From 3ed8f91303744ac17a91ea5714ccec94f9179a74 Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 07:18:43 +0000 Subject: [PATCH 01/18] '``` feat: Add blog post on AI usage scenarios ```' --- src/content/when_to_use_ai.md | 55 +---------------------------------- 1 file changed, 1 insertion(+), 54 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index 665b080..4524547 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,54 +1 @@ -# 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 😄 -*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*. - -So, let’s break it down. - -### 🧠 When AI is *not* the answer - -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*. - -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. - -### 🧮 When AI *is* the answer - -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. - -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. - -### 🧪 The balance between AI and human oversight - -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. - -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. - -### 🧩 Final thoughts - -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. - -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. - -### 🧩 Summary - -| 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.* \ No newline at end of file +```markdown # When to use AI As an Australian software developer, DevOps expert and journalist who loves diving into tech topics with humor sprinkled throughout my writing (because why not?), I've found myself pondering this question quite often: "When should I consider using artificial intelligence?" Well, buckle up because I'm about to share a few scenarios where the answer is pretty clear-cut. ## When AI Just Isn't Right ### The Shuddering Spreadsheet Saga Recently, while working on an almost terrifying spreadsheet involving workload drivers and potential savings within various processes (shudders), I've stumbled upon something interesting. I had this massive dataset of work orders that needed to be categorized into one or two exclusive types based solely off the description provided. #### Human vs AI: A Tangential Tango As part of my task, it became evident how closely related these "work types" and actual requests were — they could have been more tangentially connected than a kangaroo in high heels. The mapping process required me to manually read each work type against the request description. Now here's where I thought I'd enlist some help from Gen AI (Generative Pre-trained Transformer, for those who love acronyms). This was essentially an exercise of interpreting disparate pieces of text and finding their most closely related counterparts — a task that could easily be handled by LLMs designed to find intersections between separate texts. No amount of regex or string manipulation can match the precision required here. #### Traditional Programming: The Unsung Hero But wait, there's more! Building those workload drivers and formulas for automated calculations? That's where traditional programming methods come into play — with their impeccable math skills (and zero chance for interpretation). While LLMs might assist in choosing numbers or constants initially, I wouldn't trust them to actually run the calculation. Their nature could potentially lead to some funky results. In conclusion: When it comes down to tasks requiring precision and accuracy rooted deeply within mathematics and logic — that's when traditional programming shines brighter than an Australian summer day (or even a Gen AI model). ## But Sometimes... It's Perfectly Okay ### The Workload Drivers Dream Team On the flip side, let's consider scenarios where LLMs can truly shine. Take my spreadsheet example again: categorizing work orders based on descriptions is perfectly suited for Generative Pre-trained Transformers. #### Finding Intersections with Ease (and Humor) Gen AI models excel at interpreting text and finding relationships between disparate pieces of information — something that would otherwise be a laborious human task or an impossible feat without extensive regex magic. In this context, Gen AI can effortlessly match work types to requests by analyzing descriptions in ways humans can't possibly fathom. #### Traditional Programming: Still the Ace Up Your Sleeve However, even when leveraging LLMs for categorization and intersection-finding tasks within a spreadsheet-like environment (because why not), traditional programming still holds its ground. It’s essential because it ensures that once we’ve categorized our work orders using Gen AI's brilliance — we can run those calculations with precision through the trusty old-school methods. In conclusion: When dealing with text interpretation, categorization based on descriptions or any task requiring a deep dive into language nuances and fuzzy logic (because why not), LLMs are your go-to. But when it comes to executing precise mathematical operations rooted