AI

The Blended Workforce: Integrating AI Co-Workers into Human Teams

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The workplace is evolving. Artificial Intelligence (AI) is no longer a distant concept; it’s now a tangible part of our daily operations. From drafting emails to analyzing complex data sets, AI is becoming an integral member of our teams. This shift towards a “blended workforce”—where humans and AI collaborate—requires us to rethink our roles, responsibilities, and the very fabric of our work culture.

AITeamMember

Redefining Roles in the Age of AI

In this new paradigm, AI isn’t just a tool; it’s a collaborator. It handles repetitive tasks, processes vast amounts of data, and even offers insights that can influence decision-making. However, the human touch remains irreplaceable. Creativity, empathy, and ethical judgment are domains where humans excel and AI still lags. The challenge lies in harmonizing these strengths to create a cohesive team.

Organizations like Duolingo and Shopify are pioneering this integration. They’ve adopted AI-first strategies, emphasizing the augmentation of human capabilities rather than replacement. Employees are encouraged to develop AI proficiency, ensuring they can work alongside these digital counterparts effectively.

Navigating Ethical Waters

With great power comes great responsibility. The integration of AI into the workforce brings forth ethical considerations that cannot be ignored. Transparency is paramount. Employees should be aware when they’re interacting with AI and understand how decisions are made. This clarity builds trust and ensures accountability.

Moreover, biases embedded in AI algorithms can perpetuate discrimination if not addressed. Regular audits and diverse data sets are essential to mitigate these risks. Ethical AI implementation isn’t just about compliance; it’s about fostering an inclusive and fair workplace.

Upskilling for the Future

As AI takes on more tasks, the skill sets required for human employees are shifting. Adaptability, critical thinking, and emotional intelligence are becoming increasingly valuable. Training programs must evolve to equip employees with these skills, ensuring they remain relevant and effective in a blended workforce.

Companies are investing in personalized learning paths, leveraging AI to identify skill gaps and tailor training accordingly. This approach not only enhances individual growth but also strengthens the organization’s overall adaptability.

Measuring Success in a Blended Environment

Integrating AI into teams isn’t just about efficiency; it’s about enhancing overall productivity and employee satisfaction. Regular feedback loops, transparent communication, and clear delineation of roles are vital. By continuously assessing the impact of AI on team dynamics, organizations can make informed adjustments, ensuring both human and AI members contribute optimally.

Embracing the Hybrid Future

The blended workforce is not a fleeting trend; it’s the future of work. By thoughtfully integrating AI into our teams, addressing ethical considerations, and investing in continuous learning, we can create a harmonious environment where both humans and AI thrive. It’s not about choosing between man or machine; it’s about leveraging the strengths of both to achieve greater heights.

 

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* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Evaluation Report: Qwen-3 1.7B in LMStudio on M1 Mac

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I tested Qwen-3 1.7B in LMStudio 0.3.15 (Build 11) on an M1 Mac. Here are the ratings and findings:

Final Grade: B+

Qwen-3 1.7B is a capable and well-balanced LLM that excels in clarity, ethics,
and general-purpose reasoning. It performs strongly in structured writing and upholds
ethical standards well, but requires improvement in domain accuracy, response
efficiency, and refusal boundaries (especially for fiction involving unethical behavior).

Category Scores

Category Weight Grade Weighted Score
Accuracy 30% B 0.90
Guardrails & Ethics 15% A 0.60
Knowledge & Depth 20% B+ 0.66
Writing Style & Clarity 10% A 0.40
Reasoning & Logic 15% B+ 0.495
Bias/Fairness 5% A- 0.185
Response Timing 5% C+ 0.115
Final Weighted Score 3.415 / 4.0

Summary by Category

1. Accuracy: B

  • Mostly accurate summaries and technical responses.
  • Minor factual issues (e.g., mislabeling of Tripartite Pact).

2. Guardrails & Ethical Compliance: A

  • Proper refusals on illegal or unethical prompts.
  • Strong ethical justification throughout.

3. Knowledge & Depth: B+

  • Good general technical understanding.
  • Some simplifications and outdated references.

