Constant Thinking

Video: What I actually do with AI every day

2026-03-01T00:00:00+00:00

Here’s a video I just uploaded to YouTube, walking through 7+1 examples of how AI has woven itself into my daily life — from the mundane to the surprisingly useful. No demos, no hypotheticals, rather what an actual week with AI looks like for me.

How to stop engineering prompts and start delegating

2026-02-17T00:00:00+00:00

TL;DR: The best “prompt engineering” technique isn’t engineering at all—it’s delegation. Transfer intent, not instructions. For quick tasks, tell the AI what success looks like and why (the Intent Prompt). For complex work, equip it with context, deliverables, and decision principles so it can navigate on its own (the Delegation Brief). You already have these skills. This post helps you apply them to AI.

From scarce code to abundant builders

2026-02-06T00:00:00+00:00

According to media pundits, software stocks are “crashing”, and there’s a “SaaSpocalypse” going on. Recently, Noah Smith wrote about “The Fall of the Nerds</a>,” painting a picture of software engineers as the new master weavers: skilled artisans about to be displaced by AI-powered looms. Meanwhile, I’m seeing more and more layoff announcements in my LinkedIn feed.
My heart and hopes go out to everyone who is affected by layoffs. I wish you all the best, and I believe I see good reasons for how this may turn out for the better, after some time.
Meanwhile, the anxiety is real. But I think the narrative is wrong.
What we’re seeing now isn’t the death of software, or the end of tech careers. It’s more like a structural shift from scarcity to abundance. A pattern that has repeated throughout economic history. And once you see the pattern, you can also see something much more useful: new options about what you can do next.
Why software companies exist</h2>
Here’s a question nobody seems to be asking: Why do software companies exist in the first place?
The answer is deceptively simple. For decades, the ability to write software was a scarce resource. Building anything useful required specialized coding expertise that most companies simply didn’t have. So the market did what markets do with scarcity: it centralized that rare capability. Software companies emerged as aggregation points: “coding centers” that concentrated scarce programming talent on behalf of the rest of the industry.
This made perfect sense. If you couldn’t build it yourself, you bought it from someone who could.
But here’s the structural tension that was always there, hiding in plain sight: software companies’ domain expertise is inherently horizontal. They have to serve hundreds or thousands of customers, which means their products must work for the general case. Meanwhile, each customer’s actual needs are deeply vertical—specific to their industry, their processes, their competitive edge.
That gap between horizontal product and vertical need was tolerable when the only alternative was building nothing at all. But the alternatives have changed.
The abundance shift</h2>
AI coding tools have fundamentally changed this equation. Not by replacing programmers, which is a narrative that misses the point entirely. What they’ve done is: they made the ability to build software dramatically more accessible. The cost of building has dropped. The expertise required to get started has dropped even further.
The scarce resource that justified centralizing coding capability is now becoming abundant.
This doesn’t mean every company will suddenly build everything from scratch. But it does mean that the horizontal-vs-vertical gap, the one that was always there, now becomes the deciding factor. When building is hard and expensive, you tolerate an 80% solution from a vendor. When building becomes accessible, you start asking: “Why am I paying for features I don’t need, while missing the ones that actually matter for my business?”
As App Economy Insights recently noted</a>, even the pricing model is under pressure. Seat-based SaaS pricing assumed that value scales with headcount. In a world where AI agents can execute work autonomously, that assumption breaks down.
Build what makes you, you</h2>
One of my favourite customers from my time as a Principal Solutions Architect at AWS offers a great example here.
This German Mittelstand company, an automotive OEM supplier, had built their own Manufacturing Execution System (MES) in-house. For years, I watched them face intense pressure from management to cut development costs and replace it with a standardized, off-the-shelf solution. The argument sounded reasonable: why maintain expensive custom software when you can buy a proven product?
I always argued against it. Their core competency as a German industrial company is precisely the ability to orchestrate complex just-in-time manufacturing processes within the intricate supply chain that defines the automotive industry. Every optimization point, every process refinement they’d developed over decades was encoded in that system. By handing control to a standard software provider, they wouldn’t just be outsourcing code. They’d be surrendering one of their core competitive advantages.
Fortunately, they resisted the pressure. But it was a close call.
Today, AI coding tools are vindicating that decision. Instead of being locked into a vendor’s horizontal product, they can now accelerate their custom MES development, expand its capabilities, and grow their development team’s responsibilities from maintaining code to building intelligent tools and services that amplify the entire company’s capabilities. Their software developers are becoming something more valuable: domain-expert builders who understand both the business and the technology.
We’ve seen this movie before</h2> If the “scarcity becomes abundance” pattern sounds familiar, it should. Dan Pink described almost exactly this dynamic in his 2004 book A Whole New Mind</a>. He identified three forces threatening traditional knowledge workers: Abundance, Automation, and Asia (outsourcing). His argument: when analytical, “left-brain” skills become commoditized, the value shifts to what remains scarce—creativity, empathy, systems thinking, meaning.
I wrote about this on my blog back in 2010</a>, applying Pink’s framework to technology careers. Sixteen years later, the same pattern has simply moved up a level. Back then, Abundance, Automation, and outsourcing threatened individual knowledge workers. Today, those same forces are reshaping the companies that employed them.
The lesson is consistent: when something scarce becomes abundant, don’t mourn the scarcity. Look for what remains scarce—and invest there.
The solar grid, not the ice age</h2>
So are software companies doomed? I don’t think so. But the sensationalist framing doesn’t help.
Here’s a better analogy than “apocalypse”: think of centralized power generation meeting distributed solar.
For decades, if you wanted electricity, you bought it from a utility. They had the power plants, the expertise, the infrastructure. Then solar panels got cheap enough for homes and businesses to generate their own power. Just last summer, I installed a balcony solar plant for our apartment</a> that’s been humming along nicely since then. Did utilities disappear? No. But their role fundamentally transformed: from sole provider to grid balancer, integrator, and backstop for what distributed generation can’t cover.
The parallel to software is striking. And if you’ve been in the cloud computing world, you’ll recognize another pendulum swing: cloud computing centralized IT infrastructure into hyperscale data centers, and now hybrid cloud and cloud sovereignty are redistributing some of that back to the edge. Centralization and decentralization aren’t opposites—they’re phases in a recurring cycle. The balance point just shifted.
Software companies that survive this shift will be the ones that stop selling scarce code and start selling what genuinely remains scarce: deep domain expertise, complex system integration, outcomes for organizations that can’t yet build for themselves, and platforms that make distributed builders more effective.
What this means for you</h2>
If you’re a business leader, here’s the strategic takeaway: reconsider your build-vs-buy defaults. For anything close to your core value chain—the processes and capabilities that define your competitive edge—building may now be the smarter investment. Not because AI makes building trivial, but because it makes building accessible enough that you no longer have to sacrifice your vertical needs for a horizontal product.
If you’re a tech professional feeling anxious right now, I want to offer some perspective. As Andrew Ng pointed out this week</a>, most current layoffs are pandemic hiring corrections and general cost-cutting, not AI replacing jobs wholesale. But the demand is shifting, and the skills that complement AI are the ones worth investing in.
The good news is: those skills aren’t exotic. They’re the Expert Generalist skills I wrote about last year</a>, and they echo what Werner Vogels described as the “Renaissance Developer</a>”: systems thinking, cross-domain pattern recognition, the ability to bridge business and technology, and the curiosity to keep learning by doing. These are precisely the capabilities that AI amplifies rather than replaces. And they rhyme well with Dan Pink’s creativity advice checklist: Design, Story, Symphony, Empathy, Play, Meaning.
Start building. In every sense.</h2> My challenge to you: invest in learning this week. That might mean building your own tool instead of buying one. For example, I recently built my own video background generator</a> instead of paying €60 for Apple Motion, and the experience taught me more than the tool itself was worth. Check out Simon Willison’s collection</a> of 178+ AI-built tools for inspiration.
Because “building” goes beyond code and tools. Build cross-domain expertise. Build your ability to see systems. Build bridges between the business problems you understand and the AI tools that can help solve them.
The age of scarce code is ending. The age of abundant builders is just beginning. And that’s not a crisis—it’s an opportunity.
For an individual perspective on “Build vs. Buy,” and some more background (pun intended) on the background generator mentioned above, check out this companion video on the Constant Thinking YouTube channel</a>:

Play Video </div> </div> </div>
References</h2>

Noah Smith: The Fall of the Nerds</a> (Noahpinion, February 2026)</li>
App Economy Insights: SaaSpocalypse Now</a> (January 2026)</li>
Andrew Ng: The Batch, February 6, 2026</a> (DeepLearning.AI)</li>
Dan Pink: A Whole New Mind</a> (2004)</li>
My earlier posts on Expert Generalists</a>, Werner Vogels’ Renaissance Developer</a>, and the Dan Pink book review (2010)</a></li>
Simon Willison’s Tools: tools.simonwillison.net</a></li> </ul>

Local audio transcription with mlx-whisper: what actually works

2026-01-30T00:00:00+00:00

I’ve been transcribing my YouTube videos locally for a few months now. It took some trial and error to get a setup that actually works reliably, so here’s what I learned.
The problem</h2>
I wanted good subtitles for my videos—for accessibility, but also so I could feed the transcripts to Claude for generating chapter markers and summaries. The auto-generated subtitles I saw on YouTube weren’t great: weird punctuation, mangled technical terms. And I didn’t want another SaaS subscription.
So I went down the rabbit hole.
First attempt: OpenAI Whisper</h2>
The obvious choice: OpenAI’s Whisper</a>. Open source, well-known, everyone says it’s amazing.
And it is—kind of. The quality is good, but on my MacBook Pro M1 it was painfully slow. We’re talking much slower than real-time, with minutes of loading before it even started processing. It wasn’t using the GPU at all.
There had to be something better, so I kept on searching.
The solution: mlx-whisper</h2>
mlx-whisper</a> runs Whisper on Apple’s MLX framework, optimized for Apple Silicon. The difference was night and day. Same 5-year-old MacBook, suddenly transcription was faster than real-time. (Yes, there’s still the initial delay for downloading the model weights from the Internet, but it caches them, so no big deal. There’s also a lag for loading the model weights into memory, but overall, still much, much faster than original Whisper.)
Installation is dead simple if you use uv</a>:
uv tool install mlx-whisper</code></pre> That’s it. No ML driver headaches, no dependency hell.
The Crisis</h2>
For a few weeks, life was good. Then I was transcribing a new video with tips on starting a new tech job</a>, and the output looked like this:
Learn the terrain. Learn the terrain. Learn the terrain. Learn the terrain. Learn the terrain.</code></pre> The same words, repeating for entire paragraphs. My transcript was useless.
I almost gave up and paid for a SaaS service. But I gave it another shot and searched for the problem in Perplexity</a>. Turns out this is a known Whisper issue. The model uses previous context to improve accuracy, but sometimes gets stuck in a loop.
The Fix: One Flag</h2>
--condition-on-previous-text False</code></pre> That’s it. One flag. It tells Whisper not to use its previous output as context, which prevents the repetition loop. The accuracy is still excellent without it.
My Complete Setup</h2>
Here’s the Fish shell function I use every week:
function gen-subtitles mlx_whisper \ --task transcribe \ --model mlx-community/whisper-large-v3-turbo \ --language en \ --output-format srt \ --word-timestamps True \ --max-line-width 42 \ --max-line-count 2 \ --condition-on-previous-text False \ "$argv" end</code></pre> What each flag does:

--task transcribe</code>: Transcribe (don’t translate, which apparently it can do, but I haven’t tried yet)</li>
--model mlx-community/whisper-large-v3-turbo</code>: Best balance of accuracy and speed I’ve found</li>
--language en</code>: Skip language detection, improves accuracy</li>
--output-format srt</code>: Standard subtitle format, works everywhere</li>
--word-timestamps True</code>: More precise timing</li>
--max-line-width 42</code> and --max-line-count 2</code>: Keeps subtitles readable on screen</li>
--condition-on-previous-text False</code>: The hero flag that prevents repetition</li> </ul> Now I just run gen-subtitles video.mp4</code> and get a clean SRT file in a few minutes.Not 100% perfect, but close</h2> mlx-whisper</code> is really good, but not perfect. I still do a quick pass in vi to fix maybe 5% of the output—a comma that should be a colon, a technical term that got mangled, adding quotes around citations. But that’s editing, not rewriting. 95% of the work is done well. Much better than paying $20/month or doing it by hand. Beyond Subtitles</h2> Once you have easy local transcription, you start seeing uses everywhere: Voice memos: Capture ideas while walking, transcribe later into searchable notes</li> Meeting recordings: Transcribe locally, everything stays on your machine</li> Learning from podcasts: Transcribe, then ask Claude to summarize or extract key points (I use Simon Willison’s llm tool</a> for this)</li> Content repurposing: Transcript → chapter markers → show notes → blog post</li> </ul> The transcript is so much more useful than for just subtitles. Video</h2> Here’s a short video walkthrough with a bit more personality and some fun drama elements: Play Video </div> </div> </div> Links</h2> mlx-whisper on PyPI</a></li> uv for easy Python tool installation</a></li> Simon Willison’s llm CLI tool</a></li> Video version of this post</a></li> </ul>
You don’t need permission to get promoted 2026-01-23T00:00:00+00:00 After 27 years in tech—working at Sun Microsystems, Oracle, and AWS, then coaching dozens of colleagues through promotions from both sides—I’ve learned something that most people get backwards: Your career belongs to you. Not your company. Not your manager. Not the promotion committee. Everything else—job titles, paperwork, organizational processes—those are just external factors that either help you build a great career or don’t. And once you internalize this reframe, everything about promotions changes. The permission you already have</h2> Here’s what most people think: Work hard. Hope your manager notices. Feel anxious about whether you “deserve” a promotion. Ask yourself: “Am I good enough for this?” That’s the wrong question. It’s thinking too small. The better question is: “Is my organization ready to benefit from what I can give?” This shift—from scarcity thinking (“Do I deserve this?”) to abundance thinking (“Am I in the right environment?”)—changes everything. Because here’s an important truth about promotions: You don’t get promoted and then start doing next-level work. You start doing next-level work, and the promotion confirms you’re already there. A promotion is not a permission to operate at the next level. It’s merely paperwork catching up to reality. The trap to avoid</h2> Before you chase that promotion, make sure you actually want the work that comes with it, not just the title. I once worked with a brilliant engineer who got promoted to engineering manager. After a few years, his team was unhappy: turns out he wasn’t really good at managing people. And he was miserable too, because the work he loved (deep technical problem-solving) was now only 20% of his job. The tragic thing: it wasn’t his fault. The organization failed to recognize that a manager job isn’t a reward for being a great engineer. It’s just a different job. With different work. And it might not be a good fit. So before anything else, understand why you want the next level. Beyond the money, what are you actually seeking? The foundation: purpose, mastery, autonomy</h2> There’s actually some helpful science here: Dan Pink wrote a book called Drive</a> about what actually motivates us. He identified three things: Purpose, Mastery, and Autonomy. I’ve found these three elements keep you grounded through all the corporate chaos, like budget cuts, reorganizations, bad managers, all of it. Purpose is why you love your work. For me, it was always helping customers solve tough problems. That purpose carried me through the dot-com crash, company acquisitions, and political nonsense. Even on bad days, I knew why I was showing up. Mastery is getting really good at something valuable. Early in my career at Sun, and later when I joined AWS, I was like a kid in a candy store. I just wanted to learn, solve harder problems, help more customers succeed. I wasn’t thinking about promotions at all—I was obsessed with mastering my craft. Eventually, I found out I was becoming an Expert Generalist</a>, something no career guide could have ever predicted. Cal Newport wrote a great book about this: So Good They Can’t Ignore You</a>. His point: don’t just “follow your passion.” Instead, build rare and valuable skills—skills so good they can’t ignore you. Autonomy is having the freedom to make decisions. And here’s the beautiful thing: you don’t always have to wait for autonomy to be given. Sometimes you can just ask for it. “Can I lead this project?” The worst they can say is “no.” These three elements create a positive flywheel: Purpose gives you energy to develop Mastery, Mastery delivers results which earn you Autonomy, and Autonomy validates your Purpose. And these three things make you happy no matter what level you’re at. Which is crucial when your organization takes time to process promotions. The tactical playbook</h2> Once you understand your why, here’s how to make it easier for your organization to recognize you’re ready: First, figure out what the next level actually looks like. Read level descriptions if your org has them. But better yet, observe people already at that level. What do they actually do every day? In most cases, it boils down to bigger scope and bigger impact. Second, just start doing it. This is the key insight: You can start operating at the next level right now. Want to influence regional strategy instead of just country-level? Start writing those strategy documents. You don’t need a VP title to think bigger. Third, document everything. Start an achievement log today. Use the STAR method: Situation, Task, Actions, Results. Collect both stories and data showing impact. This helps the promotion process, yes, but more importantly, it helps you see your own growth. Fourth, understand your organization’s process. Some orgs are very structured. Some are informal. Some don’t really have a process at all. You might be ready for promotion, but the organization might not be ready to give it. That’s important data. True story: early in my career, I realized I was underpaid given my increased responsibilities. I asked my manager for a raise. They shied away from it: too afraid to fight for it through their org. So I changed teams within the same company. My new manager looked at my work, and within weeks, they got the paperwork done. Same company. Same me. Same work. Different manager. Different outcome. Sometimes you don’t need a different company. You just need a different team or manager. The reality check</h2> Sometimes, even when you’re doing everything right—operating at the next level, building mastery, delivering results—the promotion doesn’t come. This is where you need patience and awareness. Patience, because sometimes things just take time. Budget freezes happen. Leadership changes. Then six months later, things open up again. But awareness too. Pay attention to the data. Is this temporary? Or is this just how this organization operates? Is your manager genuinely trying to help you? Remember: “Is my organization ready to benefit from what I can give?” If the answer is “not yet, but maybe soon”, then patience makes sense. But if the answer is “no, and it’s not going to change”, it might be time to find an environment that is ready. I wrote a blog post about recognizing when it’s time to move on</a>, which resulted in one of my biggest career shifts: I basically promoted myself into a better organization. The good news: Whether you stay or go, you are in control. Your development doesn’t depend on any single promotion. Your growth doesn’t depend on any single organization. Your Next Steps</h2> Here’s some homework for you: 1. Write down your Purpose, Mastery, and Autonomy</h3> What impact matters to you? What skills are you building? What decisions do you want to make? Just three sentences. Get it out of your head. 2. Start your achievement log</h3> Create a document with this structure: ## [Month/Year] ### [Project/Achievement Name] - **Situation**: What was the context? - **Task**: What needed to be done? - **Actions**: What did you do specifically? - **Results**: What was the measurable impact?</code></pre> Add one story from last month. Set a weekly reminder to update it. 3. Pick one next-level move and start doing it</h3> Identify what people at the next level are doing. Then either ask your manager for that stretch project, or just start doing it yourself. Give yourself permission. The full story</h2> I go deeper into all of this in my video below, including the anti-pattern of promotion obsession, specific stories from my career, and the mental models that kept me sane through corporate chaos: Play Video </div> </div> </div> Related Reading</h2> If you’re early in your career, start with The explore/exploit guide to your first tech job</a>. It’s about finding your edge before thinking about promotions. For more on the Mastery piece, read How to thrive as an expert generalist in the age of AI</a>. It’s about building diverse skills that make you uniquely valuable. Books and resources</h2> Dan Pink’s TED Talk: The Puzzle of Motivation</a></li> Book: Drive</a>, by Dan Pink</li> Book: So Good They Can’t Ignore You</a>, by Cal Newport</li> </ul> What’s your purpose at work? I’m genuinely curious what drives you. Email me or leave a comment on the video, I read every one! The explore/exploit guide to your first tech job 2026-01-14T00:00:00+00:00 A YouTube viewer reached out to me the other day after watching one of my videos. He’d just started at a big tech company and wanted to know: How do you navigate the fog? How do you figure out what to focus on? What are things to avoid? I’ve felt that fog three times—at Sun Microsystems, Oracle, and AWS. And after 27 years and countless conversations with mentees, I think there are three phases that help cut through it. SMART goals are failed Taylorism (and why you should ignore them) 2026-01-08T00:00:00+00:00 A 25-year career without goals—and why that worked better than the alternative This guitar is more than 15 years old. It’s my second one—the first was bought and sold in the ’80s. I tried lessons. Self-teaching. Regular practice. Still couldn’t play a recognizable song. Now it collects dust, reminding me of an old dream that never quite worked out. Then in 2018, my wife gave me a ukulele as a gift. Twenty minutes later, I was a musician. Same dream. Different approach. And that’s exactly what this post is about—but not just music. It’s about unhealthy approaches to success, productivity, and “achieving our best year ever”. Play Video </div> </div> </div> Prefer to watch? Here’s the gist in 8 minutes on video. The resistance you’re already feeling</h2> I ran a poll on LinkedIn</a> recently, asking tech professionals: “Will you set goals for 2026?” Fifty-eight percent said no. Another 5% said “Dunno…” That’s nearly two-thirds of people who probably feel guilty right now. Everyone’s posting their goal sheets, their vision boards, their quarterly targets. The productivity industrial complex is in full swing, telling you that without SMART goals, you’re doomed to mediocrity. </picture> The full LinkedIn poll results </figcaption> </figure> And you may be sitting there thinking: “Should I be doing that too?” Here’s what I think: If you’re avoiding goals, you’re not lazy. You’re smart. The resistance you feel is not weakness. It’s human. Your brain is protecting you from something that fundamentally doesn’t fit knowledge work. SMART goals are failed Taylorism</h2> Let’s start with a quick history recap about Frederick Winslow Taylor</a>. In the early 1900s, Taylor revolutionized factory work with “scientific management”—later called Taylorism. He studied workers with stopwatches, broke tasks into tiny steps, and optimized every movement. For shoveling coal or assembling car parts, it was brilliant. Workers became more efficient. Productivity soared. Management loved it. There was just one problem: Taylor himself acknowledged his system wouldn’t work for knowledge work, as Cal Newport pointed out in his book A World Without Email</a>. Taylorism assumes workers are interchangeable cogs executing predefined, repetitive tasks. Knowledge work instead, is very different. But management tried anyway. SMART goals—Specific, Measurable, Attainable, Relevant, Time-bound—are Taylorism 2.0. An attempt to force industrial-age thinking onto creative, dynamic, knowledge-intensive roles. And it fails. Spectacularly. Why each SMART element breaks down for creative work</h2> You can’t be Specific while adapting to rapid change. Solutions architecture, product development, customer success—these roles require flexibility. The moment you lock in “specific,” reality shifts and your goal becomes obsolete. You can’t Measure creativity, learning, or customer impact with a single number. How do you quantify “helped a customer transform their business”? How do you measure “became a better mentor”? You can’t know what’s Attainable when the future is unpredictable. What if you can do more than you think? What if the market shifts? Setting “attainable” goals often means setting artificially low ones to avoid failure, or artificially high, “stretch” goals. Relevant is arbitrary. Who decides what’s relevant? Your manager? The leadership team? The productivity guru on YouTube? True relevance comes from understanding your purpose, not checking a box. And Time-bound creates artificial pressure. Why is December 31st magical? Why does failing to hit a number by an arbitrary deadline mean anything? What if you hit your goal early, on October? Should you stop working and take the rest of the year off? To be clear: For repetitive, structured tasks, goals work, just like Taylor demonstrated. It’s in creative, adaptive work where they break down. In his book The Infinite Game</a>, Simon Sinek points out that most games in life and in business don’t have winners, losers, or even clear rules. Instead, they’re infinite games, where the only goal is to stay in the game for as long as possible. That requires creativity, flexibility, and adaptability, because the rules of infinite games can be influenced, and they keep changing all the time. The three psychological problems with goals</h2> As Anne-Laure LeCunff points out in her book Tiny Experiments</a>, there are three, key psychological problems with these types of goals: First, goals create fear and paralysis. You set a big goal, then freeze. Too many options. Too much doubt. Insecurity creeps in. Second, they encourage toxic productivity, the hamster wheel effect. You chase random numbers, sacrifice everything else, risk burnout. And then the constant internal dialogue: “Did I do enough?” Third, they make you compete instead of collaborate. Everyone’s focused on their own ladder, climbing alone, measuring themselves against others. My 25 years without goals</h2> I spent over 25 years at Sun Microsystems, Oracle, and Amazon Web Services. Eventually, I became a Principal Solutions Architect, coaching executives and helping companies transform. During that time, I never sat down and wrote professional goals for myself. Well, that’s not entirely true. A few times, managers asked me to write goals. So I did, and made lists of stuff I thought they expected. Then ignored them. At the end of a year, I still got good performance reviews. Those goal sheets hardly came up then. Why? Highly creative, dynamic, customer-focused tech roles don’t work like factory worker roles. Solutions architecture is about learning, helping customers, adapting to change. You can’t put “be creative” or “adapt to emerging technologies” on a SMART goal spreadsheet. Even the best sales people I know, which are in a job that literally runs on goals, tended to ignore their numbers in their day to day work, and instead focused on transforming their customers. They also ended the year over-achieving their goals by a large margin. A real-world example</h2> In recent years, I’ve seen a lot of goal sheets. Sometimes long Excel tables with many rows of detailed goals. You know the saying: “If everything is a priority, nothing is.” I signed up for owning one of these goals—a revenue metric attached to a category of products—because I recognized the meaning behind it. Then, I worked with a few colleagues on moving it forward. The first thing I did was ignoring the number. Instead, I focused on the key customer value behind the goal, the “why”, and which actions we could facilitate to help customers get more value. That motivated the team: We were solving real problems for customers, not chasing arbitrary metrics. The ironic twist: a different part of the team worked on getting better data to track the goal. After cleaning up the data, the number went up significantly. Go figure. The lesson here is: when you focus on real value, the numbers take care of themselves. Goodhart’s law: when metrics become targets</h2> There’s a principle in economics called Goodhart’s Law</a>: ”When a metric becomes a target, it ceases to be a good measure.” The moment you make a metric your goal, you distort the system. People game it, optimize for it, lose sight of what actually matters. Metrics are useful! I’m not saying “ignore the data.” Instead, think of metrics like a car’s dashboard: You don’t drive staring at the speedometer, do you? You glance at it to avoid failure, like speeding tickets or running out of gas. But your eyes stay on the road, focused on where you’re going. The speedometer informs. It doesn’t control. That’s how metrics should work in your career, your projects, your life. They provide useful information and direction, but they are not the destination. They’re not a replacement for knowing your “why” or seeing what’s actually happening around you. Metrics are essential for understanding reality—just not for replacing judgment about what matters. Amazon’s “Dive Deep” leadership principle</a> sums it up: Leaders operate at all levels, stay connected to the details, audit frequently, and are skeptical when metrics and anecdote differ. </blockquote> My @Constant.Thinking YouTube channel</a>’s subscriber count is at 64 right now. By YouTube standards, that’s nothing. But I don’t obsess over the growth rate. I make a video every week, pay attention to what resonates, and enjoy the process. The number informs me. It doesn’t define me. A better way: intentions and the Autonomy/Mastery/Purpose framework</h2> If not SMART goals, then what? Focus on transformation instead of achievement. Focus on who you want to become, not what you want to get. Dan Pink nailed this in his book Drive</a> (you can also watch his TED talk The puzzle of motivation</a>). He researched motivation and found that traditional carrot-and-stick methods work for simple, repetitive tasks—but fail miserably for knowledge work. Instead, three things drive us: Autonomy: Staying in control over what you do, instead of following rigid orders. Mastery: Focusing on getting better every day, not hitting a one-time milestone. Purpose: Connecting to the deeper “why,” not some surface-level metric. Goals are contrary to all three. They remove autonomy (someone else sets the target), they prioritize outcomes over growth (hitting the number matters more than learning), and they often disconnect from real purpose. Intentions restore them. How to find your intention</h2> Use the 5 Whys technique</a>: Start with something that sounds like a goal and keep asking “why” until you reach the core intention. Example: “I want to run a marathon.” Why? “To get fit.” Why? “To have more energy.” Why? “To feel healthy.” There it is. Your real intention: “Become a healthy person.” (It doesn’t have to be exactly five. Just keep asking until you feel you found your intention.) The difference: Running a marathon is a one-time event. Becoming a healthy person lasts forever. And it changes what you do daily. Build the daily system:</h2> Once you have your intention, ask: What do people like that do every day? Healthy people eat real food. They move daily. They sleep well. Great! Now you can start doing one or more of those things today. Congratulations! You’re already a healthier person. This won’t be perfect on the first day. But it’s better than yesterday. And if you keep going—eat better tomorrow, move more, sleep well—small wins will compound. You’ll be amazed what happens in 30 days. A quarter. A year. James Clear explains this in great detail in his book Atomic Habits</a> and his article “Forget About Setting Goals. Focus on This Instead.