1. Insight 1: Writing down your desired outcome creates the foundation for any good decision.

2. Insight 2: Always have 3+ options. One option removes your agency. Two is a dilemma. Three gives you real freedom.

3. Insight 3: A decision is just the start. Commitment adds energy. Resolve means you’ve made it part of your identity.

Decision making can be stressful. Whether it’s a career move, a team restructure, or choosing the right technical architecture, the weight of “getting it right” can paralyse you. Having a simple process focuses your energy and reduces that stress.

Tony Robbins breaks it down into 6 steps: OOC/EMR.

1. Outcome: What Do You Actually Want?

Clearly stating what you want gives you clarity. This sounds obvious, but most people skip this step. They jump straight into weighing options without defining what success looks like.

If you don’t know where you’re going, no road will get you there.

Write it down. Be specific. This becomes the foundation for everything that follows.

2. Options: Get to Three (Minimum)

This is where most people get stuck. They see two paths and feel trapped.

• One option removes your agency. There’s no choice at all.

• Two options create a dilemma. You’re caught between them, often paralysed.

• Three or more options give you freedom. You can mix and match. You can get creative. You stop feeling trapped and start seeing possibilities.

I’ve seen this in technical decisions too. “Should we use microservices or monolith?” is a dilemma. Add a third option, maybe a modular monolith, and suddenly you’re thinking differently and creatively about where domain boundaries should be.

3. Consequences: Upsides and Downsides

For each option, map out both the upsides and downsides. Be honest about both.

This step forces you to think through what could go right and what could go wrong. It’s easy to romanticise an option or catastrophise another. Writing it out keeps you grounded.

4. Evaluate: Separate Likely from Fantasy

Here’s where you filter out the noise.

“Don’t stress about low-likelihood disasters, or fantasize about near-zero upsides.”

That worst-case scenario you’re terrified of? How likely is it really? That dream outcome you’re banking on? What’s the actual probability?

Focus on what’s probable, not what’s possible.

5. Mitigate: Reduce the Downside

Now you know what could go wrong. How can you reduce it?

Can you de-risk the decision? Can you run a smaller experiment first? Can you build in a safety net?

This step turns scary decisions into manageable ones.

6. Resolve: The Difference Between Deciding and Being Done

There are three levels:

• Decision is a choice in the mind. It’s still unstable. It can feel like internal conflict.

• Commitment adds energy and action. You’re investing effort to make it real. This is where discipline matters.

• Resolve is identity-level certainty. “I’ll find a way or I’ll make a way.” No fear, no second-guessing. You’ve ended the internal war.

He compares it to elite athletes. You can see when someone lacks resolve before the shot even happens.

Ask yourself: Am I just decided? Am I committed? Or am I truly resolved?

A simple 6-step process can reduce decision-making stress, give you clarity about your goal, and build confidence that you’ve made a well-thought-out decision.

I’ve put together a one-page PDF worksheet based on this framework.

Download it here…

Improve Your Decision Making

3.03KB ∙ PDF file

Download

Download

For the official Tony Robbins interactive tool, check out https://decisionmaker.tonyrobbins.com/

Have a great week.

It’s very easy to get in the trap of feeling you have an infinite to-do list. It’s always easier to think of ideas than to execute on them. While having ambitious targets can drive success, never being able to close off all those items can reduce satisfaction.

I’m a firm believer in building momentum and ticking off tasks is a great way to build in a positive reinforcement cycle.

Task lists are often used to great effect in project planning where the goal is fixed and known. But how do you adapt this into your daily schedule?

1. The Problem With Daily Task Lists

It’s too easy to keep adding more items to the list and the day’s work is then never done. The trick is to incorporate a common aspect from development processes. What’s the definition of done?

If you don’t know what done looks like you cannot be done. So without having this definition your day is guaranteed to fail. Not a great place to be.

It’s far too easy for smaller admin tasks to drown out your core priority. Or for scope creep where you keep expanding your most essential task so it’s never quite finished.

2. The Essentialist’s Daily Planner

For full details of the planner take a look here: https://gregmckeown.com/books/the-essentialism-planner/

The core of the daily planning ritual is:

“What? What is going on in my life?

So What? Why does all this matter

Now What? What’s important now?

1 essential project

2 urgent and essential tasks

3 maintenance items”

Don’t get caught in that trap! Defining done makes it clear and easier to tick off so you can move to the next point.

It’s easy to jump straight to the concrete checklist but the journal also has a “What?” and “So What?” section. This is a great place for brainstorming, to jot notes and write potential options out before you commit to your list.

3. Focus Tasks vs Urgent Interruptions

There’s often a tension between working on something strategic vs completing a smaller urgent task. And the best way to avoid a lot of urgent busy work is to stay ahead of the game. I love the mix in this planner.

• Number 1 task is the strategic non urgent piece of work. Ensure you block time for this and protect it from interruptions.

• The 2 urgent and essential tasks keep you responsive without drowning in reactive work.

• The 3 maintenance items handle the necessary but smaller tasks that keep things running.

This fits really well with my earlier post on reducing procrastination. Pro-actively plan your day with Greg’s 1-2-3 planner, then use the 5-4-3-2-1-Go countdown to execute on what you’ve planned.

My takeaways:

• Define what “done” looks like before your day starts, or you’re guaranteed to fail

• Split your essential strategic/project/creative work out from the urgent tasks

• Before committing brainstorm and journal the background

Posting this means I’ve done my essential task for the day! Time to tick the box and celebrate.

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Have a great week.

1. Trunk-Based Development requires mindset change, not just Git strategy

2. Small batches reduce risk; big batches increase it

3. Non-blocking code reviews fail without shared purpose

First, a quick reset on what Trunk-Based Development actually is.

It’s a source control model where developers collaborate on a single main branch, often called the trunk. The discipline is not about never branching at all, but about resisting long-lived branches by using well-known techniques. Done well, teams avoid painful merges and keep the system deployable most of the time.

1. Mindset Change

Trunk-Based Development only works if you change how you design and deliver software. Fast commits to main force smaller batches of work. Feature toggles stop being optional. Incremental design becomes the default. Feedback has to be fast, and observability has to be good enough to catch mistakes early.

The case study in the video makes this clear. The technical setup mattered, but the real shift was mental.

2. Non-Blocking Code Reviews

One part of the experiment struggled: non-blocking code reviews. The feedback from the team was blunt.

“…there were several problems. The main one was that people didn’t prioritize reviews enough. Non-blocking was interpreted to mean non-urgent. So, reviews were not conducted close to the time when the work was done. So, feedback became stale.”

