Update with Human Interactions

Reviewed-on: #13

.gitea/workflows/build_push.yml

@@ -1,5 +1,8 @@
 name: Build and Push Image
-on: [ push ]
+on:
+    push:
+        branches:
+            - master
 
 jobs:
     build:
@@ -40,3 +43,19 @@ jobs:
                   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/matrix_ai_integrations_with_baibot.md

@@ -0,0 +1,114 @@
+Title: Intergrating Ollama and Matrix with Baibot
+Date: 2025-06-25 20:00 
+Modified: 2025-06-30 08:00 
+Category: AI, Data, Matrix 
+Tags: ai, kubernetes, matrix 
+Slug: ollama-matrix-integration 
+Authors: Andrew Ridgway 
+Summary: Integrating a Local LLM to a personal matrix server all the fun AND data sovereignty
+
+### _Human Introduction_
+I've been experimenting with AI and integrations I'm particuarly excited by the idea of using LLM's to integrate between different systems (Stay tuned for a blog [MCP](https://modelcontextprotocol.io/introduction) at some point in the future!)
+
+Below I've thrown together some notes and had AI build a very quick how to on a cool little project that took next to no time to put together that I thought might be interesting for the group.. Enjoy!
+
+
+
+# Matrix AI Integrations with baibot: A Fun Journey into Home Automation and LLMs
+
+Alright, so I’ve been messing around with this cool project called **baibot**, which is a locally deployable bot for integrating Large Language Models (LLMs) into Matrix chatrooms. If you’re anything like me, you run your own Matrix server to keep things private and under control—whether it’s for family communication or interacting with the tech community. But one day, I thought, “Why not have my LLMs right where I’m already managing everything else?” Enter baibot.
+
+**Setting Up My Own Matrix Server with baibot**
+
+First off, I’ve got a home Matrix server running Element. Integrating baibot into this environment makes sense because it allows me to connect directly via the same platform. The key was getting the configuration right using examples from [baibot’s GitHub](https://github.com/etkecc/baibot/blob/main/docs/sample-provider-configs/ollama.yml). For instance, connecting to an Ollama gemma3 model with a specific prompt ensures it’s lighthearted yet responsive:
+
+```yaml
+base_url: http://<my_ollama_ip>:11434/v1
+text_generation:
+  model_id: gemma3:latest
+  prompt: 'You are a lighthearted bot...'
+temperature: 0.9
+max_response_tokens: 4096
+max_context_tokens: 128000
+```
+
+This gives me precise control over the bot’s behavior, ensuring each instance in Matrix rooms behaves exactly as intended.
+
+**Deploying to Kubernetes**
+
+To ensure reliability, I used Kubernetes. Here's a breakdown of the key files:
+
+*   **Deployment.yaml**: Manages pod replicas, security contexts, and volume mounts for persistence.
+
+```yaml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  labels:
+    app: ridgway-bot
+  name: ridgway-bot
+spec:
+  replicas: 1
+  strategy:
+    type: Recreate
+  template:
+    spec:
+      containers:
+      - image: ghcr.io/etkecc/baibot:v1.7.4
+        name: baibot
+        volumeMounts:
+        - name: ridgway-bot-cm
+          mountPath: /app/config.yml
+        - name: ridgway-bot-pv
+          mountPath: /data
+      volumes:
+      - name: ridgway-bot-cm
+        configMap:
+          name: ridgway-bot
+      - name: ridgway-bot-pv
+        persistentVolumeClaim:
+          claimName: ridgway-bot-storage
+```
+
+*   **Persistent Volume Claim (PVC)** ensures data storage for baibot.
+
+```yaml
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: ridgway-bot-storage
+spec:
+  accessModes:
+  - ReadWriteMany
+  resources:
+    requests:
+      storage: 500Mi
+```
+
+The deployment script handles namespace creation, config maps, PVCs, and waits for the pod to be ready before copying data.
+
+**Integrating with OpenWebUI for RAG**
+
+Another cool aspect is integrating baibot with **OpenWebUI**, which acts as an OpenAI-compatible API. This allows me to leverage models I’ve created in OpenWebUI that include knowledge bases (RAG). The config here uses OpenWebUI’s endpoints:
+
+```yaml
+base_url: 'https://<my-openwebui-endpoint>/api/'
+api_key: <my-openwebui-api-key>
+text_generation:
+  model_id: andrew-knowledge-base
+  prompt: 'Your name is Rodergast...'
+```
+
+This setup lets me access RAG capabilities directly within Matrix chats, all without writing a single line of code. It’s like having my very own AI research assistant right there in the chatroom.
+
+**Future Steps and Challenges**
+
+Now that baibot is up and running, I’m already thinking about expanding its use cases. The next step might be integrating it with **Home Assistant** for alarm notifications or other automation tasks. However, my current setup uses an older gaming PC, which struggles with computational demands. This could lead to a rearchitecting effort—perhaps moving to a dedicated server or optimizing the hardware.
+
+**Conclusion**
+
+Baibot has been a fantastic tool for experimenting with AI integrations in Matrix. By leveraging existing infrastructure and OpenWebUI’s capabilities, I’ve achieved full control over data privacy and customization. The next frontier is expanding these integrations into more practical applications like home automation. Stay tuned for updates!
+
+**Final Thoughts**
+
+It’s incredibly rewarding to see how open-source projects like baibot democratize AI access. Whether you’re a hobbyist or a pro, having tools that let you run LLMs locally without vendor lock-in is game-changing. If you’re interested in diving deeper, check out the [baibot GitHub](https://github.com/etkecc/baibot) and explore its documentation. Happy coding!