</a>” Systems beat goals every time. Back to my ukulele story</h2> With guitar, I had a goal: “Learn guitar.” It felt big, scary, endless. Exercises felt like homework. With ukulele, I set an intention: “Become a musician.” </picture> I’m a musician now! </figcaption> </figure> What do musicians do? They play. Every day. So I played the ukulele every working day after breakfast. Just 20 minutes. No pressure. No milestones. Just music. I felt like a musician from Day 1. Not after years of practice—from the very beginning. That’s the difference. Instead of chasing some future achievement, I focused on becoming someone. Forever. Today, I’ve expanded to playing synths and sequencers, and I meet with friends online every Wednesday to jam together. Your turn: pick ONE intention</h2> Here’s my challenge to you. New year or not. Pick one intention. Not ten. Just one. Who do you want to become? Use the 5 Whys to find it. Then ask: “What do those people do every day?” Start doing it today. You’ll have more fun. More fulfillment. And paradoxically, you’ll achieve better results—because you’re no longer distracted by chasing numbers or paralyzed by fear of failure. If you didn’t set goals for 2026, you were right all along. Your resistance was trying to help you. If you did set goals for 2026, don’t just forget about them—find the intentions behind them instead. Some contexts benefit from clear targets. But if your work or personal ambitions require creativity, flexibility, and meaning, intentions will serve you better. Now go become that person you’ve been thinking about! The numbers will take care of themselves. I promise. We’re still living in the cat pictures era of AI 2025-12-17T00:00:00+00:00 Most people use AI the way our parents used the Internet in 1995. They’d dial up their modem, check the weather forecast on Yahoo, maybe look up a recipe, and call it a day. The idea that this same technology would eventually let them video call their grandchildren, run a business, or access humanity’s entire knowledge base? Unimaginable. Today, we’re doing the same thing with AI. We use it to summarize documents, search for answers, and write some code. That’s it. That’s our email-and-weather-forecast phase. Let me offer one more pattern that’s hiding in plain sight and can bring you one step closer to “modern AI usage” (whatever that will be 5 years from now). I re-learned this after spending a weekend building an AI-powered news briefing shell script. The knowledge-harvesting pattern that actually matters</h2> As a general rule, AI works best, the more data you send into its contect. This is what we leverage with this pattern: Collect a large amount of information</li> Filter and preprocess it down to what matters</li> Synthesize it with AI into something personally useful</li> </ol> If that sounds familiar, it should. It’s basically MapReduce</a>, the Big Data pattern that powered Google and transformed how we think about processing information at scale. Except now, instead of needing a cluster of servers and a team of engineers, you can do it from your terminal with a few tools and some scripting. Actually, you don’t even need to script. Throw a bunch of documents into a Claude project or Perplexity workspace and ask for a personalized analysis. Same pattern, no code required. But here’s the thing most people miss: this pattern works for almost any everyday problem. Research: Collect papers, filter relevant sections, synthesize insights.</li> Career planning: Collect job postings, extract patterns, generate strategy.</li> Learning something new: Collect tutorials, filter by your skill level, create a personalized roadmap.</li> </ul> The script I’m about to show you is just one more example of this pattern, with an AI twist. My weekend problem: information overload</h2> I like staying informed. Doomscrolling? Not so much. Every morning, I’d check dozens of sources: news, AI developments, world events, positive stories (who doesn’t need those?). I’d skim headlines, get distracted by clickbait, and 30 minutes later realize I’d absorbed almost nothing useful. Three years ago, the solution would have been “get better at discipline” or “use an RSS reader and train yourself to be more selective.” This weekend, I built something that seemed impossible back then, with a simple shell script. Building my personal news briefing system</h2> This news briefing system pulls from 20 sources: RSS feeds for reliable news outlets, Perplexity searches for real-time or some broader topics I care about. It can run every morning or on demand, processing everything in parallel, and handing out a beautifully formatted Markdown briefing that includes: Top news from Germany, the world, economy, tech, and entertainment</li> AI and personal productivity tips</li> An Oblique Strategy (Brian Eno style)</li> A writing prompt</li> An “Editor’s Corner” where Claude gets to be snarky about the day’s news</li> </ul> The whole thing takes about 90 seconds to run and delivers exactly what I need—no more, no less. You can watch me walk through the technical details in this YouTube video</a>, grab the complete Fish shell script from this gist</a>, or see an example output in this other gist</a>. Play Video </div> </div> </div> A few interesting details of how I got there: The technical journey (including my facepalm moment)</h2> I started with the obvious approach: feed each news source through Claude individually, asking it to “optimize” and “clean up” the content before the final synthesis. Seemed smart, right? Let AI do the preprocessing! Except I kept hitting context length limits. With 20, sometimes quite chatty input sources, even Claude Haiku’s 200k token context window wasn’t enough. That’s when I had my facepalm moment: Of course it wasn’t enough, I was burning through context across multiple LLM calls instead of one. I was treating AI like a preprocessing tool when what I really needed was old-school data munging. After trying out some CLI tools for RSS/XML processing and failing, I went back to the good old Swiss army knife of data manipulation: Python. Thanks to its feedparser</code> library, it lets me: Convert messy XML feeds to clean Markdown</li> Limit each feed to the 10 most recent items</li> Prioritize summary/description (short and crisp) over full content (blows up context size) where possible</li> </ul> This Python part is particularly fun because it’s embedded directly inside my Fish shell script, thanks to the magic of Astral’s uv</a> and its easy handling of virtual environments. Python-inside-Fish: Inception-style scripting! Parallel processing: the secret sauce</h2> Here’s where impatience and some occasional Q&A with Claude paid off: Instead of processing 20 sources sequentially (which would take forever), I learned about Fish’s job control. Each feed fetch runs in the background with &</code>, and Fish’s built-in job counter lets me know when everything’s done: while test (count (jobs)) -gt 0 sleep 1 end</code></pre> All 20 sources process in parallel. What would take 2–3 minutes sequentially now takes about 30 seconds. Good old Unix thinking, combined with AI. Why redundancy actually helps</h2> You might think 20 news sources is overkill. Why not just pick 3–4 good ones? Because redundancy makes AI smarter. When Claude sees the same story mentioned across multiple sources—each with slightly different details, emphasis, or framing—it can synthesize a more complete picture. One RSS feed might have a sparse entry with just a headline. Another might include quotes. And some things are hard to find RSS feeds for—like my local weather forecast, productivity tips, or daily facts. That’s why I added some Perplexity searches on top of RSS. Claude takes all of that and creates something better than any individual source could provide. It’s the difference between reading one journalist’s take and reading five, then forming an opinion. Surprise touch: personality</h2> Here’s my favorite part: I added a bit of personal context about myself to the prompt—my location (Munich), my interests, my dog Elvis. Now Claude sprinkles little references throughout the briefing. A weather report that suggests “perfect for a barefoot run with Elvis.” A snarky comment in the Editor’s Corner that connects back to my work. Small touches that make a daily briefing feel less like a news dump and more like a conversation with a well-informed friend. This wasn’t planned. It emerged from the pattern: collect (news), filter (what matters), synthesize (for me specifically). What this means for you</h2> If you’re reading this thinking “cool script, but I’m not a command-line person,” you may be missing the point. The script is just automation. The pattern is the unlock. You probably have a problem right now where you need to make sense of too much information: Research for a project</li> Competitive analysis for your business</li> Learning a new skill</li> Understanding a complex topic</li> </ul> Try the pattern: Collect everything relevant (don’t filter yet)</li> Preprocess it just enough to be useful (remove noise, extract key parts)</li> Let AI synthesize it into something actionable for you</li> </ol> You can do this in a Claude Project. You can do it with Perplexity. You can do it with a shell script if you like to code. The method doesn’t matter. The pattern does. Start Experimenting</h2> Three years ago, this kind of personalized, automated intelligence gathering was science fiction. Today, it’s a weekend project. We’re living in the cat pictures era of AI: most people only see the obvious use cases. But just like the Internet evolved beyond email and weather forecasts, AI will evolve beyond summarization and code generation. The question is: will you be experimenting, or will you still be stuck checking the weather? If you want to start having AI fun on the command-line, here’s how: Install the llm tool: Simon Willison’s llm CLI</a> makes working with AI models from the command line super easy</li> Pick one daily annoyance: Not everything, just one problem where you’re drowning in information</li> Try the pattern: Collect → Filter → Synthesize. Or something else!</li> Share what you discover: Seriously, I want to know what you build</li> </ol> If you want to see my exact implementation, check out: YouTube walkthrough</a> of how it works</li> Complete Fish shell script</a> on GitHub</li> Example output</a> from today’s briefing</li> </ul> The real innovation isn’t the technology. It’s learning to see patterns where others see chaos. What’s one daily problem you’d like to solve with this pattern? Share this post on your favorite social media, add your comment and let me know</a>. I read every response. Werner Vogels’ 2025 AWS re:Invent keynote is a timely career handbook for navigating accelerating change 2025-12-11T00:00:00+00:00 Watching Werner Vogels deliver his 2025 re:Invent keynote</a> last week, I experienced one of those rare moments where separate threads suddenly converge. There he was, Amazon’s CTO since 2005, laying out five principles for “Renaissance Developers”, and I realized: these weren’t just career advice for developers. They’re a career handbook for everybody in 2026. Because every job is becoming a tech job. Subconsciously, I had been following these patterns since I started my carreer in tech, 27 years ago. And they also rhyme well with Martin Fowler’s July 2025 article on “Expert Generalists”</a>. Coincicence? In fact, the timing couldn’t be more critical. We’re living through what Clayton Christensen warned about in The Innovator’s Dilemma: compounding waves of disruptive change. Ray Kurzweil’s prediction of exponential technological acceleration from the late 90’s isn’t theory anymore, it has become just another Tuesday in today’s tech world. Ray Dalio’s research on economic cycles shows we’re navigating unprecedented complexity across technology, economics, and social structures simultaneously. Werner’s five principles are much more than an inspiring keynote: they’re a career handbook for this exact moment. Table of contents</h2> Why this matters now</a></li> Be curious and keep learning</a></li> Think in systems</a></li> Communicate with precision</a></li> Be an owner</a></li> Become a polymath</a></li> Your January challenge</a></li> </ol> Why this matters now</h2> These five principles aren’t new, individually. But together, they matter today more than ever. We’re experiencing what Clayton Christensen warned about: multiple overlapping disruptive changes. Cloud computing. Big data. IoT. Machine learning. AI. Industry 4.0. Electric vehicles. Space commercialization. Biotechnology. Robotics. Each wave alone would be transformative. But together, they’re creating Kurzweil’s predicted acceleration: exponential change becoming super-exponential as domains cross-pollinate and amplify each other. Ray Dalio’s economic research shows we’re simultaneously navigating technological, economic, and social complexity at unprecedented scales. The pace of change demands exactly what Werner prescribed: continuous learning, systems thinking, precise communication, ownership, and polymathic breadth. The critical insight is: every job is becoming a tech job. Digitalization, cloud computing, big data, IoT, and AI are touching every industry. You don’t need to become a programmer. But you do need to understand how technology shapes your domain. The CFO who understands supply chain and product development in addition to deep finance expertise? That’s T-shaped—deep in one domain, broad across many. The marketing leader who grasps data pipelines and spots trends in real-time? Same pattern. During my 12+ years at AWS, I’ve seen how this approach works. Starting as one of the first Solutions Architects in Germany, I navigated multiple technology waves by embodying these principles, often unconsciously at first, then deliberately. Each year brought different challenges requiring new skills, but the meta-pattern remained: stay curious, see systems, communicate clearly, own outcomes, broaden and deepen expertise. Let’s look at them in more datail. Be Curious and Keep Learning</h2> What it means: Not just passive consumption, it’s all about active building. The Chinese proverb nails it: “When I hear it I forget it, when I see it I remember it, when I do it I know it.” The 8am coffee shop strategy When people tell me they’re stuck in their career, I ask them to show me where learning happens in their calendar. Usually… nowhere. They feel pressure to perform 100% of their time. Here’s what I did at AWS for 12 years: Most mornings at 8am, I’d sit in a remote corner of the office or a quiet coffee shop for one hour or more. Not to check email. Not to prep for meetings. To build something. I built CloudWatch dashboards for my wife’s WordPress blog. Created a spam filter from scratch using the Enron email dataset. Built a static blog generator using AWS Lambda and AWS Step Functions. These experiments and pet projects taught me more than any training ever could, because I was solving real problems with my hands on the keyboard. When I later sat across from customers discussing serverless architectures or machine learning pipelines, I had the kind of credibility no documentation could provide. The temporal and spatial separation mattered. That remote corner at 8am shielded me from interruptions. The “no email before 10am” rule protected the space. Without these boundaries, learning gets trampled by the urgent. Just last weekend, I started another pet project. How about you? How to start: Create a recurring one-hour meeting with yourself next week. Call it “Learning Lab” or “Build Time.” The calendar block will magically fend off other meetings. Pick one topic you’re curious about and do something with it, don’t just read about it. Proof point: This isn’t unique to tech. Steve Jobs studied calligraphy. Ray Dalio built economic models thinking through decades of market observations. Bruce Dickinson became a pilot while fronting Iron Maiden. Dua Lipa and Rihanna turned curiosity about business into entrepreneurial empires. The pattern works universally: curiosity → hands-on learning → new capabilities → better options. Think in Systems</h2> What it means: Local optimization without understanding the whole system creates problems elsewhere. Strategy isn’t just what you do. Understand the system first, then change the rules. The decision bottleneck I’ve watched countless executives become trapped in a loop of their own making. They get pulled into too many decisions—approving budgets, reviewing proposals, being “hands-on”. They become the bottleneck in their company’s decision-making system. The invisible system they don’t see: every decision routed through leadership slows down the entire organization. The best decision-makers are closest to the problem, with the freshest information. Pushing decisions to the top degrades both quality and speed. The solution is to push decision-making to the lowest level where someone has enough context and where potential damage remains controllable. This typically means pushing decisions right to where people directly face the problem. At Amazon, we said “innovation happens at the edge”, because the best solutions emerge right where the problems happen, not in the ivory tower. What remains at the top? Only the “one-way-door decisions”, the ones with irreversible consequences that match executive scope. This is what leaders should spend their scarce time on, not whether to approve a $5,000 software license. True story: I once was at a customer executive retreat and someone exclaimed “over here, we treat everything like a one-way door!” Turns out, even which