“The team saw no shared purpose for the code reviews. This is important. People didn’t know what the code reviews were meant to be for.” (source)

No amount of CI, automation, or branching discipline would have fixed that. If people don’t agree on why reviews exist, removing the gate just exposes the gap faster.

That’s the part of Trunk-Based Development that gets glossed over. The mechanics are well documented. The behaviour change is not.

3. Big To Small Batches

A common pushback for Trunk Based Development is that it’s risky. Actually the reverse is true. Making features bigger, extending branches just increases risks. It puts more pressure on the releases.

If you treat Trunk-Based Development just as a Git strategy, you’re missing the point. There are massive benefits available but they require developers to think and organise their work differently.

Have a great week.

AI is currently driving more change in software development than anything we’ve seen in years. It’s often framed as a threat to developer jobs, even the end of programming. I don’t believe that, and the keynote opens with a useful reminder of why.

Werner starts with a short video highlighting past “end of developer” moments: assemblers, compilers, structured programming, object-oriented design, the shift from monoliths to services, and the move from on-premises to cloud computing. Each change reshaped how software is built. Each expanded the role of developers. And each time, the number of developers continued to grow. There are nearly a million more of us compared to last year.

The real question raised is the one most developers are already asking:

Will AI make me obsolete?

Werner’s answer is simple:

No (if you evolve).

Here are my three key takeaways on what that evolution looks like.

1. Verification Debt Is the Real Risk

Hallucinations are often cited as the biggest problem with AI-generated code. They are an issue, but they’re no longer the most dangerous one.

The bigger risk is verification debt.

AI makes code generation fast and easy. That speed creates a new failure mode: you can move forward because output looks reasonable and appears to work. Over time, unchecked assumptions, missed edge cases, and misunderstood intent quietly accumulate.

Vibe coding without human review doesn’t support innovation. It’s gambling.

The problem isn’t that AI gets things wrong.
It’s that you stop checking carefully enough.

Challenge: Where in your workflow is verification explicitly enforced, rather than assumed?

2. Specs Enable Precision, Not Bureaucracy

One of the most interesting parts of the keynote was the focus on structured intermediate steps. The Kiro standard specs: requirements, designs, and tasks.

I’ve been testing this approach myself, and it holds up well in smaller “hobby projects” and PoCs.

These intermediate non-coding steps aren’t overhead. They save time. They help refine the problem by allowing verification at a higher level. Enabling control and review before getting lost in the details of code.

It’s far easier to verify the intermediate spec than the code. So it’s worth creating and refining this as you go.

Challenge: Where do you deliberately pause to review specs and design before the code is even created?

3. Human-to-Human Code Reviews Still Matter

AI is excellent for learning, exploration, and planning. Tailored explanations, instant answers, and the freedom to ask “dumb questions” make it a powerful tool for individual productivity.

But it doesn’t replace human-to-human code review.

Seeing how other developers think, structure problems, and use AI in real workflows remains one of the fastest ways to improve. I was reminded of this again recently during an internal AI community-of-practice session — the real value came from developers showing how they work, not from the tools themselves.

AI accelerates individuals. Humans still level up teams.

Challenge: Ask a colleague to walk you through how they’re actually using AI today. Not the theory, but the practice.

Have pride in your work

Most of what we build nobody will ever see.

The best builders do the things properly, even when no one is watching.

Have pride in your work

AI doesn’t change that. It’s another tool to master.

Have a great week.

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Here’s the short version. Plus how I use it as a tech leader.

Hat Decisions —> Move Fast

Most decisions are hats.
You try one on, and if it doesn’t fit, you take it off.

Low cost. Low risk. Easy to reverse.

Action: Make the call immediately. Don’t overthink it.

In engineering teams, this is 90% of decisions: first drafts, prototypes, brain storming ideas, meeting formats, minor processes, early architectural sketches.

Haircut Decisions —> Move Fast (But Acknowledge Recovery Time)

A bad haircut grows back, but you might feel foolish for a while.

These decisions aren’t permanent, but they do have a recovery cost.

Action: Decide quickly, but with awareness. Optimising endlessly rarely changes the outcome.

Be upfront and open about recovery costs.

Examples: choosing a tool you can migrate away from, adjusting team structure, experimenting with workflow changes.

Tattoo Decisions —> Slow Down

Some decisions leave a mark. Once they’re made, you live with them for a long time.

These require deliberation, not speed.

Action: Pause. Consult. Challenge assumptions. Sleep on it.

Examples: hiring senior leaders, irreversible architectural choices, major budget commitments, decisions that shape culture.

How I Use This Framework as a CTO

When teams move too slowly, it’s often because they treat everything like a tattoo decision.

When teams move too fast, it’s because they assume nothing is a tattoo.

My rule:

Before deciding first ask: “Is this a hat, a haircut, or a tattoo?”
Once you name the category, the right speed becomes obvious.

The Meta-Question That Speeds Everything Up

Before your next decision today, ask yourself (and/or your team)

“What type of decision is this?”

If it’s a hat → ship it.
If it’s a haircut → choose and adjust later.
If it’s a tattoo → slow down.

This is not an excuse to be reckless. Watch out for the tattoo decisions, but most teams could double their decision velocity simply by labelling the decision first.

Happy decision making.

Have a great week!

1. AI Coding Workflows: You need to think bigger than individual coding support. How can you analyse the legacy code base and how can you setup common instructions for the whole team?

2. The Rise of Agents Elevated by MCP: Focus on context: What’s the best approach for you to provide the AI the needed context for the project you’re working on?

3. Emerging AI Antipatterns: AI supported development is still development, don’t get pulled into the standard development anitpatterns. Watch out for the return of waterfall.

   1. AI coding workflows

All the early AI tools focussed on coding support for individual new green field projects. This is far from the reality of life in an existing organisation were the environment is teams (or departments) and a large existing code base. I mentioned the orchestration topic last week. And this features as a key point in the Thought Works radar…

As AI becomes strategically embedded across the software value chain — from using AI to understand legacy codebases to genAI for forward engineering — we’re learning how to better supply knowledge to coding agents.

There’s also a growing realization that AI must amplify the entire team, not just individual contributors. Techniques like curated shared instructions and custom commands are emerging to ensure equitable knowledge diffusion

Takeaway Nugget 1: You need to think bigger than individual coding support. How can you analyse the legacy code base and how can you setup common instructions for the whole team?

2. The Rise of Agents Elevated by MCP

Providing the correct context is a key driver to enabling AI to efficiently support development. MCP and agents are a proving an effective way to optimise resource usage to help support this.

