Navigating Rapid Automation & AI Without Losing Human-Centric Design

August 25, 2025 / lbhuston / Leave a comment

Why Now Matters

Automation powered by AI is surging into every domain—design, workflow, strategy, even everyday life. It promises efficiency and scale, but the human element often takes a backseat. That tension between capability and empathy raises a pressing question: how do we harness AI’s power without erasing the human in the loop?

A man with glasses performing an audit with careful attention to detail with an office background cinematic 8K high definition photograph

Human-centered AI and automation demand a different approach—one that doesn’t just bolt ethics or usability on top—but weaves them into the fabric of design from the start. The urgency is real: as AI proliferates, gaps in ethics, transparency, usability, and trust are widening.

The Risks of Tech-Centered Solutions

Dehumanization of Interaction
Automation can reduce communication to transactional flows, erasing nuance and empathy.
Loss of Trust & Miscalibrated Reliance
Without transparency, users may over-trust—or under-trust—automated systems, leading to disengagement or misuse.
Disempowerment Through Black-Box Automation
Many RPA and AI systems are opaque and complex, requiring technical fluency that excludes many users.
Ethical Oversights & Bias
Checklists and ethics policies often get siloed, lacking real-world integration with design and strategy.

Principles of Human–Tech Coupling

Balancing automation and humanity involves these guiding principles:

Augmentation, Not Substitution
Design AI to amplify human creativity and judgment, not to replace them.
Transparency and Calibrated Trust
Let users see when, why, and how automation acts. Support aligned trust, not blind faith.
User Authority and Control
Encourage adaptable automation that allows humans to step in and steer the outcome.
Ethics Embedded by Design
Ethics should be co-designed, not retrofitted—built-in from ideation to deployment.

Emerging Frameworks & Tools

Human-Centered AI Loop

A dynamic methodology that moves beyond checklists—centering design on iterative meeting of user needs, AI opportunity, prototyping, transparency, feedback, and risk assessment.

Human-Centered Automation (HCA)

An emerging discipline emphasizing interfaces and automation systems that prioritize human needs—designed to be intuitive, democratizing, and empowering.

ADEPTS: Unified Capability Framework

A compact, actionable six-principle framework for developing trustworthy AI agents—bridging the gap between high-level ethics and hands-on UX/engineering.

Ethics-Based Auditing

Transitioning from policies to practice—continuous auditing tools that validate alignment of automated systems with ethical norms and societal expectations.

Prototypes & Audit Tools in Practice

Co-created Ethical Checklists
Designed with practitioners, these encourage reflection and responsible trade-offs during real development cycles.
Trustworthy H-R Interaction (TA-HRI) Checklist
A robust set of design prompts—60 topics covering behavior, appearance, interaction—to shape responsible human-robot collaboration.
Ethics Impact Assessments (Industry 5.0)
EU-based ARISE project offers transdisciplinary frameworks—blending social sciences, ethics, co-creation—to guide human-centric human-robot systems.

Bridging the Gaps: An Integrated Guide

Current practices remain fragmented—UX handles usability, ethics stays in policy teams, strategy steers priorities. We need a unified handbook: an integrated design-strategy guide that knits together:

Human-Centered AI method loops
Adaptable automation principles
ADEPTS capability frameworks
Ethics embedded with auditing and assessment
Prototyping tools for feedback and trust calibration

Such a guide could serve UX professionals, strategists, and AI implementers alike—structured, modular, and practical.

What UX Pros and Strategists Can Do Now

Start with Real Needs, Not Tech
Map where AI adds value—not hollow automation—but amplifies meaningful human tasks.
Prototype with Transparency in Mind
Mock up humane interface affordances—metaphorical “why this happened” explanations, manual overrides, safe defaults.
Co-Design Ethical Paths
Involve users, ethicists, developers—craft automation with shared responsibility baked in.
Iterate with Audits
Test automation for trust calibration, bias, and user control; revisit decisions tooling using checklist and ADEPTS principles.
Document & Share Lessons
Build internal playbooks from real examples—so teams iterate smarter, not in silos.

Final Thoughts: Empowered Humans, Thoughtful Machines

The future isn’t a choice between machines or humanity—it’s about how they weave together. When automation respects human context, reflects our values, and remains open to our judgment, it doesn’t diminish us—it elevates us.

