Constant Thinking

I was the tax office’s unpaid clerk, until I delegated it to an AI

Constantin Gonzalez — Thu, 06 Aug 2026 00:00:00 +0000

Every summer, a shoebox wins.

For about fifteen years, preparing my German tax return has cost me a weekend plus a few evenings, and quite a lot of swearing in between. Not the tax return itself: I have a Steuerberaterin for that, and she is excellent. What costs me the weekend is everything that has to happen before she can start. Remembering which receipts exist. Finding them. Naming them. Sorting them into the right folders. Building the spreadsheets that explain the numbers, and the notes that explain the spreadsheets.

This year I handed most of that to an AI. It still took a couple of days. But for the first time since I have been filing, it didn’t suck, and the result was better than anything I had produced on my own.

perplexity-agent-mcp: an open-source MCP server for Perplexity’s Agent API

Constantin Gonzalez — Mon, 27 Jul 2026 00:00:00 +0000
How I let Claude Code build the MCP server that didn’t exist.

Why working with AI agents is so tiring (and what to do about it)

Constantin Gonzalez — Thu, 16 Jul 2026 00:00:00 +0000

Earlier this week, I spent five hours at the WERK1</a> coworking space in Munich, moving three projects forward at once: the brain dump that would become this blog post, a contribution to an open source project, and a redesign of my blog’s analytics. My AI agents did the heavy lifting. Whenever one of them finished a work package, I reviewed the results, made a few decisions, wrote the next set of instructions, and hopped over to the next project. It felt great: three streams of steady progress, one me.
Then I came home for dinner and noticed that my head was buzzing. I know this particular kind of tiredness well, but from a different life: it’s how I used to feel after a day of back-to-back job interviews or customer workshops. This time, I had spent the day mostly alone, typing.
Wait, isn’t this multitasking?</h2>
At the surface level, what I did that day was an anti-pattern: I hopped between tasks all afternoon (I’ve started calling it agent hopping), and in productivity circles, multitasking is supposed to be a lie we tell ourselves. Paul Graham wrote in one of my favourite blog posts of all time that makers and managers live on different schedules</a>, and that a single meeting interrupting a long work session can ruin a maker’s afternoon. Cal Newport built a whole body of work around deep, uninterrupted focus</a>. Psychologists have measured again and again that task switching degrades our performance far beyond the seconds the switch itself takes.
And yet, the people building the newest AI tools work exactly this way, all day. Fiona Fung, who manages the Claude Code and Cowork teams at Anthropic, cheerfully described on Lenny’s Podcast</a> how she kicks off multiple agents at once, and knits through meetings while they work.
Either decades of research are wrong, or this isn’t multitasking.
You’ve been promoted to head chef</h2>
I think “agent hopping” (if this is a word) is not multitasking. And the best picture I’ve found for it comes from Gene Kim and Steve Yegge’s book Vibe Coding</a>. Working with AI agents, they say, is like running a professional kitchen: you are the head chef, and the agents are your line cooks. You don’t chop the onions yourself anymore. You write a ticket, hand it to a cook, and turn to the next dish. The switching happens at the hand-off, the natural seam between tasks, not in the middle of a thought.
There’s a second difference that I think matters even more: you choose the moment. Sophie Leroy, the researcher who discovered that part of our attention stays stuck on an unfinished task after we switch away from it</a>, found the effect is at its worst when we’re forced to switch under time pressure. A ringing phone yanks you out. An agent that finished its work waits patiently until you’re ready. The seams are self-timed.
I’ve seen a similar effect in my computer systems work before: in the mid-2000s at Sun Microsystems, I helped introduce servers that swapped one very fast processor for many slower ones working in parallel. In kitchen terms, we asked our customers to trade one brilliant, expensive chef for a whole brigade of decent cooks, and most of their software simply wasn’t organized to work as a brigade yet, so it was a hard sell. Today’s AI brigade shows up pre-trained. It even speaks English, and most other languages, too.
So no, working with agents isn’t multitasking in the classic, self-deceiving sense. It’s running a kitchen. Which leaves the question: why the buzzing head?
The kitchen in your head has no ticket rail</h2>
Because the kitchen picture breaks in two places, and the tiredness lives exactly in the breaks.
A real kitchen keeps its state out in the open: the orders hang on a ticket rail above the pass, and the chef’s head can stay comparatively empty. Your brain has no such rail. Psychologists have known since the 1920s that unfinished tasks keep occupying our minds</a>, and that we can only actively track about three or four things at a time</a>. Every dish that’s still cooking with an agent somewhere keeps simmering in your head, too.
And here’s the subtle part: handing off a ticket doesn’t finish anything. The dish is still yours until you’ve tasted it and sent it out. Delegating to an agent softens the cost of switching. It doesn’t clear the plate.
The cooks took the chopping</h2>
The second break is sneakier. The line cooks didn’t take a random part of your work. They took the easy part.
Drafting routine code and text, summarizing documents, doing research legwork: that used to be the lighter part of a working day, the part where your mind could coast a little. What tends to remain for you (tends, because there’s still plenty of mindless cleanup in AI land) is tasting and deciding: reviewing results, making judgment calls, working on strategy and direction. As Steve Yegge put it</a>: “AI has turned us all into Jeff Bezos, by automating the easy work, and leaving us with all the difficult decisions.” And decision work is the most expensive kind of thinking we do. The best evidence I know of suggests we can sustain roughly four good hours of it per day</a>; Yegge, after months of intense agent work, arrived at three to four hours as the realistic workday, too.