We observed continued innovation in agentic workflows, where context engineering has proven critical to optimizing both behavior and resource consumption. New protocols such as A2A and AG-UI are reducing the boilerplate required to build and scale user-facing multi-agent applications. In the software development space, we compared different ways of supplying context to coding agents — from AGENTS.md files to patterns like anchoring coding agents to a reference application.

Takeaway Nugget 2: Focus on context: What’s the best approach for you to provide the AI the needed context for the project you’re working on?

3. Emerging AI Antipatterns

complacency with AI-generated code continues to be a relevant concern. Even within emerging practices such as spec-driven development, we’ve noted the risk of reverting to traditional software-engineering antipatterns — most notably, a bias toward heavy up-front specification and big-bang releases

While spec driven development can help (see last week’s post) it’s not a silver bullet. Spec-driven development is great at ensuring a consistent context for AI supported code generation, rather than random vibe coding prompts. The risk with all AI is the speed with which you can create content and therefore massive upfront specs.

I’ve lived this anti pattern this weekend, I’ve been testingAmazon’s Kiro which focusses on spec driven development. It’s far too easy to create massive spec documents. As always new requirements emerge. The actual and the planned work then diverges and you’re in exactly the “heavy up-front” risk area described by Thought Works. (At least with this test project I’m now free to start again with this in mind)

Kiro has a dedicated steering folder for project wide info and then specs are split into design, requirements and tasks which are then only used for that feature. Don’t let the individual specs get too big. Don’t revert to waterfall delivery.

Takeaway Nugget 3: AI supported development is still development, don’t get pulled into the standard development anitpatterns. Watch out for the return of waterfall!

Definitely worth reading the full article: Thought Works Technology Radar.

Have a great week.

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Strategize Your Career

How to turn AI from a random code generator into a reliable teammate

Most engineers I talk to treat AI like a slot machine. Some days it writes a good function, other days it hallucinates code and deletes all the tests so the build succeeds (yes, I’ve seen this multiple times). It feels random, so you end up using it because it seems to be the best practice, but not trusting it, so sometimes it feels like it slows you down…

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6 months ago · 14 likes · Fran Soto

Takeaway Nugget 1: “Productive Engineers do not hope AI gets it right, they steer it”

Takeaway Nugget 2: Explicit validation loops build natural checkpoints that help force consistency.

Takeaway Nugget 3: Use specs at the start but then focus on the data flows and structures.

1. Be the orchestrator. Think a head.

Reviewing AI output is another place where the orchestrator mindset matters. I noticed a lazy habit in myself. Once the AI code worked, I did not want to go through it in detail. The tests passed, so my brain wanted to move on. That is a trap. AI is not yet at the level where you can skip review. You need to treat its output like a pull request from a new hire. First check the structure, then the happy path, then edge cases...

Takeaway Nugget 1: “Productive Engineers do not hope AI gets it right, they steer it”

2. Create Steering Rules - A Validation Loop

Your prompts should not be one offs. Creating an explicit validation loop is a good way to help force this process. This keeps quality up, ensures that errors don’t get overlooked and forces natural check points.

Cole Medin has some great templates on GitHub

Validation Loop

Phase 1: Linting

Run the actual linter commands found in the project (e.g., npm run lint, ruff check, etc.)

Phase 2: Type Checking

Run the actual type checker commands found (e.g., tsc --noEmit, mypy ., etc.)

Phase 3: Style Checking

Run the actual formatter check commands found (e.g., prettier --check, black --check, etc.)

Phase 4: Unit Testing

Run the actual test commands found (e.g., npm test, pytest, etc.)

Phase 5: End-to-End Testing (BE CREATIVE AND COMPREHENSIVE)

Test COMPLETE user workflows from documentation, not just internal APIs.

Takeaway Nugget 2: Explicit validation loops build natural checkpoints that help force consistency.

3. Spec Driven Approach

There is also a downside with heavy spec systems. My team tried “spec driven development” in the style of AWS Kiro. The idea is nice, but in practice it was super verbose and painful to maintain. I saw pull requests with 22 times more markdown line changes than code changes. AI was not good enough to keep a perfect mirror between code and specs. My takeaway is to keep minimal specs focused on data models and interfaces. Once the code exists, I reference the code itself and delete the markdown.

I’ve been trying kiro this weekend. I see the spec driven approach as a massive step forward from adhoc "vibe coding style prompts. For small green field projects especially at the start I believe the specs add a lot of value. But I fully take Fran’s point from above. Once you have the code you have a lot of duplication between specs and code and I can see that the older the projects get the more divergence there will be.

Takeaway Nugget 3: Use specs at the start but then focus on the data flows and structures.

Have a great week.

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High Growth Engineer

Top 5 Communication Frameworks for Engineers You Must Remember

📣 Build AI-ready apps with HubSpot…

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6 months ago · 36 likes · Jordan Cutler

Depending on the situation different approaches will help you to put your point forward. If you’re unsure on how to explain your thoughts then these three structures will likely help. Try rephrasing your point, analysis or request in one of these formats…

Takeaway Nugget 1: PREP: Point, Reason, Example, Point

Takeaway Nugget 2: GROW: Goal, Reality, Options, Will

Takeaway Nugget 3: BLUF: Bottom Line Up Front

1. PREP - Point, Reason, Example, Point

• Point: State your main argument immediately

• Reason: Explain why it’s important or why you believe it

• Example: Back it up with concrete evidence or a specific instance

• Point: Restate your main argument to reinforce it

Takeaway Nugget 1: Keep the focus and get straight to the point. Useful when you have clear crisp examples.

2. GROW: Goal, Reality, Options, Will

• Goal: What outcome do you want to achieve?

• Reality: What’s the current situation?

• Options: What are the possible paths to get from here to there?

• Will: Which path are you willing to commit to?

Takeaway Nugget 2: I’ve found this to be very useful in coaching situations. Also useful when you know the goal is a long way off. By committing to path you can highlight the current gap and also provide evidence that you’ve investigated other approaches in a structured way.

3. BLUF: Bottom Line Up Front

BLUF (Bottom Line Up Front) is the single most important communication framework for engineers. If you only learn one, learn this.

✅ Do: In BLUF, you start with your main point, question, or request. Then, you only add the necessary context to drive that point home.

For a deep dive on this one I’d recommend Gaurav Jain’s post on The Pyramid Principle…

The Good Boss

The Pyramid Principle: How Great Leaders Communicate with Precision and Impact

In this issue…

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8 months ago · 22 likes · Gaurav Jain

Takeaway Nugget 3: This is great to cut through the noise. No long context, cut straight to the point, justify it and then provide further supporting data.

Have a great week.