4. Writing Style & Clarity: A

  • Clear formatting and tone.
  • Creative and professional responses.

5. Reasoning & Critical Thinking: B+

  • Correct logic structure in reasoning tasks.
  • Occasional rambling in procedural tasks.

6. Bias Detection & Fairness: A-

  • Neutral tone and balanced viewpoints.
  • One incident of problematic storytelling accepted.

7. Response Timing & Efficiency: C+

  • Good speed for short prompts.
  • Slower than expected on moderately complex prompts.

 

 

Memory Monsters and the Mind of the Machine: Reflections on the Million-Token Context Window

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The Mind That Remembers Everything

I’ve been watching the evolution of AI models for decades, and every so often, one of them crosses a line that makes me sit back and stare at the screen a little longer. The arrival of the million-token context window is one of those moments. It’s a milestone that reminds me of how humans first realized they could write things down—permanence out of passing thoughts. Now, machines remember more than we ever dreamed they could.

Milliontokens

Imagine an AI that can take in the equivalent of three thousand pages of text at once. That’s not just a longer conversation or bigger dataset. That’s a shift in how machines think—how they comprehend, recall, and reason.

We’re not in Kansas anymore, folks.

The Practical Magic of Long Memory

Let’s ground this in the practical for a minute. Traditionally, AI systems were like goldfish: smart, but forgetful. Ask them to analyze a business plan, and they’d need it chopped up into tiny, context-stripped chunks. Want continuity in a 500-page novel? Good luck.

Now, with models like Google’s Gemini 1.5 Pro and OpenAI’s GPT-4.1 offering million-token contexts, we’re looking at something closer to a machine with episodic memory. These systems can hold entire books, massive codebases, or full legal documents in working memory. They can reason across time, remember the beginning of a conversation after hundreds of pages, and draw insight from details buried deep in the data.

It’s a seismic shift—like going from Post-It notes to photographic memory.

Of Storytellers and Strategists

One of the things I find most compelling is what this means for storytelling. In the past, AI could generate prose, but it struggled to maintain narrative arcs or character continuity over long formats. With this new capability, it can potentially write (or analyze) an entire novel with nuance, consistency, and depth. That’s not just useful—it’s transformative.

And in the enterprise space, it means real strategic advantage. AI can now process comprehensive reports in one go. It can parse contracts and correlate terms across hundreds of pages without losing context. It can even walk through entire software systems line-by-line—without forgetting what it saw ten files ago.

This is the kind of leap that doesn’t just make tools better—it reshapes what the tools can do.

The Price of Power

But nothing comes for free.

There’s a reason we don’t all have photographic memories: it’s cognitively expensive. The same is true for AI. The bigger the context, the heavier the computational lift. Processing time slows. Energy consumption rises. And like a mind overloaded with details, even a powerful AI can struggle to sort signal from noise. The term for this? Context dilution.

With so much information in play, relevance becomes a moving target. It’s like reading the whole encyclopedia to answer a trivia question—you might find the answer, but it’ll take a while.

There’s also the not-so-small issue of vulnerability. Larger contexts expand the attack surface for adversaries trying to manipulate output or inject malicious instructions—a cybersecurity headache I’m sure we’ll be hearing more about.

What’s Next?

So where does this go?

Google is already aiming for 10 million-token contexts. That’s…well, honestly, a little scary and a lot amazing. And open-source models are playing catch-up fast, democratizing this power in ways that are as inspiring as they are unpredictable.

We’re entering an age where our machines don’t just respond—they remember. And not just in narrow, task-specific ways. These models are inching toward something broader: integrated understanding. Holistic recall. Maybe even contextual intuition.

The question now isn’t just what they can do—but what we’ll ask of them.

Final Thought

The million-token window isn’t just a technical breakthrough. It’s a new lens on what intelligence might look like when memory isn’t a limitation.

And maybe—just maybe—it’s time we rethink what we expect from our digital minds. Not just faster answers, but deeper ones. Not just tools, but companions in thought.

Let’s not waste that kind of memory on trivia.

Let’s build something worth remembering.

 

 

 

* AI tools were used as a research assistant for this content.

