Private Equity’s Focus on Sports and Gaming: A Startup Playbook

May 12, 2025 / lbhuston / Leave a comment

Navigating the Current Landscape of 2025

While global M&A activity has slowed, private equity investments in the U.S. have risen by 25% year-over-year, reaching $46 billion in April 2025. This surge is fueled by a significant amount of “dry powder,” with PE firms holding $1.2 trillion in unspent cash. Notably, 24% of this capital has been idle for over four years, indicating a readiness to invest in promising ventures.

ChatGPT Image May 12 2025 at 10 22 38 AM

Sports franchises have become attractive targets due to their long-term media rights deals, which provide financial stability. For instance, the NBA’s 11-year, $77 billion deal and the NFL’s 11-year, $111 billion agreement offer predictable revenue streams. This stability has led to significant investments, such as the Boston Celtics’ $6.1 billion sale and stakes in the Buffalo Bills and Miami Dolphins by private equity firms.

The gaming industry is also experiencing a boom, with M&A activity totaling $6.6 billion in Q1 2025. A notable deal includes the $3.5 billion acquisition of Niantic, the developer of Pokémon Go, by Scopely.

Strategic Advice for Startups

Align with PE Interests: Startups should position themselves in sectors that are attracting private equity attention, such as sports tech and gaming. Demonstrating potential for stable, long-term revenue can make your company more appealing to investors.
Leverage Industry Stability: Highlight how your business can benefit from the financial stability offered by long-term contracts or recurring revenue models. This approach can mitigate perceived risks and attract investment.
Embrace Innovation: Stay ahead by integrating emerging technologies and trends into your business model. Innovative solutions in fan engagement, virtual reality, or mobile gaming can set your startup apart.
Prepare for Investment: Ensure your business is ready for investment by maintaining transparent financial records, a clear growth strategy, and a strong management team. Being prepared can expedite the investment process when opportunities arise.

Conclusion

Despite a challenging M&A environment, private equity firms are actively investing in sectors that offer stability and growth potential. Startups in sports and gaming can capitalize on this trend by aligning their strategies with investor interests and demonstrating resilience in uncertain times.

* 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

May 6, 2025 / lbhuston / Leave a comment

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.

The Heisenberg Principle, Everyday Life, and Cybersecurity: Embracing Uncertainty

May 5, 2025 / lbhuston / Leave a comment

You’ve probably heard of the Heisenberg Uncertainty Principle — that weird quantum physics thing that says you can’t know where something is and how fast it’s going at the same time. But what does that actually mean, and more importantly, how can we use it outside of a physics lab?

Here’s the quick version:
At the quantum level, the more precisely you try to measure the position of a particle (like an electron), the less precisely you can know its momentum (its speed and direction). And vice versa. It’s not about having bad tools — it’s a built-in feature of the universe. The act of observing disturbs the system.

Heis

Now, for anything bigger than a molecule, this doesn’t really apply. You can measure the location and speed of your car without it vanishing into a probability cloud. The effects at our scale are so tiny they’re basically zero. But that doesn’t mean Heisenberg’s idea isn’t useful. In fact, I think it’s a perfect metaphor for both life and cybersecurity.

Here’s how I’ve been applying it:

1. Observation Changes Behavior

In security and in business, watching something often changes how it behaves. Put monitoring software on endpoints, and employees become more cautious. Watch a threat actor closely, and they’ll shift tactics. Just like in quantum physics, observation isn’t passive — it has consequences.

2. Focus Creates Blind Spots

In incident response, zeroing in on a single alert might help you track one bad actor — but you might miss the bigger pattern. Focus too much on endpoint logs and you might miss lateral movement in cloud assets. The more precisely you try to measure one thing, the fuzzier everything else becomes. Sound familiar?

3. Know the Limits of Certainty

The principle reminds us that perfect knowledge is a myth. There will always be unknowns — gaps in visibility, unknown unknowns in your threat model, or behaviors that can’t be fully predicted. Instead of chasing total control, we should optimize for resilience and responsiveness.