src/content/the_failing_social_media_ban.md

@@ -0,0 +1,53 @@
+Title: The Failing Social Media Ban
+Date: 2025-06-19 20:00
+Modified: 2025-06-20 20:00
+Category: Politics
+Tags: politics, social meda, tech policy
+Slug: social-media-ban-fail
+Authors: Andrew Ridgway
+Summary: The Social Media ban is an abject failure of policy. Education and the use of the much better existing tools is the key
+
+## 🎯 The Goal: A Legal Framework to Protect Kids
+
+The Australian government’s or should I say Julie Inman's plan to ban social media for teens has sparked on going debate. While the intention is noble—protecting minors from online risks—it’s clear the technical and legal hurdles are massive. This government concept of relying on “facial aging” or “Proof of Age” APIs are prone to privacy violations and data breaches. Parents already have tools that let them make decisions about their children’s tech use without needing to hand over photos of their ID. The governments current approach is mired in bureaucracy and the tech world does not thrive in that environment. Instead of trying to outsource the problem to consultants, the government should **educate parents on the tools already available**.
+
+## 🧩 The Problem: Tech Giants Won’t Do It
+
+The government’s plan to enable Inman's vision is to use facial recognition or “age-based” filters. This was flawed from the start. These systems are expensive, unreliable, and not designed for the scale of a national rollout. Even if a company like Meta or Google could do it, they’d **never** do it for the same reason: **There is no money in the equation**. The only alternative is to outsource to consultants, but those consultants are not equipped to handle the complexity. The government’s plan is a joke, no one is going to build a system that’s 100% accurate, secure, and compliant with privacy laws and those that, maybe, could have no insentive to. No amount of chest thumping by The E-Safety Commissioner will change this fact and throwing frankly meaningless pieces of paper from our legislative assembly will do little more than make them laugh
+
+## 🛠️ The Tools Parents Already Have
+
+Parents ([Is it parents? is it in fact fiefdom creation on behalf of Julie Inman?](https://minister.infrastructure.gov.au/rowland/media-release/record-investment-improve-safety-australians-online)) must give up on the idea of the government fixing this. , parents should be using the **tools already in their homes**. These tools are **free, secure, and effective**. Some examples include (and I use in my own home):
+
+*   **Fritz Box Parental Controls** (https://en.fritz.com/service/knowledge-base/dok/FRITZ-Box-7530/8_Restricting-internet-use-with-the-FRITZ-Box-parental-controls/) - Allows blocking of websites and apps, setting time limits, and creating user profiles.
+*   **Microsoft Family Safety** (https://www.microsoft.com/en-au/microsoft-365/family-safety) - Provides screen time limits, content filters, and activity reporting.
+*   **Nintendo Parental Controls** (https://www.nintendo.com/au/apps/parental-controls/) - Allows managing game time, content restrictions, and communication settings on Nintendo devices.
+*   **Google Family Link** (https://families.google.com/familylink/) - Enables remote monitoring, app management, and location tracking for children's Android devices.
+*   **Apple Family Sharing** (https://support.apple.com/en-au/105121) - Allows sharing purchases, subscriptions, and location information with family members.
+
+These tools let parents **block apps, limit screen time, and monitor online activity** without needing to share sensitive data. They offer parents full control over what is available and are not dependant on some arbitrary list governed in legislation (which is in an of itself an indicator of how backwards this legislation is)