Let’s not lose the soul of design in the rush to automate. Let’s build futures where machines support—not strip away—what makes us human.

References

Arxiv.org – Calibrated Trust in AI
Arxiv.org – Human-Centered RPA Systems
Jennwv.com – Checklists for Responsible Development
ResearchGate – Balancing AI and Human Creativity
ScienceDirect – Adaptable Automation Principles
MDPI – Ethics Embedded in AI Design
UX Planet – Human-Centered AI in Practice
TA-HRI Checklist

Support My Work

If you found this useful and want to help support my ongoing research into the intersection of cybersecurity, automation, and human-centric design, consider buying me a coffee:

👉 Support on Buy Me a Coffee

* 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.

Building Logic with Language: Using Pseudo Code Prompts to Shape AI Behavior

August 8, 2025 / lbhuston / Leave a comment

Introduction

It started as an experiment. Just an idea — could we use pseudo code, written in plain human language, to define tasks for AI platforms in a structured, logical way? Not programming, exactly. Not scripting. But something between instruction and automation. And to my surprise — it worked. At least in early testing, platforms like Claude Sonnet 4 and Perplexity have been responding in consistently usable ways. This post outlines the method I’ve been testing, broken into three sections: Inputs, Task Logic, and Outputs. It’s early, but I think this structure has the potential to evolve into a kind of “prompt language” — a set of building blocks that could power a wide range of rule-based tools and reusable logic trees.

Section 1: Inputs

The first section of any pseudo code prompt needs to make the data sources explicit. In my experiments, that means spelling out exactly where the AI should look — URLs, APIs, or internal data sets. Being explicit in this section has two advantages: it limits hallucination by narrowing the AI’s attention, and it standardizes the process, so results are more repeatable across runs or across different models.

# --- INPUTS ---
Sources:
- DrudgeReport (https://drudgereport.com/)
- MSN News (https://www.msn.com/en-us/news)
- Yahoo News (https://news.yahoo.com/)

Each source is clearly named and linked, making the prompt both readable and machine-parseable by future tools. It’s not just about inputs — it’s about documenting the scope of trust and context for the model.

Section 2: Task Logic

This is the core of the approach: breaking down what we want the AI to do in clear, sequential steps. No heavy syntax. Just numbered logic, indentation for subtasks, and simple conditional statements. Think of it as logic LEGO — modular, stackable, and understandable at a glance.

# --- TASK LOGIC ---
1. Scrape and parse front-page headlines and article URLs from all three sources.
2. For each headline:
   a. Fetch full article text.
   b. Extract named entities, events, dates, and facts using NER and event detection.
3. Deduplicate:
   a. Group similar articles across sources using fuzzy matching or semantic similarity.
   b. Merge shared facts; resolve minor contradictions based on majority or confidence weighting.
4. Prioritize and compress:
   a. Reduce down to significant, non-redundant points that are informational and relevant.
   b. Eliminate clickbait, vague, or purely opinion-based content unless it reflects significant sentiment shift.
5. Rate each item:
   a. Assign sentiment as [Positive | Neutral | Negative].
   b. Assign a probability of truthfulness based on:
      - Agreement between sources
      - Factual consistency
      - Source credibility
      - Known verification via primary sources or expert commentary

What’s emerging here is a flexible grammar of logic. Early tests show that platforms can follow this format surprisingly well — especially when the tasks are clearly modularized. Even more exciting: this structure hints at future libraries of reusable prompt modules — small logic trees that could plug into a larger system.

Section 3: Outputs

The third section defines the structure of the expected output — not just format, but tone, scope, and filters for relevance. This ensures that different models produce consistent, actionable results, even when their internal mechanics differ.

# --- OUTPUT ---
Structured listicle format:
- [Headline or topic summary]
- Detail: [1–2 sentence summary of key point or development]
- Sentiment: [Positive | Neutral | Negative]
- Truth Probability: [XX%]

It’s not about precision so much as direction. The goal is to give the AI a shape to pour its answers into. This also makes post-processing or visualization easier, which I’ve started exploring using Perplexity Labs.