in traditional programming principles — that's where we still reign supreme! ## So What's the Verdict? In essence: Use AI wisely, folks! Whether it's interpreting texts or performing intricate calculations grounded firmly within a structured logical framework (because who needs fuzzy logic anyway?), knowing which tool fits best for each task is crucial. And hey, if you ever find yourself in an Australian summer debating whether to use Gen AI versus traditional programming methods — just remember that both have their unique strengths and quirks. So go forth with your tech adventures confidently! Whether you're a software developer crafting the next groundbreaking app or merely enjoying this delightful journey through technology's endless possibilities (with a hint of humor, naturally), always know when it's time to let Gen AI take over... and sometimes even more importantly — knowing when not to. ``` From 1f4b7100bcd3cc7678e1b9a5baf9cf7a8a79eaed Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 07:43:58 +0000 Subject: [PATCH 02/18] '``` git commit -m "AI: Know when to use it" ``` MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit **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. ' --- src/content/when_to_use_ai.md | 95 ++++++++++++++++++++++++++++++++++- 1 file changed, 94 insertions(+), 1 deletion(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index 4524547..b52f1bd 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1 +1,94 @@ -```markdown # When to use AI As an Australian software developer, DevOps expert and journalist who loves diving into tech topics with humor sprinkled throughout my writing (because why not?), I've found myself pondering this question quite often: "When should I consider using artificial intelligence?" Well, buckle up because I'm about to share a few scenarios where the answer is pretty clear-cut. ## When AI Just Isn't Right ### The Shuddering Spreadsheet Saga Recently, while working on an almost terrifying spreadsheet involving workload drivers and potential savings within various processes (shudders), I've stumbled upon something interesting. I had this massive dataset of work orders that needed to be categorized into one or two exclusive types based solely off the description provided. #### Human vs AI: A Tangential Tango As part of my task, it became evident how closely related these "work types" and actual requests were — they could have been more tangentially connected than a kangaroo in high heels. The mapping process required me to manually read each work type against the request description. Now here's where I thought I'd enlist some help from Gen AI (Generative Pre-trained Transformer, for those who love acronyms). This was essentially an exercise of interpreting disparate pieces of text and finding their most closely related counterparts — a task that could easily be handled by LLMs designed to find intersections between separate texts. No amount of regex or string manipulation can match the precision required here. #### Traditional Programming: The Unsung Hero But wait, there's more! Building those workload drivers and formulas for automated calculations? That's where traditional programming methods come into play — with their impeccable math skills (and zero chance for interpretation). While LLMs might assist in choosing numbers or constants initially, I wouldn't trust them to actually run the calculation. Their nature could potentially lead to some funky results. In conclusion: When it comes down to tasks requiring precision and accuracy rooted deeply within mathematics and logic — that's when traditional programming shines brighter than an Australian summer day (or even a Gen AI model). ## But Sometimes... It's Perfectly Okay ### The Workload Drivers Dream Team On the flip side, let's consider scenarios where LLMs can truly shine. Take my spreadsheet example again: categorizing work orders based on descriptions is perfectly suited for Generative Pre-trained Transformers. #### Finding Intersections with Ease (and Humor) Gen AI models excel at interpreting text and finding relationships between disparate pieces of information — something that would otherwise be a laborious human task or an impossible feat without extensive regex magic. In this context, Gen AI can effortlessly match work types to requests by analyzing descriptions in ways humans can't possibly fathom. #### Traditional Programming: Still the Ace Up Your Sleeve However, even when leveraging LLMs for categorization and intersection-finding tasks within a spreadsheet-like environment (because why not), traditional programming still holds its ground. It’s essential because it ensures that once we’ve categorized our work orders using Gen AI's brilliance — we can run those calculations with precision through the trusty old-school methods. In conclusion: When dealing with text interpretation, categorization based on descriptions or any