'```

git commit -m "AI: Know when to use it"
```

**Explanation of the commit message:**

*   **Concise:** It's short and to the point, adhering to the common 5-word limit.
*   **Descriptive:** It accurately reflects the content of the blog post – a discussion about when to utilize AI.
*   **Action-oriented:**  Implies a need for thoughtful consideration.
'

.gitea/workflows/build_push.yml

@@ -1,42 +1,61 @@
 name: Build and Push Image
-on: [ push ]
+on:
+    push:
+        branches:
+            - master
 
 jobs:
-  build:
+    build:
-    name: Build and push image
+        name: Build and push image
-    runs-on: ubuntu-latest
+        runs-on: ubuntu-latest
-    container: catthehacker/ubuntu:act-latest
+        container: catthehacker/ubuntu:act-latest
-    if: gitea.ref == 'refs/heads/master'
+        if: gitea.ref == 'refs/heads/master'
 
-    steps:
+        steps:
-    - name: Checkout
+            - name: Checkout
-      uses: actions/checkout@v4
+              uses: actions/checkout@v4
 
-    - name: Create Kubeconfig
+            - name: Create Kubeconfig
-      run: |
+              run: |
-        mkdir $HOME/.kube
+                  mkdir $HOME/.kube
-        echo "${{ secrets.KUBEC_CONFIG_BUILDX }}" > $HOME/.kube/config        
+                  echo "${{ secrets.KUBEC_CONFIG_BUILDX }}" > $HOME/.kube/config
 
-    - name: Set up Docker Buildx
+            - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
+              uses: docker/setup-buildx-action@v3
-      with:
+              with:
-        driver: kubernetes
+                  driver: kubernetes
-        driver-opts: |
+                  driver-opts: |
-          namespace=gitea-runner
+                      namespace=gitea-runner
-          qemu.install=true          
+                      qemu.install=true
 
-    - name: Login to Docker Registry
+            - name: Login to Docker Registry
-      uses: docker/login-action@v3
+              uses: docker/login-action@v3
-      with:
+              with:
-        registry: git.aridgwayweb.com
+                  registry: git.aridgwayweb.com
-        username: armistace
+                  username: armistace
-        password: ${{ secrets.REG_PASSWORD }}
+                  password: ${{ secrets.REG_PASSWORD }}
 
-    - name: Build and push
+            - name: Build and push
-      uses: docker/build-push-action@v5
+              uses: docker/build-push-action@v5
-      with:
+              with:
-        context: .
+                  context: .
-        push: true
+                  push: true
-        platforms: linux/amd64,linux/arm64
+                  platforms: linux/amd64,linux/arm64
-        tags: |
+                  tags: |
-          git.aridgwayweb.com/armistace/blog:latest    
+                      git.aridgwayweb.com/armistace/blog:latest
+
+            - name: Deploy
+              run: |
+                  echo "Installing Kubectl"
+                  apt-get update
+                  apt-get install -y apt-transport-https ca-certificates curl gnupg
+                  curl -fsSL https://pkgs.k8s.io/core:/stable:/v1.33/deb/Release.key | gpg --dearmor -o /etc/apt/keyrings/kubernetes-apt-keyring.gpg
+                  chmod 644 /etc/apt/keyrings/kubernetes-apt-keyring.gpg
+                  echo 'deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.33/deb/ /' | tee /etc/apt/sources.list.d/kubernetes.list
+                  chmod 644 /etc/apt/sources.list.d/kubernetes.list
+                  apt-get update
+                  apt-get install kubectl
+                  kubectl delete namespace blog
+                  kubectl create namespace blog
+                  kubectl create secret docker-registry regcred --docker-server=${{ vars.DOCKER_SERVER }} --docker-username=${{ vars.DOCKER_USERNAME }} --docker-password='${{ secrets.DOCKER_PASSWORD }}' --docker-email=${{ vars.DOCKER_EMAIL }} --namespace=blog
+                  kubectl apply -f kube/blog_pod.yaml && kubectl apply -f kube/blog_deployment.yaml && kubectl apply -f kube/blog_service.yaml

kube/blog_deployment.yaml

@@ -0,0 +1,24 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: blog-deployment
+  labels:
+    app: blog
+  namespace: blog
+spec:
+  replicas: 3
+  selector:
+    matchLabels:
+      app: blog
+  template:
+    metadata:
+      labels:
+        app: blog
+    spec:
+      containers:
+      - name: blog
