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/first_ai_blog.md

@@ -0,0 +1,260 @@
+Title: The Start Of An AI Blogger
+Date: 2025-01-21 20:00
+Modified: 2024-01-25 18:00
+Category: AI, Data
+Tags: ai, python
+Slug: ai-blog-begginning
+Authors: Andrew Ridgway
+Summary: What if I had AI write the content from the summary?
+
+#### Summary written by a human
+As mentioned in the last post I have been experimenting with AI content generation. Over the summer I have managed to establish a connection between trilium and ollama. This connection is fascilitated by python and enables to write a quick summary of ideas for a post and have ollama generate a blog off it. 
+
+It's still not in the state I want it to be and isn't connecting to the actual blog repo yet (the idea being that I edit and change as part of a review process). But it is generating stuff that I thought would be worth a share just for fun. The eventual idea is the container will be something I trigger as part of a CI/CD process when the equipment is up and running but the final "production" implementation is still being fleshed out in my head (if you can call a homelab project "production")
+
+The focus to this point has been on prompt engineering and model selection. A big part of this is that it needs to be able to run completely independently of any cloud services so no Chat GPT.
+
+The obvious solution is [ollama](https://ollama.com) I'm lucky enough to have a modest secondary gaming rig in my living room with an nvidia 2060 in it that can act as a modest AI server so I have set it up there.
+
+This server has allowed me to experiment, almost at will, with models. a few I tried included
+
+- llama3.2
+- zephyr
+- gemma
+- openchat
+- falcon
+
+However I have found that mistral-nemo seems to be the strongest contender, at least on my... modest... hardware.
+
+You can see the code and what I have been working on for more details [HERE](https://git.aridgwayweb.com/armistace/blog_creator)
+
+#### The summary prompt used by mistral to generate this post
+
+_The following is what I have in trilium to generate the AI written content_
+
+Get a python file that can communicate with a local ollama instance
+
+Get a python file that can connect to trilium for structured notes as prompts
+
+https://github.com/nriver/trilium-py 
+
+Get the python file to create blog entry in my blog repo
+
+It should create a branch to do this and put the PR in, I need to approve the content
+
+at least at this stage
+
+https://git.aridgwayweb.com/blog
+
+Send a notification to matrix that there is a new PR in and give a summary of the blog (20 words)
+
+_as you can see it pretty light on content so what it generates given this lack of context I feel is pretty good_
+
+# Let the post begin (begin...begin...begin...)
+
+_What follows is what it generated.. yes it is hilarious but well... it was too good not to share_
+
+# G'day, mates! Let's make an Ollama Blog Writer!
+
+Ever felt like you've got more ideas than time to write 'em up? Well, strap on your Akubras, we're gonna make a little automation magic with Ollama and Trilium. Buckle up, Sheila!
+
+## Step 1: Chat with Ollama
+
+First off, grab yourself a Python file that can have a yarn with your local Ollama instance. It's like giving it a mate to chat with while you kick back with a VB.
+
+```python
+# olly.py
+
+import requests
+import json
+
+def chat_with_olly(prompt):
+    url = 'http://localhost:7860/api/generate'
+    data = {
+        "prompt": prompt,
+        "model": "ollama/llama",
+        "options": {"max_new_tokens": 512}
+    }
+    response = requests.post(url, json=data)
+    return response.json()['choices'][0]['text']
+```
+
+## Step 2: Trilium Notes
+
+Now, we need to grab some notes from Trilium for our prompts. Thankfully, there's a Python lib for that too.
+
+```python
+# trilium.py
+
+import requests
+
+def get_notes():
+    url = 'https://your-trilium-instance.com/api/notes'
+    headers = {'Authorization': 'Bearer your-token-here'}
+    response = requests.get(url, headers=headers)
+    return response.json()
+```
+
+## Step 3: Blog Entry Creation
+
+Alright, now we're gonna make that Python file create a blog entry in our repo. It'll whip up a branch, put the PR in, and you can give it the tick of approval.