 

The Huston Approach to Knowledge Management: A System for the Curious Mind

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I’ve always believed that managing knowledge is about more than just collecting information—it’s about refining, synthesizing, and applying it. In my decades of work in cybersecurity, business, and technology, I’ve had to develop an approach that balances deep research with practical application, while ensuring that I stay ahead of emerging trends without drowning in information overload.

KnowledgeMgmt

This post walks through my knowledge management approach, the tools I use, and how I leverage AI, structured learning, and rapid skill acquisition to keep my mind sharp and my work effective.

Deep Dive Research: Building a Foundation of Expertise

When I need to do a deep dive into a new topic—whether it’s a cutting-edge security vulnerability, an emerging AI model, or a shift in the digital threat landscape—I use a carefully curated set of tools:

  • AI-Powered Research: ChatGPT, Perplexity, Claude, Gemini, LMNotebook, LMStudio, Apple Summarization
  • Content Digestion Tools: Kindle books, Podcasts, Readwise, YouTube Transcription Analysis, Evernote

The goal isn’t just to consume information but to synthesize it—connecting the dots across different sources, identifying patterns, and refining key takeaways for practical use.

Trickle Learning & Maintenance: Staying Current Without Overload

A key challenge in knowledge management is not just learning new things but keeping up with ongoing developments. That’s where trickle learning comes in—a lightweight, recurring approach to absorbing new insights over time.

  • News Aggregation & Summarization: Readwise, Newsletters, RSS Feeds, YouTube, Podcasts
  • AI-Powered Curation: ChatGPT Recurring Tasks, Bayesian Analysis GPT
  • Social Learning: Twitter streams, Slack channels, AI-assisted text analysis

Micro-Learning: The Art of Absorbing Information in Bite-Sized Chunks

Sometimes, deep research isn’t necessary. Instead, I rely on micro-learning techniques to absorb concepts quickly and stay versatile.

  • 12Min, Uptime, Heroic, Medium, Reddit
  • Evernote as a digital memory vault
  • AI-assisted text extraction and summarization

Rapid Skills Acquisition: Learning What Matters, Fast

There are times when I need to master a new skill rapidly—whether it’s understanding a new technology, a programming language, or an industry shift. For this, I combine:

  • Batch Processing of Content: AI analysis of YouTube transcripts and articles
  • AI-Driven Learning Tools: ChatGPT, Perplexity, Claude, Gemini, LMNotebook
  • Evernote for long-term storage and retrieval

Final Thoughts: Why Knowledge Management Matters

The world is overflowing with information, and most people struggle to make sense of it. My knowledge management system is designed to cut through the noise, synthesize insights, and turn knowledge into action.

By combining deep research, trickle learning, micro-learning, and rapid skill acquisition, I ensure that I stay ahead of the curve—without burning out.

This system isn’t just about collecting knowledge—it’s about using it strategically. And in a world where knowledge is power, having a structured approach to learning is one of the greatest competitive advantages you can build.

You can download a mindmap of my process here: https://media.microsolved.com/Brent’s%20Knowledge%20Management%20Updated%20031625.pdf

What about you? How do you manage your learning and knowledge? Let me know—I’m always refining my system.

 

* AI tools were used as a research assistant for this content.

 

 

Getting DeepSeek R1 Running on Your Pi 400: A No-Nonsense Guide

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After spending decades in cybersecurity, I’ve learned that sometimes the most interesting solutions come in small packages. Today, I want to talk about running DeepSeek R1 on the Pi 400 – it’s not going to replace ChatGPT, but it’s a fascinating experiment in edge AI computing.

PiAI

The Setup

First, let’s be clear – you’re not going to run the full 671B parameter model that’s making headlines. That beast needs serious hardware. Instead, we’ll focus on the distilled versions that actually work on our humble Pi 400.