Agent work packs those hours tight. Every wait invites filling, so the pauses that used to come free with the work, like a coffee break while the computer executed a long program, get filled with yet another round of deciding. That’s how a quiet maker day can feel like an interview marathon day.
You’re not imagining it</h2>
A more sensationalistic blogger would call this an “AI fatigue epidemic”, and they wouldn’t be wrong:
Simon Willison, a developer with decades of experience, describes it</a> like this: “Using coding agents well is taking every inch of my 25 years of experience as a software engineer, and it is mentally exhausting. I can fire up four agents in parallel and have them work on four different problems. And by like 11 AM, I am wiped out for the day.”
A field study of 200 tech workers found the same pattern</a> and called it work intensification: “a continual switching of attention” and “a sense of always juggling.”
Laura Summers at Pydantic, who call themselves an “end-to-end AI engineering stack”, writes: “Programming with LLMs is genuinely useful and genuinely destabilizing. These two things coexist. If we pretend the second one isn’t happening, we will all burn out.</a>” She also offers hope by pointing out that “It was always the human attention, the engineering judgment, the ability to hold a coherent vision for a system. … those human capacities are revealed as the actual scarce resource. And scarce resources are valuable.“
Speaking of burnout: in this year’s tech worker sentiment survey</a>, significant burnout jumped from 44.7% to 54.7% in a single year. Agent fatigue is at most one contributor among many there (layoffs and job insecurity surely do their part), but the direction is worrying.
Even Fiona Fung, when Lenny asked her how to handle the growing context-switching load, answered: “I haven’t cracked it yet.”
What I do about it</h2>
I don’t claim to have cracked it either. But the following practices have made my agent days noticeably lighter, so take them as field notes rather than final answers.
Expect the tiredness. It’s arithmetic, not weakness. If you delegate the light work, what remains for you is dense by definition. I stopped being surprised by the buzzing head and started accepting it, the way a chef plans for the dinner rush: it’s coming, so don’t schedule anything deep behind it.
Build yourself a ticket rail. I write long, careful briefs for my agents, something I’ve described in How to stop engineering prompts and start delegating</a>. A good brief does two jobs at once: it makes the agent better, and it takes the dish out of your head. Leroy’s research offers the same remedy for humans, a short written note on where you stopped and what happens next, so your mind can let go. Then take your break at the hand-off, not at random: after I send off a brief, I stand up, stretch, make coffee, play a move on Chess.com, or send my wife a message. Five minutes is enough. And check on your cooks on your schedule, not theirs: peeking at a still-working agent is a paid interruption with zero yield, and agent notifications that interrupt you rebuild the bad kind of task switching with extra steps. (Though I do find Peon Ping</a> hilarious.)
Cap your burners. One big dish, at most two small ones on the side. My personal Kanban</a> board enforces this limit, and my brain is grateful for it. Some days, the right number of works-in-progress is one. Above all, enjoy the light work without guilt: the lunch walk, the small tool tinkering between hand-offs. The silly pet project. Fiona Fung knits, and I think that’s the perfect extreme case: a task that occupies the hands and costs no working memory at all.
After hours: give yourself permission to play. My mother used to work as a diplomat in the German foreign ministry. Our family spent many years in foreign countries she was assigned to: Colombia, Turkey, Italy, Portugal, Spain. As I grew older, she told me about her colleagues who used Valium and alcohol after an intensive day at the office to wind down. At the time, I had introduced her to computer games and she loved playing Pac-Man, Krakout</a>, and, of course, Tetris. She told me that this was her wind down ritual, no drugs required. Today, I love playing The Legend of Zelda: Breath of the Wild. I recently saw it ranking high on a list of “best cozy games”. “Wait!” I thought to myself, “Zelda, a cozy game?” But, yes, it makes sense. There’s a lot of meditative comfort in exploring the vast world of Zelda at your own pace, collecting stuff, completing quests, and relaxing in a beautifully made imaginary world.
Stay awake at the wheel</h2> I wrote a few weeks ago that we’re in the driver’s seat with AI, if we choose to stay awake</a>. Agent fatigue is exactly where that choice gets hard, because the real risk isn’t being tired. It’s what tired brains do: accept the agent’s recommendation without thinking it through, skim the review that deserved attention, let the evening reading collapse from books into feeds. You can frequently find me reading a good non-fiction book on my morning commute, but my evening commute is more often spent on Reddit.
Air traffic controllers and truck drivers get mandated rest, because as a society, we’ve learned the hard way what vigilance fatigue does in those professions. Nobody is going to mandate rest for people running AI agents; all we get is a smartwatch reminding us to stand up. So this one is up to you: see the arithmetic of doing more executive work than ever before. Congratulate yourself on your promotion to head chef. Build the rail. Cap the burners. And step away from the pass while the cooks are cooking.
So, tell me: what’s on your burners right now, and what’s your knitting?

