Director, Software Engineering | Actionable content for leaders in tech. Software engineering, engineering leadership, online business.

15+ years building production systems. Former Director of Engineering at Oracle, leading public cloud services and working with engineers from Microsoft, AWS, Google, Netflix.

My mission: help businesses and engineers build AI systems that create real leverage.

We're entering a world where AI agents do the work and humans direct. The question is which side of the gap you're on.

Tool user: Prompts ChatGPT, starts from scratch every time.

System builder: Has agents that research, write, and code - creating output while they sleep.

Most AI content teaches you to copy paste. I teach the patterns underneath - so you can build any system, for any problem.

One person with the right systems can do the work of 10. I help people become that person.

What I do:
→ AI Engineer: Community for AI engineers. Courses, resources, downloadable AI projects
→ GradientWork: Custom AI development for businesses
→ Newsletter: Free value every week

📩 newsletter.owainlewis.com
👥 skool.com/aiengineer
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Owain Lewis

5 AI frameworks worth learning in 2026:

(And when to use none of them):

Here's the dirty secret nobody talks about:

AI frameworks are wrappers over provider SDKs with extra features bolted on.

Many professional developers I respect actively avoid them.

The complexity. The over-abstraction. The breaking changes. It adds up fast.

I call this framework tax: the ongoing cost of maintaining someone else's opinions about how AI apps should work.

Start with the SDK. Build until you hit a wall. Then reach for a framework.

When you do, here's what's worth learning:

1. Pydantic AI (Python)

If you've used FastAPI, this feels instantly familiar.

Types, validation, contracts, retries on malformed responses. Works across all major providers.

Best for: Python engineers who already think in terms of types.

2. LangGraph (Python)

Treats AI applications as graphs, not chains.

Branches, retries, parallel execution, human-in-the-loop become first-class citizens instead of afterthoughts.

Best for: Production systems with complex workflow logic.

3. Vercel AI SDK (TypeScript)

Provider-agnostic with a clean developer experience.

Swap models and build agents with minimal code.

Best for: TypeScript developers building AI apps.

4. Google ADK

The first framework that takes polyglot teams seriously.

Official support for Python, Java, TypeScript, and Go with the same mental model. Ships with observability out of the box.

Best for: Teams building on the Google ecosystem.

5. Spring AI (Java)

Exposes LLM services through familiar Spring patterns.

The value isn't innovation. It's integration.

Best for: Enterprise teams already familiar with Spring. 

The uncomfortable truth?

The best framework is often no framework at all.

The goal isn't to master frameworks. It's to build reliable systems.

Sometimes a framework helps. Sometimes it just gets in the way.

What's your current recommendation?

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Founder, GradientWork | AI Engineer | I build custom software to make your business more profitable

5 AI frameworks worth learning in 2026

The easiest way to build AI agents in 2026.

(My micro-agent architecture explained):

I built an AI agent that searches YouTube, analyses thousands of videos in seconds, downloads transcripts, writes scripts, and uploads content.

It completely changed my workflow and how I think about "personal AI assistants". I use it every day. 

The whole thing? A folder with some files and about 150 lines of Python code.

Here's the architecture:

1. The folder IS the agent

A Micro Agent is a folder where files define everything:

AGENT md = who it is and what it can do
tools/ = scripts it runs
context/ = knowledge it reads
workspace/ = where it saves files

Point any terminal coding agent at the folder. Claude Code, Codex, Amp, OpenCode. Works perfectly with all of them. 

2. Scripts are a superpower

Software engineers have been writing scripts and CLIs for decades. Nothing new.

But when you give them to an AI agent, magic happens.

Terminal-based agents are exceptional at invoking scripts. This eliminates 90% of the complexity you get with setups like MCP.

No protocols. No complexity. Just stdin/stdout.

3. Skip the infrastructure

The pain of building AI agents is the harness. The UI. The orchestration layer.

There are hundreds of coding agents now. They already have perfect UX out of the box.

Don't rebuild what exists. Focus your energy on what makes your agent unique: powerful tools and rich context.