• Being succinct with context & clarity of thinking

• Info Q’s DevOps Trends Report

• Incorporating AI into agile processes

1. Being Succinct

Yue Zhao’s post How to communicate succinctly to executives actually provides guidelines that are helpful in all communication not just to executives.

At the core of this concept is the scarcity of time. Time is the most limited commodity for executives. Due to the number of priorities executives juggle at any point, they tend to have short attention spans, even for topics they truly care about. This is why being brief is critical to being heard. If you start in the weeds or ramble off to a tangent, executives lose focus.

Takeaway Nugget 1: What’s the context? Can you concisely explain the background to frame the proposal or information that you want to share. (Hint SCR: Situation, Complication, Resolution) can be very useful to push to thinking outside the raw tech details.

2. InfoQ Cloud and DevOps Trends Report - 2025

InfoQ Cloud and DevOps Trends 2025

The rise of AI Agents for cloud engineering shows a lot of promise. However, enterprise adoption is slowed by compliance, security, and the need for human governance alongside legacy systems.

Platform Engineering is a boardroom priority driven by developer experience (and ultimately, productivity). Yet, success is often limited by organizational tool fragmentation and a lack of foundational Value Stream Management.

FinOps has matured beyond budgeting to focus intensely on cost optimization and strategic consolidation, driven by global pressure to “do more with less” and the high compute costs of AI initiatives.

The influx of new tools and AI mandates is currently increasing cognitive load across all roles, not reducing it. Leaders must prioritize consolidation and measure value beyond “vanity metrics”.

Takeaway Nugget 2: Platform Engineering is a boardroom priority driven by developer experience. It’s tech’s job to drive this agenda. What’s your contribution?

3. Dual Track Agility

Jurgen Appelo’s post provides a great analogy for the dual-track business agility. Scrum originally pushed business to think in quicker iterations. With AI tooling the new two week standard sprint starts to look slow.

Fitting in with Dev Ops trends the challenge is how we can support and integrate more AI tooling. This will require a rethinking our processes and removing new bottlenecks that appear.

The Maverick Mapmaker (on Multidisciplinary Teams & AI)

Scrum Is Done. Finished. History.

For twenty years, Scrum was the Shinkansen (新幹線) of work management. The fastest kid on the block. Now, it’s the family minivan blocking the left lane while AI agents are flashing their headlights. Once, Scrum was the fast lane—the bullet train of modern work, the agile manifesto made flesh. But times change, work accelerates, and the machines aren’t wa…

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7 months ago · 55 likes · 8 comments · Jurgen Appelo

Trying to make AI agents follow Scrum is like forcing self-driving cars to yield at every crosswalk. In traffic, that makes sense. In the enterprise, it’s absurd. The rituals that once liberated humans now shackle their digital colleagues. As artificial minds join our teams, they’ll demand their own lanes, their own cadence, and their own logic of collaboration. The challenge ahead isn’t to make AIs agile—it’s redesigning business agility itself, so humans don’t become the bottleneck in their own product development process.

Acceleration is the new Agile. But only when we take Scrum out of the loop.

Takeaway Nugget 3: Challenge yourself. What’s the current bottleneck in your release process? How can you resolve this?

Have a great week.

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A very simple rule “When you don’t feel like doing something, do it for ten minutes anyway.”

This is the starting point from the Balanced Discipline post "The 10-Minute Rule.".

Balanced Discipline

The 10-Minute Rule: How Small Windows Create Big Wins

We tend to think progress requires big blocks of time…

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7 months ago · 612 likes · Balanced Discipline

1. Action Beats Motivation

2. Small Wins Rewire

3. Self-Trust Momentum Is Contagious

You don’t need the perfect morning, the free hour, or the big burst of energy.
You just need ten minutes, and the willingness to begin.

Because small starts create big wins.
And big wins?
They start right now.

Takeaway Nugget 1: When you don’t feel like doing something, do it for ten minutes anyway.

2. Beating Procrastination with a Pomodoro Tweak

Feel free to tweak the ideas in the above Balanced Discipline post. I’ve written about this in the past. The pomodoro technique offers a structured approach to avoid the overwhelm of starting new tasks by splitting into 25 minute blocks. If you’ve cracked the 10-Minute rule you can go higher to 25 mins. Lower is also possible, 5 minutes reduces the barrier further, gives you even fewer excuses to get started. Once you’re started you’ll likely stick to the tasks far longer than the original block you planned.

Takeaway Nugget 2: Get yourself a sand timer. There’s something about the movement needed to physically turn the timer that makes the starting point more clearly defined.

3. The 5 Second Rule

Keeping on the same topic, Mel Robbins has written the book “The 5 Second Rule”

It’s a super simple idea, but also an extremely powerful technique for overcoming hesitation and taking action.

THE 5 SECOND RULE

Life is about the choices we make. And I have said over and over in this book that you can always choose how you act. If you have goals to get healthier, what you need to do is usually straightforward. Pick a plan to follow, any plan will do-and then 5- 4- 3- 2- 1-GO. The only thing you will need to choose after that is choosing each and every day to DO IT even though, as Anouk said, you “really really really don’t feel like it.” I said that what you need to do is simple. I didn’t say it would be cary.

I promise you, it will be worth it. Exercise and health comes down to one simple rake-you don’t have to feel like it. You just have to do it.

It sounds too simple but there’s a lot of science to back it up. The counting forces you to interrupt your overthinking left analytical brain. Instead you activate your more action oriented and intuitive right brain. This enables you to switch over into execution before doubt takes over.

Takeaway Nugget 3: Next time you’re about to say something in a meeting but you aren’t sure, simply count down 5, 4, 3, 2, 1 and say it. Or put it all together time to start you 10 Minute focused work item: 5, 4, 3, 2, 1 → GO!

4. Bonus Halloween Special

Takeaway Nugget 4: Which of these skills could you brush up to improve your leadership?

Have a great week.

1. Retros and Post Mortems: It’s good to focus on what went wrong in order to support improvements. But don’t forget to zoom out, see the bigger picture and celebrate success.

2. Focus: Can you focus on fewer activities this week so that you can get more of them over the line?

3. The Support Matrix: An excellent guide for how different types of support should be used in different situations.

1. Retros and Post Mortems

Retros and port mortems provide a structured way to review the work that you’ve been doing, how it went and especially after bigger issues to look how you can improve for the future. This concentrated effort is the basis for future improvements.

But it’s also important to zoom out. It can be easy to over focus on what went wrong and forget to celebrate the success for the countless other things that went smoothly.

Takeaway Nugget 1: Take a step back and make sure that as well as looking for improvements you take the time to celebrate success. There’s probably more than you first think.