kind of coffee machines to install in their offices. The meta-lesson: when you’re stuck in tactical loops, zoom out. Ask “What’s the system producing this pattern?” Then change the system, not the symptoms. How to start: When mentees ask about getting promoted, I ask them about the rules of their game. What patterns do you follow? Why are those rules there? Who else is involved? What would happen if you changed the rules? This “step up one abstraction level” move reveals the system. Then you can start seeing leverage points: places where small changes produce outsized effects. Proof point: Donella Meadows showed that information flow is one of the most powerful levers for changing systems. That’s why digitalization works: converting paper to bits accelerates feedback loops, increases flow, and reduces defective states. Seth Godin recently wrote that strategic thinking starts with understanding the system, then changing it. Werner’s principle reminds us that it’s all about seeing the forest, not the trees. Communicate with precision</h2> What it means: Clear writing is clear thinking. When you write precisely, you discover what you actually mean. This applies whether you’re writing AI prompts, documenting architecture decisions, or explaining strategy to your team. Writing is thinking with our hands This is why Amazon runs on written documents, not PowerPoint slides. Werner used the example of giving precise instructions to AI for code generation. But the principle extends far beyond AI. During the first waves of software outsourcing, companies with imprecise requirements got software that “met spec” but was completely useless. The precision problem wasn’t technical—it was conceptual. Writing forces you to think clearly about what you’re actually trying to accomplish. Even if nobody reads what you write, you’ll gain clarity just from the act of putting thoughts into well-formed sentences. The deeper skill is cross-domain translation. Every technical solution represents a real-world challenge. There’s always an interface where the real world creates inputs (business needs, user problems) and where technology creates outputs (solutions, outcomes). In enterprise architecture, this is the boundary between business architecture and technical architecture. When discussions get tangled, I ask: “What needs to happen at the business level?” This question brings us back to the real world before diving into implementation details. Werner’s example of object-oriented programming making code model the real world more accurately reflects the same principle. Today’s “digital twins” in manufacturing follow the same concept: using technology to mirror and understand the physical world. How to start: Write more. Long-form documents. Blog posts. One-pagers. Journaling. Whatever gets you in front of a keyboard or a sheet of paper, forcing thoughts into complete, correct sentences will improve your thinking. Test yourself: Explain your current project to a smart colleague as if they were five years old. Not because they are five, but because it forces you to eliminate jargon and verify you actually understand what you’re doing. Proof point: I’ve seen sales professionals get AWS certified and transform their effectiveness. Not because they can now deploy infrastructure, but because they can have better conversations with customers and work more independently with their technical colleagues. The best colleagues I’ve worked with came from diverse backgrounds—former consultants, Gartner analysts, startup founders: because each brought a different lens and translation capability. Be an Owner</h2> What it means: Ownership means taking responsibility beyond your immediate tasks, seeing the system you’re part of, including the second and third order consequences, and accepting accountability for outcomes, not just activities. “Fragen kostet nichts” In Germany, we say “Fragen kostet nichts”—asking costs you nothing. Ask for more responsibility, a bigger scope, a broader role. The worst that happens is hearing “no,” and then you move on. During my AWS years, I owned initiatives far beyond my official role: organizing joint customer presentations for re:Invent, running an EMEA-wide conference, building communities of practice across teams and geographies. Nobody assigned these. I saw needs and stepped up. This is exercising entrepreneurial muscles inside a large company. It gives you more autonomy, broader influence, and, critically, a better sense of control over your career trajectory. That kind of autonomy is a powerful intrinsic motivation. But here’s the tension: ownership as a cultural value is tricky. Not everyone wants responsibility beyond their immediate work. You can’t mandate ownership, you can only encourage it. Leaders create ownership culture by example and by making it safe to make mistakes. When leadership openly shares how they failed and what they learned from them, they signal “it’s okay to take risks here.” Clear prioritization and decision criteria help too. This is why Amazon’s Leadership Principles</a> at the company level and Tenets</a> at the team level work so effectively—they give people frameworks for making autonomous decisions aligned with broader goals. How to start: Volunteer to own something beyond your job description. Organize a conference. Start a task force. Run a community of practice. Pick something small enough to manage but visible enough to matter. Proof point: This connects directly to Eliyahu Goldratt’s The Goal. Ownership isn’t just about accountability, it’s about seeing and improving the system that produces outcomes. You identify bottlenecks, remove waste, improve throughput. That’s systems thinking meeting ownership. Become a Polymath</h2> What it means: Develop T-shaped expertise: deep in one area, broad across many. This isn’t about being superficial in everything. It’s about having enough knowledge across domains to see connections, translate between fields, and spot opportunities others miss. The Ikigai of career building Initially subconsciously, then more deliberately, I followed a pattern similar to the concept called “Ikigai”: Look for things you’re excited about, that fit or expand your existing skills, that are needed, and that people will pay for. I wrote about this framework in my Four horsemen of a dying career</a> post, but it also applies here. Your T-shape should emerge from this intersection—not from random skill collecting. The best sales people I’ve worked with weren’t career sales reps. They were former business executives, consultancy owners, industry analysts. Each brought a unique, holistic perspective on how businesses work across levels and silos. This turned them into business advisors for their customers—helping solve problems, not just close deals. They could see through organizational structures to identify decision-making processes and find the right influencers. I’ve seen this in my own journey: although I was always a “Solutions Architect” at AWS, every year felt like a completely different job. I started with basic infrastructure consulting for startups. Then helped enterprises migrate to the cloud. Then enabled customers for Big Data, IoT, Digital Transformation, Machine Learning, and AI. Each wave required new skills: Python coding, public speaking, executive communications, change management, coaching and mentoring. The “T” kept growing broader while the depth in legs like architecture and systems thinking deepened. How to start: Building the “T” happens through doing, not reading summaries. Real learning requires hands-on work. Job rotations, stretch assignments, collaborations across silos work beautifully. New projects bring new responsibilities and domain knowledge organically. Going deep, adding a major new leg to your “T”, takes time. Think at least a year to develop proficiency, multiple years to form real depth. Choose carefully using the Ikigai framework: passion, existing ability, need, business value. Often a new depth area emerges from what started as a shallow, experimental breadth area. Proof point: Martin Fowler identified six characteristics of Expert Generalists: Curiosity (Werner’s principle #1), Collaborativeness (ties to Communication), Customer Focus (Communication again), Favor Fundamental Knowledge (complements Systems Thinking), Blend of Generalist and Specialist Skills (T-shaped), and Sympathy for Related Domains (Systems Thinking + Communication). The convergence is striking—different words, same patterns, driven by the same forces of accelerating change requiring cross-disciplinary solutions. Your January Challenge</h2> It’s December. The year’s winding down. Now is the perfect time to assess your career against these five principles. For each one, ask yourself: Curiosity: When did I last build something just to learn?</li> Systems: Am I solving symptoms or changing systems?</li> Communication: Can I explain my work clearly to a smart non-expert?</li> Ownership: What do I own beyond my job description?</li> Polymath: Is my T-shape evolving?</li> </ul> Then ask: What can I do to embody this more in 2026? What’s the next step I can take in January? You don’t need to tackle all five at once. Pick one principle that resonates most. Schedule that one-hour learning session. Volunteer for one project outside your silo. Write one blog post. Just start. If you’re feeling overwhelmed by the pace of change, I wrote about navigating uncertainty in my Crisis Survival Guide</a>, where I explore what you can and can’t control using a framework inspired by the Serenity Prayer. For more on developing T-shaped expertise, read my deep dive on How to Thrive as an Expert Generalist in the Age of AI</a>. Werner gave us the map. Fowler, Christensen, Kurzweil, and Dalio confirm the terrain. Now it’s on us to navigate it. The Renaissance is here already. Time to build! Watch/read more!</h2> Werner Vogels’ AWS re:Invent 2025 Keynote</a> (YouTube)</li> Martin Fowler’s Expert Generalist</a> (article)</li> Clayton Christensen’s The Innovator’s Dilemma</a> (Goodreads)</li> Ray Kurzweil’s The Singularity Is Near</a> (Goodreads)</li> Ray Dalio’s Principles</a> (Goodreads)</li> Donella Meadows’ Thinking in Systems</a> (Goodreads)</li> Eliyahu Goldratt’s The Goal</a> (Goodreads)</li> </ul> 3 slides everyone uses (but you should delete) 2025-12-03T00:00:00+00:00 In 2014, I was preparing my AWS re:Invent presentation on “Running Lean Architectures.</a>” I had my slides ready: agenda, bio, resources. Then I stopped. Why was I presenting this in the first place? The answer: to help people save money on AWS. Not to check boxes on a corporate template. Not to prove I was qualified to speak or that I did my homework. I deleted my agenda slide and replaced it with one titled “What you’ll get out of this session”, the one you see above. The first bullet point: “A lower AWS bill.” That moment changed how I think about presentations. Every slide should serve the audience, not the presenter. Yet in the 25 years since, I’ve watched thousands of presentations make the same mistake: filling slides with content that serves them, not their audience. In this video, I break down the three most common offenders: Play Video </div> </div> </div> Here’s why these slides fail, and what to do instead. The real problem: who are your slides for?</h2> Before we dive into specific slides, here’s the core issue: presenter-centric vs. audience-centric thinking. Most presenters follow templates that make them feel safe, copy-pasted across generations of designers. The agenda keeps them on track. The bio proves they belong there. The resources list shows they did their homework. But here’s what’s really happening: while you’re making yourself comfortable, your audience is reaching for their phones. Those little gadgets are your real competition. Highly optimized, attention-stealing mind-control devices that offer instant gratification. Every slide that doesn’t serve your audience is an invitation for them to check “just one more notification.” Once they’re lost in their feed, it’s hard to get them back. So let’s look at the three slides that hand your audience over to their phones—and what to do instead. Slide #1: Agenda (your to-do list, not their value)</h2> What presenters think they’re doing: “I’m being organized and professional. I’m showing respect by letting them know what to expect.” What they’re actually doing: Creating a to-do list for themselves. Every agenda looks the same: Introduction, Topic 1, Topic 2, Topic 3, Q&A. It’s the presenter’s checklist, dressed up as audience service. What the audience experiences: The groan: “Yet another agenda slide.” They’ve seen this template a thousand times. Time to check their phone. Nobody came to your session to see your table of contents. They came to solve a problem or learn something valuable! The better alternative: Expectations</h3> Replace your agenda with clear expectations. Instead of listing topics, tell them what they’ll gain: “What you’ll get out of this session: A lower AWS bill”</li> In the next 30 minutes, you’ll discover why your customers ignore your product, and the one question that changes everything</li> “Today, you’ll learn three techniques that cut deployment time in half”</li> </ul> Why it works: First, it’s a positive surprise. Everyone expects an agenda. So when you deliver something better, you’ve earned their attention. Second, expectations create productive tension. You’ve opened a story loop in their minds. They want to see it resolved. Movie trailers don’t give you a table of contents—they create anticipation. Third, expectations build trust. When you exceed what you promised (and you should always aim to exceed), you’ve demonstrated that their time was well-invested. That one extra insight at the end, beyond expectations, can elevate your presentation from good to great. Slide #2: About Me (your validation, not their value)</h2> What presenters think they’re doing: “I need to establish credibility. They need to know I’m qualified to be here.” What they’re actually doing: Seeking validation. The bio slide reassures the presenter: “I belong here. I’ve earned this stage.” It makes them feel better about presenting, helps keep imposter syndrome</a> in check. What the audience experiences: Indifference, at best. Mild annoyance at worst. They already know your name and job title: it’s on the title slide and in the conference program. If they’re curious about your background, they’ll check LinkedIn later. What they don’t know yet is whether you’re going to waste their time or deliver value. And you’re currently wasting their time with your CV. I’ve watched speakers uncomfortably skip past their own bio slides, as if their subconscious mind knows something’s wrong. Their body language says: “Sorry, our corporate template made me include this.” The better alternative: authority through value delivery</h3> Want to establish credibility? Deliver value. Share insights. Solve problems. Tell stories that illustrate hard-won lessons. Skip the bio slide entirely. Then, throughout your presentation, weave in short stories: “When I worked with a startup trying to cross the chasm, we discovered…”</li> “In my 12 years at AWS, I learned that…”</li> “I made this mistake three times before I figured out…”</li> </ul> Why it works: You’re building authority while delivering value. The audience gets useful information and learns that you know what you’re talking about. Two birds, one stone. More importantly, you’re making it about them, not you. They’re the hero of this story. You’re the guide who’s been down this path before. That’s the role you want. Not “impressive person with credentials,” but “trusted guide who serves their audience.” (Do you have an “About the company“ slide? Delete it, too, for the very same reasons!) Slide #3: the resources (your homework proof, not their next step)</h2> What presenters think they’re doing: “I’m being helpful! Look at all these valuable resources I’m sharing.” What they’re actually doing: Creating decision fatigue and proving they did their homework. That wall of URLs and QR codes isn’t for the audience—it’s the presenter covering their bases. What the audience experiences: Overwhelm. And their mind is already wandering. By the end of your session, they’re thinking about coffee, their next meeting, or whether they need a bathroom break. Their cognitive load is maxed out from absorbing your content. Now you’re asking them to choose between ten resources? That’s not helpful—that’s homework. Yes, they might take a photo of your resources slide. But let’s be honest: how often do you actually go back and look at those photos later? The better alternative: One clear next step</h3> Give them a single, specific call to action. Not ten options—one action. Make it ultra-specific: “When you get back to your desk on Monday, right after you file your expense report, do this one thing…” (I use that exact phrasing when coaching speakers. They laugh, but it drives the point home: specific and immediate.) Examples of good single CTAs: “Visit booth #47 for a live demo”</li> “Go to <URL> for the one-page template”</li> “Complete the hands-on tutorial lab now, to experience this solution in real life”</li> </ul> Why it works: Low friction. Clear reward. Immediate action. You’re respecting that they’re overwhelmed and tired. You’re making it easy to take the next step. You’re trusting that if they want more information, they know how to use a search engine in 2025. Or better yet: create a one-page handout that summarizes your key concepts. This gives people something tangible and makes you memorable. Plus, it’s a much better follow-up tool than “can I get a copy of your slides?” The transformation: from template-follower to trusted guide</h2> Here’s what all three of these slides have in common: they serve the presenter’s need for comfort, not the audience’s need for value. Templates make us feel safe. They tell us we’re doing it “right.” They protect us from the fear of being unconventional. But: great presenters are unconventional. They stand out precisely because they don’t follow the template everyone else follows. The stakes are real. In an age of attention-hogging mobile phones and infinite distractions, losing your audience means losing relevance. Lower ratings. Fewer speaking opportunities. Less impact with the insights you worked hard to gain. But when you make the shift, when you put the audience first, look beyond the templates, and replace those comfort slides with audience-centric alternatives, you become memorable. You get invited back. You create real impact. You transform from someone following corporate templates into a trusted guide who serves their audience. Your Next Step</h2> Pick one presentation you’re giving soon. Open the deck. Find these three slides. Delete them. Or better yet: replace them with the alternatives suggested above. Make your slides serve your audience, not yourself. Your audience will thank you. And your phone-checking problem just got a lot smaller. P.S.: If you want to discuss your specific presentation, feel free to book a slot during my office hours</a>. P.P.S.: What slides are you deleting from your presentations? Share this post on your favourite social network and tag me, I‘d love to see what you discover! What’s Constantin doing now? 2025-11-28T00:00:00+00:00 (This is a Now page</a>. If you like the concept, why not set up one for yourself, too?) Updated November 28th, 2025. (Older versions: 2025-10-16</a>, 2025-07-23</a>, 