productivity

The Pyramid I Operate From

/ lbhuston / Leave a comment

Over the years I’ve come to realize that the way I operate—both in business and in life—can be visualized as a pyramid.

At the top are mental models. Beneath those sit the systems that operationalize those models. And forming the foundation are the tools that allow those systems to run efficiently and, when possible, automatically.

The pyramid matters because it enforces something simple but powerful:

Tools should never drive thinking. Thinking should drive systems, and systems should determine the tools.

Too often organizations start with tools and hope good outcomes emerge. I prefer the opposite approach.

ChatGPT Image Mar 11 2026 at 11 35 04 AM

The Top Layer: Mental Models

The top of the pyramid is the smallest but most important layer. These are the mental models that shape how I interpret problems, make decisions, and allocate effort.

I first encountered many of these ideas through Charlie Munger and then spent more than thirty years collecting, testing, and refining them through experience.

Some of the models that influence how I operate include:

  • First-principles thinking

  • Pareto optimization (80/20)

  • The entourage effect

  • Inversion

  • Compounding

  • Second- and third-order thinking

  • The Five Whys root cause analysis

  • Risk = Probability × Impact (and sometimes × Novelty, borrowing from Taleb)

  • Creating more value than I harvest

Together these form what Munger described as a latticework of mental models.

They influence everything I do—from cybersecurity architecture to business strategy to personal productivity.

Mental models are powerful because they allow you to reason from principles rather than reacting to symptoms.

But by themselves they are abstract.

Which brings us to the second layer.

The Second Layer: Systems

Mental models shape thinking.
Systems turn that thinking into repeatable behavior.

Over time I’ve developed several systems that embody the mental models above.

TaskGrid

One of the most important is a task and project management system I built called TaskGrid.

It’s based loosely on the Eisenhower Matrix, but evolved into something closer to a personal operations dashboard across the planes of my life.

Each day TaskGrid tracks three types of activity:

  • Things I must do

  • Things I should do

  • Things I want to do

The system keeps me focused on high-value tasks while also revealing patterns where urgency and importance diverge.

One unexpected benefit is psychological.

TaskGrid signals when the day is finished.

When the items on the grid are complete, my brain gets a clear signal that it’s time to stop working and return to full optionality—the freedom to explore, learn, or simply disengage.

That boundary is incredibly valuable.

AI-Driven Knowledge Distillation

Another system focuses on information analysis.

The modern information environment produces far more content than any human can realistically process. Yet buried inside that flood are small amounts of extremely valuable insight.

To deal with that, I use AI to analyze large volumes of articles, research, and news.

But the goal isn’t just summarization.

The goal is to apply models like Pareto, inversion, and second-order thinking to extract the few ideas that actually matter.

Often the most valuable insights are the ones that are uncommon, overlooked, or hidden inside noise.

AI helps surface those signals.

Risk Analysis Systems

Risk has always been central to my work in cybersecurity, but I apply the same thinking more broadly.

Over the years I’ve built systems—initially using traditional analytics and now increasingly using AI—that monitor and evaluate risk across multiple areas:

  • Information security

  • Financial decisions

  • Business operations

  • Personal life decisions

These systems analyze probability, impact, and occasionally novelty to produce actionable insights rather than just dashboards.

The goal is simple: better decisions under uncertainty.

The Foundation: Tools

At the base of the pyramid are the tools.

Tools are important, but they are also the least important layer conceptually.

They exist to support systems—not the other way around.

I primarily operate within the Apple ecosystem, using multiple devices that are often configured for specific types of work such as AI experimentation, automation, research, or communication.

One principle I try to enforce aggressively is asynchronous operation.

Optionality disappears when your time is constantly interrupted.

So I try to push as much of life and business into asynchronous workflows as possible.

That includes things like:

  • Automated scheduling and calendar management

  • Routing unscheduled calls to voicemail that becomes email

  • Automated email management that surfaces only meaningful messages

  • Time-boxing tasks, research, and projects on my calendar

In many ways, I live and die by my calendar.