Conclusion

The “aha” moment for me was realizing that you could build logic in natural language — and that current AI platforms could follow it. Not flawlessly, not yet. But well enough to sketch the blueprint of a new kind of rule-based system. If we keep pushing in this direction, we may end up with prompt grammars or libraries — logic that’s easy to write, easy to read, and portable across AI tools.

This is early-phase work, but the possibilities are massive. Whether you’re aiming for decision support, automation, research synthesis, or standardizing AI outputs, pseudo code prompts are a fascinating new tool in the kit. More experiments to come.

* 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.

The Second Half: Building a Legacy of Generational Knowledge

August 4, 2025 / lbhuston / Leave a comment

“Build, establish, and support a legacy of knowledge that not only exceeds my lifetime, but exceeds generations and creates a generational wealth of knowledge.”

That’s the mission I’ve set for the second half of my life. It’s not about ego, and it’s certainly not about permanence in the usual sense. It’s about creating something that can outlast me—not in the form of statues or plaques, but in the ripples of how people think, solve problems, and support each other long after I’m gone.

Three Pillars of a Legacy

There are three key prongs to how I’m approaching this mission. Each one is interwoven with a sense of service and intention. The first is about altruism—specifically, applying a barbell strategy to how I support systems and organizations. The middle of the bar is the consistent, proven efforts that deliver value today. But at the ends are the moonshots—projects like the psychedelic science work of MAPS or the long-term frameworks for addressing food insecurity and inequality. These aren’t about tactics; they’re about systems-level, knowledge-driven approaches that could evolve over the next 50 to 100 years.

The second pillar is more personal. It’s about documenting how I think. Inspired in part by Charlie Munger, I’ve come to realize that just handing out solutions isn’t enough. If you want to make lasting impact, you have to teach people how to think. So I’ve been unpacking the models I use—deconstruction, inversion, compounding, Pareto analysis, the entourage effect—and showing how those can be applied across cybersecurity, personal health, and even everyday life. This is less about genius and more about discipline: the practice of solving hard problems with reusable, teachable tools.

The third leg of the stool is mentoring. I don’t have children, but I see the act of mentorship as my version of parenting. I’ve watched people I’ve mentored go on to become rock stars in their own right—building lives and careers they once thought were out of reach. What I offer them isn’t just advice. It’s a commitment to help them design lives they want to live, through systems thinking, life hacking, and relentless self-experimentation.

Confidence and Competence

One of the core ideas I try to pass along—both to myself and to my mentees—is the importance of aligning your circle of confidence with your circle of competence. Let those drift apart, and you’re just breeding hubris. But keep them close, and you cultivate integrity, humility, and effective action. That principle is baked into everything I do now. It’s part of how I live. It’s a boundary check I run daily.

The Long Game

I don’t think legacy is something you “leave behind.” I think it’s something you put into motion and let others carry forward. This isn’t about a monument. It’s about momentum. And if I can contribute even a small part to a future where people think better, solve bigger, and give more—then that’s a legacy I can live with.

* 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.

Why Humans Suck at Asymmetric Risk – And What We Can Do About It

July 2, 2025 / lbhuston / Leave a comment

Somewhere between the reptilian wiring of our brain and the ambient noise of the modern world, humans lost the plot when it comes to asymmetric risk. I see it every day—in security assessments, in boardroom decisions, even in how we cross the street. We’re hardwired to flinch at shadows and ignore the giant neon “Jackpot” signs blinking in our periphery.

The Flawed Lens We Call Perception

Asymmetric risk, if you’re not familiar, is the art and agony of weighing a small chance of a big win against a large chance of a small loss—or vice versa. The kind of math that makes venture capitalists grin and compliance officers lose sleep.

But here’s the kicker: we are biologically terrible at this. Our brains were optimized for sabertooth cats and tribal gossip, not venture portfolios and probabilistic threat modeling. As Kahneman and Tversky so elegantly showed, we’re much more likely to run from a $100 loss than to chase a $150 gain. That’s not risk aversion. That’s evolutionary baggage.

Biases in the Wild

Two of my favorite culprits are the availability heuristic and the affect heuristic—basically, we decide based on what we remember and how we feel. That’s fine for picking a restaurant. But for cybersecurity investments or evaluating high-impact, low-probability threats? It’s a disaster.