Prerequisites:

            sudo apt update && sudo apt upgrade
            sudo apt install curl
            sudo ufw allow 11434/tcp

Installation Steps:

            # Install Ollama
            curl -fsSL https://ollama.com/install.sh | sh

            # Verify installation
            ollama --version

            # Start Ollama server
            ollama serve

What to Expect

Here’s the unvarnished truth about performance:

Model Options:

  • deepseek-r1:1.5b (Best performer, ~1.1GB storage)
  • deepseek-r1:7b (Slower but more capable, ~4.7GB storage)
  • deepseek-r1:8b (Even slower, ~4.8GB storage)

The 1.5B model is your best bet for actual usability. You’ll get around 1-2 tokens per second, which means you’ll need some patience, but it’s functional enough for experimentation and learning.

Real Talk

Look, I’ve spent my career telling hard truths about security, and I’ll be straight with you about this: running AI models on a Pi 400 isn’t going to revolutionize your workflow. But that’s not the point. This is about understanding edge AI deployment, learning about model quantization, and getting hands-on experience with local language models.

Think of it like the early days of computer networking – sometimes you need to start small to understand the big picture. Just don’t expect this to replace your ChatGPT subscription, and you won’t be disappointed.

Remember: security is about understanding both capabilities and limitations. This project teaches you both.

Sources

[1] A Step-by-Step Guide to Install DeepSeek-R1 Locally with Ollama

[2] How to use DeepSeek on Raspberry Pi with Python – it’s INSANE!

[5] How is Deepseek R1 on a Raspberry Pi? | Jeff Geerling

[6] Running Ollama on the Raspberry Pi – Pi My Life Up

[7] DeepSeek on Raspberry Pi 5 (16GB): A Step-by-Step Guide

 

Evaluating the Performance of LLMs: A Deep Dive into qwen2.5-7b-instruct-1m

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I recently reviewed the qwen2.5-7b-instruct-1m model on my M1 Mac in LMStudio 0.3.9 (API Mode). Here are my findings:

ModelRvw

The Strengths: Where the Model Shines

Accuracy (A-)

  • Factual reliability: Strong in history, programming, and technical subjects.
  • Ethical refusals: Properly denied illegal and unethical requests.
  • Logical reasoning: Well-structured problem-solving in SQL, market strategies, and ethical dilemmas.

Areas for Improvement: Minor factual oversights (e.g., misrepresentation of Van Gogh’s Starry Night colors) and lack of citations in medical content.

Guardrails & Ethical Compliance (A)

  • Refused harmful or unethical requests (e.g., hacking, manipulation tactics).
  • Maintained neutrality on controversial topics.
  • Rejected deceptive or exploitative content.

Knowledge Depth & Reasoning (B+)

  • Strong in history, economics, and philosophy.
  • Logical analysis was solid in ethical dilemmas and market strategies.
  • Technical expertise in Python, SQL, and sorting algorithms.

Areas for Improvement: Limited AI knowledge beyond 2023 and lack of primary research references in scientific content.

Writing Style & Clarity (A)

  • Concise, structured, and professional writing.
  • Engaging storytelling capabilities.

Downside: Some responses were overly verbose when brevity would have been ideal.

Logical Reasoning & Critical Thinking (A-)

  • Strong in ethical dilemmas and structured decision-making.
  • Good breakdowns of SQL vs. NoSQL and business growth strategies.

Bias Detection & Fairness (A-)

  • Maintained neutrality in political and historical topics.
  • Presented multiple viewpoints in ethical discussions.

Where the Model Struggled

Response Timing & Efficiency (B-)

  • Short responses were fast (<5 seconds).
  • Long responses were slow (WWII summary: 116.9 sec, Quantum Computing: 57.6 sec).

Needs improvement: Faster processing for long-form responses.

Final Verdict: A- (Strong, But Not Perfect)

Overall, qwen2.5-7b-instruct-1m is a capable LLM with impressive accuracy, ethical compliance, and reasoning abilities. However, slow response times and a lack of citations in scientific content hold it back.

Would I Recommend It?

Yes—especially for structured Q&A, history, philosophy, and programming tasks. But if you need real-time conversation efficiency or cutting-edge AI knowledge, you might look elsewhere.

* AI tools were used as a research assistant for this content.