Both local AI and cloud AI have also become central tools in this layer. They help automate routine work, accelerate learning, and simplify repetitive tasks.

But automation itself requires judgment.

To help decide what should and should not be automated, I rely on a framework I developed called FRICT, which I described previously on notquiterandom.com.

FRICT helps identify tasks that benefit from automation while protecting areas where human judgment still matters.

Why the Pyramid Matters

Many organizations invert this pyramid.

They start with tools, bolt on processes, and hope good decisions emerge.

But tools alone rarely create good outcomes.

Instead, I think it works better in this order:

Mental Models → Systems → Tools

Start with the models that shape how you think.

Build systems that embody those models.

Then choose tools that make those systems easier, faster, and more automated.

When the layers align, something interesting happens.

Complexity decreases.
Optionality increases.
Decisions improve.

And over time, the entire structure begins to compound.

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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.

Hybrid Work, Cognitive Fragmentation, and the Rise of Flow‑Design

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Context: Why hybrid work isn’t just a convenience

Hybrid work isn’t a fringe experiment anymore — it’s quickly becoming the baseline. A 2024–25 survey in the U.S. shows that 52% of employees whose jobs can be remote work in a hybrid mode, and another 27% are fully remote.

Other recent studies reinforce the upsides: hybrid arrangements often deliver similar productivity and career‑advancement outcomes as fully on-site roles, while improving employee retention and satisfaction.

Redheadcoffee

In short: hybrid work is now normal — and that normalization brings new challenges that go beyond “working from home vs. office.”

The Hidden Cost: Cognitive Fragmentation as an Engineering Problem

When organizations shift to hybrid work, they often celebrate autonomy, flexibility, and freedom from commutes. What gets less attention is how hybrid systems — built around multiple apps, asynchronous communication, decentralized teams, shifting time zones — cause constant context switching.

  • Each time we jump from an email thread to a project board, then to a chat, then to a doc — that’s not just a change in window or tab. It is a mental task switch.

  • Such switches can consume as much as 40% of productive time.

  • Beyond lost time, there’s a deeper toll: the phenomenon of “attention residue.” That’s when remnants of the previous task linger in your mind, degrading focus and decreasing performance on the current task — especially harmful for cognitively demanding or creative work.

If we think about hybrid work as an engineered system, context switching is a kind of “friction” — not in code or infrastructure, but in human attention. And like any engineering problem, friction can — and should — be minimized.

Second‑Order Effects: Why Cognitive Fragmentation Matters

Cognitive fragmentation doesn’t just reduce throughput or add stress. Its effects ripple deeper, with impacts on:

  • Quality of output: When attention is fragmented, even small tasks suffer. Mistakes creep in, thoughtfulness erodes, and deep work becomes rare.

  • Long-term mental fatigue and burnout: Constant switching wears down cognitive reserves. It’s no longer just “too much work,” but “too many contexts” demanding attention.

  • Team performance and morale: At the organizational level, teams that minimize context switching report stronger morale, better retention, and fewer “after‑hours” overloads.

  • Loss of strategic thinking and flow states: When individuals rarely stay in one mental context long enough, opportunities for deep reflection, creative thinking, or coherent planning erode.

In short, hybrid work doesn’t just shift “where” work happens — it fundamentally alters how work happens.

Why Current Solutions Fall Short

There are many popular “help me focus” strategies:

  • The classic — Pomodoro Technique / “deep work” blocks / browser blockers.

  • Calendar-based time blocking to carve out uninterrupted hours.

  • Productivity suites: project/task trackers like Asana, Notion, Linear and other collaboration tools — designed to organize work across contexts.

And yet — these often treat only the symptoms, not the underlying architecture of distraction. What’s missing is a system‑level guidance on:

  • Mapping cognitive load across workflow architecture (not just “my calendar,” but “how many systems/platforms/contexts am I juggling?”).