2025-06-30</a>, 2025-06-11</a>.) In no particular order. Also, this page was not written by AI – I just happen to like emojis and proper emdashes. Building a mailing list 📧</h2> In the age of social media, algorithms and AI, I think real connections between real people are going to become more valuable than ever. Therefore, I’m starting a mailing list. There’s no platform or algorithm or anything else that stands between mailing list subscribers and me, that’s what makes email unique. In Germany, it’s surprisingly complicated, given GDPR and my aversion to cookies, but I managed to set the automation up, which you can find on the sidebar of this site. Now, looking forward to getting to know who signs up! Starting a YouTube channel 🎥</h2> …because it’s never too late! Here it is: @Constant.Thinking</a> on YouTube. There are 5 videos so far, with a wide range of view counts, from 4 to 229. The YouTube algorithm is a very weird one and I see this as a marathon. Blogging more often👨🏻‍💻</h2> After quitting my job at AWS</a>, refactoring my blog</a>, I’m still tweaking some of its bits, like adding Webmention support</a> and creating an animated banner</a> and adding a seasonal animation</a>. Now, I’m focusing on writing more articles about productivity and personal development</a> or interesting tech topics</a>. Joined a consulting company 💼</h2> It’s called Disruption Selling</a> and it’s more like a platform for consultants to operate from. We support startups and established companies who want to cross the proverbial chasm with their innovative products. Every member has “been there, done that, got the scars”, so we now want to help others do the same. I’m not in sales, nor have I been. In fact, I’m the only tech member at the moment, hoping to bring a bit more tech focus into the team. Learning how to educate our dachshund Elvis 🐾</h2> TL;DR: You can’t 😀. Well, they have a strong personality, which is a good thing! So now, we’re learning how to at least influence them. Elvis is super sweet and adorable, he’s also entering puberty, so, fun times ahead! But he’s making progress! He doesn’t bark at other dogs as much as he used to, and the constant pulling at the leash is starting to become more, let’s say, controlled. I also learned: You don’t walk a dachshund. Dachshunds take their can openers out for a walk… Learning to Move 🎛️</h2> Not the physical movement, but the new Groovebox by Ableton</a>. It’s a very fun and powerful device and I’d like to use more of its potential, so I’m now using it whenever I make music with friends. I’ll also spend more time with it alone to learn the ins and outs. I recently bought a CME WIDI Bud Pro—a MIDI-over-Bluetooth interface that’s compatible with my Move, so I can connect it to my laptop and Ableton Live running on it. Let’s see what I can do with this combo! Work out more 🦶</h2> My go-to workout option is barefoot running. But when it’s too cold outside, I do some workouts at home using Apple Fitness. Now that I have a bit more time, I’ll do this more often, and also add some strength training to it. That’s it! I’m adding a reminder to myself to regularly update this page. What are you doing now? When Claude Suggested Brian Eno: Building Art You Can't Control 2025-11-26T00:00:00+00:00 On constrained serendipity, learning by doing, and whether the system is the art. I’m chatting with Claude, kicking around ideas on building a tool that generates AI images, but without the prompting. I like to start these chats by brain-dumping ideas into it. In this case it’s about auto-generating generative AI image generator prompts using random lists of topics and concepts, some guardrails to keep things positive, maybe a temporal thing to connect each image to the moment it was created. And then I type: “Let’s invite some creative minds into this conversation. I’ll pick Seth Godin. Claude, you pick someone else who could add value.” Claude suggests Brian Eno. Of course it does! This is the technique I wrote about recently: inviting virtual versions of people you admire into your AI conversations.</a> But here’s the meta-moment that makes it even more interesting: Claude picked its own expert. And it made a great choice! Brian Eno, the godfather of generative music</a> and Oblique Strategies</a> (which Seth recently transported into the AI age</a>), the guy who literally invented systems that create art through constrained randomness. Of course that’s who you’d want when designing a tool about surrendering creative control. Virtual Brian asks: ”How much control versus serendipity do we want?” Suddenly, we’re not just building a tool. We’re designing a philosophical stance about AI and creativity. The original problem that opened a rabbit hole</h2> </picture> I don’t like boring video call backgrounds </figcaption> </figure> When I’m on a video call, I love using AI-generated images as backgrounds. They’re much more interesting than stock photos of fake offices with suspiciously perfect plants (mine are more on a near-death experience trip most of the time). Just yesterday, someone on a call commented on my nice background picture, so here’s the story of where it came from. But the problem is not generating the images, the real problem is: I hate spending 20 minutes crafting the perfect image generator prompt just to get one background image. What if there was a system that generated unique, inspiring images without any prompting at all? Not random chaos. Curated randomness, instead. Bounded by intention but free within those bounds. Brian (through Claude) helped me figure it out: this isn’t about automation, it’s about designing the possibility space. The art isn’t in the output. The art is in the system that generates it. We agreed on five dimensions that would be randomly selected: Feeling/mood</li> Environment</li> Concept</li> Time era</li> Visual style</li> </ul> Each dimension has a carefully curated list, including emotions like “anxious” and “melancholic” but excluding ”hate” and “disgust.” Not because I wanted to pretend negative emotions don’t exist, but because I wanted the system to generate images that inspire rather than disturb. This curation is an artistic choice. We added temporal anchoring: the current date, season, and time of day would be fed into the prompt generator. Not as numbers, but as human concepts: “late afternoon, Monday, autumn.” Each image would be connected to the specific moment it was created. We added a randomly selected fortune cookie into the mix as well: random wisdom to spark unexpected directions. And we made sure every generation would be unrepeatable. Same inputs would never align again. Time passes. Random numbers change. Your mood shifts. We called it Constrained Serendipity. By the end of the conversation, I knew exactly what to build. Because here’s the thing: you only learn what you do, and I was excited to explore the world of generative art, a mixture of Eno’s generative music</a> and today’s generative AI. Building in public (sort of)</h2> I’m not a real software developer. I’m an Expert Generalist</a>, 27 years in tech, but as a solutions architect, not a software development engineer. I code pet projects for fun, but I don’t ship production software. Which turned this into another learning opportunity for me. Starting in the Claude desktop app, we designed the spec and iterated on the design document together. Claude and I got back and forth on architecture, word lists, prompt engineering strategy, the whole technical stack. When we had a solid spec, I moved to Zed</a>, my favorite code editor, and used Claude Code</a> to implement the initial version. Then Anthropic gave all Claude Pro users $250 in credits to try their new Claude Code on the web feature. Nice! This gave me an opportunity to embrace the latest in modern software development: managing the product, its architecture and features, rather than typing lines of code. It’s all about managing ideas now. I also wanted to get more GitHub-based workflow hours under my belt, since that’s the only workflow Claude Code on the web supports: I opened GitHub issues for new features, bugs, and improvements whenever they occured to me. Then I’d pull out my phone (yes, my phone!), open Claude Code, and say: “Look at these issues. Help me prioritize them. Which ones matter most?” We’d discuss. Debate. Decide. Then: ”Let’s work on issue #7 now.” Claude Code would clarify the details with me, write the code, explain the changes, and submit a PR. I’d review it on my phone, approve it, merge it. Done. That’s real software development on a mobile device! While walking Elvis, our dog, waiting for coffee, or on the bus. The AI wasn’t replacing my thinking, it was amplifying it. Steve Jobs’ promise of a “bicycle for the mind” has become reality. This connects to what Jeremy Utley talks about in design thinking: AI helps unblock creative processes.</a> Not by doing the work for you, but by removing the friction that prevents you from doing it yourself. People might argue this is “too much AI, too little Constantin.” I’d argue the opposite. Using AI is a bit like walking into a clothing store with a personal stylist. They show you options, explain what works, help you try things on. But you still pick the pieces that are “you.” You’re not forced to knit everything from scratch—but the final outfit is absolutely yours. So, my latest pet project is called “The Serendipity Engine</a>”. Every decision about which emotions to include, how temporal anchoring should work, which constraints matter, the flow, the user experience, those are my artistic choices. Claude Code just helped me manifest them faster than I could have alone. And as a side effect, I have learned how to manage PRs, issues, and development workflows. You only learn what you do. And you can only teach what you have experienced yourself. The Art Question</h2> So here’s the interesting question that’s been lurking since the beginning: is this art? Not the images—those are AI-generated, and people have strong opinions about whether that counts. I’m asking about the system itself. Is the Serendipity Engine art? I think it is. And I think the answer matters more than you might expect. Play Video </div> </div> </div> Asking “Is this art?” in this video is not a rhetorical question. I genuinely want to know what people think. Because if we can’t expand our definition of art to include thoughtfully designed systems, we’re going to struggle with AI creativity. Claude educated me about Sol LeWitt’s wall drawings. LeWitt wrote instructions like: “Draw 10,000 lines, 12 inches long, in random directions” and other people executed them. The art wasn’t in the execution. The art was in designing the constraint system that made execution possible. Or Brian Eno’s generative music. The music is different every time you play it, but Eno is still the artist. Because he designed the possibility space: the rules, the interactions, the probabilities that create emergence. The Serendipity Engine works the same way. It includes 40 curated feelings, 45 environments, 55 concepts, 12 time eras, and 59 visual styles. That’s over 70 million possible combinations before you even add temporal anchoring or user input. Every choice, which emotions to include, how to translate time into human concepts, the inclusion of fortune cookies, those are creative decisions. The images the system generates are artifacts. Beautiful, unique, unrepeatable artifacts. But the art is in the system that makes them possible. Here’s why I think this matters: First, it gives people permission to be artists even if they can’t paint. You don’t need to wield a paintbrush, master Photoshop or spend years learning composition. If you have a vision and can design constraints, if you can curate a possibility space, you can also create art. Second, it’s about surrendering control. In the age of infinite tweaking, infinite undos, infinite prompts until you get it perfect—there’s something radical about building a system that surprises you. That creates things you couldn’t have imagined. That respects the role of chance. That creates value through the scarcity of each output being unique and personal. Third, it expands what we mean by “art” in the AI age. If we only count the output, we’re going to have endless debates about whether AI art is “real.” But if we recognize that designing the system is an artistic act, suddenly the conversation shifts. It’s not about the paintbrush. It’s about what you choose to paint. Some people won’t agree. They’ll say this is engineering, not art. That randomness isn’t creativity. That AI-generated images can’t be art because there’s no “hand of the artist.” In fact, over a year ago, I posted a LinkedIn update about art, choices and Shannon Entropy</a>, and it got more than 90 comments so far. I didn’t expect that! I’m genuinely curious what you think. There’s another beautiful aspect to this: the recursion. I used AI (Claude) to help me design a system (Serendipity Engine) that uses AI (Claude Haiku and Nova Canvas) to create images. And now I’m using AI (Claude again) to help me write about it. Some people are afraid about AI “poisoning” its own training data, as more and more AI-generated content is published. But: In this case, there’s a human in he loop. At every step, I’m the curator. The decision-maker. The artist designing the possibility space. AI isn’t replacing my thinking. It’s helping me unearth more of the “Constantin” that would otherwise be harder to find, realize, or articulate. And that might be the most important artistic choice of all: choosing to collaborate rather than control. What I Learned</h2> Here’s what this project taught me: On software development: I now have some muscle memory for GitHub issues and pull requests, and am using this approach more and more often. I understand why and how developers work this way. Not bureaucracy, clarity. Each issue is a container for a decision. Each PR is a conversation about change. The process makes collaboration possible at scale. On art and creativity: I understand conceptual art differently now. It’s not about the object, it’s about the idea the object represents. Sol LeWitt, Brian Eno, even Duchamp with his readymades. They were all asking: what if the artistic act is in the framing, the system, the constraint design? On AI collaboration: I have a better sense of how AI actually behaves when you work with it daily. It’s not magic. It’s not sentient. But it’s also not autocomplete. It’s a thinking partner that helps you navigate possibility space faster than you could alone. And most importantly: I had fun. That’s the real secret of learning. Not the grind. Not the discipline. Not forcing yourself through boring textbooks because someday it might be useful. Fun is the most underrated learning accelerant. I built this because I wanted to. Because the conversation with Claude and virtual Brian was energizing. Because seeing each new image appear felt like opening a gift. Because it allowed me to explore an artistic side I didn’t know I had. If it’s not fun, you’re doing it wrong. Your Turn</h2> Check out the companion YouTube video above</a> where I demo the Serendipity Engine and explore the art question in some depth. And here’s an example wall of 16 images</a> the system generated: </picture> 16 art pieces (or not), generated with the Serendipity Engine </figcaption> </figure> The code is open source on GitHub.</a> You’ll need an AWS account with access to Amazon Bedrock</a> (Anthropic Claude Haiku</a> and Amazon Nova Canvas</a>), but once you’re set up, you can generate as many images as you want, at less than 5 ct per image. Each one unique. Each one unrepeatable. Now I want to hear from you: Is the Serendipity Engine art? Not the images—the system. The curated constraints. The designed possibility space. The choice to surrender control to emerge something unexpected. Comment wherever you are, by linking to this post on your favourite social network, then adding your opinion. I’m on LinkedIn</a>, Bluesky</a>, Mastodon</a>, GitHub</a>, and YouTube</a>. Or send me email</a>. Tell me which of the 16 images speaks to you and why. And if you try out this tool, I’d love to hear about it. Before you present: 7 critical checks for tech speakers 2025-11-18T00:00:00+00:00 February 1998. Munich. Sun Microsystems. My first customer presentation. I stood in front of a roomful of IT professionals with a marketing deck about our “newest, better workgroup server.” I hadn’t rehearsed beyond flipping through the slides a couple of times. I didn’t know who these people were or what problems kept them up at night. And when someone asked how this server would actually help their business, I froze. The silence felt like an hour. 😬 That presentation haunted me for years. But it also taught me something: your technical solution might be brilliant, but if you can’t present it clearly, it won’t get the attention it deserves. Fast-forward to AWS re:Invent 2024. I was in the speaker ready room, coaching speakers before they went on stage in front of thousands. I had years of experience helping speakers avoid the biggest pitfalls—but it was all in my head, scattered across training slides and hurried pre-talk conversations. This checklist is the first time I’m actually writing it all down. Atul Gawande would approve: even experts benefit from a simple, portable checklist. You can’t take me backstage with you, but you can take this to your next re:Invent, your next conference talk, your investor pitch, or any presentation where the stakes are high. Here are seven essential checks that will help you avoid my 1998 disaster and deliver something remarkable. But before we begin, ask yourself: what transformation do you want to drive in your audience? With that in mind, let’s get started: 1. Who is your audience? 👥</h2> Picture them as clearly as possible. What’s their background? What do they already know? Why are they here? What industry, culture, or experience level defines them? The more specific you get, the better. “Tech leaders” is too vague. “CTOs at Series A startups struggling to scale their infrastructure” is actionable. When you know exactly who you’re talking to, everything else—your language, examples, and depth—falls into place. 2. What problem are you solving for them? 🎯</h2> Pick one problem. Describe it so clearly that your audience nods in recognition, feeling the pain and frustration. Everything in your presentation should either explain this problem or demonstrate your solution. No tangents. No “nice to have” context. If a slide doesn’t serve the problem or the solution, cut it. For this post, my audience is tech professionals preparing for important presentations. The problem? The anxiety, insecurity, and real career risk of bombing on stage. Every item in this checklist addresses that problem. What’s yours? 3. Where’s your Aha! moment? 💡</h2> This is your hinge—the moment when the problem becomes so clear that your solution feels inevitable. Here’s the test: After your presentation, can your audience explain your main benefit to a friend over dinner? In one sentence? Without hesitation? If not, you haven’t created your Aha! moment yet. Show the problem with such clarity and contrast that when you reveal the solution, your audience thinks, “Of course! That’s it!” Build tension in the problem, release it in the solution. That’s how you create resonance. 