4. Version control native

Your agent is just files. Share the folder with your team and they can use the agent.

Track changes. Roll back mistakes. Collaborate on improvements. No deployment pipeline needed.

The craft is knowing what you don't need:

Most agents don't need vector databases. Or distributed orchestration. Or complex memory systems.

They need clear instructions, relevant context, and powerful tools that do useful things.

Is this suitable for every case? No. 
Is it suitable for multi user production situations? No
Is it a great way to quickly build useful agents for your own use? Yes.

What's your current setup for running AI agents?

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The easiest way to build AI agents in 2026

How I use Claude Code. 

7 tactics to make you a power user:

Claude Code is an AI agent that lives in your terminal. People from all industries are using it for far more than just writing code. Imagine it as the smartest personal assistant you've ever had. 

But the tool alone isn't enough. Here are 9 tactics I use daily:

1. Design before you build (think first)

Separate planning from execution. Use plan mode (Shift+TAB) to think through what you're building before Claude writes any code. What's the approach? What are the edge cases? Think first, code second. This is how senior engineers work. 

2. Break big work into tasks (plan the work)

I take a large spec/plan and automatically break it into smaller tasks to feed to an agent. Each includes what to build, why it matters, and what success looks like. Claude executes one at a time with a clean context window.

3. Iterate, don't one-shot (refine as you go)

No engineer writes code perfectly first time. So why expect AI to? 

After any first draft, I ask: "Review this and suggest improvements" or "What edge cases are we missing?" "How would you improve this code?".

4. Save commands you repeat (systematize patterns)

Use slash commands: I have a .claude/commands/ folder. /design starts a planning session. /review checks code before shipping. /commit writes proper commit messages. Write once, run forever.

5. Set up your project brain (configure once)

CLAUDE {dot} md sits in your project root. Claude reads it at the start of every session. Put your tech stack, code style rules, and things Claude should always (or never) do. You stop repeating yourself. Claude just knows.

6. Automate your standards (automate quality)

I use hooks to run tasks on every edit (tests, linting etc). Quality happens automatically. Problems get caught immediately, not discovered later.

7. Document more things (the mindset shift)

LLMs have no memory. Store important context in static docs. I keep a docs/ folder with SOPs, examples of good output, and rules for how I want things done. 

Claude is a powerful builder with amnesia. My job isn't writing better prompts. It's building docs and systems that make every session start strong.

The future of work = humans using AI agents to become superhuman. 

Which of these are you using yourself?

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7 Claude Code tactics

Most AI agents never make it to production.

Because no one knows how to deploy them.

Everyone is vibe-coding great looking demos on YouTube.

But how do you take a demo and make it work for real customers?

Most production AI systems fit three patterns:

1. API service

User calls your endpoint, gets a response.

→ Chat APIs for customer support
→ Document analysis endpoints
→ AI-powered search

Use when you need synchronous responses or human-in-the-loop.

Pro tip: Stream the response. Standard request/response feels too slow for LLMs.

2. Scheduled job

Runs on a schedule. No trigger needed.

→ Daily report generation
→ Hourly data sync
→ Weekly newsletter drafts

Use for batch processing at regular intervals. Set it and forget it.

3. Event-driven

External event triggers your automation.

→ New email arrives, process and respond
→ File uploaded, analyse and categorise
→ Webhook fires, take action

Use when reacting to the world in real-time.

The pattern you choose determines your infrastructure.

On Google Cloud, for example:

API service → Cloud Run Service (scales to zero, scales to thousands)
Scheduled job → Cloud Run Job + Scheduler (runs to completion, stops)
Event-driven → Pub/Sub + Cloud Run (decouples source from processing)

AWS and Azure have equivalents. The patterns stay the same.

That's the gap most people miss.

The engineers getting hired aren't the ones with impressive demos.

They're the ones who can answer: "How do we give this to real customers?"

Which pattern are you building with?

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3 production AI pattersn

If you want to build AI applications, you need to know how to deploy them.

Most tutorials skip this part entirely.

They end with "and now you have a working chatbot." Running on localhost. Useless for real customers.

