productivity

The Pyramid I Operate From

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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

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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.

How to Hack Your Daily Tech Workflow with AI Agents

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Imagine walking into your home office on a bright Monday morning. The coffee’s fresh, you’re seated, and before you even open your inbox, your workflow looks something like this: your AI agent has already sorted your calendar for the week, flagged three high‑priority tasks tied to your quarterly goals, summarised overnight emails into bite‑sized actionable items, and queued up relevant research for the meeting you’ll give later today. You haven’t done anything yet — but you’re ahead. You’ve shifted from reactive mode (how many times did I just chase tasks yesterday?) to proactive, future‑ready mode.

If that sounds like science fiction, it’s not. It’s very much within reach for professionals who are willing to treat their daily tech workflow as a system to hack — intentionally, strategically, and purposefully.

A digital image of a brain thinking 4684455

1. The Problem: From Tech‑Overload to Productivity Guilt

In the world of tech and advisory work, many of us are drowning in tools. Think of the endless stream: new AI agents cropping up, automation platforms promising to “save” your day, identity platforms, calendar integrations, chatbots, copilots, dashboards, the list goes on. And while each is pitched as helping, what often happens instead is: we adopt them in patches, they sit unused or under‑used, and we feel guilt or frustration. Because we know we should be more efficient, more futuristic, but instead we feel sloppy, behind, reactive.

A recent report from McKinsey & Company, “Superagency in the workplace: Empowering people to unlock AI’s full potential”, notes that while most companies are investing in AI, only around 1 % believe they have truly matured in embedding it into workflows and driving meaningful business outcomes. McKinsey & Company Meanwhile, Deloitte’s research shows that agentic AI — systems that act, not just generate — are already being explored at scale, with 26 % of organisations saying they are deploying them in a large way.

What does this mean for you as a professional? It means if you’re not adapting your workflow now, you’ll likely fall behind—not just in your work, but in your ability to stay credible as a tech advisor, consultant, or even just a sharp individual contributor in a knowledge‑work world.

What are people trying today? Sure: adopting generic productivity tools (task managers, calendar automation), experimenting with AI copilots (e.g., chat + summarisation), outsourcing/virtual assistants. But many of these efforts miss the point. They don’t integrate into your context, they don’t align with your habits and goals, and they lack the future‑readiness mindset needed to keep pace with agentic AI and rapid tool evolution.

Hence the opportunity: design a workflow that isn’t just “tool‑driven” but you‑driven, one built on systems thinking, aligning emerging tech with personal habits and long‑term readiness.

2. Emerging Forces: What’s Driving the Change

Before we jump into the how, it’s worth pausing on why the shift matters now.

Agentic AI & moving from “assist” → “act”

As McKinsey argues in Why agents are the next frontier of generative AI, we’re moving beyond “knowledge‑based tools” (chatbots, content generation) into “agentic systems” — AI that plans, acts, co‑ordinates workflows, even learns over time. McKinsey & Company

Deloitte adds that multi‑agent systems (role‑specific cooperating agents) are already implemented in organisations to streamline complex workflows, collaborate with humans, and validate outputs. 

In short: the tools you hire today as “assistants” will become tomorrow’s colleagues (digital ones). Your workflow needs to evolve accordingly.

Remote / Hybrid Work & Life‑Hacking

With remote and hybrid work the norm, the boundary between work and life is blurrier than ever. Home offices, irregular schedules, distributed teams — all require a workflow that’s not rigid but modular, adaptive, and technology‑aligned. The professionals who thrive aren’t just good at meetings — they’re good at systems. They apply process‑thinking to their personal productivity, workspace, and tech stack.

Process optimisation & systems thinking

The “workflow” you use at work is not unlike the one you could use at home — it’s a system: inputs, processes, outputs. When you apply systems thinking, you treat your email, meetings, research, client‑interaction, personal time as parts of one interconnected ecosystem. When tech (AI/automation) enters, you optimise the system, not just the tool.

These trends intersect at a sweet spot for tech advisors, consultants, professionals who must not only advise clients but advise themselves — staying ahead of tool adoption, improving their own workflows, and thereby modelling future‑readiness.

3. A Workflow Framework: 4 Steps to Future‑Readiness

Here’s a practical, repeatable framework you can use to hack your tech workflow:

3.1 Audit & Map Your Current Workflow

  • Track your tasks for one week: Use a simple time‑block tool (Excel, Notion, whatever) to log what you actually do — meetings, email triage, research, admin, client work, personal time.

  • Identify bottlenecks & waste: Which tasks feel reactive? Which take more time than they should? Which generate low value relative to effort?

  • Set goals for freed time: If you can reclaim 1‑2 hours per day, what would you do? Client advisory? Deep work? Strategic planning?

  • Visualise the flow: Map out (on paper or digitally) how work moves from “incoming” (email, Slack, calls) → “processing” → “action” → “outcome”. This becomes your baseline.

Transition: Now that you’ve mapped how you currently work, you can move to where to plug in the automation and agentic tools.