+
+## 📚 The Real Solution: Education, Not Tech
+
+The government’s plan is a **mistake**. Instead of trying to build a new system, parents should be **educating themselves on the tools already available**.
+
+### 🔄 Flexibility for Every Family
+
+*   **Approved apps**
+*   **Blacklisted content**
+*   **Screen time limits**
+*   **Privacy controls**
+
+These tools let parents **make decisions tailored to their children’s needs**. No one-size-fits-all approach. It gives parents autonomy over their online decision making whilst better respecting everyones privacy, including the childs. Already Julie is making calls to expand the list, this is unacceptable, it is no one but MY choice what is acceptable in my house and for my family. 
+
+## 🧩 Why the Government’s Plan Fails
+
+The government’s plan is a **disaster**. It’s not about fixing the problems of social media use in teens, it’s about giving the perception they are doing something about it using archaic methods and tools that don't go to the root cause. The tools parents already have are **better, cheaper, and more secure**. The only way to make this work is for the government to **stop trying to solve a social problem with tech** and **focus on the real solution: education and parental autonomy**. Stop Letting Julie create her cartel and create her own version of the Chinese firewall
+
+## 📝 Summary: The Right Tools, Not the Tech
+
+The government’s plan is a dead monkey. Instead of trying to build a system that’s 100% accurate and secure, parents should be using the **tools already in their homes**. These tools are **free, effective, and preserve privacy**. They let parents **make decisions about their children’s tech use on a true case by case basis** without needing to hand over sensitive data.
+
+## 🧩 Final Thoughts
+
+The Government's plan, at the behest of Julie Inman, is a **disaster**. It’s not about fixing the problem with social media, it’s about creating the perception they are solving a problem that is already solved. [The E-Safety Commissioner has made clear her plans are to take control out of our hands when it comes to what we can do online](https://www.esafety.gov.au/newsroom/media-releases/online-industry-asked-address-esafetys-concerns-draft-codes-0#:~:text=Online%20industry%20asked%20to%20address%20eSafety%27s%20concerns%20with%20draft%20codes,-Share&text=Australia%27s%20eSafety%20Commissioner%20has%20asked,safeguards%20for%20users%20in%20Australia.)  Parents should be using the **tools already in their homes**. The real solution is not to expect a government to fix this, but to **educate themselves on the tools that already exist**. Until we accept that this is our responsbility the problem will continue propogate because the only place it can be fixed is in the home and not my Julie Inam.

src/content/when_to_use_ai.md

@@ -1,5 +1,16 @@
+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!?
+
+
 # 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
@@ -12,48 +23,60 @@ Anyways, without further ado, I present to you the first, pipeline written, AI c
 
 ---
 
-# When to Use AI: Navigating the Right Moments for Machine Learning and Beyond
+# When to Use AI: Navigating the Right Scenarios
 
-In today's tech landscape, the question "When should we use AI?" is as common as it is critical. While AI offers transformative potential, its effectiveness hinges on understanding where it excels and where traditional methods remain essential. Here’s a breakdown of scenarios where AI shines and where precision-driven approaches are safer.
+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.
 