Here's what they don't show you:

- TLS certificates
- Load balancers
- Database connections
- Environment variables and secrets
- CI/CD pipelines
- Networking and DNS
- Async work and message queues
- Container orchestration

This is real architecture. Even if a platform team handles it at your company, understanding how software gets to production matters.

I deploy AI applications three different ways depending on the situation:

1. PaaS (Render, Railway)

Push code. They handle infrastructure.

Best for: Side/solo projects, MVPs, "just get it live today."

Trade-off: Costs add up quickly once you scale.  

2. Cloud Platforms (AWS, GCP, Azure)

Managed services with real infrastructure control.

GCP Cloud Run is my default: containerised, scales to zero, pay for what you use. AWS has ECS and Lambda. Azure has Container Apps. All three work - pick one and learn it deeply.

Best for: Client work, production apps, anything enterprise.

Trade-off: Steep learning curve. Each platform has its own approach to IAM, networking, and secrets. Budget real time to learn.

3. VPS (DigitalOcean, Hetzner)

Rent a server. SSH in. Run everything yourself.

Best for: Cost optimisation, GPU workloads, learning how it actually works.

Trade-off: You're the ops team. Security, backups, uptime: all you.

There's no universal right answer.

Shipping a side project? Start with Render.

Building for clients who need scale? Learn a cloud platform.

Want full control and lowest cost? VPS.

Is this the complete picture? No. Every app has different needs.

What's your go-to deployment path?

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How to deploy AI apps to production

Qoder just launched their Teams Plan which brings agentic coding to enterprise software teams:

One of the challenges with agentic coding is working on larger real-world codebases. 

Qoder is an agentic coding platform that can search and reason across 100,000+ files, giving you deeper codebase understanding - not just individual files.. 

I use it as both a dedicated IDE and a CLI.

Here are the features worth knowing about:

1. Quest mode

This is a killer feature. Describe what you want. Qoder generates a spec, breaks it into tasks, and executes asynchronously.

You delegate, walk away, review completed work later.

This matches how we work as engineers: Plan > [tasks] > execute.

2. Repo wiki

Auto-generates documentation and keeps it in sync as code changes. Architecture overviews, module summaries, dependency maps.

3. Smart model routing

Qoder routes to the right model based on task complexity which makes token usage way more efficient. 

4. Teams Plan

→ $30/seat/month (minimum 2 seats)
→ 2,000 credits per seat
→ Centralized billing and admin console
→ SSO for team-wide access
→ Shared credit packages coming soon

Qoder is moving agentic coding from “nice to have” to serious infrastructure for enterprise software teams.

Check it out here: https://aisecret.co/Owain

If you want to get reliable data from LLMs, learn this essential technique:

The problem is simple:

LLMs return text. Your application needs structured data.

You ask for JSON. Sometimes you get JSON. Sometimes you get JSON wrapped in markdown. Sometimes you get a paragraph explaining the JSON.

Your code expects one format. The model gives you three.

This is why structured outputs exist.

What are structured outputs?

A way to force an LLM to return data in an exact format you define.

Instead of asking the model to return any old answer, you give it a schema. The model's output must match that schema. No exceptions.

When to use them:

→ Extracting information from text (names, dates, entities)
→ Classifying content into categories
→ Routing decisions in AI workflows
→ Any time downstream code expects specific fields

If your AI application needs predictable data from an LLM, don't rely on instructions.

Use structured outputs.

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Getting Senior Leaders to Optimal Performance and Sustainable Growth Without Burnout

You’re capable, experienced, and used to handling pressure. Yet certain moments quietly destabilise even strong leaders.
► When you step into a new role and realise the rules have changed
► When a restructuring, missed promotion, or sudden shift knocks confidence more than you admit
► When performance still looks “fine”, but decision-making feels heavier and slower than it should

These moments don’t look dramatic from the outside. 
They look like coping.

And this is where many leaders hesitate —
waiting for clarity to return on its own,
until the cost shows up as loss of judgment, credibility, or energy.

👉 This is where my work begins.
I partner with senior leaders and leadership teams during high-stakes transitions — where clarity, authority, and direction matter more than speed.