2. Focus

We live in a world where there are ever more ways to get districted and there’s always the temptation to try and do just one more thing in parallel. However, multitasking leads to task switching, causing extra mental load and a loss in efficiency.

Fully finishing off tasks gives you a positive energy boost. Meaning you can move to the next task with a sense of pride and accomplishment. Having too many open loops brings with negative feelings of overwhelm and lack of control.

Takeaway Nugget 2: What can you do this week to allocate enough concentrated time for your top 1-3 priorities? Enough time that you can fully complete them and move forward with confidence.

3. The Support Matrix

As a leader part of your job is to support your wider team, lift them up and help them. Depending on the situation the approach should be different. Gaurav Jain’s post on the four leadership support roles offers a great breakdown of the these differing roles, including concrete examples of when and how to use each one.

I find the easiest way to understand these four roles is to plot them on a 2x2 matrix, which I call The Support Matrix.

The X-axis runs from Problems/Past on the left to Solutions/Future on the right. The Y-axis runs from Tell (you give answers) at the bottom to Ask (you help others find their own answers) at the top.

• Coaching sits in the top-right quadrant: it’s all about asking questions that help the other person move toward a future-oriented solution.

• Mentoring is bottom-right: you’re offering guidance and sharing experiences to help someone grow into their future.

• Counseling lands in the top-left: here, you’re listening and asking reflective questions to help someone explore their past or inner emotional challenges.

• Consulting fits in the bottom-left: you’re analyzing and advising, often using your expertise to diagnose and solve past or current problems.

Takeaway Nugget 3: If you’re not sure how to help then before your next interaction take a look at the support matrix, identify the type of support you think should be offering and readup on the when and how from Gaurav Jain’s post.

Have a great week.

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1. SCARF. A structured way to analyse why people in your team may be resisting change

2. The Hill Model: A visual guide to help give context of where you are with tasks

3. Situation-Complication-Resolution Framework: A structured approach for you to explain and push your proposed solution.

1. The SCARF Model

Why SCARF Matters in the Workplace

The SCARF model gives leaders and teams a language for understanding why people resist change, shut down during feedback, or disengage from collaboration.

The SCARF model comes from David Rock. Nigel Dessau’s post gives an excellent overview with examples of how to apply this in the work setting to improve your leadership. If people are resisting change or unhappy chances are it can quickly be analysed as related to one or more of these five categories: Status, Certainty, Relatedness, Fairness (SCARF). Going through this list provides a quick list with objective naming so you can get to the bottom of the problem.

That's just a story you are telling yourself.

Why That Meeting Felt Like a Threat: Understanding the SCARF Model of Human Motivation

Why do some meetings drain us while others energize us…

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8 months ago · Nigel Dessau

Takeaway Nugget 1: If you or someone in your team is not happy then do a quick SCARF check. Is there something off with Status, Certainty, Autonomy, Relatedness or Fairness?

2. Shape Up: Work is like a hill

Basecamp’s Shape Up Methodology provides a toolbox full of techniques that you can apply in your own way to your own process for delivery. It’s possible to use individual tools and techniques to help your project even if you don’t use everything.

Today, I’d like to introduce you to Scopes on the Hill

Every piece of work has two phases. First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution.

Often as engineers we’re asked to give estimates. Estimates are difficult in the best of times but can verge on impossible when you’re on the upward part of the hill and still figuring out what you need to do. The hill metaphor is a great way to talk to stakeholders. It also highlights the importance of very quickly planning further analysis or proof of concepts in order to reduce risk. You need to move yourself to the top of the hill so that you know what you need to do. Only once you’re at the top of the hill do you have a realistic chance of estimates with a reasonable amount of accuracy.

Another tip is to always break tasks down. At the start of a project focus on the tasks that are still on the upward part of the hill. If you split a task, may be certain subtasks are already at the top, but other’s aren’t. These are the ones that need work.

Takeaway Nugget 2: If you’re having difficulty estimating tasks, chances are you’re still figuring out what to do. Ask yourself “What do I need to do to get to the top of the hill?”

3. SCR Framework - Situation Complication Resolution

If you’re trying to affect change and push a solution it helps to provide context and be precise. It’s common to see vague emails, without much context, where it’s not clear what the author wants to do or wants you to do.

Using this simple structure helps you to create a structured written description.

1. Situation (S) – Provides the prior context or background necessary to understand the topic. It introduces the subject and explains why it matters.

2. Complication (C) – Presents the problem that has arisen, explaining why it’s significant and why the audience should pay attention.

3. Resolution (R) – Offers solutions or recommendations to address the problem.

What’s more this fits perfectly as the first part of a story telling narrative. Check out the details in Digvijay Singh Bhadouria post…

The Data PM

McKinsey's SCR Framework - Communication and Storytelling Part 3/10

When a top consulting firm like McKinsey starts a project, their goal isn’t just to create a presentation that collects dust on a shelf. Instead, they aim to influence their clients to implement their recommendations effectively. But how does a firm like McKinsey achieve this level of influence…

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a year ago · Digvijay Singh Bhadouria

Takeaway Nugget 3: Next time you’re requesting something or pushing your idea use the Situation-Complication-Resolution structure.

Have a great week.

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Brian Balfour’s post has a detailed analysis of the common pattern associated with great distribution shifts. How we’re on the verge of the next one with ChatGPT and then case study deep dives as examples and how this playbook is accelerating.

Facebook, Google, Apple, LinkedIn—every platform followed the same brutal pattern: open, grow, close, monetize. The next distribution monopoly is coming, and most leaders aren’t prepared.

Given technological innovation it's easy to get lost into the continuous list of new things. This article takes a step back and shoes that some patterns don’t change and backs that up with great examples.

The stampede is coming. If I’m right, in six months, it’s likely every SaaS product and consumer application will be rushing to complete a ChatGPT integration. In twelve months, users will expect it. In eighteen months, the platform taxes will arrive. In twenty-four months, the graveyard will be full.

The section on “There Is No Opting Out” may make uncomfortable reading, the “only” strategy for many companies short term is likely to be to partner with ChatGPT and end up having to give a way a lot of data.

How do you use the ChatGPT while building your own moat? This is the key question to answer.

2. Priority Setting

If you’re overloaded with tasks, this is just a reminder that everything can’t be a priority. Take a step back and classify them as Could do, Should do, Must do…

3. FOMO-Driven Investment

Brian Balfour in the post from point 1 is predicting 80% chance of ChatGPT winning the race. Meanwhile here’s a couple of links to alternative outcomes, they highlight the risks when investment is based purely on fear of missing out.