-### AI’s Sweet Spot: Where Humans Fail
+## The Spreadsheet Dilemma: Where AI Can help, and where it hurts
 
-1. **Unstructured Data Analysis**
+**Scenario:** Mapping work types to categories in a spreadsheet with thousands of entries, like distinguishing between "Painting," "Repainting," "Deck Painting," or "Stucco Repainting."
-   - **Example**: Categorizing customer reviews, emails, or social media posts for sentiment analysis.
-   - **Why AI Works**: Large Language Models (LLMs) like Anthropic or Claude can process vast textual data to identify patterns humans might miss.
-2. **Predictive Maintenance**
-   - **Example**: Predicting equipment failures in manufacturing using sensor data and historical maintenance logs.
-   - **Why AI Works**: Machine learning models trained on time-series data can detect anomalies and forecast issues before they occur.
-3. **Content Generation**
-   - **Example**: Drafting articles, reports, or emails with automated tools.
-   - **Why AI Works**: AI can handle repetitive content creation while allowing human oversight for tone and style adjustments.
 
-### Where AI Falls Short: Precision Over Flexibility
+**Where AI Helps:**
 
-1. **Critical Financial Calculations**
+*   **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.
-   - **Example**: Tax calculations or financial models requiring exact outcomes.
-   - **Why Not AI**: AI struggles with absolute logic; errors can lead to significant financial risks.
-2. **Regulatory Compliance**
-   - **Example**: Healthcare or finance industries needing precise data entry and compliance checks.
-   - **Why Not AI**: AI might misinterpret rules, leading to legal issues.
-3. **Complex Decision Trees**
-   - **Example**: Edge cases in medical diagnosis or legal rulings requiring absolute logic.
-   - **Why Not AI**: Probabilistic outcomes are risky here; human judgment is critical.
 
-### Hybrid Approaches for Success
+**Where AI Struggles:** 
 
-- **Data Collection & Initial Analysis**: Use AI to gather insights from unstructured data.
+*   **Precision Over Ambiguity:** Calculations requiring exact values (e.g., average durations) are better handled by deterministic algorithms rather than AI’s probabilistic approach.
-- **Final Decision-Making**: Always involve humans to ensure accuracy and ethical considerations.
 
-**Case Study: My Spreadsheet Experience**
+**Traditional Methods Are Easier for Deterministic Problems:**
 
-I analyzed thousands of work orders, mapping them into two categories via an LLM. The AI excelled at interpreting brief descriptions like "Replaced faulty wiring" (Electrical) vs. "Fixed AC unit" (Plumbing). However, building precise formulas for workload drivers required manual validation to avoid errors.
+*   **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.
 
-### Conclusion: Balancing AI and Traditional Methods
+## When AI Shines: Contextual and Unstructured Tasks
 
-AI is ideal for tasks involving natural language understanding, prediction, or handling large datasets. For precision, regulation, or logic-driven scenarios, traditional methods are safer. The key is combining both approaches smartly:
+**Scenario:** Automating customer support with chatbots or analyzing social media sentiment.
 
-- **Use AI** for unstructured data analysis and automation.
+**Why AI Works Here:**
-- **Stick to traditional methods** for critical calculations and compliance.
 
-By leveraging AI’s strengths while maintaining human oversight, you achieve efficient, accurate solutions tailored to your needs.
+*   **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.
+
+**Why Traditional Methods Don't:**
+
+* **There is no easily discernable pattern:** If the pattern doesn't exist in a deterministic sense there will be little someone can do without complex regex and 'whack a mole' style programming. 
+
+## Hybrid Approaches: The Future of Efficiency
+
+While traditional methods remain superior for precise calculations, AI can assist in setting up initial parameters or generating insights. For example:
+
+*   **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.
+
+## Key Takeaways
+
+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).
+
+## Conclusion
+
+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.
+
+**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.
+
+🚀