My clients typically come to this point when they need to:

✔️ Make clear, grounded decisions under pressure 
✔️ Re-establish authority and presence in a new or changing role 
✔️ Navigate career transitions or restructuring without loss of momentum 
✔️ Lead teams through complexity, transformation, or sustained change 
✔️ Perform at a high level without sacrificing health, focus, or integrity

I bring 17+ years of experience from Fortune 500 organisations (P&G, General Mills, GSK, Syngenta), combined with professional executive coaching and mentoring (ICF PCC, EMCC SP). 

I understand the realities of senior leadership — competing priorities, political complexity, and the expectation to “hold it together” while everything shifts.

My clients often describe the biggest shift not as improved performance alone — but as clearer thinking, steadier confidence, and the ability to lead without constantly operating in reactive mode.

If you’re navigating a meaningful transition — whether you’re still in role or between chapters — and sense that waiting is no longer the right strategy, you’re welcome to message me here on LinkedIn to explore whether working together makes sense.

— 
🌐 tatianarueff.com 
📩 tatiana@tatianarueff.com 

For organisations, I also design and deliver senior leadership workshops and keynotes on leading through high-stakes transitions — enabling sustainable performance, clarity, and resilience under pressure.

Tatiana Rueff

𝐓𝐡𝐞𝐫𝐞’𝐬 𝐚 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐦𝐨𝐫𝐞
𝐚𝐧𝐝 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐜𝐥𝐞𝐚𝐫𝐥𝐲 𝐞𝐧𝐨𝐮𝐠𝐡 𝐭𝐨 𝐚𝐜𝐭.

Most of the leaders I work with aren’t stuck 
because they are not thinking enough.

They see what’s no longer working.
They understand the implications.
They have ideas to test.
They’ve thought it through - repeatedly.

What’s missing isn’t insight.

It’s a space where clarity turns into decisions,
and decisions turn into movement -
without rushing, proving, or forcing outcomes.

Some moments don’t need more effort.
They need the right kind of containment.

P.S. If something you’ve been circling feels ready to be addressed, this is the kind of transition that benefits from the right container.

Partnering with Leaders through High-Stakes Transitions | Clear Decisions & Sustainable Growth under Pressure | Executive & Leadership Transition Coach (ICF PCC) | Ex-Fortune 500

Tatiana Rueff, Executive and Leadership Transition Coach, standing in a snowy mountain village, looking calmly at the camera, with alpine houses and snow-covered peaks (Mont Blanc) behind her on a clear winter day.

𝐓𝐡𝐞 𝐡𝐚𝐫𝐝𝐞𝐬𝐭 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐚 𝐜𝐚𝐫𝐞𝐞𝐫 𝐭𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧
𝐢𝐬 𝐧𝐨𝐭 𝐤𝐧𝐨𝐰𝐢𝐧𝐠 𝐡𝐨𝐰 𝐥𝐨𝐧𝐠 𝐢𝐭 𝐰𝐢𝐥𝐥 𝐭𝐚𝐤𝐞.

Between roles many leaders feel an invisible clock ticking.

They don’t want to rush.
They don’t want to repeat old patterns.
But they also don’t want to get stuck.

So they stay busy instead.

Networking.
Thinking.
Sending applications daily.
Scrolling LinkedIn.

Yet the discomfort isn’t really about time.
It’s about not having enough structure for the transition.

Without that, even thoughtful leaders start doubting themselves.

This is where transitions quietly stretch longer than necessary -
not because skills are missing,
but because support is.

P.S. Being between chapters isn’t a gap to fill. 
It’s a phase to take seriously.

A quiet home office with a wooden desk by a window, a beige leather chair slightly pulled back, an open laptop displaying emails, a notebook and pen, a coffee mug, and a digital clock showing 13:30, suggesting a paused workday and time passing.

𝐋𝐢𝐟𝐞 𝐢𝐬𝐧’𝐭 𝐚 𝐪𝐮𝐢𝐞𝐭 𝐥𝐚𝐤𝐞.
𝐈𝐭’𝐬 𝐚𝐧 𝐨𝐜𝐞𝐚𝐧.