  • Designing environments (digital and physical) that reduce cross‑system interference instead of piling more tools.

  • Considering second‑ and third‑order consequences — not just “did I get tasks done?” but “did I preserve attention capacity, quality, and mental energy?”

In other words: we lack a rationalist, engineered approach to hybrid‑work life hacking.

Toward Flow‑Preserving Systems: A Pareto Model of Attention

If we treat attention as a finite resource — and work systems as pipelines — then hybrid work demands more than discipline: it demands architecture. Here’s a framework rooted in the 80/20 (Pareto) principle and “flow‑preserving design.”

1. Identify your “attention vector” — where does your attention go?

List the systems, tools, communication modes, and contexts you interact with daily. How many platforms? How many distinct contexts (e.g., team A chat, team B ticket board, email, docs, meetings)? Rank them by frequency and friction.

2. Cull ruthlessly. Apply the 80/20 test to contexts:

Which 20% of contexts produce 80% of meaningful value? Those deserve high-bandwidth attention and uninterrupted time. Everything else — low‑value, context‑switch‑heavy noise — may be candidates for elimination, batching, or delegation.

3. Build “flow windows,” not just “focus zones.”

Rather than hoping “deep work days” will save you, build structural constraints: e.g., merge related contexts (use fewer overlapping tools), group similar tasks, minimize simultaneous cross-team demands, push meetings into consolidated blocks, silence cross‑context notifications when in flow windows.

4. Design both digital and physical environments for flow.

Digital: reduce number of apps, unify communications, use integrated platforms intelligently.
Physical: fight “always on” posture — treat work zones as environments with their own constraints.

5. Monitor second‑order effects.

Track not just output quantity, but quality, mental fatigue, clarity, creativity, and subjective well‑being. Use “collaboration analytics” if available (e.g., data on meeting load, communication frequency) to understand when fragmentation creeps up.

Conclusion: Hybrid Work Needs More Than Tools — It Needs Architecture

Hybrid work is now the baseline for millions of professionals. But with that shift comes a subtle and pervasive risk: cognitive fragmentation. Like a system under high load without proper caching or resource pooling, our brains start thrashing — switching, reloading, groggy, inefficient.

We can fight that not (only) through willpower, but through design. Treat your mental bandwidth as a resource. Treat hybrid work as an engineered system. Apply Pareto-style pruning. Consolidate contexts. Build flow‑preserving constraints. Track not just tasks — but cognitive load, quality, and fatigue.

If done intentionally, you might discover that hybrid work doesn’t just offer flexibility — it offers the potential for deeper focus, higher quality, and less mental burnout.

References

  1. Great Place to Work, Remote Work Productivity Study: greatplacetowork.com

  2. Stanford University Research on Hybrid Work: news.stanford.edu

  3. Reclaim.ai on Context Switching: reclaim.ai

  4. Conclude.io on Context Switching and Productivity Loss: conclude.io

  5. Software.com DevOps Guide: software.com

  6. BasicOps on Context Switching Impact: basicops.com

  7. RSIS International Study on Collaboration Analytics: rsisinternational.org

Support My Work

If this post resonated with you, and you’d like to support further writing like this — analyses of digital work, cognition, and designing for flow — consider buying me a coffee: 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.

System Hacking Your Tech Career: From Surviving to Thriving Amid Automation

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There I was, halfway through a Monday that felt like déjà-vu: a calendar packed with back-to-back video calls, an inbox expanding in real-time, a new AI-tool pilot landing without warning, and a growing sense that the workflows I’d honed over years were quietly becoming obsolete. As a tech advisor accustomed to making rational, evidence-based decisions, it hit me that the same forces transforming my clients’ operations—AI, hybrid work, and automation—were rapidly reshaping my own career architecture.