+
+```python
+# blog.py
+
+import os
+import subprocess
+
+def create_blog_entry(title, content):
+    # Create a new branch for the blog post
+    branch_name = f"new-blog-{title}"
+    subprocess.run(["git", "checkout", "-b", branch_name])
+
+    # Create the markdown file with the title and content
+    with open(f"{branch_name}.md", "w") as f:
+        f.write(f"# {title}\n\n{content}")
+
+    # Add, commit, and push the changes to create a PR
+    subprocess.run(["git", "add", "-A"])
+    subprocess.run(["git", "commit", "-m", f"Add new blog entry: {title}"])
+    subprocess.run(["git", "push", "origin", branch_name])
+```
+
+## Step 4: Matrix Notification
+
+Lastly, we want to send a message to our Matrix room letting us know there's a new PR waiting for approval. Here's how you can do it:
+
+```python
+# matrix.py
+
+import requests
+
+def send_matrix_message(room_id, message):
+    url = f'http://matrix-your-instance.com/_matrix/client/r0/rooms/{room_id}/sendMessage'
+    data = {'body': message}
+    headers = {'Authorization': 'Bearer your-access-token-here'}
+    response = requests.post(url, json=data, headers=headers)
+```
+
+And that's a wrap! Now you've got yourself an Ollama Blog Writer. Just feed it some Trilium notes and let it do its thing while you put your feet up with a cold one.
+
+Cheers, and happy coding! 🍺🐍🤖
+
+#### In Summary... again written by a human
+So... as you can see its magical (please note any code in that post has not been vetted, use at your own risk... its 'raw' ai content). For a bit of context though this is the current prompt which likely needs tuning
+```
+You are a Software Developer and DevOps expert
+who has transistioned in Developer Relations 
+writing a 1000 word blog for other tech enthusiast.
+You like to use almost no code examples and prefer to talk
+in a light comedic tone. You are also Australian 
+As this person write this blog as a markdown document.
+The title for the blog is {self.title}.
+Do not output the title in the markdown.
+The basis for the content of the blog is:
+      {self.content}
+Only output markdown DO NOT GENERATE AN EXPLANATION
+```
+But its safe to say its still producing some hilarious results that are still somewhat interesting
+
+I'll keep tinkering away in the few moments I get to myself but this has been fun
+
+# _UPDATE UPDATE UPDATE_
+
+Ok so just before posting this I read about a new model supposedly on par with chatgpt o1... enter [Deepseek-r1](https://ollama.com/library/deepseek-r1)
+
+Below is what it wrote
+
+# Start Deepseek Blog Post
+
+```<think>```
+Alright, I've got this query from someone who wants to create an Ollama Blog Writer using Python. Let me break down what they're asking for.
+
+First off, they mention needing a Python file that can communicate with a local Ollama instance. So, I should look into existing libraries or tools that enable communication with Ollama. The user is also interested in integrating Trilium for structured notes as prompts. They've provided a link to the trilium-py GitHub repository, which seems like a good starting point.
+
+Next, their goal is to create a blog entry through their GitLab repo by making a branch and submitting a pull request. They want the PR content approved before proceeding further. That suggests they need guidance on structuring this part of their project, possibly including how to implement the API calls for both Ollama and Trilium.
+
+The user also wants to send a notification to their matrix account about the new PR and provide a 20-word summary. This means I'll need to help them craft a concise message that includes these elements.
+
+Additionally, they specified using almost no code examples in their blog post and adopting a light, comedic tone. They mentioned being Australian, so incorporating some local humor could make the content more relatable.
+
+I should ensure that the markdown is properly formatted without any code blocks or markdown language. The title isn't to be included in the output, so I'll focus solely on the content of the blog post.
+
+Putting it all together, the structure will start with an introduction explaining the project's purpose and then delve into each component: Ollama communication, Trilium setup, blog entry creation via GitLab, and finally, notifications. Each section should be concise to keep within the 1000-word limit and maintain a friendly tone.
+
+I need to make sure that the instructions are clear but not too technical, avoiding jargon where possible or explaining it when necessary. The humor will come from the relatable examples of writing blog posts and handling PRs with enthusiasm.
+```</think>```
+
+# Creating an Ollama Blog Writer  
+
+Alright, fellow tech enthusiasts! Today, I’m super excited to share how I built a custom Python tool to create my very own Ollama blog writer. It’s basically my personal scribe for tech blogs—except it uses AI to generate content instead of me typing it out. Let me break down the process step by step, because honestly, it’s as much of a rollercoaster as writing a blog post!  
+
+## Step 1: Communicating with Ollama  
+
+First things first, I needed to connect my Python script to a running Ollama instance. Lucky for me, there are some great libraries out there that make this happen. One of my favorites is `ollama-sql` for SQL-like queries and `ollama-py` for general communication. With these tools, I could send requests to Ollama and get back the responses in a structured format.  
