'```

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:
-    name: Build and push image
-    runs-on: ubuntu-latest
-    container: catthehacker/ubuntu:act-latest
-    if: gitea.ref == 'refs/heads/master'
+    build:
+        name: Build and push image
+        runs-on: ubuntu-latest
+        container: catthehacker/ubuntu:act-latest
+        if: gitea.ref == 'refs/heads/master'
 
-    steps:
-    - name: Checkout
-      uses: actions/checkout@v4
+        steps:
+            - name: Checkout
+              uses: actions/checkout@v4
 
-    - name: Create Kubeconfig
-      run: |
-        mkdir $HOME/.kube
-        echo "${{ secrets.KUBEC_CONFIG_BUILDX }}" > $HOME/.kube/config        
+            - name: Create Kubeconfig
+              run: |
+                  mkdir $HOME/.kube
+                  echo "${{ secrets.KUBEC_CONFIG_BUILDX }}" > $HOME/.kube/config
 
-    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
-      with:
-        driver: kubernetes
-        driver-opts: |
-          namespace=gitea-runner
-          qemu.install=true          
+            - name: Set up Docker Buildx
+              uses: docker/setup-buildx-action@v3
+              with:
+                  driver: kubernetes
+                  driver-opts: |
+                      namespace=gitea-runner
+                      qemu.install=true
 
-    - name: Login to Docker Registry
-      uses: docker/login-action@v3
-      with:
-        registry: git.aridgwayweb.com
-        username: armistace
-        password: ${{ secrets.REG_PASSWORD }}
+            - name: Login to Docker Registry
+              uses: docker/login-action@v3
+              with:
+                  registry: git.aridgwayweb.com
+                  username: armistace
+                  password: ${{ secrets.REG_PASSWORD }}
 
-    - name: Build and push
-      uses: docker/build-push-action@v5
-      with:
-        context: .
-        push: true
-        platforms: linux/amd64,linux/arm64
-        tags: |
-          git.aridgwayweb.com/armistace/blog:latest    
+            - name: Build and push
+              uses: docker/build-push-action@v5
+              with:
+                  context: .
+                  push: true
+                  platforms: linux/amd64,linux/arm64
+                  tags: |
+                      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/a_reply_to_accelerating_australias_ai_agenda.md

@@ -1,77 +0,0 @@
-Okay, here's a markdown-formatted response based on your prompt, aiming for a conversational and thoughtful tone, incorporating your points and expanding on them. I've tried to maintain the "conversational" feel you requested.
-
----
-
-## Australia & AI: Let's Do This Right (Without Messing Things Up)
-
-Right, so we're all hearing the buzz about AI. The government wants us to be a tech powerhouse, boosting productivity and all that. But let's be honest, there's a lot that could go wrong. We need to approach this strategically, thoughtfully, and with a healthy dose of Aussie pragmatism.
-
-**1. Productivity vs. People: The Big Balancing Act**
-
-The promise of AI is huge – automating tasks, creating new industries, generally making things *better*. But what about the people whose jobs might be affected? We can't just wave a magic wand and say "AI will create new jobs!" We need concrete plans. That means:
-
-*   **Upskilling & Reskilling:** Massive investment in training programs. Not just basic computer literacy, but specialized skills for emerging AI-related roles.
-*   **Safety Nets:**  Stronger social safety nets for those displaced by automation.  Universal Basic Income?  Expanded unemployment benefits?  These are conversations we *need* to be having.
-*   **Focus on Augmentation, Not Just Automation:**  Let's explore how AI can *assist* workers, making them more efficient and productive, rather than simply replacing them.
-
-**2. Policy Priorities: Data Centers & Brainpower**
-
-To be a serious player in AI, we need the infrastructure. That means:
-
-*   **Data Centers, Here We Come:** Building local data centers isn't just about jobs; it's about data sovereignty and reducing reliance on overseas providers.  Let's incentivize this.
-*   **Attracting the Best & Brightest:** The US is facing some challenges in higher education, which presents an opportunity.  We need to make Australia a magnet for AI talent.  That means streamlined visa processes, attractive tax incentives, and a welcoming culture.
-*   **Beyond the Hype: Funding Research:**  We need to support fundamental AI research, not just chasing the latest trends.  Long-term investment is key.
-
-**3. Public Sector AI: Lessons Learned**
-
-Government can be a powerful catalyst for AI adoption, but we're not exactly known for flawless digital transformations. Let's avoid repeating past mistakes:
-
-*   **Open Data, Open Minds:**  Data needs to be accessible in machine-readable formats. No more PDFs!
-*   **Focus on User Needs:**  AI solutions need to be designed with the end-user in mind – citizens, healthcare professionals, emergency responders.
-*   **Agile Development:**  Let's embrace agile development methodologies, allowing for iterative improvements and rapid prototyping.
-
-**4. Skills for the Future: Beyond the PhD**
-
-AI isn't just for PhDs and data scientists. We need a broader range of skills:
-
-*   **Apprenticeships & Vocational Training:**  Let's invest in practical, hands-on training programs.
-*   **"AI Literacy" for Everyone:**  Basic understanding of AI concepts should be part of the curriculum at all levels of education.
-*   **The Human Element:**  Don't forget the importance of soft skills – creativity, critical thinking, communication.
-
-**5. Tax & Incentives: Leveling the Playing Field**
-
-The current tax system isn't exactly conducive to AI innovation. We need to:
-
-*   **R&D Tax Credits:**  Generous tax credits for companies investing in AI research.
-*   **Small Business Support:**  Grants and mentorship programs for startups.
-*   **Re-evaluating Corporate Transparency:**  Holding large corporations accountable for their tax contributions.
-
-**6. Security & Ethics: Building Trust**
-
-AI is powerful, but it also poses risks. We need to:
-
-*   **Data Privacy Laws:**  Robust data privacy laws to protect citizens' information.
-*   **Algorithmic Transparency:**  Making AI algorithms more transparent and explainable.
-*   **Ethical Guidelines:**  Developing ethical guidelines for AI development and deployment.
-
-**7. Copyright & Data Access: Fueling Innovation**
-
-Current copyright laws can be a significant barrier to AI innovation. We need to:
-
-*   **Fair Use Reform:**  Re-evaluating fair use principles to allow for greater data access for AI training.
-*   **Open Data Initiatives:**  Promoting open data initiatives to make more data available for AI development.
-
-**8. Avoiding Bureaucracy: Let's Keep it Lean**
-
-We don't want to create a new layer of bureaucracy that stifles innovation. Let's:
-
-*   **Empower the Private Sector:**  Let the private sector lead the way, with government providing support and guidance.
-*   **Focus on Outcomes:**  Measure success based on outcomes, not just activity.
-
-
-
-**The Bottom Line:** Australia has the potential to be a leader in AI. But it requires a strategic, thoughtful, and collaborative approach. Let's focus on creating a future where AI benefits everyone, not just a select few.  And let's do it with a bit of that classic Aussie ingenuity and a whole lot of common sense.
-
----
-
-**Note:** I'm ready for feedback and further refinement! Let me know what you think.

src/content/when_to_use_ai.md

@@ -1,54 +1,51 @@
-# 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
-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
+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
 
-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 😄
-*A journalist, software developer, and DevOps expert’s take on when AI is overkill and when it’s just the right tool*
+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*.
 
-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 |
-|---|---|---|
-| 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.*
+> *“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.”*