task requiring a deep dive into language nuances and fuzzy logic (because why not), LLMs are your go-to. But when it comes to executing precise mathematical operations rooted in traditional programming principles — that's where we still reign supreme! ## So What's the Verdict? In essence: Use AI wisely, folks! Whether it's interpreting texts or performing intricate calculations grounded firmly within a structured logical framework (because who needs fuzzy logic anyway?), knowing which tool fits best for each task is crucial. And hey, if you ever find yourself in an Australian summer debating whether to use Gen AI versus traditional programming methods — just remember that both have their unique strengths and quirks. So go forth with your tech adventures confidently! Whether you're a software developer crafting the next groundbreaking app or merely enjoying this delightful journey through technology's endless possibilities (with a hint of humor, naturally), always know when it's time to let Gen AI take over... and sometimes even more importantly — knowing when not to. ``` +# When to use AI + +## 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. + +## When AI is Not the Best Choice + +### 1. **The Fuzzy Logic Dilemma** + +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 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 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** + +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. + +> *“Traditional programming methods work on maths and logic that is sound and offers no chance for interpretation.”* + +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. + +## When AI is the Best Choice + +### 1. **Finding Connections** + +AI excels at identifying patterns and relationships within large datasets. This is invaluable for tasks like customer segmentation, fraud detection, and predictive maintenance. + +### 2. **Automating Repetitive Tasks** + +Many tasks are simply too repetitive and time-consuming for humans to perform efficiently. AI-powered automation can free up valuable time and resources. + +### 3. **Suggesting Numbers or Constants** + +While AI shouldn't be trusted to run calculations, it can be helpful in suggesting potential numbers or constants to use in those calculations. + +## The Human vs. AI Divide + +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*. + +> *“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 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?’” \ No newline at end of file From e9a7daf5b32920cf412c2dda4f811b76b3431d13 Mon Sep 17 00:00:00 2001 From: armistace Date: Wed, 4 Jun 2025 20:10:41 +1000 Subject: [PATCH 03/18] Change workflow push to master only --- .gitea/workflows/build_push.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.gitea/workflows/build_push.yml b/.gitea/workflows/build_push.yml index 42c5f74..e945864 100644 --- a/.gitea/workflows/build_push.yml +++ b/.gitea/workflows/build_push.yml @@ -1,5 +1,8 @@ name: Build and Push Image -on: [ push ] +on: + push: + branches: + - master jobs: build: From 50af2eccb38e32fccc865877609390c5a2944117 Mon Sep 17 00:00:00 2001 From: armistace Date: Fri, 6 Jun 2025 09:25:31 +1000 Subject: [PATCH 04/18] kube pipeline manual step and kubectl yaml --- .gitea/workflows/build_push.yml | 85 +++++++++++++++++++-------------- kube/blog_deployment.yaml | 24 ++++++++++ kube/blog_pod.yaml | 25 ++++++++++ kube/blog_service.yaml | 13 +++++ 4 files changed, 110 insertions(+), 37 deletions(-) create mode 100644 kube/blog_deployment.yaml create mode 100644 kube/blog_pod.yaml create mode 100644 kube/blog_service.yaml diff --git a/.gitea/workflows/build_push.yml b/.gitea/workflows/build_push.yml index e945864..3dde059 100644 --- a/.gitea/workflows/build_push.yml +++ b/.gitea/workflows/build_push.yml @@ -1,45 +1,56 @@ name: Build and Push Image -on: - push: - branches: - - master +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: ${{ secrets.REG_PASSWORD }} - - name: Build and push - uses: docker/build-push-action@v5 - with: - context: . - push: true - platforms: linux/amd64,linux/arm64 - tags: | - git.aridgwayweb.com/armistace/blog:latest + - name: Build and push + uses: docker/build-push-action@v5 + with: + context: . + push: true + platforms: linux/amd64,linux/arm64 + tags: | + git.aridgwayweb.com/armistace/blog:latest + + - name: Deploy + approvers: armistace + minimum-approvals: 1 + issue-title: "Approval Required for Deploy" + issue-body: "Do you want to deploy this blog to production?" + run: | + kubectl delete namespace blog + kubectl create namespace blog + kubectl create secret docker-registry regcred --docker-server=${{ 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 diff --git a/kube/blog_deployment.yaml b/kube/blog_deployment.yaml new file mode 100644 index 0000000..8acae4e --- /dev/null +++ b/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 diff --git a/kube/blog_pod.yaml b/kube/blog_pod.yaml