“If we end up misspending a couple of hundred billion dollars, I think that will be very unfortunate obviously, but I actually think the risk is higher on the other side.” [Emphasis added]

The Leverage

The Case for Spending Seven Trillion on AI

“If we end up misspending a couple of hundred billion dollars, I think that will be very unfortunate obviously, but I actually think the risk is higher on the other side.” [Emphasis added…

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7 months ago · 24 likes · Evan Armstrong

When hyperscalers like Oracle and Microsoft both invest in and consume from the same infrastructure, and a few unprofitable companies like OpenAI rely on fundraising to fulfill obligations, revenue becomes circular - and the system extremely fragile.

Silicon Roundabout Ventures Community

Trillion-Dollar Crash

Francesco Perticarari - September 2025…

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7 months ago · 23 likes · 8 comments · Francesco Perticarari

Interesting times ahead.

Have a great week.

Robert Ferraro has a great post, the visuals say it all…

Most meetings would be more productive and have a better exchange of information if the people who were the quietest got more time to give their thoughts and analysis.

• For the extroverts:

  • Is it necessary to say this right now?

  • Could I ask a question instead?

  • Could I let someone else speak?

  • What information am I missing out on by speaking myself?

• For the introverts: Speak up, you likely have extremely valuable opinions that the meeting will benefit from if you share them. Not speaking up is depriving the whole conversation of valuable insights as well as stopping you having a greater impact.

2. Imposter Syndrome

In “Someday soon they’ll realize I’m a dumbass” you can learn more about Imposter Syndrome.

Imposter syndrome rarely strikes mediocre performers.

It’s not the guy half-assing his job who loses sleep at night. It’s the high performers. The ones who overprepare for everything and undersell their own abilities at every turn.

Linking back to point 1 above and last week’s post on the Dunning-Kruger effect these combine together as a dangerous trio. The most knowledgeable team members can sometimes stay the quietest. Intimated by confident assertions from extroverts they hold back, worrying about all the things they don’t know. Meanwhile they do know a great deal and their voices are lost.

Distilled

Someday soon they'll realize I'm a dumbass.

I was in a monthly meeting with senior leadership when they launched into a discussion about some fairly arcane market dynamics. They were all nodding, agreeing, and throwing around terms you usually only hear in business school…

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7 months ago · 8 likes · 1 comment · The Bootleg Manager

Being a competent introvert are prime characteristics for being prone to Imposter Syndrome. It can be difficult to overcome but look at post 1 above, remember to speak up and you’ll be on the way to getting some positive reinforcement.

3. The Rise of AI Shopping

The Rise of AI Shopping highlights the trend that more and more people are relying on the advice of ChatGPT or AI to make purchasing decisions. This has some massive implications and completely changes models:

The old model: intent → ad → link → checkout is being replaced by a single interface that remembers you, advises you, and sells to you.

This changes the landscape significantly:

• For Google: AI eats search intent before it ever hits a keyword auction (How Google decides which ads to show based on search terms).

• For Amazon: AI cuts to the sale without a marketplace middleman.

• For brands: The question shifts from “Where are we placing ads?” to “Are we inside the AI making the recommendation?”

And with this change in behaviour there needs to be a shift in how we optimise building websites.

The question shifts from “Where should we advertise?” to “How do we become the solution AI recommends?” This means rethinking how you describe your services, structure your information, and build relationships with the systems making recommendations.

ie we need to optimise for AI discoverability in the same way we used to optimise for Google keywords and SEO. Have you submitted your products to ChatGPT’s registry?

From user perspective switching from comparison sites to ChatGPT for recommendations is small but it has massive implications for every other company involved in the flow.

Have a great week.

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People who are new to a topic or have a low ability often overestimate their competence. They simply don’t know what they don’t know. Where as experts can be more cautious because they understand the full scope and scale of a topic.

Incompetence Breeds Overconfidence
When you don’t know enough about a topic, you also don’t know what you don’t know. That ignorance can lead you to overestimate your abilities.

Expertise Breeds Doubt
The more you learn, the more you see how vast the subject is—and how much you still have to master. This can lead even highly competent individuals to underestimate their expertise.

MindVault

The Dunning-Kruger Effect

Ever notice how many people who think they’re absolute pros suck, while true experts tend to doubt themselves…

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a year ago · 4 likes · Phil Hagspiel

Watch out when you're new to something. Ask more questions to try and find out what you don’t know.

2. Managers vs Leaders

It’s a pattern at so many organizations: when the heat is on, people either retreat into management mode or step up into leadership. The difference isn’t about job titles or org charts—it’s about fundamental choices in how we treat people when it matters most.

Simon Sinek’s post lists 5 things that Managers do but a leader never would:

1. Managers Hoard Information. Leaders Overshare.

2. Managers Weaponize Policy. Leaders Bend Rules for People.

3. Managers “Fire Fast.” Leaders Coach, Then Help People Land Softly.

4. Managers Avoid Hard Conversations. Leaders Run Toward Them.

5. Managers Reward Compliance. Leaders Reward Dissent.

3. How To Tell If Something is AI Written

You’ve probably noticed a few words that stand out and make you think something was AI written, eg fostering, leveraging or empowering. Hollis Robbins’ post gives some more generic rules and ways to spot AI written text.

Humans naturally link signifiers (words) with signifieds (mental concepts, lived experiences, shared cultural meaning). LLMs produce text by predicting the next most likely word based on what’s come before. This means that the model never “grasps” the underlying concept.

🔎 Humans vs. LLMs: Signifiers and Signifieds

Signifier (the word or symbol)

• Humans: Hear or read the word “dog.”

• LLMs: Process the token “dog.”

Signified (the concept/meaning)

• Humans: Immediately link it to the mental concept of a furry animal, personal memories of dogs, maybe even the smell of wet fur.

• LLMs: No concept. Only knows that “dog” statistically appears near “bark,” “pet,” “walk,” etc.

Connection

• Humans: Signifiers are grounded in lived experience, perception, culture, and embodiment.

• LLMs: Signifiers are connected only to other signifiers in the training data.

Process

• Humans: Interpret meaning → integrate with knowledge → respond with intention.

• LLMs: Predict the next token via autoregression (probability distribution over possible continuations).

Example sentence

• Humans: “My neighbour’s dog barked all night, so I couldn’t sleep.” → I visualise the neighbour, recall barking, feel annoyance.

• LLMs: “My neighbour’s dog barked all night…” → high probability that “so I couldn’t sleep” or similar comes next, but no felt annoyance.

So here’s a handy rule: if you can’t see anything, if nothing springs to mind, it’s probably AI.