And waiting won’t make it calmer.

Many people are waiting.

Waiting for the market to improve.
Waiting for work to slow down.
Waiting for life to feel easier before they finally breathe again.

They hope the waters will calm.

But life is rarely a quiet lake.
Most of the time, it’s a wavy ocean - 
unpredictable, demanding, alive.

And while you’re waiting for perfect conditions,
your life is still passing by.

Sustainable growth doesn’t come from hoping the waves disappear.
It comes from learning how to surf.

From building balance, clarity, and resilience 𝘪𝘯𝘴𝘪𝘥𝘦 you -
so you can navigate change without burning out
and even enjoy the ride.

𝐓𝐡𝐞 𝐰𝐚𝐯𝐞𝐬 𝐚𝐫𝐞 𝐧𝐨𝐭 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦.
𝐖𝐚𝐢𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐭𝐡𝐞𝐦 𝐭𝐨 𝐬𝐭𝐨𝐩 𝐢𝐬.

P.S. Where in your life are you still waiting for “better times”?

Guiding Senior Leaders & Teams Through Transitions | From Pressure to Clarity, Confidence & Sustainable Growth | Executive & Leadership Transition Coach (ICF PCC) | Ex-Fortune 500

A split image contrasting two approaches to challenge: on the left, a person stands at the edge of a turbulent ocean, watching the waves; on the right, a surfer actively rides a large wave, moving with strength and balance.

𝐁𝐮𝐫𝐧𝐨𝐮𝐭 𝐫𝐚𝐫𝐞𝐥𝐲 𝐬𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 𝐞𝐱𝐡𝐚𝐮𝐬𝐭𝐢𝐨𝐧. 
𝐈𝐭 𝐬𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 𝐦𝐢𝐬𝐚𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭.

Many senior leaders don’t collapse.
They continue performing.

What changes first is something quieter.

Decisions take longer.
Recovery takes more effort.
Quality thinking time disappears.
Weekends bring more screen time than joy.

They tell themselves it’s temporary.
That pressure comes with the role.

Sometimes that’s true.
Often it’s a signal.

Sustainable growth isn’t about pushing less.
It’s about noticing when the cost is no longer worth the output.

That moment is easy to rationalise away -
until it forces a decision later, 
under worse conditions.

P.S. If the price of “doing well” has been rising for a while, it might be asking for your attention now.

Tatiana Rueff, executive and leadership transition coaching, seated on a wooden balcony overlooking snow-covered mountains and alpine houses, appearing thoughtful and composed in a calm winter setting.

𝐄𝐯𝐞𝐫𝐲 𝐩𝐫𝐨𝐦𝐨𝐭𝐢𝐨𝐧 𝐚𝐧𝐧𝐨𝐮𝐧𝐜𝐞𝐦𝐞𝐧𝐭 𝐨𝐧 𝐬𝐨𝐜𝐢𝐚𝐥 𝐦𝐞𝐝𝐢𝐚
𝐦𝐚𝐤𝐞𝐬 𝐬𝐨𝐦𝐞𝐨𝐧𝐞 𝐞𝐥𝐬𝐞 𝐪𝐮𝐢𝐞𝐭𝐥𝐲 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐭𝐡𝐞𝐢𝐫 𝐥𝐢𝐟𝐞.

Scroll through LinkedIn on any given day and you’ll see it.

New role announcements.
Promotions.
Bigger titles.
Public celebrations of progress.

Most people could make these updates invisible - 
but they don’t.

Because being seen, acknowledged, and validated matters.
That’s human.

What we don’t talk about enough is the other side of those posts.

The quiet comparison they trigger in leaders who are already carrying a lot.