WorkingWithRobot1

The shift is no longer theoretical. Hybrid work is now a structural expectation across the tech industry. AI tools have moved from “experimental curiosity” to “baseline requirement.” Client expectations are accelerating, not stabilising. For rational professionals who have always relied on clarity, systems, and repeatable processes, this era can feel like a constant game of catch-up.

But the problem isn’t the pace of change. It’s the lack of a system for navigating it.
That’s where life-hacking your tech career becomes essential: clear thinking, deliberate tooling, and habits that generate leverage instead of exhaustion.

Problem Statement

The Changing Landscape: Hybrid Work, AI, and the Referral Economy

Hybrid work is now the dominant operating model for many organisations, and the debate has shifted from “whether it works” to “how to optimise it.” Tech advisors, consultants, and rational professionals must now operate across asynchronous channels, distributed teams, and multiple modes of presence.

Meanwhile, AI tools are no longer optional. They’ve become embedded in daily workflows—from research and summarisation to code support, writing, data analysis, and client-facing preparation. They reduce friction and remove repetitive tasks, but only if used strategically rather than reactively.

The referral economy completes the shift. Reputation, responsiveness, and adaptability now outweigh tenure and static portfolios. The professionals who win are those who can evolve quickly and apply insight where others rely on old playbooks.

Key Threats

  • Skills Obsolescence: Technical and advisory skills age faster than ever. The shelf life of “expertise” is shrinking.

  • Distraction & Overload: Hybrid environments introduce more communication channels, more noise, and more context-switching.

  • Burnout Risk: Without boundaries, remote and hybrid work can quietly become “always-on.”

  • Misalignment: Many professionals drift into reactive cycles—meetings, inboxes, escalations—rather than strategic, high-impact advisory work.

Gaps in Existing Advice

Most productivity guidance is generic: “time-block better,” “take breaks,” “use tools.”
Very little addresses the specific operating environment of high-impact tech advisors:

  • complex client ecosystems

  • constant learning demands

  • hybrid workflows

  • and the increasing presence of AI as a collaborator

Even less addresses how to build a future-resilient career using rational decision-making and system-thinking.

Life-Hack Framework: The Three Pillars

To build a durable, adaptive, and high-leverage tech career, focus on three pillars: Mindset, Tools, and Habits.
These form a simple but powerful “tech advisor life-hack canvas.”

Pillar 1: Mindset

Why It Matters

Tools evolve. Environments shift. But your approach to learning and problem-solving is the invariant that keeps you ahead.

Core Ideas

  • Adaptability as a professional baseline

  • First-principles thinking for problem framing and value creation

  • Continuous learning as an embedded part of your work week

Actions

  • Weekly Meta-Review: 30 minutes every Friday to reflect on what changed and what needs to change next.

  • Skills Radar: A running list of emerging tools and skills with one shallow-dive each week.

Pillar 2: Tools

Why It Matters

The right tools amplify your cognition. The wrong ones drown you.

Core Ideas

  • Use AI as a partner, not a replacement or a distraction.

  • Invest in remote/hybrid infrastructure that supports clarity and high-signal communication.

  • Treat knowledge-management as career-management—capture insights, patterns, and client learning.

Actions

  • Build your Career Tool-Stack (AI assistant, meeting-summary tool, personal wiki, task manager).

  • Automate at least one repetitive task this month.

  • Conduct a monthly tool-prune to remove anything that adds friction.

Pillar 3: Habits

Why It Matters

Even the best system collapses without consistent execution. Habits translate potential into results.

Core Ideas

  • Deep-work time-blocking that protects high-value thinking

  • Energy management rather than pure time management

  • Boundary-setting in hybrid/remote environments

  • Reflection loops that keep the system aligned

Actions

  • A simple morning ritual: priority review + 5-minute journal.

  • A daily done list to reinforce progress.

  • A consistent weekly review to adjust tools, goals, and focus.

  • A quarterly career sprint: one theme, three skills, one major output.

Implementation: 30-Day Ramp-Up Plan

Week 1

  • Map a one-year vision of your advisory role.