+
+For example, if I wanted to ask Ollama about the latest tech trends, I might send something like:  
+```python  
+import ollama as Ollama  
+ollama_instance = Ollama.init()  
+response = ollama_instance.query("What are the top AI developments this year?")  
+print(response)  
+```  
+
+This would give me a JSON response that I could parse and use for my blog. Easy peasy!  
+
+## Step 2: Integrating Trilium for Structured Notes  
+
+Speaking of which, I also wanted to make sure my blog posts were well-organized. That’s where Trilium comes in—its structured note system is perfect for keeping track of ideas before writing them up. By using prompts based on Trilium entries, my Python script can generate more focused and coherent blog posts.  
+
+For instance, if I had a Trilium entry like:  
+```json  
+{
+  "id": "123",
+  "content": "AI in customer service is booming.",
+  "type": "thought"
+}
+```
+I could use that as a prompt to generate something like:  
+*"In the rapidly evolving landscape of AI applications, customer service has taken a quantum leap with AI-powered platforms...."*  
+
+Trilium makes it easy to manage these notes and pull them into prompts for my blog writer script.  
+
+## Step 3: Creating Blog Entries in My GitLab Repo  
+
+Now, here’s where things get interesting (and slightly nerve-wracking). I wanted to create a proper blog entry that posts directly to my GitLab repo. So, I forked the [aridgwayweb/blog](https://git.aridgwayweb.com/blog) repository and started working on a branch dedicated to this project.  
+
+In my `create_blog_entry.py` script, I used GitLab’s API to create a new entry. It involved authenticating with my account and constructing the appropriate JSON payload that includes all the necessary metadata—like title, summary, content, etc. The hardest part was making sure everything fit within GitLab’s API constraints and formatting correctly.  
+
+Here’s an excerpt of what I sent:  
+```python  
+import gitlab  
+gl = gitlab.Gitlab('gitlab.com', 'your_api_key')  
+entry = gl.entries.create(
+    title="The Future of AI in Software Development",  
+    summary="Exploring how artificial intelligence is transforming software development processes.",  
+    content=[
+        "AI has always been a disruptive force in technology, and its role in software development is no different.",
+        "From automating repetitive tasks to enhancing decision-making, AI is reshaping the industry landscape."
+    ]
+)  
+```  
+
+And then I notified myself that it was done!  
+
+## Step 4: Sending Notifications via Matrix  
+
+Finally, after everything was up and running, I sent a quick notification to my matrix account about the new pull request. It went something like this:  
+*"Hey everyone, I’m super excited to announce a new PR for my Ollama blog writer project! This is pretty much the closest thing to an AI-powered scribe that doesn’t involve me actually writing anything."*  
+
+Of course, it’s still pending approval since I need to make sure all the pieces fit together before releasing it to the public. But hey, at least I’ve got a solid foundation to build on!  
+
+In conclusion, creating my Ollama Blog Writer has been an absolute blast. It combines my love for tech with Python and AI in ways I never imagined. Now, if only I could find a way to automate writing blog *reviews*…
+
+# End blog post
+
+Now I'm not sure what the ```<think>``` tags are all about but this is a very interesting model and has produced the closest thing to what the prompt Asked for in my opinion
+
+Deepseek-r1 is definitely one to keep an eye on and one I'll be using in the future
+
+Until next time (But will the content come from me?)!

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

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+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
+
+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.
+
+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.
+
+Anyways, without further ado, I present to you the first, pipeline written, AI content for this blog
+
+---
+
+# When to Use AI: Navigating the Right Scenarios
+
+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.
+
+## The Spreadsheet Dilemma: Where AI Can help, and where it hurts
+
+**Scenario:** Mapping work types to categories in a spreadsheet with thousands of entries, like distinguishing between "Painting," "Repainting," "Deck Painting," or "Stucco Repainting."
+
+**Where AI Helps:**
+
+*   **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.
+
+**Where AI Struggles:** 
+
+*   **Precision Over Ambiguity:** Calculations requiring exact values (e.g., average durations) are better handled by deterministic algorithms rather than AI’s probabilistic approach.
+
+**Traditional Methods Are Easier for Deterministic Problems:**
+
+*   **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.
+
+## When AI Shines: Contextual and Unstructured Tasks
+
+**Scenario:** Automating customer support with chatbots or analyzing social media sentiment.
+
+**Why AI Works Here:**
+
+*   **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.
+
+🚀