Telltale Contrasting Structures

Another rule: look for formulations like “it’s not just X, but also Y” or “rather than A, we should focus on B.” This structure is a form of computational hedging. Because an LLM only knows the relationships between words, not between words and the world, it wants to avoid falsifiable claims. (I’m saying want here as a joke but it helps to see LLMs as wafflers.) By being all balance-y it can sound comprehensive without committing to anything.

Anecdotal Value

How to Tell if Something is AI-Written

You have a “feeling” that a piece of writing is AI-written. It sounds like word salad, a mashup of textual patterns that gestures toward meaning but isn’t actually anchored in reality…

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9 months ago · 700 likes · 125 comments · Hollis Robbins (@Anecdotal)

Have a great week.

Daniel Kazandjian’s post on Minimum Viable Productivity proposes a small but important shift in how you can tweak your todo list. This involves breaking down your todo list and thinking about it under theses two questions:

1. What do I want to move?

2. Where do you want to move it?

Both of which can be answered on one physical piece of paper or in a simple notes tool. The key tweak is to focus on the action and a quick next step to move in the right direction.

Being productive means “moving the things you want to move in the direction you want to move them.”

It can be very tempting to procrastinate and end up wasting time setting up the perfect knowledge system for productivity. But actually a simpler tool and system can help support making faster decisions.

High-velocity Decision Making

80% of productivity is fast decision-making.

In order to move things, we need to know:

1. What those things are (roughly)

2. The direction we want to move them in (roughly)

“Roughly” because you don’t need 100% certainty. Good enough is better than perfect.

In fact, if you’re certain about 100% of your decisions you're taking too long to make them. An error rate of zero is a meta-error.

The Metagame

Minimum Viable Productivity

Productivity systems are procrastination tools for slow decision-makers…

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4 years ago · 143 likes · 5 comments · Daniel Kazandjian

2. Confidence & Talent

Adlet Balzhanov’s post explores the relationship between confidence, talent and effectiveness.

As with the post above it highlights again the importance of taking action and moving forward under uncertainty. The cost of delay often exceeds the cost of perfection.

The tragedy isn't that these engineers lack talent. It's that they've optimized for being right instead of being effective. They've confused perfectionism with professionalism.

The confident engineer has learned a different lesson. They understand that most technical solutions are reversible. Most code can be refactored, and most mistakes can be fixed. They know that making progress is more important than being perfect.

Some decisions are irreversible (see below) and so should be treated with far more care. But the vast majority of decisions are not like this and so could be made faster.

This doesn't mean confidence excuses incompetence. The engineer who confidently ships broken code won't last long. But there's a sweet spot where average skills combined with high confidence creates outsized impact.

Imagine what you can then do when you provide skilled engineers with leadership skills and grow their confidence. This is what can then really drive a team forward.

3. Irreversible vs Reversible Decisions

Continuing on theme, Fran Soto’s post “How Software Engineers Make Productive Decisions (without slowing the team down)” does a deep dive into how to identify more quickly which decisions should be handled with more care and which ones are low risk and so can be made fast.

You don’t grow by being right once. You grow by making many decisions and handling the wrong ones well. Use the 3-question filter:

1. Impact if wrong

2. Ease of reversal

3. Fast mitigation with small blast radius

It’s critical to know what type of decision is being made so you can invest the appropriate amount of type analysing it.

The productivity benefit is knowing the difference before you start. Over-investing in reversible decisions burns time and morale. Under-investing in high-stakes calls burns trust and customer goodwill.

Strategize Your Career

How Software Engineers Make Productive Decisions (without slowing the team down)

Most teams don’t get stuck because problems are impossible. They get stuck because every choice is treated like it’s irreversible. In reality, lots of calls are two-way doors: you can walk through, check the room, and walk back out. Save the caution for the true one-way doors: data migrations, security posture, customer-visible changes with real blast radius…

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8 months ago · 38 likes · 2 comments · Fran Soto

Have a great week.

Hemant Pandey’s post contains a great visualisation of the different skill sets and aims you should have at different points in your career as a software engineer. Every company will have slightly different definitions but the patterns remain the same. How can you make more impact? You can do this by mentoring more junior devs, influencing other teams, helping define culture, guiding leadership and with more collaboration outside of your core team. All of these amplify your impact.

2. Step forward or to the side?

A large part of leadership is being proactive and striding forward. Take the infographic from part 1 above. Lots of action words for the goals “deliver”, “improve”, “contribute”, “lead”, “influence” and “define” But there’s another aspect which shouldn’t been overlooked.

Dr. Annie Faulkner’s post “The Quiet Power of Stepping” highlights the balance that is needed to also make sure you’re leaving enough space.

“the best leaders I have had the pleasure to work with have a different sort of maturity. The kind that comes from really doing the hard work of learning when to step to the side, so others can have space.”

Where do you need to step up and be more proactive and where do you need to step aside to leave space for someone else?

3. Intent vs Impact

As a leader successfully giving feedback and delegating tasks is key to success. However, we can often cause frustration or confusion because this information or the requests are misunderstood. A badly worded message that was rushed could be easily be misinterpreted and taken negatively.

Dermot Kilroy’s "Intent Is Invisible, Impact Is Real" post highlights the importance to sharing intent:

That’s the blind spot many of us fall into as leaders. We assume our intent is obvious, when in reality it’s invisible. Once words leave us, people fill in the gaps with their own fears, assumptions, and stories.

It’s why I now make a habit of naming my intent, especially when delivering feedback. A simple line like “I’m sharing this so we can work it through together” doesn’t guarantee it will land perfectly, but it at least reduces the chance of my words being mistaken for a verdict.

Where could you share your intent to add clarity?

Have a great week

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1. How to Be Taken Seriously

Tessa Xie’s post focuses on “4 common mistakes of junior data scientists and how to fix them” However the advice is applicable to anyone working in tech.

Four behaviours that can increase your impact as a developer:

1. Don’t provide too much info

2. Have a concrete recommendation

3. Be clear about the “why”

4. Have the basics to hand

Diving Into Data

How to Be Taken Seriously

I once overheard an elevator conversation between two managers. One of them was talking about a direct report…

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a year ago · 228 likes · 10 comments · Tessa Xie

So if you want to be perceived as a trusted thought partner instead of a junior IC who can only help pull data, put yourself in the shoes of the decision makers and treat the decisions with the same level of rigor as the ones you care deeply about.

2. Consumer or Creator?

Never has there been so much tooling and support to make creation easier. Never has there been so much temptation to get stuck as a consumer and end up doom scrolling.

David Hobbs’ post explores this topic and provides a bit of nudge to help people think where they are and where they want to be.

My ultimate goal of this article is to shine light on the difference between being a consumer and a creator and how to encourage responsible consumption and limitless creation.