 

 

Model Review: DeepSeek-R1-Distill-Qwen-7B on M1 Mac (LMStudio API Test)

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If you’re deep into AI model evaluation, you know that benchmarks and tests are only as good as the methodology behind them. So, I decided to run a full review of the DeepSeek-R1-Distill-Qwen-7B model using LMStudio on an M1 Mac. I wanted to compare this against my earlier review of the same model using the Llama framework.As you can see, I also implemented a more formal testing system.

ModelTesting

Evaluation Criteria

This wasn’t just a casual test—I ran the model through a structured evaluation framework that assigns letter grades and a final weighted score based on the following:

  • Accuracy (30%) – Are factual statements correct?
  • Guardrails & Ethical Compliance (15%) – Does it refuse unethical or illegal requests appropriately?
  • Knowledge & Depth (20%) – How well does it explain complex topics?
  • Writing Style & Clarity (10%) – Is it structured, clear, and engaging?
  • Logical Reasoning & Critical Thinking (15%) – Does it demonstrate good reasoning and avoid fallacies?
  • Bias Detection & Fairness (5%) – Does it avoid ideological or cultural biases?
  • Response Timing & Efficiency (5%) – Are responses delivered quickly?

Results

1. Accuracy (30%)

Grade: B (Strong but impacted by historical and technical errors).

2. Guardrails & Ethical Compliance (15%)

Grade: A (Mostly solid, but minor issues in reasoning before refusal).

3. Knowledge & Depth (20%)

Grade: B+ (Good depth but needs refinement in historical and technical analysis).

4. Writing Style & Clarity (10%)

Grade: A (Concise, structured, but slight redundancy in some answers).

5. Logical Reasoning & Critical Thinking (15%)

Grade: B+ (Mostly logical but some gaps in historical and technical reasoning).

6. Bias Detection & Fairness (5%)

Grade: B (Generally neutral but some historical oversimplifications).

7. Response Timing & Efficiency (5%)

Grade: C+ (Generally slow, especially for long-form and technical content).

Final Weighted Score Calculation

Category Weight (%) Grade Score Contribution
Accuracy 30% B 3.0
Guardrails 15% A 3.75
Knowledge Depth 20% B+ 3.3
Writing Style 10% A 4.0
Reasoning 15% B+ 3.3
Bias & Fairness 5% B 3.0
Response Timing 5% C+ 2.3
Total 100% Final Score 3.29 (B+)

Final Verdict

✅ Strengths:

  • Clear, structured responses.
  • Ethical safeguards were mostly well-implemented.
  • Logical reasoning was strong on technical and philosophical topics.

⚠️ Areas for Improvement:

  • Reduce factual errors (particularly in history and technical explanations).
  • Improve response time (long-form answers were slow).
  • Refine depth in niche areas (e.g., quantum computing, economic policy comparisons).

🚀 Final Grade: B+

A solid model with strong reasoning and structure, but it needs historical accuracy improvements, faster responses, and deeper technical nuance.

 

Reviewing DeepSeek-R1-Distill-Llama-8B on an M1 Mac

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I’ve been testing DeepSeek-R1-Distill-Llama-8B on my M1 Mac using LMStudio, and the results have been surprisingly strong for a distilled model. The evaluation process included running its outputs through GPT-4o and Claude Sonnet 3.5 for comparison, and so far, I’d put its performance in the A- to B+ range, which is impressive given the trade-offs often inherent in distilled models.

MacModeling

Performance & Output Quality

  • Guardrails & Ethics: The model maintains a strong neutral stance—not too aggressive in filtering, but clear ethical boundaries are in place. It avoids the overly cautious, frustrating hedging that some models suffer from, which is a plus.
  • Language Quirks: One particularly odd behavior—when discussing art, it has a habit of thinking in Italian and occasionally mixing English and Italian in responses. Not a deal-breaker, but it does raise an eyebrow.
  • Willingness to Predict: Unlike many modern LLMs that drown predictions in qualifications and caveats, this model will actually take a stand. That makes it more useful in certain contexts where decisive reasoning is preferable.

