interactive Tutorial** and mastered the fundamentals of MLOps with ZenML!
+You've successfully completed the **ZenML Interactive Tutorial** and mastered the fundamentals of using ZenML for MLOps!
 
 </div>
 <div class="right-column">

pipelines/fanOut/fanOut.md

@@ -1,7 +1,3 @@
-# Fan-out/Fan-in - Parallel Processing
-
-Learn how to create parallel workflows that split work across multiple steps and then combine the results.
-
 ## What you'll learn
 
 - How to create parallel processing patterns
@@ -29,4 +25,5 @@ def fan_pipeline(parallel: int = 4):
 
 ## Try it yourself
 
-Run this pipeline to see how it processes data in parallel across multiple steps, then combines the results!
+Run this pipeline to see how it processes data in parallel across multiple
+steps, then combines the results!

pipelines/fanOut/fan_pipeline.py

@@ -1,11 +1,3 @@
-"""
-Shows parallel fan-out (multiple identical steps) and a fan-in step that
-gathers all their outputs via the Client API.
-
-Run it once and observe the printed summary.  Re-run: cache is OFF so you
-see the steps execute every time.
-"""
-
 from typing_extensions import Annotated
 from zenml import get_step_context, pipeline, step
 from zenml.client import Client

pipelines/helloWorld/helloWorld.md

@@ -1,5 +1,3 @@
-# Hello World - Steps & Pipelines
-
 Welcome to your first ZenML pipeline! This tutorial introduces the fundamental concepts of **steps** and **pipelines**.
 
 ## What you'll learn
@@ -30,4 +28,5 @@ def hello_pipeline():
 
 ## Try it yourself
 
-Click the **Run Pipeline** button to execute your first ZenML pipeline and see the output!
+Click the **Run Pipeline** button to execute your first ZenML pipeline and see
+the output!

pipelines/metadata/metadata.md

@@ -1,7 +1,3 @@
-# Metadata - Recording Useful Facts
-
-Learn how to log metadata to track important information about your pipeline runs.
-
 ## What you'll learn
 
 - How to use `log_metadata()` to record key information
@@ -29,4 +25,4 @@ def compute_accuracy() -> Annotated[float, "accuracy_metric"]:
 
 ## Try it yourself
 
-Run this pipeline and check the ZenML dashboard to see the metadata card!
+Run this pipeline and check the ZenML dashboard to see the metadata card!

pipelines/parameters/parameters.md

@@ -1,7 +1,3 @@
-# Parameters - Configurable Behavior
-
-Learn how to make your pipelines configurable with parameters.
-
 ## What you'll learn
 
 - How to add parameters to steps and pipelines
@@ -31,4 +27,4 @@ def param_pipeline(number: int = 3, factor: int = 2):
 
 ## Try it yourself
 
-Run this pipeline to see how parameters control the multiplication operation!
+Run this pipeline to see how parameters control the multiplication operation!

pipelines/retries/retries.md

@@ -1,7 +1,3 @@
-# Retries & Hooks - Robust Pipelines
-
-Learn how to build resilient pipelines that can handle failures gracefully with automatic retries and hooks.
-
 ## What you'll learn
 
 - How to configure automatic retries for flaky steps
@@ -32,4 +28,5 @@ def flaky() -> Annotated[str, "result"]:
 
 ## Try it yourself
 
-Run this pipeline multiple times! Sometimes it succeeds immediately, sometimes it needs retries to handle the random failures.
+Run this pipeline multiple times! Sometimes it succeeds immediately, sometimes
+it needs retries to handle the random failures.

pipelines/retries/robust_pipeline.py

@@ -1,8 +1,3 @@
-"""
-Demonstrates automatic retries *and* a failure hook.
-Run it a few times – roughly half will need 1–2 retries.
-"""
-
 import random
 import time
 

pipelines/stepIO/stepIO.md

@@ -1,7 +1,3 @@
-# Step I/O - Typed Inputs and Outputs
-
-Now let's learn how to pass data between steps with proper type annotations.
-
 ## What you'll learn
 
 - How to define typed step outputs using `Annotated` types
@@ -37,4 +33,5 @@ def count_rows(
 
 ## Try it yourself
 
-Run this pipeline to see how data flows from the `load_data` step to the `count_rows` step!
+Run this pipeline to see how data flows from the `load_data` step to the
+`count_rows` step!

pipelines/tagging/tagged_pipeline.py

@@ -1,12 +1,3 @@
-"""
-Artifact Tagging Demonstration
-This example shows different ways to tag artifacts in ZenML:
-1. Regular tags on artifacts using ArtifactConfig
-2. Cascade tags from pipeline to artifacts
-3. Dynamic tagging within steps
-4. Filtering artifacts by tags
-"""
-
 from typing import Annotated
 
 import pandas as pd

pipelines/tagging/tagging.md

@@ -1,7 +1,3 @@
-# Tagging - Keep Runs Organized
-
-Learn how to use tags to organize and categorize your pipeline runs.
-
 ## What you'll learn
 
 - How to add simple tags to pipelines
@@ -27,4 +23,5 @@ def tagged_pipeline():
 
 ## Try it yourself
 
-Run this pipeline multiple times and watch how the exclusive tag automatically moves to the latest run!
+Run this pipeline multiple times and watch how the exclusive tag automatically
+moves to the latest run!

pipelines/visualizations/visualizations.md

@@ -1,7 +1,3 @@
-# Visualizations - Custom Charts
-
-Learn how to create automatic and custom visualizations that appear in the ZenML dashboard.
-
 ## What you'll learn
 
 - How ZenML automatically visualizes common data types
@@ -30,6 +26,9 @@ def scatter(df: pd.DataFrame) -> Annotated[HTMLString, "scatter_plot"]:
 - **HTMLString type** lets you embed rich content in the dashboard
 - **Dashboard integration** makes results easily shareable
 
+![Two visualizations generated by this pipeline](./pipelines/visualizations/visualizations-dashboard.png)
+
 ## Try it yourself
 
-Run this pipeline and check the dashboard to see both the automatic DataFrame visualization and the custom scatter plot!
+Run this pipeline and check the dashboard to see both the automatic DataFrame
+visualization and the custom scatter plot!

pipelines/welcome/welcome.md

@@ -1,7 +1,7 @@
 <div class="two-column-content">
 <div class="left-column">
 
-# Welcome to ZenML Interactive Tutorial!
+# Welcome to the ZenML Interactive Tutorial!
 
 This hands-on tutorial will teach you **ZenML fundamentals** through 10 progressive lessons. Each lesson introduces exactly **one new concept** with copy-paste code examples.