Anxiety, in particular, makes us avoid even minimal risks, while optimism bias has us chasing dreams on gut feeling. The result? We miss the upsides and ignore the tripwires. We undervalue data and overvalue drama.

The Real World Cost

These aren’t just academic quibbles. Misjudging asymmetric risk leads to bad policies, missed opportunities, and overblown fears. It’s the infosec team spending 90% of their time on threats that look scary on paper but never materialize—while ignoring the quiet, creeping risks with catastrophic potential.

And young people, bless their eager hearts, are caught in a bind. They have the time horizon to tolerate risk, but not the experience to see the asymmetric goldmines hiding in plain sight. Education, yes. But more importantly, exposure—to calculated risks, not just textbook theory.

Bridging the Risk Gap

So what do we do? First, we stop pretending humans are rational. We aren’t. But we can be reflective. We can build systems—risk ladders, simulations, portfolios—that force us to confront our own biases and recalibrate.

Next, we tell better stories. The framing of a risk—description versus experience—can change everything. A one-in-a-thousand chance sounds terrifying until you say “one person in a stadium full of fans.” Clarity in communication is power.

Finally, we get comfortable with discomfort. Real asymmetric opportunity often lives in ambiguity. It’s not a coin toss—it’s a spectrum. And learning to navigate that space, armed with models, heuristics, and a pinch of skepticism, is the real edge.

Wrapping Up

Asymmetric risk is both a threat and a gift. It’s the reason bad startups make billionaires and why black swan events crash markets. We can’t rewire our lizard brains, but we can out-think them.

We owe it to ourselves—and our futures—to stop sucking at asymmetric risk.

Shoutouts:

This post came from an interesting discussion with two friends: Bart and Jason. Thanks, gentlemen, for the impetus and the shared banter!

* 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.

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

June 5, 2025 / lbhuston / Leave a comment

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.

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.

Support the creation of high-impact content and research. Sponsorship opportunities are available for specific topics, whitepapers, tools, or advisory insights. Learn more or contribute here: Buy Me A Coffee

* 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.

Navigating the Noise: A Personal Take on Digital Asset Investing

May 27, 2025June 5, 2025 / lbhuston / Leave a comment

The last few years have seen digital assets storm from the periphery of tech geek circles to the forefront of institutional portfolios. We’ve moved from whispering about Bitcoin at hacker conferences to hearing it discussed on earnings calls by publicly traded companies. And while the hype machines are louder than ever, so is the regulatory drumbeat. The digital asset world has matured—but it hasn’t gotten simpler.

DigitalAssets

Here’s my personal attempt to cut through the noise, and talk about what really matters.

From Curiosity to Core Holdings

It used to be that crypto was a side hustle for technophiles and libertarians. Today, with over 617 million crypto holders globally and institutions dedicating 10% or more of their portfolios to digital assets, this thing is mainstream. Even BlackRock, the same folks behind the traditional investment portfolios of yesteryear, have rolled out a Bitcoin ETF that’s become the fastest-growing in history.

That tells us something: digital assets are no longer the fringe. They’re foundational.

The Seven Faces of Digital Assets

This market is anything but monolithic. From my perspective, it’s better understood as an ecosystem with seven distinct species: Network tokens, Security tokens, Company-backed tokens, Arcade tokens, Collectible tokens (NFTs), Asset-backed tokens, and Memecoins. Each category carries different risk profiles and regulatory considerations. Understanding them is critical—especially if you’re trying to build a resilient, well-diversified portfolio.

Risk Isn’t a Bug—It’s a Feature

One of the biggest lies I see in mainstream discourse is the framing of crypto risk as something to be eliminated. But risk isn’t just part of the deal—it’s the entire point. Risk is the price of opportunity.

That said, you need a framework. I like the four-step approach: identify, analyze, assess, and plan treatments. It’s not rocket science, but you’d be surprised how many people skip step one.

Regulation: The Double-Edged Sword

For years, regulation was the bogeyman. Now, it’s becoming the moat. The EU’s MiCA framework is setting the global standard with its methodical categorization of tokens and service providers. Meanwhile, the U.S. is going through its own regulatory renaissance. Under the Trump administration, we’ve seen a pro-crypto tilt—rescinding anti-custody policies, establishing a Crypto Task Force, and explicitly banning CBDCs.