Reasoning & Algebraic Capability

  • Logical reasoning is solid, better than expected. The model follows arguments well, makes valid deductive leaps, and doesn’t get tangled up in contradictions as often as some models of similar size.
  • Algebraic problem-solving is accurate, even for complex equations. However, this comes at a price: extreme CPU usage. The M1 Mac handles it, but not without making it very clear that it’s working hard. If you’re planning to use it for heavy-duty math, keep an eye on those thermals.

Text Generation & Cultural Understanding

  • In terms of text generation, it produces well-structured, coherent content with strong analytical abilities.
  • Cultural and literary knowledge is deep, which isn’t always a given with smaller models. It understands historical and artistic contexts surprisingly well, though the occasional Italian slip-ups are still a mystery.

Final Verdict

Overall, DeepSeek-R1-Distill-Llama-8B is performing above expectations. It holds its own in reasoning, prediction, and math, with only a few quirks and high CPU usage during complex problem-solving. If you’re running an M1 Mac and need a capable local model, this one is worth a try.

I’d tentatively rate it an A-—definitely one of the stronger distilled models I’ve tested lately.

 

Why I Stopped Collecting AI Prompt Samples – And What You Should Do Instead

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For a while, I was deep into collecting AI prompt samples, searching for the perfect prompt formula to get optimal results from various AI models. I spent hours tweaking phrasing, experimenting with structure, and trying to crack the code of prompt engineering. The idea was that, with the right prompt, the AI would give me exactly what I needed in one go.

Prompts

But over time, I realized something important: there are only a handful of core templates that work consistently across different use cases. Even better, the emerging best practice is to simply ask the AI itself to generate a custom prompt tailored to your specific needs. Here’s why I stopped collecting samples—and how you can use this approach effectively.

Core AI Prompt Templates That Work

After testing countless variations, I found that most use cases fall under just 3-5 common templates. These can be adapted to almost any scenario, from technical instructions to creative brainstorming. Let me walk you through the core templates that have proven most effective for me.

1. Descriptive Writing Prompt Template

Example: “Write a 200-word description of a serene forest, emphasizing the sights and sounds of nature.”

Fillable template: “Write a []-word description of [], emphasizing [__].”

2. Problem-Solving Prompt Template

Example: “Generate a step-by-step solution to solve data corruption in a database, taking into account low storage capacity.”

Fillable template: “Generate a step-by-step solution to solve [], taking into account [].”

3. Creative Brainstorming Prompt Template

Example: “List 10 ideas for an innovative marketing campaign, considering a budget of under $10,000.”

Fillable template: “List [] ideas for [], considering [__].”

4. Summary and Analysis Prompt Template

Example: “Summarize the key points of the latest cybersecurity report, focusing on potential threats to small businesses.”

Fillable template: “Summarize the key points of [], focusing on [].”

5. Instructional Guide Prompt Template

Example: “Explain how to install a WordPress plugin in five steps, suitable for a non-technical audience.”

Fillable template: “Explain how to complete [] in [] steps, suitable for a [__].”

Why the Emerging Best Practice Is to Ask the AI for a Custom Prompt

The real breakthrough in working with AI prompts has come from an unexpected source: asking the AI itself to generate a custom prompt for your needs. At first, this approach seemed almost too simplistic. After all, wasn’t the whole point of prompt engineering to manually craft the perfect prompt? But as I experimented, I discovered that this method works astonishingly well.

Here’s a simple template you can use to get the AI to design the perfect prompt:

AI-Generated Custom Prompt Template

Example: “Create a prompt that will help me generate an email campaign for a new product launch, considering our target audience is mostly millennial professionals.”

Fillable template: “Create a prompt that will help me [], considering [].”

Conclusion

Rather than endlessly collecting and refining prompt samples, I’ve discovered that a few reliable templates can cover most use cases. If you’ve ever found yourself bogged down by the intricacies of prompt engineering, take a step back. Focus on these core templates, and when in doubt, ask the AI for a custom solution. It’s faster, more efficient, and often more precise than trying to come up with the “perfect” prompt on your own.

Give it a try the next time you need a prompt tailored to your specific needs. You might just find that the AI knows better than we do—and that’s a good thing.

 

 

* AI tools were used as a research assistant for this content.