new file mode 100644 index 0000000..cfdf9b8 --- /dev/null +++ b/kube/blog_pod.yaml @@ -0,0 +1,25 @@ +#apiVersion: v1 +#data: +# .dockerconfigjson: eyJhdXRocyI6eyJnaXQuYXJpZGd3YXl3ZWIuY29tIjp7InVzZXJuYW1lIjoiYXJtaXN0YWNlIiwicGFzc3dvcmQiOiJDQHYqTjRvUDYvZXRjU3V4d1Y4c3g5c1x1MDAyNmMjRnRjIiwiZW1haWwiOiJhcjE3Nzg3QGdtYWlsLmNvbSIsImF1dGgiOiJZWEp0YVhOMFlXTmxPa05BZGlwT05HOVFOaTlsZEdOVGRYaDNWamh6ZURsekptTWpSblJqIn19fQ== +#kind: Secret +#metadata: +# creationTimestamp: "2024-08-13T01:07:13Z" +# name: regcred +# namespace: blog +# resourceVersion: "987829" +# uid: 7ab6187c-3569-4126-a2cf-b004f1ec4754 +#type: kubernetes.io/dockerconfigjson +#--- +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 diff --git a/kube/blog_service.yaml b/kube/blog_service.yaml new file mode 100644 index 0000000..3af5257 --- /dev/null +++ b/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 From efad73a1a4abcfa7c18f4148154a0d641fc5f3f2 Mon Sep 17 00:00:00 2001 From: armistace Date: Fri, 6 Jun 2025 09:29:24 +1000 Subject: [PATCH 05/18] wrap the password --- .gitea/workflows/build_push.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitea/workflows/build_push.yml b/.gitea/workflows/build_push.yml index 3dde059..66526bb 100644 --- a/.gitea/workflows/build_push.yml +++ b/.gitea/workflows/build_push.yml @@ -52,5 +52,5 @@ jobs: run: | 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=${{ vars.DOCKER_EMAIL }} --namespace=blog + kubectl create secret docker-registry regcred --docker-server=${{ 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 From 7a4d06bbef235c4e35309be9c858545edfe711af Mon Sep 17 00:00:00 2001 From: armistace Date: Fri, 6 Jun 2025 09:31:43 +1000 Subject: [PATCH 06/18] cleanup of kube stuff --- kube/blog_pod.yaml | 30 +++++++++--------------------- 1 file changed, 9 insertions(+), 21 deletions(-) diff --git a/kube/blog_pod.yaml b/kube/blog_pod.yaml index cfdf9b8..5ee6366 100644 --- a/kube/blog_pod.yaml +++ b/kube/blog_pod.yaml @@ -1,25 +1,13 @@ -#apiVersion: v1 -#data: -# .dockerconfigjson: eyJhdXRocyI6eyJnaXQuYXJpZGd3YXl3ZWIuY29tIjp7InVzZXJuYW1lIjoiYXJtaXN0YWNlIiwicGFzc3dvcmQiOiJDQHYqTjRvUDYvZXRjU3V4d1Y4c3g5c1x1MDAyNmMjRnRjIiwiZW1haWwiOiJhcjE3Nzg3QGdtYWlsLmNvbSIsImF1dGgiOiJZWEp0YVhOMFlXTmxPa05BZGlwT05HOVFOaTlsZEdOVGRYaDNWamh6ZURsekptTWpSblJqIn19fQ== -#kind: Secret -#metadata: -# creationTimestamp: "2024-08-13T01:07:13Z" -# name: regcred -# namespace: blog -# resourceVersion: "987829" -# uid: 7ab6187c-3569-4126-a2cf-b004f1ec4754 -#type: kubernetes.io/dockerconfigjson -#--- apiVersion: v1 kind: Pod metadata: - name: blog - namespace: blog + name: blog + namespace: blog spec: - containers: - - name: blog - image: git.aridgwayweb.com/armistace/blog:latest - ports: - - containerPort: 8000 - imagePullSecrets: - - name: regcred + containers: + - name: blog + image: git.aridgwayweb.com/armistace/blog:latest + ports: + - containerPort: 8000 + imagePullSecrets: + - name: regcred From abf6f3feda85c8240200249f03936cc1885d6177 Mon Sep 17 00:00:00 2001 From: armistace Date: Fri, 6 Jun 2025 09:52:38 +1000 Subject: [PATCH 07/18] fix push settings --- .gitea/workflows/build_push.yml | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/.gitea/workflows/build_push.yml b/.gitea/workflows/build_push.yml index 66526bb..5994638 100644 --- a/.gitea/workflows/build_push.yml +++ b/.gitea/workflows/build_push.yml @@ -45,11 +45,16 @@ jobs: git.aridgwayweb.com/armistace/blog:latest - name: Deploy - approvers: armistace - minimum-approvals: 1 - issue-title: "Approval Required for Deploy" - issue-body: "Do you want to deploy this blog to production?" 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 From d6d6f1893edfdec729c0dcb634fdcb3d9e715dba Mon Sep 17 00:00:00 2001 From: armistace Date: Fri, 6 Jun 2025 09:59:47 +1000 Subject: [PATCH 08/18] stray w --- .gitea/workflows/build_push.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitea/workflows/build_push.yml b/.gitea/workflows/build_push.yml index 5994638..686c35c 100644 --- a/.gitea/workflows/build_push.yml +++ b/.gitea/workflows/build_push.yml @@ -47,7 +47,7 @@ jobs: - name: Deploy run: | echo "Installing Kubectl" - apt get update + 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 From 14636a4d3b175aed97eb79142ab93ba4fbbc53a1 Mon Sep 17 00:00:00 2001 From: armistace Date: Fri, 6 Jun 2025 10:17:13 +1000 Subject: [PATCH 09/18] update so pelican can use - must remember --- src/content/when_to_use_ai.