The Future Is Multi-Token, Multi-Strategy

Digital assets aren’t one-size-fits-all. Institutional investors are moving beyond Bitcoin and Ethereum into DeFi tokens, gaming assets, and stablecoins. That’s not diversification for its own sake—it’s strategy.

Final Thoughts

This isn’t a post about getting rich. It’s about getting ready. Digital assets are here to stay. They’re volatile, yes. They’re complex, absolutely. But they also represent one of the most transformative shifts in the financial landscape since the creation of the internet.

References

Disclaimer:
This content is provided for informational and research purposes only. It does not constitute financial, investment, legal, or tax advice. I am not a licensed financial advisor, and nothing in this document should be interpreted as a recommendation to buy, sell, or hold any financial instrument or pursue any specific strategy. Always consult a qualified financial professional before making any financial decisions.

Inversion Thinking: Solving Backward to Live Forward

May 21, 2025 / lbhuston / Leave a comment

I’ve always been a fan of breaking things down to figure out how they work—sometimes that means disassembling old electronics, other times it means turning a question on its head. That’s where inversion comes in.

InversionThinking

Inversion is this strange, elegant mental model—popularized by Charlie Munger but rooted in the mathematical mind of Carl Jacobi—built around a simple idea: if you want to solve something, try solving the opposite. Don’t just ask, “How can I succeed?” Ask, “How might I fail?” Then avoid those failures.

This flipped way of thinking has helped me untangle everything from tricky team dynamics to gnarly security architecture. It’s not magic. It’s just honest thinking. And it’s surprisingly useful—in life and cybersecurity.

Everyday Life: Living by Avoiding the Dumb Stuff

In personal productivity, inversion’s like having a brutally honest friend. Don’t ask how to be productive—ask what makes you waste time. Suddenly you’re cancelling useless meetings, setting agendas, trimming the invite list. It’s not about optimizing your calendar, it’s about not being a dumbass with your calendar.

When it comes to tasks, the question isn’t “How do I get more done?” but “What distracts me?” Turns out, for me, it’s that one open browser tab I swear I’ll close later. Close it now.

Even wellness gets better when you flip the lens. Don’t chase the best workout plan—just ask “Why do I skip the gym?” Too far away, crappy equipment, bad timing. Fix those.

Same with food. I stopped keeping junk in plain sight. I eat better now, not because I have more willpower, but because I don’t trip over the Oreos every time I pass the kitchen.

Inversion also made me rethink how I spend money. Don’t ask “How do I save more?” Ask “What makes me blow cash unnecessarily?” That late-night Amazon scroll? Canceled. That gym membership I never use? Gone.

Relationships: Avoiding Trust Bombs

In relationships—especially the ones you care about—you want to build trust. But instead of obsessing over how to build it, ask “What destroys trust?” Lying. Inconsistency. Oversharing someone’s private stuff. Don’t do those things.

Want better communication? Don’t start with strategies. Just stop interrupting, assuming, or trying to fix everything when people just want to be heard.

Cybersecurity: Think Like the Adversary

Now let’s pivot to my day job: security. Inversion is baked into the best security thinking. It’s how I do architecture reviews: don’t ask, “Is this secure?” Ask, “If I were going to break this, how would I do it?”

It’s how I approach resource planning: “What failure would hurt us the most?” Not “Where should we invest?” The pain points reveal your priorities.

Even in incident response, I run pre-mortems: “Let’s assume this defense fails—what went wrong?” It’s bleak, but effective.

Want to design better user behavior? Don’t pile on password rules. Ask “What makes users work around them?” Then fix the root causes. If people hate your training, ask why. Then stop doing the thing that makes them hate it.

The Big Idea: Don’t Try to Be Smart. Just Don’t Be Stupid.

“It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.” — Charlie Munger

We don’t need to be clever all the time. We need to stop sabotaging ourselves.

Inversion helps you see the hidden traps. It doesn’t promise easy answers, but it gives you better questions. And sometimes, asking the right wrong question is the smartest thing you can do.