You’re in the driver’s seat: will you choose to stay awake with AI?

Constantin Gonzalez — Mon, 22 Jun 2026 00:00:00 +0000

„Nah ist / Und schwer zu fassen der Gott. / Wo aber Gefahr ist, wächst / Das Rettende auch.” — Friedrich Hölderlin, Patmos (1803) </blockquote>

(Near is, / and difficult to grasp, the god. / But where danger is, grows / the saving power also.) </blockquote>
A few weeks ago, I wrote a post titled Thinking with Claude: why Cyborg writing works better than Centaur writing.</a> In the LinkedIn comments, a reader named Christopher pushed back: Leaning on AI to help me write, he argued, was training my brain to “surrender executive function to external sources.” Examples: freed prisoners overwhelmed by everyday choices, ex-military struggling with civilian life, people leaving long-term institutional care. The source doesn’t matter, he wrote. The offloading pattern is the same.
While my initial answer was along the lines of “using your executive function is a choice”, I think it’s not that simple, after all, and I owe Christopher a real answer. In fact, his comment pushed me into a weeks-long thinking journey, during which I looked for research, came across interesting related articles, and reflected a lot on my own AI use. I also jammed with Claude, and yes: I used the very tool Christopher warned me about to think harder about his warning. We’ll get to that contradiction later.

Beyond coding agents: how AI rewires your organization

Constantin Gonzalez — Wed, 10 Jun 2026 00:00:00 +0000

TL;DR: AI made the coding part of software fast, and that speed didn’t create chaos so much as expose where your organization was already slow: the hand-offs between teams. The fix isn’t a better tool, it’s better wiring. We rewired Dev and Ops once already and called it DevOps. The next wall, between business and engineering, is coming down the same way. Here’s how to see it, and one thing you can do tomorrow.

AI is the rubber duck that pushes back

Constantin Gonzalez — Thu, 14 May 2026 00:00:00 +0000
On working with AI as a self-reflection exercise — a companion to my conversation with Cris Roata on the Catching More Green Lights in Life podcast.