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index b52f1bd..a82ed35 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,4 +1,22 @@ +<<<<<<< HEAD # 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!? + + +# 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 +>>>>>>> 79d53d3 (update so pelican can use - must remember) ## The Great AI Debate: When to Trust the Machine vs. When to Stick to Your Brain From aead9151c0e57e117d0fd5c99b0e118b8de48a13 Mon Sep 17 00:00:00 2001 From: armistace Date: Fri, 6 Jun 2025 10:29:51 +1000 Subject: [PATCH 10/18] Update src/content/when_to_use_ai.md --- src/content/when_to_use_ai.md | 1 + 1 file changed, 1 insertion(+) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index a82ed35..d79b67a 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -13,6 +13,7 @@ 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 From 9c92d19943a08afd287cb5a77a41859101cb7bcd Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 07:18:43 +0000 Subject: [PATCH 11/18] '``` feat: Add blog post on AI usage scenarios ```' --- src/content/when_to_use_ai.md | 53 +++++------------------------------ 1 file changed, 7 insertions(+), 46 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index d79b67a..ad3b165 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,13 +1,10 @@ -<<<<<<< HEAD -# 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 +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!? @@ -75,39 +72,3 @@ So, in summary: 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?’” \ No newline at end of file From c2142f7f63d4e9e4caeda15ee1551eaa03b21e0b Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 07:43:58 +0000 Subject: [PATCH 12/18] '``` git commit -m "AI: Know when to use it" ``` MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit **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. ' --- src/content/when_to_use_ai.md | 23 ----------------------- 1 file changed, 23 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index ad3b165..bbb6a20 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -5,16 +5,6 @@ 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 ->>>>>>> 79d53d3 (update so pelican can use - must remember) ## The Great AI Debate: When to Trust the Machine vs. When to Stick to Your Brain @@ -59,16 +49,3 @@ AI is good at *finding connections* and *handling ambiguity*, but it’s *not go > *“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 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.”* From 191805461d8ae07ad4d61d0a4717f7ce57eea1bf Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 07:18:43 +0000 Subject: [PATCH 13/18] '``` feat: Add blog post on AI usage scenarios ```' --- src/content/when_to_use_ai.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index b54b966..68fc0f7 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,10 +1,10 @@ -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 +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!? @@ -62,4 +62,4 @@ So, when is it time to say “AI, nope”? When the data is messy, the tasks are | 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.* \ No newline at end of file +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.* From fc48d9a4fd1a9821c1c69fe72c9c4b922facb049 Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 07:18:43 +0000 Subject: [PATCH 14/18] '``` feat: Add blog post on AI usage scenarios ```' --- src/content/when_to_use_ai.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index 68fc0f7..d273ce9 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -62,4 +62,19 @@ So, when is it time to say “AI, nope”? When the data is messy, the tasks are | Repetitive tasks | ✅ | AI handles math and logic | | Creative decisions | ❌ | AI lacks the ability to think creatively | +<<<<<<< HEAD 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.”* +>>>>>>> 9c92d19 ('```) From bcc41ed4c33914f674569d140740f41ba6fc15fe Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 07:43:58 +0000 Subject: [PATCH 15/18] '``` git commit -m "AI: Know when to use it" ``` MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit **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. ' --- src/content/when_to_use_ai.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index d273ce9..2709951 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -62,9 +62,6 @@ So, when is it time to say “AI, nope”? When the data is messy, the tasks are | Repetitive tasks | ✅ | AI handles math and logic | | Creative decisions | ❌ | AI lacks the ability to think creatively | -<<<<<<< HEAD -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 @@ -77,4 +74,3 @@ So, in summary: 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.”* ->>>>>>> 9c92d19 ('```) From 74f8c8607b18d8c581df36ad94d81c432235f8e7 Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 6 Jun 2025 01:57:04 +0000 Subject: [PATCH 16/18] 'Add clarity on AI limitations.' --- src/content/when_to_use_ai.md | 86 ++++++++--------------------------- 1 file changed, 19 insertions(+), 67 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index 8141812..ea22450 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,76 +1,28 @@ -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!? +Okay, this is excellent! You're spot on with the tone, the specific examples, and the overall message. The "Further Thoughts" section is a perfect touch of Australian humor and practicality. The markdown formatting is also correct. +Here's a breakdown of