4. Think Probabilistically

Security decisions (and life choices) benefit from probability thinking. Nothing is 100% secure or 100% safe. But you can estimate, adapt, and prepare. The world’s fuzzy — accept it, work with it, and use it to your advantage.

Final Thought

The Heisenberg Principle isn’t just for physicists. It’s a sharp reminder that trying to know everything can actually distort the system you’re trying to understand. Whether you’re debugging code, designing a threat detection strategy, or just navigating everyday choices, uncertainty isn’t a failure — it’s part of the system. Plan accordingly.

* 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.

Bayseian Analysis For Everyday Life HowTo

May 1, 2025 / lbhuston / Leave a comment

I pulled together a Perplexity page on personal use of Bayesian Analysis for folks interested.

Thinking

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

Systems Thinking and Mental Models: My Daily Operating System

April 23, 2025April 23, 2025 / lbhuston / Leave a comment

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.

Building My Daily Framework

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:

Don’t be a soldier; be a general—empower the team to overcome challenges.
Avoid checking email outside scheduled times.
Refrain from engaging in inane arguments.
Before agreeing to something, ask, “Does this make me happy?”

Time-blocking is another critical component. It allows me to dedicate specific periods to tasks and long-term projects,
ensuring consistent progress.

Mental Models in Action

Throughout my day, I apply various mental models to enhance decision-making and efficiency:

EDSAM: Eliminate, Delegate, Simplify, Automate, and Maintain—my approach to task management.
Pareto Principle: Focusing on the 20% of efforts that yield 80% of results.
Occam’s Razor: Preferring simpler solutions when faced with complex problems, and looking for the path with the least assumptions.
Inversion: Considering what I want to avoid to understand better what I want to achieve.
Compounding: Recognizing that minor, consistent improvements lead to significant long-term gains.

These models serve as lenses through which I view challenges, ensuring that my actions are timely, accurate, and valuable.

Teaching and Mentorship

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.

The Power of Compounding

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.

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

April 17, 2025 / lbhuston / Leave a comment

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.

MATTO: A Lens for Measuring the True Cost of Anything

April 11, 2025 / lbhuston / Leave a comment

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

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.

M is for Money

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?

A is for Attention

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.

T is for Time

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?

T is for Turbulence

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.

O is for Opportunity

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.

MATTO in the Real World

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.

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

March 16, 2025 / lbhuston / Leave a comment

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

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

The Mental Models of Smart Travel: Planning and Packing Without the Stress

March 3, 2025 / lbhuston / Leave a comment

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.

Travel

Here are the most useful mental models I rely on when preparing for a trip.

1. The Inversion Principle: Pack for the Worst, Plan for the Best

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?”

Weather disasters – Do you have the right clothing for unexpected rain or temperature drops?
Tech failures – What’s your backup plan if your phone dies or your charger fails?
Health issues – Are you prepared for illness, minor injuries, or allergies?

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.

2. The Pareto Packing Rule: 80% of What You Pack Won’t Matter

The Pareto Principle (80/20 Rule) states that 80% of results come from 20% of efforts. In travel, this means:

80% of the time, you’ll wear the same 20% of your clothes.
80% of your tech gear won’t see much use.
80% of the stress comes from overpacking.

3. The MVP (Minimum Viable Packing) Approach

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?”

4. The Rule of Three: Simplifying Decisions

When faced with too many choices, the Rule of Three keeps decision-making simple. Apply it to:

Clothing – Three tops, three bottoms, three pairs of socks/underwear.
Shoes – One for walking, one for casual/dress, and one for special activities.
Daily Carry Items – If it doesn’t fit in your three most-used pockets or compartments, rethink bringing it.

5. The Anti-Fragile Itinerary: Build in Buffer Time

Nassim Taleb’s concept of antifragility (things that gain from disorder) applies to travel.

6. The “Two-Week” Packing Test

A great test for overpacking is to ask: “If I had to live out of this bag for two weeks, would it work?”

7. The “Buy It There” Mindset

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.

Wrapping Up: Travel Lighter, Plan Smarter

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.

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

February 9, 2025 / lbhuston / Leave a comment

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.

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.