𝘈𝘮 𝘐 𝘧𝘢𝘭𝘭𝘪𝘯𝘨 𝘣𝘦𝘩𝘪𝘯𝘥?
𝘞𝘩𝘺 𝘢𝘮 𝘐 𝘯𝘰𝘵 𝘵𝘩𝘦𝘳𝘦 𝘺𝘦𝘵?
𝘏𝘰𝘸 𝘥𝘪𝘥 𝘐 𝘦𝘯𝘥 𝘶𝘱 l𝘦𝘢𝘷𝘪𝘯𝘨 𝘮𝘺 𝘳𝘰𝘭𝘦 𝘸𝘩𝘪𝘭𝘦 𝘵𝘩𝘦𝘺 𝘴𝘵𝘦𝘱𝘱𝘦𝘥 𝘪𝘯𝘵𝘰 𝘮𝘪𝘯𝘦?
𝘞𝘩𝘢𝘵 𝘥𝘰𝘦𝘴 𝘵𝘩𝘪𝘴 𝘴𝘢𝘺 𝘢𝘣𝘰𝘶𝘵 𝘮𝘺 𝘷𝘢𝘭𝘶𝘦?

These questions don’t stop early in a career.

I hear them from VPs, CEOs, and founders -
especially in moments of transition.

The problem isn’t ambition.

The problem is the false idea that there’s a universal success ladder we’re supposed to follow.

There isn’t.

Careers are not straight lines.
They pause, accelerate, reset, and sometimes fall apart - 
often for reasons far beyond effort or talent.

When you measure yourself against someone else’s highlight reel, you risk seeing personal failure where there is none.

You’re not late.
You’re not early.

You are where your life has brought you - 
and you still get to decide what comes next.

𝐏.𝐒.  Which “should” have you been carrying that no longer serves you?

Man lying in bed at night looking at his phone, reflecting on social media comparison and career self-doubt.

𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐚 𝐧𝐞𝐰 𝐫𝐨𝐥𝐞 𝐢𝐬 𝐦𝐨𝐫𝐞 𝐝𝐞𝐦𝐚𝐧𝐝𝐢𝐧𝐠 
𝐭𝐡𝐚𝐧 𝐦𝐨𝐬𝐭 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐞𝐱𝐩𝐞𝐜𝐭 𝐚𝐧𝐝 𝐭𝐡𝐞𝐫𝐞’𝐬 𝐚 𝐫𝐞𝐚𝐬𝐨𝐧.

It’s not because you’re unprepared.
Or lacking confidence.
Or suddenly less capable.

It’s because you’re stepping into a system that already exists -
with unspoken expectations, inherited dynamics, 
and histories you didn’t create.

You’re being read before you fully understand the context.
Your decisions matter before you’ve had time to see the full picture.

And the pressure to “add value quickly” is constant, 
even when no one says it out loud.

Most leaders respond by accelerating.

They work harder.
They say yes more often.
They try to prove themselves early.

But early overcompensation is how misalignment starts.

The leaders who land well do something different.

They slow their thinking before they speed up their actions.
They take time to understand what’s really being asked of them, 
not just what’s written.

They decide what not to change yet.

This isn’t about confidence.
It’s about orientation.

The first moves you make set patterns that last far longer than people expect.

P.S. If you’re stepping into something new and feel the pressure to get it “right” quickly, that’s not a flaw. It’s the moment.

A professional woman stands by a large window overlooking a lake and mountains, reflecting quietly before stepping into a new role.

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐧𝐨𝐭𝐢𝐜𝐞𝐝 𝐭𝐡𝐞 𝐬𝐮𝐧𝐠𝐥𝐚𝐬𝐬𝐞𝐬.
Very few talked about what that reaction reveals about leadership.

When Emmanuel Macron wore sunglasses indoors at Davos, 
the world listened - and still speculated.

And this is the interesting part:
an explanation had already been given.

It was presented as a benign eye issue -
nothing strategic, nothing symbolic.

Still -

Memes spread.
Commentary escalated.
The company behind the sunglasses saw its share price jump 28%.

Not because eyewear matters -
but because when a leader breaks an unspoken norm, 
explanation alone doesn’t stop interpretation.

This happens inside organisations every day.

A leader goes quiet.
Delays a decision.
Shows up differently than expected.

Even when there is a rational reason,
the system keeps reading signals.

Stories fill the gap:

– “Something’s off.”
– “A restructuring is coming.”
– “There must be more going on.”