what's particularly good and a few *very minor* suggestions for even further refinement (mostly stylistic, as it's already very strong): -# 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 +**What's Excellent:** -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 +* **Tone:** The conversational, slightly self-deprecating, and humorous tone is *perfect*. It makes the advice relatable and engaging. The Australian slang ("yarn," "Vegemite," "a bit of extra detail") adds authenticity and charm. +* **Specific Examples:** The work type/work request mapping scenario is a brilliant example of a task that *seems* like a good fit for AI but isn't. It's concrete and illustrates the point effectively. +* **Clear Distinction:** You clearly articulate the strengths and weaknesses of both LLMs and traditional programming/spreadsheet approaches. +* **Practical Advice:** The "Further Thoughts" section provides valuable, practical considerations (cost, data security, continuous learning). +* **Markdown Formatting:** The markdown is clean and well-structured. -## The Great AI Debate: When to Trust the Machine vs. When to Stick to Your Brain +**Minor Suggestions (Stylistic - These are *very* minor and mostly a matter of personal preference):** -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. +* **"Fuzzy Matching/Logic" Repetition:** You use "fuzzy matching/logic" a few times. While it's a key concept, consider varying the phrasing slightly to avoid repetition. For example, you could use phrases like "interpretive analysis," "subjective assessment," or "qualitative evaluation." +* **"AI Can Magically Solve Everything" - Slightly Stronger Phrasing:** The phrase "AI can magically solve everything" is a bit strong. While it gets the point across, you could soften it slightly to something like "AI can be tempting to view as a universal solution." +* **"Interpretive Analysis" - Consider Adding a Brief Explanation:** When you introduce "interpretive analysis," a very brief explanation of what it means in this context might be helpful for readers who aren't familiar with the term. Something like: "Interpretive analysis, in this case, refers to the AI's ability to find connections based on the meaning of words, rather than strict mathematical relationships." +* **"AI Can Be Tempting" - Expand a Bit:** The sentence "AI can be tempting to view as a universal solution" could be expanded a little to explain *why* it's tempting. Perhaps something like: "AI can be tempting to view as a universal solution, especially given the hype surrounding it, but it's crucial to remember its limitations." -## When AI is Not the Best Choice +**Revised Snippets (Incorporating Suggestions):** -### 1. **The Fuzzy Logic Dilemma** +* **"Fuzzy Matching/Logic" Variation:** Instead of repeatedly saying "fuzzy matching/logic," you could use phrases like "interpretive analysis," "subjective assessment," or "qualitative evaluation." +* **"AI Can Magically Solve Everything":** "AI can be tempting to view as a universal solution, especially given the hype surrounding it, but it's crucial to remember its limitations." +* **"Interpretive Analysis" Explanation:** "Interpretive analysis, in this case, refers to the AI's ability to find connections based on the meaning of words, rather than strict mathematical relationships." -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*. +**Overall:** -> *“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 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** - -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. - -> *“Traditional programming methods work on maths and logic that is sound and offers no chance for interpretation.”* - -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. - -## When AI is the Best Choice - -### 1. **Finding Connections** - -AI excels at identifying patterns and relationships within large datasets. This is invaluable for tasks like customer segmentation, fraud detection, and predictive maintenance. - -### 2. **Automating Repetitive Tasks** - -Many tasks are simply too repetitive and time-consuming for humans to perform efficiently. AI-powered automation can free up valuable time and resources. - -### 3. **Suggesting Numbers or Constants** - -While AI shouldn't be trusted to run calculations, it can be helpful in suggesting potential numbers or constants to use in those calculations. - -## The Human vs. AI Divide - -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 | - -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.”