3.2 Identify High‑Leverage Automation Opportunities

  • Recurring and low‑context tasks: calendar scheduling, meeting prep, note‑taking, email triage, follow‑ups. These are automation ripe.

  • Research and summarisation: you gather client or industry research — could an AI agent pre‑read, summarise, flag key insights ahead of you?

  • Meeting workflows: prep → run → recap → action items. Automate the recap and task creation.

  • Client‑advisory prep: build macros or agents that gather relevant data, compile slide decks, pull competitor info, etc.

  • Personal life integration: tech‑stack maintenance, home‑office scheduling, recurring tasks (bills, planning). Yes – this matters if you work at home.

Your job: pick 2‑3 high‑leverage tasks this quarter that if optimised will free meaningful time + mental bandwidth.

3.3 Build Your Personal “Agent Stack”

  • Pick 1‑2 AI tools initially — don’t try to overhaul everything at once. For example: a generative‑AI summarisation tool + a calendar automation tool.

  • Integrate with workflow: For instance, connect email → agent → summary → task manager. Or calendar invites → agent → prep doc → meeting.

  • Set guardrails: As with any tech, you need boundaries: agent output reviewed, human override, security/privacy considerations. The Deloitte report emphasises safe deployment of agentic systems.

  • Habit‑build the stack: You’re not just installing tools – you’re building habits. Schedule agent‑reviews, prompts, automation checks. For example: “Every Friday 4 pm – agent notes review + next‑week calendar check.”

  • Example mini‑stack:

    • Agent A: email summariser (runs at 08:00, sends you 5‑line summary of overnight threads)

    • Agent B: calendar scheduler (looks for open blocks, auto‑schedules buffer time and prep time)

    • Agent C: meeting‑recap (after each invite, automatically records in notes tool, flags action items).
      *Balance: human + agent = hybrid system. Because the best outcomes happen when you treat the agent as a co‑worker, not a replacement.

3.4 Embed a Review & Adapt Loop

  • Monthly review: At month end, ask: Did the tools free time? Did I use it for higher‑value work? What still resisted automation?

  • Update prompts/scripts: As the tools evolve (and they will fast), your agents’ prompts must also evolve. Refinement is part of the system.

  • Feedback loop: If an agent made an error, log it. Build a “lessons‑learned” mini‑archive.

  • Adapt to tool‑change: Because tech changes fast. Tomorrow’s AI agent will be more capable than today’s. So design your system to be modular and adaptable.

  • Accountability: Share your monthly review with a peer, your team, or publicly (if you’re comfortable). It increases rigour.

Transition: With the framework set, let’s move into specific steps to implement and a real‑world example to bring things alive.

4. Implementation: Step‑by‑Step

Here’s how you roll it out over the next 4–6 weeks.

Week 1

  • Log your tasks for 5 working days. Note durations, context, tool‑used, effort rating (1‑5).

  • Map the “incoming → processing → action” flow in your favourite tool (paper, Miro, Notion).

  • Choose your goal for freed time (e.g., “Reclaim 1 hour/day to focus on strategic client work”).

Week 2

  • Identify 3 high‑leverage tasks from your map. Prioritise by potential time saved + value increase.

  • Choose two tools/agent‑apps you will adopt (or adapt). Example: Notion + Zapier + GPT‑based summariser.

  • Build a simple workflow — e.g., email to summariser to task manager.

Week 3

  • Install/integrate tools. Create initial prompts or automation rules. Set calendar buffer time, schedule weekly review slot.

  • Test in “pilot” mode for the rest of the week: review results each evening, note errors or friction points.

Week 4

  • Deploy full. Make it real. Use the automation/agent workflows from Monday. At week end, schedule your review for next month.

  • Add the habit of “Friday at 4 pm: review next week’s automation stack + adjust”.

Week 5+

  • Monthly retrospective: What worked? What didn’t? What agent prompt needs tweaking? What task still manual?

  • Update workflow map if necessary and pick 1 new tasks to automate next quarter.

5. Example Case Study

Meet “Alex”, a tech‑consultant working in an advisory firm. Alex found himself buried: 40 % of his day spent prepping for client meetings (slide decks, research), 30 % in internal meetings, 20 % in email/Slack triage, only 10 % in client‑advisory deep work. He felt stuck.

Here’s how he applied the framework:

  • Audit & Map: Over 1 week he logged tasks — confirmed the 40/30/20/10 breakdown. He chose client‑advisory impact as his goal.

  • High‑Leverage Tasks: He picked: (1) meeting‑prep research + deck creation; (2) email triage.

  • Agent Stack:

    • Agent A: receives meeting‑invite, pulls project history, recent slides, latest research, produces a 1‑page summary + recommend structure for the next deck.

    • Agent B: runs each morning 08:00, summarises overnight email into “urgent/action” vs “read later”.

  • Review Loop: Each Friday 3 pm he reviews how much time freed, and logs any missed automation opportunities or errors.