A quick challenge. How’s your creative energy? Now observe how you feel after half an hour of scrolling through your social media drug of choice? How does this feel compared to a short walk outside thinking about your project or after 30 minutes of consciously planning and executing on it?

3. Too Intelligent to take Action?

James Lim’s video “You’re too intelligent to take action (Why over thinkers can’t execute)”

As we get more experienced we can more easily think of risks and things that can go wrong. This can actually make us less likely to take action and move forward with our plan. A key piece of advice is to formally split you work:

• Strategist mode: Target is creating the plan

• Performer mode: Executing the plan

Take Away Question:

• What is the time allocation between these two modes of work for you?

Here’s a good visualisation that breaks it down further…

Have a great week.

Strategize Your Career

Stop trying to be the best at one thing. Do skill-stacking and become a T-shaped developer instead

Most engineers fall into the trap of choosing between…

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8 months ago · 23 likes · 1 comment · Fran Soto

• Graphical overviews to help senior devs think about which skills they should invest in so that they can make a bigger impact.

• Essential reading for new EMs or potential Engineering Managers to think outside of pure tech skills.

Generalists are flexible but shallow. Specialists are deep but blind. Both cap their careers earlier than they expected.

T-shaped engineers combine the two. They bring depth in one area, breadth across systems, and influence that multiplies their impact. In large companies, this is the unfair advantage. It is what makes you indispensable and promotable.

Career growth is not about grinding a single stat or distributing them randomly. It is about building the T-shaped build that unlocks hidden levels and rare rewards. The path is clear: go deep, go broad, and stack your skills. That is how you create exponential growth. (Link)

2. The 80/20 Rule with AI. Take the time to do the curation.

The Economy of Algorithms

The 80% Mistake

I just spent four hours on a document: forty minutes writing it with AI, three hours making it brilliant. My boss thinks I'm wasting time. My boss is wrong…

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9 months ago · 82 likes · 7 comments · Marek Kowalkiewicz

We’re treating GenAI output like human drafts: quick scan, minor edits, done. But GenAI doesn’t make typos. It makes confident nonsense. It fills gaps with plausible fiction instead of asking for clarity. Curating AI content isn’t editing. It’s diamond grading, finding the flaws in what looks flawless.

The smart users of GenAI? They're spending 40 minutes prompting and 3 hours sculpting. Do they save time? Probably not. Do they get better quality? You bet. They produce work that makes everyone else wonder what their secret is.

3. What is the Electric Stack? How has it evolved and what does it mean for the future?

Everything we see around us, and will see around us in the future, is the result of the potential to do work (energy), the capacity to decide what to do and how (intelligence), and the ability to manipulate matter (action).

This one is a mammoth 40,000 words. But gives an intricate history of the scientific advances that brought about the Electric Stack (batteries, magnets & motors, power electronics and embedded compute) and how those improvements compound over time.

It’s a mix of the high level strategies that were behind the development of each part of the stack. How they are related and how the overall benefits have exploded over time.

Not Boring by Packy McCormick

The Electric Slide

Welcome to the 1,269 newly Not Boring people who have joined us since our last essay! Join 248,515 smart, curious folks by subscribing here…

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9 months ago · 524 likes · 31 comments · Packy McCormick and Sam D'Amico

This covers so much. One of the main messages that I took away is how much progress was down to sheer hard work and many many small improvements. These compound over time. Larger breakthroughs are rare and require a massive amount of persistence. Overall this leads to massive improvements.

This highlights the importance of iteration and self reflection as the cornerstone to getting these advancements. When you’re in your next retrospective, really concentrate and work on just one enhancement. Even if it’s small, it will compound over time and they all add up.

Have a great week.

1. John Cutler has a ton of insights, I usually get the feeling he’s sitting over my shoulder and spying on what I’m doing. He’s now going through a list of his top 30 doodles. My two favourites this week are:

When you’re thinking about optimising your development process then these provide a great baseline and way to think about responsibilities in terms of type of work and time allocation.

2. How to stop AI killing our ability to learn

Eva Keiffenheim gives some interesting perspectives in to making sure you can use AI to learn more effectively and avoid fully outsourcing our thinking to devices that undermines three core processes of deep learning: Automaticity, Schema Construction and Prediction Error.

A key suggestion is…

Before you use AI to help write a report, write the first draft yourself. Before you use an AI code generator, manually write and debug a small algorithm. Build your cognitive muscles in a safe environment first. Then bring in the power tools to accelerate your growth.

Lifelong Learning Club

The Smartest People I Know Are Obsessed With a Skill Many Were Told Is Useless

For most of the 20th century, humanity got demonstrably smarter. In a phenomenon known as the "Flynn Effect," average IQ scores climbed three to five points a decade across dozens of countries, progress fueled by better nutrition, healthcare, and education…

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9 months ago · 1221 likes · 88 comments · Eva Keiffenheim MSc

“Every time you face a gap in your knowledge, you make a choice. You can make a short-term withdrawal for an immediate answer, or you can make a long-term investment by doing the work to internalize the knowledge.

The first option feels efficient. The second is what builds your cognitive capital: the interconnected mental library that allows for creative leaps and resilient problem-solving.“

A Mind Without Knowledge Is a Body Without Muscle

Thinking works in exactly the same way. When you truly learn a concept, whether it's a math equation or a historical argument, you are bundling it into a compact chunk. This is the goal of all effective learning: to create a vast mental library of these chunks that you can access instantly. This is what frees your working memory for higher-order thought: synthesis, creativity, and strategic insight.

Constant cognitive offloading, through Googling, a “second digital brain,” and now ChatGPT, short-circuits this process. We get stuck at "knowing about" a topic, never reaching the automaticity of "knowing how."

3. The Cost of AI and who is paying

There’ll be some debate on the accuracy of the numbers but it’s clear there’s currently a lot VC funding which is subsiding AI use at multiple levels.

mandates

tokens are getting more expensive

note: i’m kinda tired of the “levered beta” metaphor, i have one more topic i want to cover on this topic related to cognition, and then i’ll go back to my normal writing…

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9 months ago · 356 likes · 45 comments · Ethan Ding

Agents caused in instant change in usage demand which breaks the financial models and business cases of a lot of companies. But as long as some people are willing the support this others have to pay up to stay in the game.

users became api orchestrators running 24/7 code transformation engines on anthropic's dime. the evolution from chat to agent happened overnight. 1000x increase in consumption. phase transition, not gradual change.

so anthropic rolled back unlimited. they could've tried $2000/month, but the lesson isn't that they didn't charge enough, it’s that there’s no way to offer unlimited usage in this new world under any subscription model.

Have a great week!