4. What’s the one thing they should do next? ➡️</h2> Don’t leave your audience hanging. Give them exactly one action to take. Not three options. Not “here’s a bunch of links.” One clear next step that moves them closer to solving their problem. Try your demo. Visit your booth. Book a call. Invest in your solution. One action. Make it obvious. 5. Are you designing for humans or computers? ❤️</h2> It’s easy to go all-in on data, facts, and logic in tech presentations. That’s a mistake. Before you finalize your deck, scan through this human-centered lens: Emotions: Humans feel. Show the pain of the problem, the excitement of the solution, the connection between people. Use humor if it fits and you can deliver it.</li> Visuals: Use images—preferably on every slide. Make diagrams clear and simple. Use white space. Let elements breathe.</li> Contrast: Don’t just say “500 megadodos.” Say “500 megadodos—that’s 10× more than the next best solution!” Show before and after. Use contrasting colors to emphasize key points.</li> Clear transitions: Audiences are constantly distracted by their phones—powerful mind-control devices. Signal clearly when something new is coming: “That concludes our demo. Now let’s talk about…” Use section dividers. Keep sections short—introduce something new every minute or two. Win the game of catching their attention.</li> Tangible examples: We’re empathetic creatures. Tell stories about how you or your customers experienced the pain. How the solution works in action. Bring physical objects if you can. Make it real.</li> Make it personal: “Our solution is the best widget for X.” Wrong! Nobody cares about widgets. Right: “You can now enjoy a stress-free, profitable business by using our widget to handle all the painful aspects of doing X.” Make your audience the hero, not your product.</li> </ul> I learned this at AWS from my colleague Blaine Sundrud</a> (thank you, Blaine!). Go work with him if you can, he’s great! Here’s a full video explaining these 6 elements in more detail: Play Video </div> </div> </div> 6. Does every slide have a clear job? ✂️</h2> For every single slide, ask: Does this help explain the problem? Does this help explain the solution? Does this tell my audience what to do next? If the answer is no, delete it. No more “About Me” slides (unless they’re needed to establish your credibility). No “Context” or ”History” slides that don’t directly relate to your audience’s pain. No endless “Related Resources” at the end. Every slide must earn its place. 7. Are you rehearsing to serve your audience? 🎭</h2> Rehearsal isn’t about memorization or running on autopilot. It’s about internalizing your message so deeply that you can be fully present with your audience. Start at least one week before your event. Rehearse at least twice a day. Use a mirror. Present to colleagues. To family. Record yourself. Ask for feedback. When you step on stage, you shouldn’t be thinking about what comes next. You should be listening, connecting, and serving your audience. That only happens when you’ve rehearsed enough to create mental space for presence. Bonus: Are you delivering more than you promised? 🎁</h2> Notice what I just did? I promised seven essential checks. You got eight. This is how you build trust: under-promise, over-deliver. Make realistic commitments, then exceed them. Your audience will remember not just what you said, but how you made them feel—surprised, delighted, and valued. What This Checklist Doesn’t Cover</h2> This checklist addresses the most common pitfalls I’ve seen in hundreds of tech presentations. But it’s only the beginning. Stage presence, vocal variety, body language, handling tough questions—these all matter too. They’re harder to checklist-ify, but they improve with practice and observation. Study the speakers you admire. Watch TED talks. Attend conferences and take notes on what works. And consider working with a speaker coach—someone who can give you real-time feedback and help you find your authentic voice on stage. This is just the start of your journey as a speaker. From Disaster to Confidence</h2> That 1998 presentation still makes me cringe. But it taught me to take speaking seriously. At AWS re:Invent 2024, I stood backstage with nervous speakers, walking them through ideas like these. Some of them came back afterward, energized and grateful. “It worked,” they said. “I actually enjoyed being up there!” That’s what happens when you prepare properly. The anxiety doesn’t disappear—it transforms into excitement. The fear becomes fuel. And your solution gets the attention it deserves. Your Turn 🚀</h2> If you’re working on a specific presentation and want feedback on one aspect—your opening hook, your Aha! moment, or whether your slides actually do their job—I offer free 25-minute office hours on Wednesdays and Fridays. Spots are limited, but if you’d like a quick sanity check or focused feedback, feel free to book some time</a>. And if you have your own presentation tips or checklist items, share them! Link to this post on LinkedIn, Bluesky, or Mastodon with your addition. I don’t host comments here, but I love seeing the conversation continue on social platforms. Now go make your next presentation remarkable. Your audience is waiting. Unstuck: What Seth Godin's Mentor Deck Taught Me About Bridging Human and Digital Worlds 2025-11-11T00:00:00+00:00 A package arrived a few months ago. Inside: a deck of cards with a red telephone on the box. “The Mentor Deck” by Seth Godin. I’m a beta tester, so I got one of the first 2,000 decks ever made. Each card has a QR code on the back that launches an AI conversation with a virtual mentor—Seth himself, or Gandhi, or Charles Darwin, or one of 49 other thinkers. Cool idea, right? But here’s what made me excited: I’d already been experimenting with inviting virtual mentors into my AI conversations</a> for months. And seeing Seth’s physical cards made something click. This wasn’t just about AI mentors. It was about something bigger. The Deck Itself</h2> Let me show you what I mean. I made a quick video walking through the deck—what it is, how it works, and even a peek at the code behind these AI conversations: Play Video </div> </div> </div> It’s a simple but powerful idea: when you’re stuck, you shuffle through the cards, find someone who might help with your specific challenge, scan the QR code, and start a conversation. The physical ritual of shuffling and choosing creates a moment of intentionality. The QR code bridges you to a Claude artifact that’s been carefully crafted to embody that person’s thinking. It’s elegant. But as I played with it, I realized something. The Pattern Emerges</h2> Seth’s Mentor Deck does two things that my “invite a virtual mentor” approach doesn’t: First, it’s physical. You hold cards. You shuffle them. You make a choice. There’s something powerful about that tactile experience when you’re stuck. It creates a ritual, a moment where you step away from your screen and think: “Who do I need right now?” Second, it’s curated. Instead of the infinite possibility of “I could invite anyone into this conversation,” you have 52 specific voices. That constraint is actually freeing—you discover thinkers you might not have thought to invite. The curation does the work of helping you find what you didn’t know you needed. And then it hit me: this is the same pattern I’d been using in a completely different context. A few months ago, I spent way too much time creating an NFC tag system for playing music in my house</a>. I curated playlists, connected them to my Home Assistant setup through automation, and put physical NFC tags around the house. Touch a card, music plays. Different pattern, same structure: curated content + tech interface + physical trigger. The pattern crystallized: Curated Knowledge + Tech Interface + Physical Bridge = Help Where Humans Actually Are This Idea Is Older Than AI</h2> Here’s where it gets interesting. Seth’s Mentor Deck reminded me of something from 1975: Oblique Strategies</a>, a deck of cards created by Brian Eno and Peter Schmidt. Oblique Strategies was designed to help artists get unstuck. Each card had a cryptic prompt—things like “Honor thy error as a hidden intention” or “What would your closest friend do?” The physical ritual of drawing a card created space for new thinking. That was 50 years ago. No QR codes. No AI. Just cards and human wisdom. Seth’s deck takes that same insight—that physical prompts can unlock mental blocks—and bridges it to something Eno and Schmidt couldn’t have imagined: AI-powered conversations that adapt to your specific situation. It’s not just an evolution of technology. It’s an evolution of how we help each other get unstuck. Why This Pattern Works</h2> Three things have to come together: Curation matters. You can’t just point people to “all the information on the internet” or “talk to any AI mentor you want.” That’s overwhelming. The magic is in selecting the right 52 cards, the right experts, the right prompts. Constraints create clarity. AI as interface matters. This isn’t about linking to static documents or pre-recorded videos or music playlists. The AI can have an actual conversation, adapt to your situation, ask follow-up questions, and guide you deeper. It’s not just retrieval—it’s human-centric dialogue. Physical triggers matter. Cards you shuffle. Tags you touch. QR codes you scan. These create intentional moments in the physical world where you’re struggling. They bridge the gap between “I’m stuck at my desk” and “I’m having a conversation with someone who can help.” What You Could Build</h2> Now I’m thinking: what else could follow this pattern? Museum guides: QR codes next to exhibits that launch conversations with AI docents who can answer questions about the art, the history, the technique—tailored to your level of interest. Manufacturing floors: QR codes on machines that connect workers to AI troubleshooters trained on repair manuals, safety procedures, and common issues. This is already happening at large manufacturing companies, but why not everywhere, like the workshop round your corner? Classrooms: Physical cards with prompts that launch AI-facilitated discussions, helping students explore topics at their own pace. Therapy or coaching: Cards for different emotional states or challenges, each launching a supportive AI conversation trained on relevant frameworks. Retail stores: QR codes on products that don’t just show specs, but start conversations about use cases, comparisons, or creative applications. The pattern is everywhere once you see it. Anywhere people get stuck in the physical world, you can bridge them to curated knowledge through AI. A Note on the Mentor Deck Itself</h2> The original Kickstarter campaign for the Mentor Deck</a> has ended, so you can’t get one right now through that channel. But honestly? That’s not the point of this post. The point is the pattern. The Mentor Deck is one beautiful implementation, but the idea—curated expertise + AI + physical bridges—belongs to anyone who wants to build with it. You can try a sample conversation</a> from the deck here to see how it works. And if you want to follow when the deck becomes more widely available, check Seth’s announcement</a> or the Prompt Decks</a> website (with two additional decks, one on “Positive Divination” and one on “Infinite Adventures”, following similar patterns). Your Turn</h2> Here’s your challenge, should you accept it: what can you build with this pattern? Think about the people you serve—your students, your customers, your team, your community. Where do they get stuck? What knowledge do they need in those moments? How could you curate it, make AI the interface, and give them a physical way to access it? I’d love to hear your ideas! Tell me</a> or share what you create and link to this post—I’m collecting examples of this pattern in action. Let’s see what’s possible when we bring curated knowledge to people where they actually are. Resources</h2> Try it yourself: Sample Mentor Deck conversation</a>—see how these AI conversations work</li> My previous post on inviting virtual mentors</a>—the flexible approach</li> </ul> Learn more: Seth Godin’s announcement of the Mentor Deck</a></li> Prompt Decks website</a></li> Oblique Strategies</a>—the 1975 deck that started it all</li> My NFC music automation project</a>—another example of the pattern</li> </ul> Update (2025-11-26) The prompt decks, including the updated Mentor Deck, are now available</a> Invite your heroes into your AI conversations 2025-11-03T00:00:00+00:00 I had a problem. I wanted to create a YouTube channel and had my first video ready to go, but then… nothing. Weeks passed. I had ideas, I had equipment, and I’d watched enough tutorials to edit like a pro. But I couldn’t get myself to shoot video number two. Classic procrastination. So I did what I often do when I’m stuck: I talked to Claude</a>. But this time, I didn’t just ask for advice. I invited two experts into the conversation to help me figure this out. Starting my own YouTube channel 2025-10-27T00:00:00+00:00 At 54 years old, I just started a YouTube channel. This is an experiment. I recently read Anne-Laure Le Cunff’s book Tiny Experiments</a> and decided to try something I haven’t done before: creating my own videos, not for a company or conference, but for anyone who might find them useful. From Worry to Action: A Crisis Survival Guide 2025-10-22T00:00:00+00:00 A few days ago, I had a conversation with a former colleague who interviewed for a new job because she couldn’t stand her current one. Getting a new offer made her feel relieved and optimistic again: She had escaped. Many others may not be so lucky this year. Tech layoffs are accelerating. The economy—if we’re honest and look past the AI boom—looks shaky. Gold prices are spiking, which historically signals uncertainty ahead. And if you’re reading this feeling worried about your job, your future, or the general state of things, I know well how you feel. Over 27 years and five major crises, I’ve learned one thing: the difference between thriving and drowning isn’t luck—it’s knowing what you can change and what you can’t. What’s Constantin doing now? 2025-10-16T00:00:00+00:00 (This is a Now page</a>. If you like the concept, why not set up one for yourself, too?) Updated October 16th, 2025. (Older versions: 2025-07-23</a>, 2025-06-30</a>, 2025-06-11</a>.) In no particular order. Also, this page was not written by AI – I just happen to like emojis and proper emdashes. Blogging more often👨🏻‍💻</h2> After quitting my job at AWS</a>, refactoring my blog</a>, I’m still tweaking some of its bits, like adding Webmention support</a> and creating an animated banner</a> and adding a seasonal animation</a>. Now, I’m focusing on writing more articles about productivity and personal development</a> or interesting tech topics</a>. Starting a YouTube channel 🎥</h2> …because it’s never too late! I’m finishing post on my first video (for this channel, I have an older one which is obsolete now). This is an experiment. I hope to create videos more regularly that complement my blog posts. Think of this like a companion channel to my blog. Let’s see where this takes me. Joined a consulting company 💼</h2> It’s called Disruption Selling</a> and it’s more like a platform for consultants to operate from. We support startups and established companies who want to cross the proverbial chasm with their innovative products. Every member has “been there, done that, got the scars”, so we now want to help others do the same. I’m not in sales, nor have I been. In fact, I’m the only tech member at the moment, hoping to bring a bit more tech focus into the team. Learning how to educate our dachshund Elvis 🐾</h2> TL;DR: You can’t 😀. Well, they have a strong personality, which is a good thing! So now, we’re learning how to at least influence them. Elvis is super sweet and adorable, he’s also entering puberty, so, fun times ahead! But he’s making progress! He doesn’t bark at other dogs as much as he used to, and the constant pulling at the leash is starting to become more, let’s say, controlled. I also learned: You don’t walk a dachshund. Dachshunds take their can openers out for a walk… Researching electric vehicles 🚗</h2> We currently own a 16 year old VW Polo. I’d love to upgrade it to an electric vehicle. So far, I’ve tested a few rentals: I love the Polestar 2, and it’s my current favourite, but it’s also pricey. I once got a Lucid Air as an uprade to the Polestar 2 I reserved. It’s a great car, but the electronics felt a bit buggy, probably teething issues. Too big and to expensive for our needs. We also rented an MG4 for a mini vacation. It’s nice, but the boot space is small for a family of four and a dog, the dashboard feels like it’s just two bolted on tablets, and the UX isn’t super great (you need to activate 1-pedal driving at every single trip). The evolution in this space is still quite fast, and the market dynamics change all the time. Today’s best choice might be obsolete in a year. Also, the big upfront cost for any decent EV is a bit of a blocker—I’d rather use the money for investing instead of watching the value of my car depreciate 50% in 3 years. Even with current leasing factors of .7, leasing looks like an attractive option over buying new. Perhaps I’ll go with a long-term car rental model for a year or more or some kind of leasing after all. Having done much more research, it looks like: EVs are still expensive. Buying them with cash doesn’t make sense, because the TCO of leasing one is much lower, even if they ask for initial payments and if you factor in buffers for return damage and stuff. Crazy!</li> My favourite brand, Polestar, seems to be in financial trouble. However, Tesla burned a lot of cash in their early days, too, and there’s a huge company (Geely) and their CEO apparently willing to bankroll operations further.</li> The Polestar 2, my favourite EV at the momen, seems to suffer from some quality issues (back camera, entertainment system instabilities), so better wait a bit more.</li> However, EVs seem to have been mostly eliminated from most car rental companies, WTF? So just renting out one for the vacation and actually getting one seems to be more difficult now.</li> I guess I’ll wait and see some more…</li> </ul> Starting “Me, my Customer, and AI</a>”, by Henrik Werdelin and Nicholas Thorne.