Outcome: Within 3 months, Alex reported his meeting‑prep time dropped by ~30 % (from 4 hours/week to ~2.8 hours/week), email triage slashed by ~20 %, and his “deep client advisory” time moved from 10 % to ~18 % of his day. Just as importantly, his mindset shifted: he stopped feeling behind and started feeling ahead. He now advises his clients not only on tech strategy but on his own personal tech workflow.

6. Next Steps: Your Checklist

Here’s your launch‑pad checklist – print it, paste it, or park it in Notion.

  •  Log my tasks for one week (incoming→processing→action).

  •  Map my current workflow visually.

  •  Set a “freed‑time” goal (how many hours/week, what for).

  •  Identify 2 high‑leverage tasks to automate this quarter.

  •  Choose 1‑2 tools/agents to adopt and integrate.

  •  Build initial prompts and automation rules.

  •  Schedule weekly habit: Friday, 3‑4 pm – automation review.

  •  Schedule monthly habit: Last Friday – retrospective + next‑step selection.

  •  Share your plan with a peer or public (optional) for accountability.

  •  Reassess in 3 months: how many hours freed? What value gained? What’s next?

Reading / tool suggestions:

  • Read McKinsey’s Why agents are the next frontier of generative AI. McKinsey & Company

  • Browse Deloitte’s How AI agents are reshaping the future of work.

  • Explore productivity tools + Zapier/Make + GPT‑based summarisation (your stack will evolve).

7. Conclusion: From Time‑Starved to Future‑Ready

The world of work is shifting. The era of passive productivity apps is giving way to agentic AI, hybrid human–machine workflows, and systems thinking applied not only to enterprise tech but to your personal tech stack. As professionals, especially those in advisory, consulting, tech or hybrid roles, you can’t just keep adding tools — you must integrate, align, optimize. This is not just about saving minutes; it’s about reclaiming mental space, creative bandwidth, and strategic focus.

When you treat your workflow as a system, when you adopt agents intentionally, when you build habits around review and adaptation, you shift from being reactive to being ready. Ready for whatever the next wave of tech brings. Ready to give higher‑value insight to your clients. Ready to live a life where you work smart, not just hard.

So pick one task this week. Automate it. Start small. Build momentum. Over time, you’ll look back and realise you’ve reclaimed control of your day — instead of your day controlling you.

See you at the leading edge.

 

* 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.

Tool Deep Dive: Mental Models Tracker + AI Insights

/ lbhuston / Leave a comment

The productivity and rational-thinking crowd has long loved mental models. We memorize them. We quote them. We sprinkle them into conversations like intellectual seasoning. But here’s the inconvenient truth: very few of us actually track how we use them. Even fewer build systems to reinforce their practical application in daily life. That gap is where this tool deep dive lands.

MentalModels

The Problem: Theory Without a Feedback Loop

You know First Principles Thinking, Inversion, Opportunity Cost, Hanlon’s Razor, the 80/20 Rule, and the rest. But do you know if you’re actually applying them consistently? Or are they just bouncing around in your head, waiting to be summoned by a Twitter thread?

In an increasingly AI-enabled work landscape, knowing mental models isn’t enough. Systems thinking alone won’t save you. Implementation will.

Why Now: The Implementation Era

AI isn’t just a new toolset. It’s a context shifter. We’re all being asked to think faster, act more strategically, and manage complexity in real-time. It’s not just about understanding systems, but executing decisions with clarity and intention. That means our cognitive infrastructure needs reinforcing.

The Tracker: One Week to Conscious Application

I ran a simple demo: one week, one daily journal template, tracking how mental models showed up (or could have) in real-world decisions.

  • A decision or scenario I encountered
  • Which models I applied (or neglected)
  • The outcome (or projected cost of neglect)
  • Reflections on integration with MATTO

You can download the journal template here.

AI Prompt: Your On-Demand Decision Partner

Here’s the ChatGPT prompt I used daily:

“I’m going to describe a situation I encountered today. I want you to help me analyze it using the following mental models: First Principles, Inversion, Opportunity Cost, Diminishing Returns, Hanlon’s Razor, Parkinson’s Law, Loss Aversion, Switching Costs, Circle of Competence, Regret Minimization, Pareto Principle, and Game Theory. First, tell me which models are most relevant. Then, walk me through how to apply them. Then, ask me reflective questions for journaling.”

Integration with MATTO: Tracking the True Cost

In my journaling system, I use MATTO (Money, Attention, Time, Trust, Opportunity) to score decisions. After a model analysis, I tag entries with their relevant MATTO implications:

  • Did I spend unnecessary attention by failing to invert?
  • Did loss aversion skew my sense of opportunity?
  • Was trust eroded due to ignoring second-order consequences?

Final Thought: Self-Awareness at Scale

We don’t need more models. We need mechanisms.

This is a small experiment in building them. Give it a week. Let your decisions become a training dataset. The clarity you’ll gain might just be the edge you’re looking for.

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