+        image: git.aridgwayweb.com/armistace/blog:latest
+        ports:
+        - containerPort: 8000
+      imagePullSecrets:
+        - name: regcred

kube/blog_pod.yaml

@@ -0,0 +1,13 @@
+apiVersion: v1
+kind: Pod
+metadata:
+    name: blog
+    namespace: blog
+spec:
+    containers:
+        - name: blog
+          image: git.aridgwayweb.com/armistace/blog:latest
+          ports:
+              - containerPort: 8000
+    imagePullSecrets:
+        - name: regcred

kube/blog_service.yaml

@@ -0,0 +1,13 @@
+apiVersion: v1
+kind: Service
+metadata:
+  name: blog-service
+  namespace: blog
+spec:
+  type: NodePort
+  selector:
+    app: blog 
+  ports:
+    - port: 80
+      targetPort: 8000
+      nodePort: 30009

src/content/when_to_use_ai.md

@@ -0,0 +1,51 @@
+Title: When to use AI
+Date: 2025-06-05 20:00
+Modified: 2025-06-06 08:00
+Category: AI, Data
+Tags: ai, python
+Slug: when-to-use-ai
+Authors: Andrew Ridgway
+
+## The Great AI Debate: When to Trust the Machine vs. When to Stick to Your Brain
+
+As a journalist, software developer, and DevOps expert, I’ve spent years navigating the murky waters of AI’s “what if” scenarios. The question that keeps coming up professionally is: *“When to use AI?”* And the answer, like many things in life, is *“It depends.”* But when I’m not using my brain, I’m using my shudders. Let’s dive into the scenarios where AI isn’s the best choice—and where it *is* the best choice.
+
+## When AI is Not the Best Choice
+
+### 1. **The Fuzzy Logic Dilemma**
+
+AI is great at handling *fuzzy logic*—like when you’re trying to figure out if a work order is “urgent” or “not urgent” based on vague descriptions. But when you’re trying to map a work type to a specific work request, AI is *not* the best choice. Imagine this: You have a dataset of work orders, each with a type (e.g., “installation,” “repair,” “maintenance”) and a duration in days. You’re trying to group these into two categories: *“high-priority”* and *“low-priority.”* The problem? The work types and work requests are *tangentially related*.
+
+> *“I was given a list of work types that could be grouped into 1 of 2 categories exclusively.”*
+> *“The problem was… the ‘work types’ and work requests were at best tangentially related.”*
+
+So I had to manually read each work type and map it to a work request. It was a *shudder-inducing* task. AI, on the other hand, would have *automated* that. But it would also have *misunderstood* the relationship between the two.
+
+### 2. **The Math and Logic Job**
+
+AI is great at math and logic. If you need to calculate the total time spent on a process, or create a formula that accounts for variables like labor hours, weather, or equipment downtime, AI can handle it. For example, I once built a spreadsheet that calculated potential savings in a process. The formulas were precise, and the logic was sound.
+
+> *“Traditional programming methods work on maths and logic that is sound and offers no chance for interpretation.”*
+
+So even if AI could suggest the numbers, I’d never trust it to actually run the calculation. It’s like trusting a robot to drive a car when it’s not built for that.
+
+## When AI is the Best Choice
+
+### 1. **Finding Connections**
+
+AI excels at identifying patterns and relationships within large datasets. This is invaluable for tasks like customer segmentation, fraud detection, and predictive maintenance.
+
+### 2. **Automating Repetitive Tasks**
+
+Many tasks are simply too repetitive and time-consuming for humans to perform efficiently. AI-powered automation can free up valuable time and resources.
+
+### 3. **Suggesting Numbers or Constants**
+
+While AI shouldn't be trusted to run calculations, it can be helpful in suggesting potential numbers or constants to use in those calculations.
+
+## The Human vs. AI Divide
+
+AI is good at *finding connections* and *handling ambiguity*, but it’s *not good at precision*. When you need to ensure that a calculation is accurate, or that a mapping is flawless, AI is not the best choice. And that’s where humans shine. We’re good at *seeing* the connection between two things, even if it’s not obvious. We’re good at *precision* and *accuracy*.
+
+> *“I was given a list of work types that could be grouped into 1 of 2 categories exclusively.”*
+> *“The problem was… the ‘work types’ and work requests were at best tangentially related.”*