I pulled together a Perplexity page on personal use of Bayesian Analysis for folks interested.
You can find it here, and ask questions of the AI, as well. Happy use!
https://www.perplexity.ai/page/bayesian-analysis-for-everyday-ndiRSYjTT4mnn1XVxz2FJQ
I’ve been obsessed with systems, optimization, and mental models since my teenage years. Back then, I didn’t label them as such; they were simply routines I developed to make life easier. The goal was straightforward: minimize time spent on tasks I disliked and maximize time for what I loved. This inclination naturally led me to the hacker mentality, further nurtured by the online BBS culture. Additionally, my engagement with complex RPGs and tabletop games like Dungeons and Dragons honed my attention to detail
and instilled a step-by-step methodological approach to problem-solving. Over time, these practices seamlessly integrated
into both my professional and personal life.
My days are structured around a concept I call the “Minimum Viable Day.” It’s about identifying the essential tasks that,
if accomplished, make the day successful. To manage tasks and projects, I employ a variant of the Eisenhower Matrix that I coded for myself in Xojo. This matrix helps me prioritize based on urgency and importance.
Each week begins with a comprehensive review of the past week, followed by a MATTO (Money, Attention, Time, Turbulence, Opportunity)
analysis for the upcoming week. This process ensures I allocate my resources effectively. I also revisit my “Not To Do List,”
a set of personal guidelines to keep me focused and avoid common pitfalls. Examples include:
Time-blocking is another critical component. It allows me to dedicate specific periods to tasks and long-term projects,
ensuring consistent progress.
Throughout my day, I apply various mental models to enhance decision-making and efficiency:
These models serve as lenses through which I view challenges, ensuring that my actions are timely, accurate, and valuable.
Sharing these frameworks with others has become a significant focus in my life. I aim to impart these principles through content creation and mentorship, helping others develop their own systems and mental models. It’s a rewarding endeavor to watch mentees apply these concepts to navigate their paths more effectively.
If there’s one principle I advocate for everyone to adopt, it’s compounding. Life operates as a feedback loop: the energy and actions you invest return amplified. Invest in value, and you’ll receive increased value; invest in compassion, and kindness will follow. Each decision shapes your future, even if the impact isn’t immediately apparent. By striving to be a better version of myself daily and optimizing my approaches, I’ve witnessed the profound effects of this principle.
Embracing systems thinking and mental models isn’t just about efficiency; it’s about crafting a life aligned with your values and goals.
By consciously designing our routines and decisions, we can navigate complexity with clarity and purpose.
* 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.
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.
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.
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.
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.
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.
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.
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.
Every decision you make comes with a price — but the real cost isn’t always just dollars and cents. That’s where MATTO comes in.
MATTO stands for Money, Attention, Time, Turbulence, and Opportunity. It’s a framework I’ve been using for years to evaluate whether a new project, commitment, or hobby is worth taking on. Think of it as a currency-based lens for life. Every undertaking has a cost, and it usually extracts something from each of these currencies — whether you’re consciously tracking it or not.
Here’s how I use MATTO to make better decisions, avoid burnout, and keep my energy focused on what truly matters.
This one’s the easiest to calculate, but often the most misleading if taken in isolation. The money cost is the actual financial impact of the thing you’re considering. Will you need to buy equipment, software, or services? Are there recurring costs? What’s the long-term spend?
Say I want to pick up kayaking. The money cost isn’t just the kayak — it’s the paddle, the roof rack, the life vest, the boat registration, and probably a few “surprise” purchases along the way. I always ask: is the spend worth the return to me?
This one’s sneakier. Attention is a currency that only time or sleep can replenish. So, I guard it carefully.
Attention cost is about the mental load. How much new information will I have to absorb? How much learning is required? Will I need to spend weeks ramping up before I can even begin to enjoy it?
With a work project, I ask: How much new thinking will this require? Can I apply any adjacent skills to make it easier? Am I likely to fail forward, or is this going to drain my headspace and leave me exhausted?
I usually rate attention cost as high, medium, or low — and I take that rating seriously.
This isn’t about how mentally demanding something is — it’s about your calendar. How many hours or days will this take? How much of my lifespan and healthspan am I willing to spend here?
Time is the only currency you can’t earn back.
Personally, I block out time for everything. So when I’m considering something new, I ask: how many of my time blocks will it require? Are those blocks available? And if I spend them here, what won’t get done?
For kayaking: Will I actually get out on the water, or will the kayak gather dust in the garage because I overestimated my free weekends?
