'```

feat: Add blog post on AI usage scenarios
```'

src/content/a_reply_to_accelerating_australias_ai_agenda.md

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

src/content/when_to_use_ai.md

@@ -1,54 +1 @@
-# 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.*
+```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. ```