Michael Leibovich: How I Built My AI Chief of Staff

Constantin Gonzalez — Wed, 13 May 2026 00:00:00 +0000

Detailed write-up by Michael Leibovich on how he used Claude Code (inside the Claude Desktop app) for implementing his own Chief of staff setup.
As someone running a very similar setup myself</a>, I particularly liked his use-case descriptions and the insights coming out of “externalizing your thinking”. I can totally relate to that!
Also, I agree that installing Obsidian</a> and giving Claude access to it through its REST API plugin and MCP are great for giving it a structured, extra memory layer beyond its own memory mechanism. Claude can write Obsidian-style Markdown documents directly to the file system which is faster than going through the MCP/API, but the real value is the ability to search for existing documents through MCP that allows Claude to discover notes, make cross-correlations and more.
I would add a few things from my own perspective here:

Thinking with Claude: why Cyborg writing works better than Centaur writing

Constantin Gonzalez — Tue, 05 May 2026 00:00:00 +0000

In February this year, I wrote about why I stopped trying to engineer prompts and started delegating intent</a> instead. That post argued the skill of working with AI is managerial, not technical. I still think that’s right. But over the last couple months I’ve noticed something else: even with good delegation, the AI’s output sometimes lands close to what I want and sometimes lands feeling oddly not me. So I started investing more time in working more closely with AI (I like Claude) to figure this out and try to come up with a good method that more closely matches my own voice, style and way of writing, while allowing me to also become a better writer and improve the quality of my work. Here’s what I found:

Generic AI output is a structural, not a prompting problem. A better way is to engage in deeper conversation: a back-and-forth where the AI gets to absorb your reasoning, your style, your taste over time. Ethan Mollick calls this Cyborg mode (vs. Centaur mode where the AI just does the work). There are three elements that I found make Cyborg mode work well for me, and a fourth shift in how you “edit” that I became aware of only recently. </blockquote>
If you’re more the audio-visual type, here’s an eight-minute video shot in a forest near Munich on a sunny morning: Thinking with Claude on YouTube</a>.

</a> </div> </div>
And here are the details:
AI slop is breaking the reading ecosystem</h2>
Here’s an LLM-style LinkedIn post. The prompt: “Write a LinkedIn update about human-AI collaboration.” This is a typical output:

🚀 Excited to share some game-changing thoughts on human-AI collaboration!
The future of work isn’t about humans OR AI — it’s about humans AND AI working together. Not a tool. Not a replacement. Just a perfect partnership.
In my experience, the best teams treat AI as:
✨ A trusted thought partner 🎯 An always-on creative engine 🚀 A multiplier of human potential
The future belongs to those who embrace this shift.
What’s YOUR take on human-AI collaboration? 👇
#AI #FutureOfWork #Innovation #ThoughtLeadership </blockquote>
You’ve probably seen posts like this a hundred times this month. Possibly this week. “AI slop” has become so over-used and thus recognizable that most readers spot it in seconds. And they scroll past.
Three things bother me about these slop posts:

They’re painful to read.</li>
They signal that their authors didn’t put much effort into writing them.</li>
They’re damaging the entire reading ecosystem.</li> </ol>
When LinkedIn (or other social networks, or your blog feed) becomes dominated by AI-generated posts, readers stop reading. They scroll faster. They engage less. They walk away. Honest authors who put real work into their writing (using AI or not) are the ones who pay the price. Even when the work is genuinely theirs, the slop around them poisons the well.
I think that’s why so much good online writing feels like it’s being read less. Not because it’s worse, but because the trust budget is collapsing.
The usual response to all of this is “prompt better.” Get more specific. Use better instructions. Try this format. Use that framework. There are entire industries selling cures for AI slop that operate at this level.
It doesn’t really work. Even good prompting produces AI-shaped output. Even careful delegation can leave you with text that’s correct and useful but feels off when you read it back. The voice isn’t quite yours. The reasoning isn’t quite yours. The output is about you, but not of you.
I argued in the article about delegation that the skill of working with AI is managerial, not technical: transfer intent, don’t engineer prompts. That’s the right move for proper, delegation-driven AI work. But noticeably better Centaur output is still Centaur output, not “you”.
This article is about putting the “you” back into your work.
Centaur and Cyborg are different modes, not different skill levels</h2>
Ethan Mollick, in Co-Intelligence (2024), distinguishes two ways humans work with AI:

Centaur mode: clean handover. You describe the task, AI does the work, you take the result. Like a horse and rider, two beings doing different things in coordinated fashion.</li>
Cyborg mode: integration. You and the AI work together throughout, blending decisions and steps, hard to draw a line between who did what. Like a single being with extended cognition.</li> </ul>
Most people use AI in Centaur mode. That’s perfectly fine for many tasks. “Explain how this code works,” “summarize this PDF,” “draft an outline for a proposal” — these don’t need you in the loop while the AI works. Centaur is the right tool. Quick, clean, scales well.
The catch: Centaur mode can’t absorb you. By design. You hand the task off, and the AI doesn’t get to see how you’d think about it, where you’d push back on yourself, what you’d cut and why. It can’t learn your taste because you’re not there while it’s working. So when the task is voice-loaded, when “sounds like me” matters, Centaur mode hits a limit that no amount of prompting can break through.
Cyborg mode is for voice-loaded work. It’s slower per task, doesn’t scale the same way, and feels weird at first. But it produces something Centaur mode can’t: output that actually sounds like you, because you stay in the lead and the AI got to participate in how you got there.
Here’s a simple heuristic to spot which mode you’re in: Watch how much you type versus how much the AI outputs. Eyeballing is fine here, just look at the overall amount. In a Centaur conversation, the ratio is usually something like 10:90, maybe 20:80 if you’re being thorough with your delegation brief. In a Cyborg conversation, you’re typing more, longer, more iteratively, with many turns. The ratio moves to something like 40:60, maybe closer to 50:50. The conversation stops feeling like request-and-response and starts feeling like an intense working session with a trusted colleague.
The ratio of you-in to AI-out is the simplest tell for which mode you’re actually in.
Three elements that make Cyborg mode work</h2>
Cyborg mode isn’t a single move. It’s three elements that work together. They’re not strict steps in a strict order. You can cycle between them, blur their boundaries, double back. Each one is doing something distinct, so it’s worth naming them separately.
1. Deep dialoging (the resonance chamber)</h3>
Most people start an AI session by typing a prompt. It can be simple, or elaborate, boilerplate, with or without delegation patterns. But still: a prompt.
In Cyborg mode, I prefer to start with a brain dump. That’s where you write down what you’re thinking about the topic, what you’re stuck on, what you’ve half-considered, what you’ve ruled out and why. You ask the AI to poke holes in your thinking. You let it suggest alternatives. You react to those alternatives. You bring in people you admire</a> (“what would Derek Sivers say about this?”) and let the AI play their voices back to you.
This is where one important shift happens. People talk about social media as echo chambers: an algorithmic loop that amplifies your existing views back to you, narrowing rather than expanding. Done wrong, AI conversations can become echo chambers very easily, because LLMs are trained to be agreeable.
The positive side of the coin is what I’ve started calling a resonance chamber, which has the opposite effect: You deliberately shape what resonates. You bring in challengers. You ask for blind spots. You explore the opposite view. You instruct the AI to give you its honest opinion, not flatter you. You curate the dialogue so it expands rather than confirms.
The mechanic underneath all of this is simple. The more “you” you put into the conversation, the more “you” the AI has to absorb. Brain-dumps, ramblings, half-formed thoughts, side notes about why you do or don’t believe something aren’t waste. They’re the raw material the AI can use to model your reasoning. You can’t ask an AI to “sound like you” if it doesn’t know you. The dialog is where it starts to know you.
2. Drafting, with a twist (and the wrongness reflex)</h3>
Most people, when it’s time to write something, write a draft and ask the AI to edit it. This feels natural. The AI is the assistant, you’re the author, drafts are the author’s job.
I found the reverse to work much better. After a long dialog (Element 1), I ask Claude to write the draft. Not me.
This produces a strange and useful effect that I’ve started calling the wrongness reflex. You’ve probably heard about the “blank page syndrome”, where an author sits in front of a blank page, freezing. The blank page is the hardest part of writing, always has been. But when I see an imperfect draft Claude wrote based on our conversation, it’s different: I react. “Wait, that’s not quite right.” “That part needs more nuance.” “That’s actually backwards.” “This whole section can go.”
Correcting wrongness is much easier than trying to fill a page. Like an itch, each “wrongness” screams at you: “Fix me!” If the blank page is paralysis, the wrong draft is a flow of corrections, bringing the content closer and closer to the finishing line.
It’s a bit of a reverse-the-order trick. I let Claude write first, not because Claude is a better writer, but because reading and reacting is a fundamentally different cognitive task than originating. By letting it fill the blank page, I create an environment that invites a flow of writing for me.
3. Editing, with and without a text editor</h3>
Which brings us to the part that took me the longest to realize.
The traditional move once you have a draft is: open the text editor, type changes directly. Move sentences. Cut paragraphs. Rewrite the awkward bits. This is what “editing” has always meant, and it’s been the part of writing where my author voice fully takes over.
When going Cyborg mode, I discovered a different move: editing through dialogue. Tell the AI what you want changed, and why. Let it apply the changes. Iterate.
The first time I did this consistently, I felt lazy. I love my text editor (vi, in my case). Editing has always been the part of writing where I’m most clearly the author, the moment where my voice asserts itself over whatever raw material I started with. Delegating that step felt like cheating.
Two things shifted my view.
First, I noticed the connection back to the delegation post. When you tell an AI what to change and why, you’re not actually delegating editing. You’re delegating the typing while keeping all the editorial decisions. The decisions are still yours. The keystrokes aren’t.
Second, and this is the bigger one: when you explain your edits, the AI absorbs your reasoning. Not just the change, the reason for the change. And it remembers.
The explanation bit is the key thing here: Like taking your time to train a new employee, you should take your time to help your AI generate the output you want to get. In fact, I’m editing this very article right now in Zed</a> (vi mode, of course), but I’m also keeping a log of change explanations in Claude’s chat window, so it can take notes. When I’m done, I’ll feed back my finished article to Claude, and ask it to update its voice.md</code> file (where we keep a description of my personal writing voice and style) with my explanations and any other patterns it notices.