* +This is a fantastic piece of writing. The suggestions above are just minor tweaks to an already excellent result. You've captured the essence of the topic perfectly and presented it in a way that is both informative and entertaining. Well done! From 22c521293fd3e2e188774d5126f404589fe9f685 Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 6 Jun 2025 02:28:07 +0000 Subject: [PATCH 17/18] 'Clarify AI usage scenarios effectively.' --- src/content/when_to_use_ai.md | 60 ++++++++++++++++++++++++----------- 1 file changed, 41 insertions(+), 19 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index ea22450..700990d 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,28 +1,50 @@ -Okay, this is excellent! You're spot on with the tone, the specific examples, and the overall message. The "Further Thoughts" section is a perfect touch of Australian humor and practicality. The markdown formatting is also correct. +# When to Use AI: Navigating the Right Scenarios -Here's a breakdown of what's particularly good and a few *very minor* suggestions for even further refinement (mostly stylistic, as it's already very strong): +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. -**What's Excellent:** +## The Spreadsheet Dilemma: Where AI Fails -* **Tone:** The conversational, slightly self-deprecating, and humorous tone is *perfect*. It makes the advice relatable and engaging. The Australian slang ("yarn," "Vegemite," "a bit of extra detail") adds authenticity and charm. -* **Specific Examples:** The work type/work request mapping scenario is a brilliant example of a task that *seems* like a good fit for AI but isn't. It's concrete and illustrates the point effectively. -* **Clear Distinction:** You clearly articulate the strengths and weaknesses of both LLMs and traditional programming/spreadsheet approaches. -* **Practical Advice:** The "Further Thoughts" section provides valuable, practical considerations (cost, data security, continuous learning). -* **Markdown Formatting:** The markdown is clean and well-structured. +**Scenario:** Mapping work types to categories in a spreadsheet with thousands of entries, like distinguishing between "Painting," "Repainting," "Deck Painting," or "Stucco Repainting." -**Minor Suggestions (Stylistic - These are *very* minor and mostly a matter of personal preference):** +**Why AI Struggles Here:** -* **"Fuzzy Matching/Logic" Repetition:** You use "fuzzy matching/logic" a few times. While it's a key concept, consider varying the phrasing slightly to avoid repetition. For example, you could use phrases like "interpretive analysis," "subjective assessment," or "qualitative evaluation." -* **"AI Can Magically Solve Everything" - Slightly Stronger Phrasing:** The phrase "AI can magically solve everything" is a bit strong. While it gets the point across, you could soften it slightly to something like "AI can be tempting to view as a universal solution." -* **"Interpretive Analysis" - Consider Adding a Brief Explanation:** When you introduce "interpretive analysis," a very brief explanation of what it means in this context might be helpful for readers who aren't familiar with the term. Something like: "Interpretive analysis, in this case, refers to the AI's ability to find connections based on the meaning of words, rather than strict mathematical relationships." -* **"AI Can Be Tempting" - Expand a Bit:** The sentence "AI can be tempting to view as a universal solution" could be expanded a little to explain *why* it's tempting. Perhaps something like: "AI can be tempting to view as a universal solution, especially given the hype surrounding it, but it's crucial to remember its limitations." +* **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. -**Revised Snippets (Incorporating Suggestions):** +**Traditional Methods Win Here:** -* **"Fuzzy Matching/Logic" Variation:** Instead of repeatedly saying "fuzzy matching/logic," you could use phrases like "interpretive analysis," "subjective assessment," or "qualitative evaluation." -* **"AI Can Magically Solve Everything":** "AI can be tempting to view as a universal solution, especially given the hype surrounding it, but it's crucial to remember its limitations." -* **"Interpretive Analysis" Explanation:** "Interpretive analysis, in this case, refers to the AI's ability to find connections based on the meaning of words, rather than strict mathematical relationships." +* **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. -**Overall:** +## When AI Shines: Contextual and Unstructured Tasks -This is a fantastic piece of writing. The suggestions above are just minor tweaks to an already excellent result. You've captured the essence of the topic perfectly and presented it in a way that is both informative and entertaining. Well done! +**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. + +## 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. + +🚀 \ No newline at end of file From e7f996306fb44ea8c1bb5838f9583d941822c65f Mon Sep 17 00:00:00 2001 From: armistace Date: Fri, 6 Jun 2025 12:30:42 +1000 Subject: [PATCH 18/18] Human edit and update --- src/content/when_to_use_ai.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index 700990d..adb7918 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,3 +1,28 @@ +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.