The real risk for leaders isn’t deviation.
It’s assuming clarity has been received just because it was provided.

Strong leadership isn’t about over-explaining.

It’s about recognising when perception has momentum -
and choosing whether to actively frame it, hold it, or redirect it.

Because whether you intend it or not,
people are always reading the signals.

PS: Where have you seen a small, unintended leadership signal take on disproportionate meaning?

A pair of blue-tinted aviator sunglasses on a light background.

𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐚𝐜𝐭𝐢𝐨𝐧 𝐪𝐮𝐢𝐞𝐭𝐥𝐲 𝐞𝐱𝐩𝐢𝐫𝐞𝐬.

Many leaders value reflection.

They think deeply.
They notice patterns.
They see what’s no longer working.

And then… they wait.

They tell themselves they’re “not ready yet”.
That they need a bit more time.
That there’s something urgent to take care of first.
That they’ll figure it out.

But insight has a shelf life.

What starts as awareness slowly turns into friction.
The same questions resurface.
The same tension returns, just louder.

At some point, not acting costs more than acting.

The tricky part is that nothing breaks immediately.

You keep functioning.
You keep thinking about it.
You keep living your life.

But over time, the cost shows up elsewhere -
in energy, focus, and the quality of decisions.

That’s when waiting stops being a pause
and starts shaping the outcome.

P.S. If something has been unaddressed for a while, this might be the moment it’s asking for a different response from you.

A man at a home office desk looks at an hourglass as time passes, reflecting on decisions and waiting.

𝐓𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 90 𝐝𝐚𝐲𝐬 𝐝𝐨𝐧’𝐭 𝐟𝐚𝐢𝐥 𝐥𝐨𝐮𝐝𝐥𝐲.
𝐓𝐡𝐞𝐲 𝐝𝐫𝐢𝐟𝐭.

Most leaders prepare hard for interviews.
Very few prepare for what comes after.

They step into a role already moving.
With inherited dynamics they didn’t design.
And expectations that aren’t fully spoken.

So they compensate early.

They try to add value fast.
They change things quickly.

And sometimes… 
they lock in patterns they later have to undo.

The leaders who land well don’t rush alignment.
They create space to think before they accelerate action.

That window is brief.
And often underestimated.

P.S. If you’ve recently stepped into a new role and feel the pressure to move fast, this is the moment I help leaders slow their thinking before their actions lock in.
You don’t need to navigate it alone.

A silhouetted man standing indoors by a window, looking out at blurred car lights moving quickly through the night, suggesting contrast between stillness and speed.

𝐘𝐨𝐮 𝐜𝐚𝐧 𝐛𝐞 𝐝𝐨𝐢𝐧𝐠 𝐰𝐞𝐥𝐥 𝐨𝐧 𝐩𝐚𝐩𝐞𝐫
𝐚𝐧𝐝 𝐬𝐭𝐢𝐥𝐥 𝐛𝐞 𝐩𝐚𝐲𝐢𝐧𝐠 𝐭𝐨𝐨 𝐡𝐢𝐠𝐡 𝐚 𝐩𝐫𝐢𝐜𝐞.

Many senior leaders look like they’re doing fine.

They deliver.
They show up.
They keep things moving.

But privately, the cost is rising.

More effort to stay focused.
More recovery time after non-stop meetings.
Less space to think clearly.

They tell themselves:

“𝘛𝘩𝘪𝘴 𝘪𝘴 𝘫𝘶𝘴𝘵 𝘢 𝘥𝘦𝘮𝘢𝘯𝘥𝘪𝘯𝘨 𝘱𝘩𝘢𝘴𝘦.”
“𝘐 𝘴𝘩𝘰𝘶𝘭𝘥 𝘣𝘦 𝘢𝘣𝘭𝘦 𝘵𝘰 𝘩𝘢𝘯𝘥𝘭𝘦 𝘵𝘩𝘪𝘴.”
“𝘖𝘵𝘩𝘦𝘳𝘴 𝘴𝘦𝘦𝘮 𝘵𝘰 𝘮𝘢𝘯𝘢𝘨𝘦.”