  • Pick one AI tool and integrate it into your workflow.

  • Start the morning ritual and daily “done list.”

Week 2

  • Build your skills radar in your personal wiki.

  • Audit your tool-stack; remove at least one distraction.

  • Protect two deep-work sessions this week.

Week 3

  • Revisit your vision and refine it.

  • Automate one repetitive task using an AI-based workflow.

  • Practice a clear boundary for end-of-day shutdown.

Week 4

  • Reflect on gains and friction.

  • Establish your knowledge-management schema.

  • Identify your first 90-day career sprint.

Example Profiles

Advisor A – The Adaptive Professional

An advisor who aggressively integrated AI tools freed multiple hours weekly by automating summaries, research, and documentation. That reclaimed time became strategic insight time. Within six months, they delivered more impactful client work and increased referrals.

Advisor B – The Old-Model Technician

An advisor who relied solely on traditional methods stayed reactive, fatigued, and mismatched to client expectations. While capable, they couldn’t scale insight or respond to emerging needs. The gap widened month after month until they were forced into a reactive job search.

Next Steps

  • Commit to one meaningful habit from the pillars above.

  • Use the 30-day plan to stabilise your system.

  • Download and use a life-hack canvas to define your personal Mindset, Tools, and Habits.

  • Stay alert to new signals—AI-mediated workflows, hybrid advisory models, and emerging skill-stacks are already reshaping the next decade.

Support My Work

If you want to support ongoing writing, research, and experimentation, you can do so here:
https://buymeacoffee.com/lbhuston

References

  1. Tech industry reporting on hybrid-work productivity trends (2025).

  2. Productivity research on context switching, overload, and hybrid-team dysfunction (2025).

  3. AI-tool adoption studies and practitioner guides (2024–2025).

  4. Lifecycle analyses of hybrid software teams and distributed workflows (2023–2025).

  5. Continuous learning and skill-half-life research in technical professions (2024–2025).

 

* 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.

Introducing The Workday Effectiveness Index

/ lbhuston / Leave a comment

Introduction:

I recently wrote about building systems for your worst days here. 

J0309621

That got me thinking that I need a system to measure how my systems and optimizations are performing on my worst (and average days for that matter) days. Thus: 

WDEI: Workday Effectiveness Index

What it is:

A quick metric for packed days so you know if your systems are carrying you or if there’s a bottleneck to fix.

Formula:

WDEI = (top‑leverage tasks completed ÷ top‑leverage tasks planned) × (focused minutes ÷ available “maker” minutes)

How to use (2‑minute setup):

Define top‑leverage tasks (3 max for the day).

Estimate maker minutes (non‑meeting, potentially focusable time).

Log focused minutes (actual deep‑work blocks ≥15 min, no context switches).

Compute WDEI at day end.

Interpretation:

≥ 0.60 → Systems working; keep current routines.

0.40–0.59 → Friction; tune meeting hygiene, buffers, or task slicing.

< 0.40 → Bottleneck; fix in the next weekly review (reprioritize, delegate, or automate).

Example (fast math):

Planned top‑leverage tasks: 3; completed: 2 → 2/3 = 0.67

Maker minutes: 90; focused minutes: 55 → 55/90 = 0.61

WDEI = 0.67 × 0.61 = 0.41 → bottleneck detected

Common fixes (pick one):

Reduce same‑day commitment: drop to 1–2 top‑leverage tasks on heavy days.

Pre‑build micro‑blocks: 3×20 min protected focus slots.

Convert meetings → async briefs; bundle decisions.

Pre‑stage work: checklist, files open, first keystroke defined.

Tiny tracker (copy/paste):

Date: __

TL planned: __ | TL done: __ | TL ratio: __

Maker min: __ | Focused min: __ | Focus ratio: __

WDEI = __ × __ = __

One friction to remove tomorrow: __

Support My Work:

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.