Human edit and update

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

@@ -1,94 +1,75 @@
-# When to use AI
+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 
+Summary: Should we be using AI for ALL THE THINGS!?
 
-## 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.
+# Human Introduction
+Well.. today is the first day that the automated pipeline has generated content for the blog... still a bit of work to do including
 
-## When AI is Not the Best Choice
+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
 
-### 1. **The Fuzzy Logic Dilemma**
+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.
 
-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'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.
 
-> *“I was given a list of work types that could be grouped into 1 of 2 categories exclusively.”*
+Anyways, without further ado, I present to you the first, pipeline written, AI content for this blog
-> *“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**
+# When to Use AI: Navigating the Right Scenarios
 
-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.
+Okay, so I've been getting this question a lot lately: "When should we use AI?" or even more frustratingly, "Why can't AI do this?" It's like asking when to use a hammer versus a screwdriver. Sometimes AI is the perfect tool, other times it's better left in the toolbox. Let me break down some scenarios where AI shines and where it might not be the best bet.
 
-> *“Traditional programming methods work on maths and logic that is sound and offers no chance for interpretation.”*
+## The Spreadsheet Dilemma: Where AI Fails
 
-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.
+**Scenario:** Mapping work types to categories in a spreadsheet with thousands of entries, like distinguishing between "Painting," "Repainting," "Deck Painting," or "Stucco Repainting."
 
-## When AI is the Best Choice
+**Why AI Struggles Here:**
 
-### 1. **Finding Connections**
+*   **Fuzzy Matching & Contextual Understanding:** AI excels at interpreting relationships between words (e.g., recognizing "Deck Painting" as a subset of "Painting"). However, traditional methods with regex or string manipulation fail here because they lack the nuanced judgment needed to handle ambiguity.
+*   **Precision Over Ambiguity:** Calculations requiring exact values (e.g., average durations) are better handled by deterministic algorithms rather than AI’s probabilistic approach.
 
-AI excels at identifying patterns and relationships within large datasets. This is invaluable for tasks like customer segmentation, fraud detection, and predictive maintenance.
+**Traditional Methods Win Here:**
 
-### 2. **Automating Repetitive Tasks**
+*   **Formula-Based Logic:** Building precise formulas for workload analysis relies on clear, unambiguous rules. AI can’t replace the need for human oversight in such cases.
 
-Many tasks are simply too repetitive and time-consuming for humans to perform efficiently. AI-powered automation can free up valuable time and resources.
+## When AI Shines: Contextual and Unstructured Tasks
 
-### 3. **Suggesting Numbers or Constants**
+**Scenario:** Automating customer support with chatbots or analyzing social media sentiment.
 
-While AI shouldn't be trusted to run calculations, it can be helpful in suggesting potential numbers or constants to use in those calculations.
+**Why AI Works Here:**
 
-## The Human vs. AI Divide
+*   **Natural Language Processing (NLP):** AI understands context, tone, and intent in unstructured data, making it ideal for tasks like chatbot responses or content analysis.
+*   **Pattern Recognition:** AI identifies trends or anomalies in large datasets that humans might miss, such as predictive maintenance in industrial settings.
 
-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*.
+## Hybrid Approaches: The Future of Efficiency
 
-> *“I was given a list of work types that could be grouped into 1 of 2 categories exclusively.”*
+While traditional methods remain superior for precise calculations, AI can assist in setting up initial parameters or generating insights. For example:
-> *“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 Proposes Formulas:** An LLM suggests a workload calculation formula based on historical data.
+*   **Human Checks Validity:** A human ensures the formula’s accuracy before deployment.
 
-## The Final Thought
+## Key Takeaways
 
-So, in summary:
+1.  **Use AI** for tasks involving:
+    *   Unstructured data (e.g., text, images).
+    *   Contextual understanding and interpretation.
+    *   Pattern recognition and trend analysis.
+2.  **Stick to Traditional Methods** for:
+    *   Precise calculations with deterministic logic.
+    *   Tasks requiring error-free accuracy (e.g., financial modeling).
 
-*   **AI is not the best choice** when you need precision, accuracy, or a human touch.
+## Conclusion
-*   **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.
+AI is a powerful tool but isn’t a one-size-fits-all solution. Match the right approach to the task at hand—whether it’s interpreting natural language or crunching numbers. The key is knowing when AI complements human expertise rather than replaces it.
 
-> *“When in doubt, just do it.”*
+**Final Tip:** Always consider the trade-offs between precision and context. For tasks where nuance matters, AI is your ally; for rigid logic, trust traditional methods.
 
-## 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?’”