'```

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

@@ -1,61 +1,42 @@
 name: Build and Push Image
-on:
+on: [ push ]
-    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

@@ -1,24 +0,0 @@
-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

@@ -1,13 +0,0 @@
-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

@@ -1,13 +0,0 @@
-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

@@ -1,54 +1,94 @@
-# Human Introduction
+# When to use AI
-Well.. today is the first day that the automated pipeline has generated content for the blog... still a bit of work to do including
-1. establishing a permanent vectordb solution (chromadb? pg_vector?)
-2. Notification to Matrix that something has happened
-3. Updating Trilium so that the note is marked as blog_written=true
 
-BUT it can take a note from trilium, generate drafts with mulitple agents, and then use RAG to have an editor go over those drafts.
+## The Great AI Debate: When to Trust the Machine vs. When to Stick to Your Brain
 
-I'm particularly proud of the randomness I've applied to temperature, top_p and top_k for the different draft agents. This means that each pass is giving me quite different "creativity" (as much as that can be applied to an algorithm that is essentially munging letters together that have a high probability of being together) It has created some really interesting variation for the editor to work with and getting some really interesting results.
+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.
 
-Anyways, without further ado, I present to you the first, pipeline written, AI content for this blog
+## When AI is Not the Best Choice
 
----
+### 1. **The Fuzzy Logic Dilemma**
 
-# When to use AI 😄
+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*.
-*A journalist, software developer, and DevOps expert’s take on when AI is overkill and when it’s just the right tool*
 
-When I was building a spreadsheet called “shudders,” I was trying to figure out how to automate the process of mapping work types to work requests. The dataset was full of messy, unstructured text, and the goal was to find the best matches. At first, I thought, “This is a perfect use case for AI!” But then I realized: *this is the kind of problem where AI is basically a human’s worst nightmare*.
+> *“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, let’s break it down.
+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.
 
-### 🧠 When AI is *not* the answer
+### 2. **The Math and Logic Job**
 
-AI is great at pattern recognition, but it’s not great at *understanding context*. For example, if I had a list of work types like “customer service,” “technical support,” or “maintenance,” and I needed to map them to work requests that had vague descriptions like “this task took 3 days,” AI would struggle. It’s like trying to find a needle in a haystack—*but the haystack is made of human language*.
+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.
 
-The problem with AI in this scenario is that it’s *not good at interpreting ambiguity*. If the work types are vague, the AI might mislabel them, leading to errors. Plus, when the data is messy, AI can’t keep up. I remember one time I tried to use a chatbot to classify work requests. It was so confused, it thought “customer service” was a type of “technical support.” 😅 The result? A spreadsheet full of “unknown” entries.
+> *“Traditional programming methods work on maths and logic that is sound and offers no chance for interpretation.”*
 
-### 🧮 When AI *is* the answer
+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.
 
-There are some scenarios where AI is *definitely* the way to go. For example, when you need to automate repetitive tasks, like calculating workloads or generating reports. These tasks are math-heavy and don’t require creative thinking. Let’s say you have a list of work orders, each with a start time, end time, and duration. You want to calculate the average time per task. AI can do that with precision. It’s like a calculator, but with a personality.
+## When AI is the Best Choice
 
-Another example: if you need to generate a report that summarizes key metrics, AI can handle that. It’s not about creativity, it’s about logic. And that’s where traditional programming shines.
+### 1. **Finding Connections**
 
-### 🧪 The balance between AI and human oversight
+AI excels at identifying patterns and relationships within large datasets. This is invaluable for tasks like customer segmentation, fraud detection, and predictive maintenance.
 
-AI is a tool, not a replacement for human judgment. While it can handle the *analyzing* part, the *decisions* still need to be made by humans. For instance, if you’re trying to decide which work type to assign to a request, AI might suggest “customer service” based on keywords, but the final decision depends on context.
+### 2. **Automating Repetitive Tasks**
 
-So, in the end, AI is a *helper*, not a *replacement*. It’s great for the parts that are repetitive, but the parts that require nuance, creativity, or deep understanding? That’s where humans step in.
+Many tasks are simply too repetitive and time-consuming for humans to perform efficiently. AI-powered automation can free up valuable time and resources.
 
-### 🧩 Final thoughts
+### 3. **Suggesting Numbers or Constants**
 
-AI is like a superpower—great at certain things, not so great at others. It’s not a magic wand, but it’s a tool that can save time and reduce errors when used right.
+While AI shouldn't be trusted to run calculations, it can be helpful in suggesting potential numbers or constants to use in those calculations.
 
-So, when is it time to say “AI, nope”? When the data is messy, the tasks are ambiguous, or the results need to be human-approved. And when is it time to say “AI, yes”? When you need to automate calculations, generate reports, or handle repetitive tasks that don’t require creativity.
+## The Human vs. AI Divide
 
-### 🧩 Summary
+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*.
 
-| Scenario | AI? | Reason |
+> *“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.”*
-| Ambiguous data | ❌ | AI struggles with context |
-| Repetitive tasks | ✅ | AI handles math and logic |
-| Creative decisions | ❌ | AI lacks the ability to think creatively |
 
-In the end, AI is just another tool. Use it when it works, and don’t let it define your workflow. 😄 *And if you ever feel like AI is overstepping, remember: it’s just trying to be helpful. Sometimes it’s not the best choice. Sometimes it’s the only choice.*
+So I had to manually read each work type and map it to a work request. It was a *shudder-inducing* task.
+
+## The Final Thought
+
+So, in summary:
+
+*   **AI is not the best choice** when you need precision, accuracy, or a human touch.
+*   **AI is the best choice** when you need to handle ambiguity, find connections, or automate repetitive tasks.
+
+And that’s why I’m a journalist, a developer, and a DevOps expert. I know when to trust the machine and when to rely on my brain.
+
+> *“When in doubt, just do it.”*
+
+## Editable Structure for Easy Customization
+
+### 📝 Title When to use AI
+
+### 🧠 Introduction
+
+*   Briefly explain the question and the tone.
+
+### 🧩 Sections
+
+1.  **When AI is Not the Best Choice**
+    *   Subpoints:
+        *   Fuzzy logic dilemmas
+        *   Manual mapping tasks
+        *   Precision vs. AI’s “fuzzy” logic
+2.  **When AI is the Best Choice**
+    *   Subpoints:
+        *   Math and logic tasks
+        *   Automation of repetitive tasks
+        *   Suggesting numbers or constants
+3.  **The Human vs. AI Divide**
+    *   Highlight the importance of human judgment in critical tasks
+4.  **Final Thought**
+    *   Encourage readers to think critically about when to use AI
+
+### 📝 Conclusion
+
+*   Wrap up the key points and reinforce the message.
+
+## Tips for Editing
+
+*   Replace the example about the spreadsheet with your own scenario.
+*   Add a personal anecdote or a relatable example.
+*   Use humor to keep the tone light and engaging.
+*   Add a call to action: “Next time you’re stuck, ask yourself: ‘Is this a task for AI or a task for me?’”