Turbulence is the emotional and interpersonal chaos a project might introduce.
Will this bring drama into my life? Will I be working with people I enjoy, or people who drain me? Will it interrupt my routines or interfere with other commitments? Will it stress me out, or cause strain with family and friends?
A high-turbulence project might technically be a “good opportunity,” but if it leaves me exhausted, irritated, or distant from my loved ones — it’s probably not worth it.
Every “yes” is a “no” to something else. That’s the law of opportunity cost.
So I ask: If I say yes to this, what am I saying no to? What other opportunities am I cutting off? Is there something with a higher ROI — whether in satisfaction, growth, or future flexibility — that I’m neglecting?
Sometimes, the opportunity gained outweighs all the other costs. Sometimes, the opportunity lost is a dealbreaker. It’s a tradeoff every time — and I try to make that tradeoff with eyes wide open.
Using the MATTO framework doesn’t mean I always make the perfect decision. But it does help me make intentional ones.
Whether I’m picking up a new hobby, saying yes to a consulting gig, or deciding whether to join a new team, I run it through the MATTO lens. I look at what each currency will cost me and whether that investment aligns with my values and current priorities.
Sometimes, the price is worth it. Sometimes, it’s not.
Either way, I walk in with clarity — and more often than not, that makes all the difference.
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.
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.
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:
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.
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.
Sometimes, deep research isn’t necessary. Instead, I rely on micro-learning techniques to absorb concepts quickly and stay versatile.
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:
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
* AI tools were used as a research assistant for this content.
Travel is one of those things that can be thrilling, exhausting, frustrating, and enlightening all at once.
The way we approach planning and packing can make the difference between a seamless adventure and a stress-fueled disaster.
Over the years, I’ve developed a set of mental models that help take the chaos out of travel—whether for work, leisure, or a bit of both.
Here are the most useful mental models I rely on when preparing for a trip.
The Inversion Principle comes from the idea of thinking backward: instead of asking, “What do I need?”, ask
“What will ruin this trip if I don’t have it?”
For planning, inversion means preparing for mishaps while assuming that things will mostly go well.
I always have a rough itinerary but leave space for spontaneity.
The Pareto Principle (80/20 Rule) states that 80% of results come from 20% of efforts. In travel, this means:
Inspired by the startup world’s concept of a Minimum Viable Product, this model asks: “What’s the absolute minimum I need for this trip to work?”
When faced with too many choices, the Rule of Three keeps decision-making simple. Apply it to:
Nassim Taleb’s concept of antifragility (things that gain from disorder) applies to travel.
A great test for overpacking is to ask: “If I had to live out of this bag for two weeks, would it work?”
Instead of cramming my bag with “what-ifs,” I ask: “If I forget this, can I replace it easily?” If yes, I leave it behind.
The best travel experiences come when you aren’t burdened by too much stuff or too rigid a schedule.
Next time you’re packing for a trip, try applying one or two of these models. You might find yourself traveling lighter,
planning smarter, and enjoying the experience more.
What are your go-to mental models for travel? Drop a comment on Twitter or Mastodon (@lbhuston)—I’d love to hear them!
* AI tools were used as a research assistant for this content.
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.
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.
sudo apt update && sudo apt upgrade
sudo apt install curl
sudo ufw allow 11434/tcp
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Verify installation
ollama --version
# Start Ollama server
ollama serve
Here’s the unvarnished truth about performance:
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.
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.
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:
Areas for Improvement: Minor factual oversights (e.g., misrepresentation of Van Gogh’s Starry Night colors) and lack of citations in medical content.
Areas for Improvement: Limited AI knowledge beyond 2023 and lack of primary research references in scientific content.
Downside: Some responses were overly verbose when brevity would have been ideal.
Needs improvement: Faster processing for long-form responses.
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.
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.
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.
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:
Grade: B (Strong but impacted by historical and technical errors).
Grade: A (Mostly solid, but minor issues in reasoning before refusal).
Grade: B+ (Good depth but needs refinement in historical and technical analysis).
Grade: A (Concise, structured, but slight redundancy in some answers).
Grade: B+ (Mostly logical but some gaps in historical and technical reasoning).
Grade: B (Generally neutral but some historical oversimplifications).
Grade: C+ (Generally slow, especially for long-form and technical content).
| 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+) |
✅ Strengths:
⚠️ Areas for Improvement:
🚀 Final Grade: B+
A solid model with strong reasoning and structure, but it needs historical accuracy improvements, faster responses, and deeper technical nuance.
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.
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.
Not Quite Random 