High performance can mask misalignment for a long time.

Until one day, the question shifts from

“𝘊𝘢𝘯 𝘐 𝘥𝘰 𝘵𝘩𝘪𝘴?”
to
“𝘋𝘰 𝘐 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘬𝘦𝘦𝘱 𝘥𝘰𝘪𝘯𝘨 𝘪𝘵 𝘵𝘩𝘪𝘴 𝘸𝘢𝘺?”

That’s not weakness.
That’s information.

P.S. Where has “pushing through” quietly become your default?

𝐓𝐢𝐦𝐞 𝐢𝐧 𝐭𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧 𝐢𝐬𝐧’𝐭 𝐝𝐚𝐧𝐠𝐞𝐫𝐨𝐮𝐬. 
Poor positioning is.

Most senior leaders don’t struggle because transitions take too long.
They struggle because they stay too broad for too long.

Time itself isn’t the problem.
Ambiguity is.

When your message sounds like:

“𝘐’𝘮 𝘰𝘱𝘦𝘯.”
“𝘐 𝘤𝘢𝘯 𝘥𝘰 𝘴𝘦𝘷𝘦𝘳𝘢𝘭 𝘵𝘩𝘪𝘯𝘨𝘴.”
“𝘐’𝘮 𝘧𝘭𝘦𝘹𝘪𝘣𝘭𝘦.”

You make it harder for others to place you -
and easier for the wrong opportunities to absorb your energy.

At senior level, clarity isn’t a constraint.
It’s a service.

The leaders who land well don’t rush.
They narrow deliberately - often with support - 
long before urgency starts making decisions for them.

The right support doesn’t just protect the outcome.

It sharpens positioning 
and that’s what changes the timeline.

If this resonates, you’re already thinking at the right level.

A narrow mountain path running along a green ridge under a clear blue sky, with steep slopes on both sides, symbolising focus and deliberate career progress.

𝐓𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐟𝐟𝐞𝐜𝐭 𝐢𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥𝐬. 
𝐈𝐭 𝐪𝐮𝐢𝐞𝐭𝐥𝐲 𝐫𝐞𝐬𝐡𝐚𝐩𝐞𝐬 𝐭𝐞𝐚𝐦 𝐝𝐲𝐧𝐚𝐦𝐢𝐜𝐬. 

When an organisation goes through transition - 
a leadership change, restructuring, strategy shift - 
the impact isn’t limited to roles or org charts. 

Team dynamics change first. 

What I often observe is not open resistance, but subtle shifts: 

– Decision-making slows 
– Ownership becomes blurred 
– Informal leaders go quiet 
– High performers start self-protecting 

On the surface, work continues. 
Underneath, alignment erodes. 

This isn’t a people problem. 
It’s a leadership transition problem. 

Teams need clarity on: 

– what still matters 
– what has changed 
– where decisions now sit 
– and what “good performance” looks like in the new context 

Without that, leaders spend months compensating with more meetings, more pressure, and more effort - 
often burning out their best people in the process. 

Sustainable leadership in transition isn’t about pushing harder. 
It’s about stabilising the system so people can perform again. 

That’s the work I do with leaders and organisations navigating change.

An empty leadership meeting room in a modern office with panoramic views over Zurich, representing organisational transition, decision-making, and the need for leadership clarity.

𝐌𝐨𝐬𝐭 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐝𝐨𝐧’𝐭 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞𝐲 𝐥𝐚𝐜𝐤 𝐢𝐧𝐬𝐢𝐠𝐡𝐭.

They notice when something starts to feel off.
When the role demands more than it gives back.
When effort increases but clarity doesn’t.

What’s harder is knowing how to respond in that moment.

Do you pause and think it through alone?
Do you give it time, hoping the pressure will ease?
Do you reach for an external perspective or structured support early enough?

I see very capable leaders make very different choices at this point.

And those choices quietly shape how long transitions last and how costly they become.

I’m curious what tends to be true for you in practice, not in theory.

When you realise something in your career is no longer sustainable, what do you tend to do first?

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