Note: today’s LLMs don’t “learn” the way we do: their training is fixed at release. But modern AI tools support persistent memory. Claude Cowork, for example, keeps a CLAUDE.md</code> file with project-specific notes in its folder and can use tools to update a “memory” file across sessions. You can also formalize learning by curating specific files for AI to reference during its work. For example, Ruben Hassid suggested letting AI interview you for generating your voice file in his I am just a text file</a> article.
That’s the difference between the caffeinated intern most people experience with AI (eager, fast, generic, no memory of you between sessions) and a colleague who knows you well. The intern stays an intern as long as you stay in Centaur mode. The colleague emerges over time when you stay in dialogue through editing.
The blur</h3> I called these three elements out separately, so they’re distinguishable, but in practice they don’t stay separated. During a Cyborg session, they tend to blur together. You start brain-dumping. The AI offers a thought you hadn’t considered. You react, and your reaction is more dialog. The AI suggests a draft of one paragraph. You read it, kill half of it, type why. The AI offers the next paragraph based on what you just told it. You’re not doing Element 1, then Element 2, then Element 3. You’re doing all three at once, often inside the same exchange, often inside the same paragraph. After a couple of such “blurs”, and reflecting on the process, Claude and I started calling this state a mind-jam. It’s when the brainstorm-draft-edit boundaries dissolve into one continuous, deep conversation. You stop noticing which mode you’re in. It has its own flow-like momentum, and you’re fully engaged in it, working. Like an artist immersed in their work, becoming one with their tools. Beyond writing, I also started to use this mind-jamming approach for taking, editing, and organizing notes and thoughts, which is a topic for its own article, someday. What this looks like in practice</h2> The article you’re reading is itself a product of this practice. I started with a brief from a working session with Claude, where we’d extracted the spine of what I wanted to say. We had a transcript of a video (created using the same approach) on the same topic from the week before, so we knew the core argument. Before drafting, Claude proposed a structure. I pushed back. We assembled a panel of three coaches in dialog: Derek Sivers (for value and minimalism), Ethan Mollick (for accuracy on his own taxonomy), and Nancy Duarte (for narrative engagement). Each was instructed to actively suggest improvements, not just rubber-stamp. The panel changed three things I’d missed. Sivers pointed out that the spine had two competing ideas in it, so we tightened to one. Mollick reminded us not to strawman prompting craft, since better delegation is a real and useful skill. Cyborg mode complements it rather than replaces it. Duarte said the problem section was too short to earn the solution, and pushed for lengthening it from 300 to 500 words. And that’s just a tiny bit of the dozen or so pages of dialog that went into improving the article meaningfully before a single word was written. The 10:90-vs-40:60 ratio handle came up in this same article-planning conversation. I was trying to explain to Claude how the difference between Centaur and Cyborg sessions feels to me, and the ratio thought-experiment surfaced. It’s a good heuristic that I wouldn’t have produced on my own. The dialog did. A summary, and a question for you</h2> Generic AI output isn’t a prompting problem. It’s a structural problem. Centaur mode is fine for certain tasks, like quick chunks of work, or when the voice doesn’t matter. For voice-loaded work, where “sounds like me” is the whole point, Cyborg mode is the way to go. And vice versa, you wouldn’t use Cyborg mode if all you want is to get a simple task done. The real question is how to use AI more intentionally. The three elements (dialog, draft, edit-through-dialogue) work together rather than in sequence. They blur into a state that’s noticeably different from prompting, and they produce output that’s noticeably more you. The simple heuristic: how much of the conversation are you typing? If it’s 10–20%, you’re probably in Centaur mode. If it’s 40% or more, you’re closer to Cyborg territory. It’s not a goal to optimize for. It’s a guideline that points at what mode you’re probably working in. If you want to try this, pick one piece of work where the voice matters. Don’t prompt-engineer it. Brain-dump instead. Ask Claude to draft. React to what comes back, in dialogue, not in the text editor. See how the conversation feels. Overcome any feelings of laziness, and instead dive into the flow of collaboration. Here are some questions for you to ponder: What’s the longest dialogue you’ve had with an AI?</li> How did you feel about it afterwards?</li> What was the rough ratio of you-typing to AI-output?</li> </ul> Drop me a line on Bluesky, LinkedIn, or via the contact link on this site. I’ll read everything you write me, as long as it is true “you”, with or without AI.
Welcome Claude, my Chief of Staff Constantin Gonzalez — Sun, 22 Mar 2026 00:00:00 +0000 How I accidentally turned Claude into my Chief of Staff—and why my Bullet Journal got better because of it. Plus: a full, unedited interview with Claude about what it’s really like. Video: What I actually do with AI every day Constantin Gonzalez — Sun, 01 Mar 2026 00:00:00 +0000 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 Constantin Gonzalez — Tue, 17 Feb 2026 00:00:00 +0000 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 Constantin Gonzalez — Fri, 06 Feb 2026 00:00:00 +0000 The “SaaSpocalypse” isn’t the death of software—it’s a structural shift from scarcity to abundance. And we’ve seen this pattern before. Local audio transcription with mlx-whisper: what actually works Constantin Gonzalez — Fri, 30 Jan 2026 00:00:00 +0000 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: </a> </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 Constantin Gonzalez — Fri, 23 Jan 2026 00:00:00 +0000 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: </a> </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 Constantin Gonzalez — Wed, 14 Jan 2026 00:00:00 +0000 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) Constantin Gonzalez — Thu, 08 Jan 2026 00:00:00 +0000 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”. </a> </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> LinkedIn screenshot 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> CT 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 Constantin Gonzalez — Wed, 17 Dec 2025 00:00:00 +0000 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>. </a> </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 Constantin Gonzalez — Thu, 11 Dec 2025 00:00:00 +0000 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>