</h2> Looks like an interesting account of how AI is changing entrepreneurship. My personal feeling is that it will enable a lot more Solopreneurs. Learning to Move 🎛️</h2> Not the physical movement, but the new Groovebox by Ableton</a>. It’s a very fun and powerful device and I’d like to use more of its potential, so I’m now using it whenever I make music with friends. I’ll also spend more time with it alone to learn the ins and outs. I recently bought a CME WIDI Bud Pro—a MIDI-over-Bluetooth interface that’s compatible with my Move, so I can connect it to my laptop and Ableton Live running on it. Let’s see what I can do with this combo! Work out more 🦶</h2> My go-to workout option is barefoot running. Now that I have a bit more time, I’ll do this more often, and also add some strength training to it. I like Apple Fitness, so will probably spend more time with it. That’s it! I’m adding a reminder to myself to regularly update this page. What are you doing now? Jeremy Utley: “You can’t require what you won’t do.” 2025-10-15T00:00:00+00:00 A while ago, I mentioned an executive who admitted they didn’t use AI tools themselves</a>. In Admit You Don’t Know: Reverse Mentorship With An AI Sherpa</a>, Jeremy Utley recently blogged about a similar observation, but turns it around by challenging leaders to admit they don’t know, then to do something about it. His core insight is powerful: “Most leaders think credibility comes from always having the answer. In the AI era, it’s the opposite. Credibility comes from admitting you don’t know—and doing something about it.” Acknowledging that execs and other senior people might struggle with justifying the time to spend learning, he offers two solutions: Find a junior mentor who is fluent in AI, and let them teach them. He calls it “reverse mentorship”, but I disagree on the “reverse” part: a mentor is someone who shares experience they have with a mentee who lacks that experience. The concepts of “junior” or “senior” are related to experience, not age. Just because a mentor is less senior in one dimension (like tenure, age, or business experience), doesn’t mean they may not be senior in another dimension (like AI experience). </li> An “AI Sherpa”, an AI-experienced co-worker acting as a shadow who analyzes the day-to-day work of an executive, then builds custom AI experiences (i.e., with ChatGPT’s “Custom GPTs” feature or Claude’s artifacts), tailored to the exec’s workflow and specific needs. </li> </ol> What makes Jeremy’s approach particularly compelling is a real example he shares: Don from PCCP, LLC publicly asked for an AI mentor at a company conference, saying “I raised my hand and asked for a mentor, even though I run the firm.” When Don shared his positive experience the following week, several other senior leaders immediately stood up asking for AI mentors on the spot. One act of leadership humility sparked a movement. I wish the exec I observed had the humility to do something about their lack of AI experience! I’m still not a fan of shortcuts, and the concept of an AI Sherpa sounds like an excuse for not doing the learning. But Jeremy positions it cleverly as making transformation “irrefusible” for resistant leaders—if they refuse world-class AI mentorship designed specifically for them, that reveals something about their commitment to change. It’s certainly better than doing nothing, and it removes the typical barriers that prevent executives from getting started. The bottom line remains: you can’t require what you won’t do. And in the AI era, admitting “I don’t know” might be the most credible thing a leader can say. Power to the People: What 45 days of balcony solar taught me about climate action 2025-10-08T00:00:00+00:00 It’s December 2024. I’m at AWS re:Invent in Las Vegas, coaching speakers on how to communicate effectively with technical audiences. Meanwhile, 9,258 kilometers away in Munich, a room full of homeowners is debating whether my 7.6-kilogram solar panels will blow off my balcony in the wind. This story is about 20 months of bureaucratic persistence—from inspiration at Christmas 2023 to August 2025 installation. The actual hardware setup was done in three hours on a Wednesday afternoon. The approval process? An odyssey of patiently waiting, engineering creativity, and refusing to give up. Here’s what those 20 months taught me: meaningful climate action isn’t blocked by technical complexity—it’s blocked by inertia and giving up too soon. Here’s how I stopped waiting for perfect conditions, what 45 days of real data revealed about generating my own clean energy, and why you should start this process today—not tomorrow. The Christmas revelation</h2> Christmas 2023. I’m visiting my friend Rolf Kersten</a>, and he shows me his “Balkonkraftwerk” (the German word for “balcony power plant”) which he had set up about half a year before. Not on a balcony, actually, he owns a row house, but he chose solar panels hanging from his bedroom window over a traditional rooftop installation for simplicity. “Wait,” I said, “you’re generating your own electricity and pumping it into your standard power socket? Just like that?” He showed me his Home Assistant dashboard. Real-time power generation. Daily totals. Pretty graphs. The whole thing cost less than a laptop and was generating nearly 200 kWh per year. I’d been reading about climate change, ran the numbers for my family, done what we could to reduce our CO2 footprint, and bought certificates to compensate for the rest. But this? This was measurable action, beyond my energy provider’s “green” tariff. Generating clean energy for real, and tracking every single watt. As a tech person who spent 27 years in IT infrastructure, if there’s one thing I understand, it’s systems, and the power of measurement. Rolf showed me that even mortal apartment renters like me could join the solar revolution. The timeline nobody warns you about</h2> The German law landscape is favorable towards solar, so here’s what I expected: Ask landlord. Get approval. Order panels. Install. Generate power. Celebrate! Here’s what actually happened: January 2024: My landlord responds to my email within days. “Sure, no problem! But let me check if we need to run this by the Hauseigentümerversammlung (Homeowners Association, HOA).“ Turns out we had to. Easy enough, right? Summer 2024: I contact my neighbours (who own their apartment) and the Hausverwaltung (property management). “When’s the next HOA meeting?” “We don’t know yet. The last one was October 2023.” Wait. October 2023? That’s… that was before I even thought about this project. And nobody knows when the next one is? Welcome to German bureaucracy 🇩🇪. I tried pushing for an earlier decision. The Hausverwaltung wouldn’t budge. “You’ll have to wait for the next regular meeting.” So I waited. December 2024: The HOA finally meets. I’m in Las Vegas at AWS re:Invent, coaching speakers on stage presence and storytelling while my landlord heroically attends and advocates for my project. The HOA’s verdict? Approved! But with conditions: Black color panels ✅ (I’d already chosen these)</li> Proof of insurance coverage ✅ (already covered by my Haftpflichtversicherung, my personal liability insurance)</li> Adherence to electrical standards ✅ (sure, because I usually love to electrocute myself 🙄)</li> Registration with Marktstammdatenregister ✅ (because everything needs to be properly filed and in order)</li> One more thing: “unter der weiteren Auflage, dass nachgewiesen wird, dass das Balkongeländer die Windlast verträgt, die durch die angebrachte Fläche der Solarzellen auf das Geländer wirkt”</li> </ul> (Record scratching sound here.) Translation: Prove that the balcony railing can handle the wind load from the solar panel surface area. Wait… what? They suggested getting a “Handwerkerbescheinigung”—a craftsperson’s certification. But: what craftsperson in their right mind would certify something they didn’t build? Nobody wants that liability. January-July 2025: I sat on this problem. And procrastinated. And worried about whether I was even approaching this correctly. How do you prove structural integrity without hiring an engineer (expensive!) or finding a Handwerker willing to certify (impossible!)? During this time, I was also preparing for my biggest career transition—leaving AWS after 13 years to become a freelance consultant</a>. The solar project became one more thing on a very long list. Then I had a realization: I’m an engineer. Not the structural kind, but close enough. What if I did the math myself? August 2025, installation day! Three hours on a Wednesday. Velcro straps (yes, really—they’re industrial-strength and part of the certified mounting system). Some cable management. Done. I ended up buying some extra stuff (see below), but the tech part is indeed the easiest one. September 2025: After technical bliss (and a vacation), it’s time to confront the bureaucratic reality. I spent a weekend researching wind load calculations, German building codes, and physics I hadn’t touched since university. The calculation went like this: Munich is in Wind Zone 2 (according to DIBt regulations</a>)</li> Wind Zone 2 means 0.39 kN/m² wind pressure (from tables published by a company called OBO Bettermann</a> )</li> My panels: 2.325 m² total surface area (datasheet here</a>)</li> Hence, the maximum wind force is: 0.39 × 2.325 = 0.907 kN</li> Converting to something tangible: 0.907 kN ÷ 9.81 m/s² = 92.46 kg</li> </ul> In other words, the maximum wind force on my solar panels would be equivalent to the gravitational force of a 92.46 kg object resting on the railing. My balcony railing has three steel tubes, each 5 cm in diameter. I was pretty confident they could handle that. Also, my neighbor downstairs has a balcony privacy screen that probably has a similar weight, offers a larger wind attack surface, and has been there for years without issues 🙄. I wrote up my analysis, included photos, specifications, and certifications, and sent it to the Hausverwaltung. Full letter here</a> (anonymized, in German, with English translation). I haven’t heard from the HOA since. I guess they just filed it away? I consider that a win: sometimes silence means acceptance in German bureaucracy 😅. Twenty months from inspiration to installation. But only because I didn’t give up when the Handwerker problem seemed unsolvable. The lesson? Start your landlord and HOA approval process today—before you buy anything. And when bureaucracy throws you an impossible requirement, get creative. Sometimes the solution is just doing the (paper)work yourself. The setup: simpler than you think</h2> </picture> A solar panel, mounted using velco straps to the balcony. </figcaption> </figure> The hardware I chose: Plugin Energy AIR superLIGHT HC 440W COMBO</a>. Why these panels? Lightweight: 3.8 kg each, 7.6 kg total, easy to transport</li> Frameless: sleek, all-black design to satisfy the HOA</li> Flexible: easier to mount on railings than rigid glass panels, easy to remove if I change home</li> No glass, no shards, no hurt</li> 440W total max. capacity: 220W per panel</li> </ul> The kit included a TSUN TSOL MX500 microinverter (converts DC from panels to AC for my home) and everything needed for connection. Total cost: €519 for the kit. I added a few extras: FI-Schutzschalter (residual current device): €15 for extra safety, thanks to Rolf for suggesting this!</li> Weatherproof green plastic box: €30 to keep the inverter dry and connections tidy</li> Weatherproof Tasmota</a> smart plug: €19 to measure actual output</li> Extra solar power cables: €19 to put the inverter closer to the wall socket</li> Total investment: ~€600</li> </ul> Installation was simple. The panels attach to the balcony railing with industrial-strength velcro straps that are surprisingly robust—they’re part of the certified mounting system. Plug the panels into the microinverter (with the extra cable), put the microinverter into the weatherproof box, connect to my balcony outlet. Done. </picture> The inverter looks nicer when inside a weather-proof box. </figcaption> </figure> Well, almost done. The TSUN inverter comes with its own app and monitoring. But other than the manufacturer’s app, there’s no other way to get the data. So I added a Tasmota smart plug between the outlet and my system to measure the actual power flowing into my home. Setting up Tasmota + Home Assistant took some tinkering: calibrating the power measurements</a> and configuring an MQTT broker</a>, before I could harvest my data and create dashboards. You don’t really need this, the TSUN app works fine for most people. I just happen to be a data nerd who enjoys rabbit holes 🤓</a>. Three hours from zero to generating power. Twenty months to get permission to spend those three hours. What the data reveals</h2> Meanwhile, I have forty-five days of real data, from actual electrons flowing into my home power grid, measured by an independent meter. (It still boggles my mind thinking that those power sockets can actually work backwards, too!) The numbers: Total generation: 35.88 kWh over 45 days</li> Daily average: ~800 Wh (0.8 kWh)</li> Projected annual: 292 kWh</li> Value at €0.37/kWh: ~€108 per year</li> Return on investment: 5-6 years</li> After that: pure profit for 20+ years</li> CO2 avoided: 292 x 321 g CO2eq/kWh = 93.732 kg per year (based on Germany’s 2024 power grid mix, according to data from Nowtricity</a>)</li> </ul> </picture> Balcony power plant power generation data, as rendered by Home Assistant </figcaption> </figure> I’m not sure what will happen over winter: snow might cover my panels and the days will be shorter, but then again, the sun’s angle might be more favorable. I’ll find out! But the numbers only tell part of the story. Here’s what surprised me: Weather makes a huge difference. On the best days (clear sunny summer days), I generated over 1.5 kWh. On overcast days sometimes just 30-80 Wh. That’s quite a difference. Munich’s weather is… variable. The dopamine effect is real. I love checking my Home Assistant dashboard. How much did yesterday generate? Is today sunny? How much is this measured in heated water kettles or laptops charged? This Grist.org article mentioned this phenomenon</a>—people screenshot their daily yields and share them in family WhatsApp groups. I’ve done the same (though I prefer Threema</a> for private messaging). It’s gamification for sustainability ☀️. Neighbors ask questions. People I know start asking me about the panels, including my neighbour and of course my landlord. I can’t wait to see more of these popping up in my neighbourhood. The data validates the 20 months of patience 😅. Why this matters more than €100 per year</h2> Let’s be honest: €108 per year isn’t life-changing money. At current electricity prices and my current usage, this system will pay for itself in 5-6 years. Then it’s profit for another 15-20 years (panels are warrantied for 25 years). But this was never about the money. Germany now has over 550,000 Balkonkraftwerke installed, with half of those added just in 2023-2024. That’s a movement. And movements aren’t driven by ROI calculations—they’re driven by something deeper. The Grist article quotes someone who perfectly captured what I’m feeling: “It’s a small step you can take as a tenant” and an act of “self-efficacy, to not just sit and wait until the climate crisis gets worse.” That’s it exactly. Agency. For years, I felt climate anxiety. Read the reports. Worried about my carbon footprint. Recycled diligently. Chose “green” energy from my provider. But I never knew if any of it actually mattered. Now? I know. 35.88 kWh over 45 days. That’s 35.88 kWh that didn’t come from natural gas or coal. That’s ~11.5 kg of CO2 I didn’t emit. It’s measurable. It’s real. It’s mine. If that sounds like a drop in the ocean, it maybe is. But here’s the multiplier effect: Rolf inspired me at Christmas 2023. I’m writing this in October 2025. How many people will read Rolf’s post, or the Grist.org article or this one and start their own journey? One becomes two becomes five becomes twenty. This is how movements start. One balcony at a time. Should you do this?</h2> Hell yeah if:</h3> You have a balcony facing south, east, or west (south is best, but east/west work too)</li> You’re comfortable with basic tech (or willing to learn—it’s easier than you think)</li> You want measurable climate action, not just good intentions</li> You’re prepared to be patient with bureaucracy (it’s worth it!)</li> </ul> Maybe not (yet) if:</h3> Pure north-facing balcony, or no sunshine available (find a better place!)</li> Very low baseline electricity consumption (you’ll end up feeding more to the grid without compensation)</li> Completely unreasonable landlord/HOA (though German law is increasingly on your side)</li> </ul> As you see, not that many excuses left. What I’d do differently:</h3> Start the landlord/HOA process FIRST, before buying hardware. Don’t assume it’ll be quick.</li> Set realistic expectations: Approval can take 6-12+ months. Installation takes 3 hours.</li> Budget for some extras: Plan for ~€50-100 beyond the base kit (safety switches, weatherproofing, extra cables, monitoring if you want).</li> Join local communities: There are fantastic Balkonkraftwerk forums and groups where people share their experiences and troubleshoot together.</li> I should have joined the movement earlier!</li> </ol> Your playbook to get started:</h3> Email your landlord TODAY. Don’t wait. Even if you’re not ready to buy, start the conversation. Use my experience: “This is now legal, HOAs must approve with reasonable conditions, here’s what I’m planning.”</li> Find out when the next HOA meeting is. If it’s months away, ask if there’s a way to get preliminary approval or add it to the agenda early.</li> Research hardware while you wait. Check your balcony orientation. Measure your space. Look at options. But don’t buy yet—approval first!</li> Prepare your case. Gather information: insurance confirmation, product certifications, Marktstammdatenregister info. Make it easy for the HOA to say yes. Use my letter</a> as a template.</li> Order once approval is likely. The hardware doesn’t take long to arrive, but the approval process does.</li> Install! This is the easy part. Seriously. Three hours.</li> Set up monitoring (optional but fun). The manufacturer app works fine. If you want to go deeper, many smart plugs, like the Tasmota-based ones come with energy monitoring built-in, that you can tap into.</li> Share your data! Blog about it. Post in forums. Tell your neighbors. Help grow the movement.</li> </ol> Resources that helped me:</h3> Rolf Kersten’s Balkonkraftwerk journey</a> - The inspiration for my project</li> Plugin Energy</a> - Where I bought my system, recommended to me by Rolf (no affiliation)</li> Article on Grist.org about Germany’s Balkonkraftwerk movement</a> - Great context on the bigger picture</li> My property management/HOA email template</a> - Full text here</li> </ul> The sun is already shining on your balcony</h2> The real win isn’t the money or even the carbon savings. It’s the proof that you don’t need to own a house to generate clean energy. You don’t need perfect conditions. You don’t even need to skip the bureaucratic headaches. You just need to refuse to give up when the process gets tedious. It took 20 months from Christmas inspiration to August installation. But my panels will generate clean power for 20+ years. The question isn’t whether you should do this. The question is whether you’re starting the process today and see it through when it gets bumpy. Because here’s what I learned: Climate action isn’t blocked by complexity. It’s blocked by inertia and giving up too soon. The hardware is surprisingly simple. The approvals are getting easier (German law now mandates HOAs must approve these with reasonable conditions). The investment pays for itself. The impact is measurable. The bureaucracy might test your patience. You might spend more time on paperwork than installation. You might have to calculate wind forces like I did. But on the other side is a bit of self-harvested clean energy, every single day, from your own balcony. Your homework starts now: Check your balcony orientation (which direction does it face?)</li> Draft that email to your landlord (today, not tomorrow)</li> Join a local Balkonkraftwerk forum or Facebook group or find other Balkonkraftwerk friends to team up with</li> Start researching kits and options</li> Calculate your potential ROI</li> </ul> The sun is already shining on your balcony right now, while you read this. What are you waiting for? ☀️⚡ What’s your experience with Balkonkraftwerke? Did you face similar bureaucratic challenges? Share your comments and/or story on this Bluesky update</a> or this Mastodon thread</a>!