Would love to hear how you’ve used inversion in your own life or work. Leave a note or shoot me an email. Always curious how others are flipping the script.

* 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.

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.

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.

The Entourage Effect in Cybersecurity and Life: Amplifying Results with Minimal Effort

September 25, 2024 / lbhuston / Leave a comment

In the world of cybersecurity, business, and even personal growth, we’re often told to focus on the few things that drive the majority of outcomes. The Pareto Principle, or the “80/20 rule,” is often cited as the key to efficiency: 20% of inputs will lead to 80% of results. But what about the remaining 80% of factors that don’t seem to hold the same weight? Is it wise to ignore them entirely, or is there a way to harness them strategically?

DefenseInDepth

In my experience, both in cybersecurity and life, I’ve found that while the core interventions drive most results, there’s power in layering smaller, easy-to-implement actions around these key elements. I call this the entourage effect: by combining secondary controls or interventions that may not be game-changers by themselves, we amplify the success of the critical 20%.

Deconstructing Problems and Applying Pareto

At the heart of my approach is first principles thinking. I break down a problem to its most fundamental components and from there, apply the Pareto Principle to find the highest-impact solutions. This is typically straightforward once the problem is deconstructed: the core 20% emerges naturally, whether it’s in optimizing cybersecurity systems, designing business processes, or improving personal routines like fitness recovery.

For instance, in my workout recovery routine, the 20% that delivers 80% of the results is clear: sleep optimization and hydration. These are the most critical factors, requiring focus and discipline. However, it doesn’t stop there.

The Entourage Effect: Supporting and Amplifying Results

The next step is where the entourage effect comes into play. Once I’ve identified the big drivers, I start looking at the remaining 80% of possible interventions. I evaluate them based on two simple criteria:

Ease of implementation
Potential for return

If a smaller action is easy to integrate, has minimal downside, and can offer any form of return—whether it’s amplifying the main effort or providing an incremental improvement—it gets added to my solution set. In the case of workout recovery, these might include cold exposure, hot tub or sauna use, consuming turmeric, or simple massage. These steps don’t require much time, focus, or resources. They can be done passively or alongside other activities throughout my day.

By adding these smaller steps, I’m essentially surrounding the big actions with a layer of support, making it easier to achieve the overall goal—recovery, in this case—even on days when I’m not at my best.

Applying the Entourage Effect in Cybersecurity

In cybersecurity, the same logic applies. The Pareto control for many systems is strong authentication. But in the real world, focusing solely on one control leaves room for exploitation in unexpected ways. This is where compensating controls, or secondary measures, come in—defense in depth, as we often call it.

Take authentication. The “Pareto” 20% is clear: a solid, multi-factor authentication system. But smaller compensating controls such as honeypots, event thresholding, or additional prevention and detection mechanisms around attack surfaces add extra layers of security. These controls may not block every attack, but they can amplify the core defense by alerting you early or deterring certain threat actors.

Much like the entourage effect in personal routines, these smaller cybersecurity controls don’t require large resources or attention. Their purpose is to amplify the main defense, providing that extra buffer against potential threats.

Knowing When to Stop

However, it’s equally important to know when to stop. Not everything needs to be 100% optimized. Sometimes the 80% solution is good enough, depending on the risk appetite of the individual or organization. I make decisions based on the resource-to-return ratio: if a secondary intervention takes too much effort for a minimal return, I skip it.

Ultimately, the decision to add or ignore smaller actions comes down to practicality. Does this smaller step cost more in time, resources, or complexity than it delivers? If yes, I leave it out. But if it’s low effort and provides even a small return, it becomes part of the system.

Conclusion: Leveraging the Entourage Effect for Efficiency

The entourage effect, when layered on top of Pareto’s principle, helps drive sustained success. By focusing on the 20% that matters most while strategically adding easy, low-cost interventions around it, we create a system that works even when resources are low or attention is divided. Whether it’s in cybersecurity, business, or personal growth, understanding how to build a system that amplifies its own core interventions is key to both efficiency and resilience.

As with all things, balance is crucial. Overloading your system with unnecessary layers can lead to diminishing returns, but if done right, these secondary measures become a powerful way to enhance the performance of your core efforts.

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