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

</a> </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>

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>

Constant Thinking

I was the tax office’s unpaid clerk, until I delegated it to an AI

perplexity-agent-mcp: an open-source MCP server for Perplexity’s Agent API

Why working with AI agents is so tiring (and what to do about it)

You’re in the driver’s seat: will you choose to stay awake with AI?

Beyond coding agents: how AI rewires your organization

AI is the rubber duck that pushes back

Michael Leibovich: How I Built My AI Chief of Staff

Thinking with Claude: why Cyborg writing works better than Centaur writing

Welcome Claude, my Chief of Staff

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

Constant Thinking

I was the tax office’s unpaid clerk, until I delegated it to an AI

perplexity-agent-mcp: an open-source MCP server for Perplexity’s Agent API

Why working with AI agents is so tiring (and what to do about it)

You’re in the driver’s seat: will you choose to stay awake with AI?

Beyond coding agents: how AI rewires your organization

AI is the rubber duck that pushes back

Michael Leibovich: How I Built My AI Chief of Staff

Thinking with Claude: why Cyborg writing works better than Centaur writing

AI slop is breaking the reading ecosystem</h2> Here’s an LLM-style LinkedIn post. The prompt: “Write a LinkedIn update about human-AI collaboration.”</em> This is a typical output:</p>

Welcome Claude, my Chief of Staff

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

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

</a> </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>

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>