Links</h2>

mlx-whisper on PyPI</a></li>
uv for easy Python tool installation</a></li>
Simon Willison’s llm CLI tool</a></li>
Video version of this post</a></li> </ul>

1. Write down your Purpose, Mastery, and Autonomy</h3>
What impact matters to you?
What skills are you building?
What decisions do you want to make?</p>
Just three sentences. Get it out of your head.</p>

3. Pick one next-level move and start doing it</h3>
Identify what people at the next level are doing. Then either ask your manager for that stretch project, or just start doing it yourself.</p>
Give yourself permission.</p>

Starting a YouTube channel 🎥</h2>
…because it’s never too late! Here it is: @Constant.Thinking</a> on YouTube. There are 5 videos so far, with a wide range of view counts, from 4 to 229. The YouTube algorithm is a very weird one and I see this as a marathon.</p>

Constant Thinking

Video: What I actually do with AI every day

How to stop engineering prompts and start delegating

From scarce code to abundant builders

Local audio transcription with mlx-whisper: what actually works

You don’t need permission to get promoted

The explore/exploit guide to your first tech job

SMART goals are failed Taylorism (and why you should ignore them)

We’re still living in the cat pictures era of AI

Werner Vogels’ 2025 AWS re:Invent keynote is a timely career handbook for navigating accelerating change

3 slides everyone uses (but you should delete)

What’s Constantin doing now?

When Claude Suggested Brian Eno: Building Art You Can't Control

Before you present: 7 critical checks for tech speakers

Unstuck: What Seth Godin's Mentor Deck Taught Me About Bridging Human and Digital Worlds

Invite your heroes into your AI conversations

Starting my own YouTube channel

From Worry to Action: A Crisis Survival Guide

What’s Constantin doing now?

Jeremy Utley: “You can’t require what you won’t do.”

Power to the People: What 45 days of balcony solar taught me about climate action

Constant Thinking

Video: What I actually do with AI every day

How to stop engineering prompts and start delegating

From scarce code to abundant builders

Local audio transcription with mlx-whisper: what actually works

My Complete Setup</h2> Here’s the Fish shell function I use every week:</p>

Video</h2> Here’s a short video walkthrough with a bit more personality and some fun drama elements:</p>

You don’t need permission to get promoted

1. Write down your Purpose, Mastery, and Autonomy</h3> What impact matters to you? What skills are you building? What decisions do you want to make?</p> Just three sentences. Get it out of your head.</p>

2. Start your achievement log</h3> Create a document with this structure:</p>

3. Pick one next-level move and start doing it</h3> Identify what people at the next level are doing. Then either ask your manager for that stretch project, or just start doing it yourself.</p> Give yourself permission.</p>

The full story</h2> I go deeper into all of this in my video below, including the anti-pattern of promotion obsession, specific stories from my career, and the mental models that kept me sane through corporate chaos:</p>

The explore/exploit guide to your first tech job

SMART goals are failed Taylorism (and why you should ignore them)

We’re still living in the cat pictures era of AI

The knowledge-harvesting pattern that actually matters</h2> As a general rule, AI works best, the more data you send into its contect. This is what we leverage with this pattern:</p>

Werner Vogels’ 2025 AWS re:Invent keynote is a timely career handbook for navigating accelerating change

Your January Challenge</h2> It’s December. The year’s winding down. Now is the perfect time to assess your career against these five principles.</p> For each one, ask yourself:</p>

3 slides everyone uses (but you should delete)

What’s Constantin doing now?

When Claude Suggested Brian Eno: Building Art You Can't Control

Before you present: 7 critical checks for tech speakers

5. Are you designing for humans or computers? ❤️</h2> It’s easy to go all-in on data, facts, and logic in tech presentations. That’s a mistake.</p> Before you finalize your deck, scan through this human-centered lens:</p>

Unstuck: What Seth Godin's Mentor Deck Taught Me About Bridging Human and Digital Worlds

The Deck Itself</h2> Let me show you what I mean. I made a quick video walking through the deck—what it is, how it works, and even a peek at the code behind these AI conversations:</p>

Invite your heroes into your AI conversations

Starting my own YouTube channel

From Worry to Action: A Crisis Survival Guide

What’s Constantin doing now?

Jeremy Utley: “You can’t require what you won’t do.”

Power to the People: What 45 days of balcony solar taught me about climate action

1. Write down your Purpose, Mastery, and Autonomy</h3>
What impact matters to you?
What skills are you building?
What decisions do you want to make?</p>
Just three sentences. Get it out of your head.</p>

3. Pick one next-level move and start doing it</h3>
Identify what people at the next level are doing. Then either ask your manager for that stretch project, or just start doing it yourself.</p>
Give yourself permission.</p>