Verstreuen [ver-ˈstrɔɪ̯-ən]

verb (German)

1. to scatter; to spread widely.

2. (versehentlich) to spill, often by accident.

Verstreuen is my weekly ritual - revisiting notes added to my 🗃️ Zettelkasten to find the ideas worth taking into the next week.

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🗃️ This Week’s Highlights

This week's notes come from 112 new additions to the Zettelkasten (lots of reading on the train this week) - here’s the three that stood out most to share with you:

🟨 How focusing on what I could control made my results worse

🟦 Why the best way to find a great idea is by being inefficient

🟥 What my worst sales call taught me about being useful

Over the past few weeks, I’ve been sending lots of cold outreach for my business. At first, I was obsessed with getting replies, conversations and customers.

The problem was that none of those things were actually under my control.
I realized I needed to move the goalpost.

Instead of measuring my success by how many people responded, I decided I would focus on what I could control - sending 10 messages every day. The responses could take care of themselves; my only job was to make sure those 10 messages went out.

When I came across the idea of “acting without attachment to the fruits of your actions”, it felt like vindication of the mindset shift I was trying to make: a detachment from the outcome. So I implemented it literally. I stopped worrying about responses and gave myself a much simpler measure of success: did I send 10 messages today? If the answer was yes, I had done my job.

But after a few weeks, I started noticing that my response rates had actually dropped. I had become so focused on detaching from the outcome that I wasn't particularly concerned about what I was sending.

I had successfully detached from the outcome, but I had also successfully detached from the responsibility for the action.

Detachment from the outcome doesn't mean detachment from the action. I can't control whether someone replies, but I can control the message I send.

I had confused not caring about the outcome with not needing to care about the action.

The point of detachment isn't to give yourself permission to do mediocre work because the result isn't under your control. It is to free yourself from needing the result to justify the work.

You can detach from the outcome without detaching from the responsibility to do the work well.

So I'm still going to send 10 messages a day. I'm just no longer treating the number as an excuse to send 10 anything.

The goal is to put as much care as I reasonably can into the things that are mine to control, and then let go of the outcome.

Give up attachment to the outcome, not responsibility for the action.

📎 Takeaway: You don't have to care whether your work succeeds to care deeply about doing the work well.

—🗃️—

If you want to discover something new, you need to be willing to be inefficient.

That sounds backwards. It’s usually assumed better results come from better selection: clearer criteria, stronger filters, and a better ability to identify the strongest option.

But that only works when you already know what good looks like. When you don't, variance becomes more valuable than optimization.

Consider hiring ten people. You could ask one person to choose all ten, or ask ten people to choose one each. The first approach repeatedly applies one person's definition of good. The second introduces ten different perspectives into the selection process.

A strong group isn't just ten individually impressive people. It is different experiences, perspectives, skills, and ways of seeing problems.

The same principle applies to ideas, books, research, and creative work. If one person recommends ten books, you'll probably get ten books that fit their existing definition of a good book. Ask ten people for one recommendation each, and you're more likely to encounter something you would never have chosen yourself.

Some of those recommendations will be worse. That's not necessarily a problem. The goal isn't to make every choice better; it's to increase the odds that one choice surprises you.

This is also why I like using a Zettelkasten. Not every idea needs to prove its value immediately. Some notes are obviously useful, while others seem irrelevant or even contradictory until they unexpectedly connect to something years later. If I filtered everything for immediate usefulness, I would eliminate many of those possibilities before I knew what they were worth.

Optimization is good at finding the best version of what you already understand. Variance gives you a chance to discover what you don't.

When the objective is clear, use better filters. But when you're exploring, researching, creating, hiring, or solving a problem you've never encountered before, don't optimize too early.

Sometimes the best way to find a winner is to create more opportunities for something you wouldn't have picked to become one.

📎 Takeaway: When you know what good looks like, optimize for it. When you don't, create enough variance for surprise.

—🗃️—

The other day I had what might go down as my worst sales call ever…

The person I was speaking with had built an impressive content distribution system connecting APIs to turn a single essay into a podcast, platform-specific videos, social posts, and a finished article. It was exactly the kind of system I would normally be excited to talk about.

Instead, I spent most of the call trying to prove I should be talking to him.

He had around 30,000 subscribers and immediately asked me how many I had. I have around 200, but noticed the tone shift after I replied. That gap changed how I approached the conversation. I started worrying about whether I was qualified to help him, or even qualified to be talking to him, and the more I worried about that, the less curious I became about what we had actually scheduled the call to discuss.

I spent the rest of the call trying to assure him of my skills. It ended without a follow-up, and I walked away feeling like I had completely flopped.

I texted a friend afterward, and he gave me a different perspective: I had treated a learning opportunity like a credibility test.

Looking back, the 30,000 vs. 200 comparison was relevant to one question: whether I could help him grow his newsletter. But I had let it answer a much bigger question about whether I had anything valuable to contribute to the conversation at all.

My background is in systems engineering. I could have spent the call understanding how the pieces fit together, where the bottlenecks were, what was creating value, what feedback loops existed, and what would need to change if he wanted to turn the system into something other people could use. Instead, I was wasting time trying to establish that I had credibility as a newsletter author.

That distinction feels important as I think about the whiteboarding work I’m building. My newsletter subscribers, paid subscribers, and revenue are useful evidence of what I can do, but they aren't the only evidence. In a conversation about systems, my experience as an engineer is extremely relevant.

The mistake wasn't that I failed to convince him I could help. The mistake was deciding that convincing him was the purpose of the call.

If I had gone in trying to understand what he had built, I might have discovered that there was a way I could help. I might have discovered that there wasn't. Either way, I would have learned something.

Instead, I turned a conversation I was genuinely interested in into an audition I was afraid of failing.

I thought the goal of the call was to prove I could help him. The goal should have been to find out whether I could.

📎 Takeaway: When you change the way you see a situation, you often change what becomes possible within it.

—🗃️—

Closing Thoughts

There’s a pattern I noticed across these three ideas: you can get very good at optimizing without noticing that you’ve lost sight of what you’re optimizing for.

You can focus so intensely on the outcome that you stop paying attention to the work. You can optimize your choices until you eliminate the possibility of surprise. You can become so focused on proving you're useful that you stop being curious.

None of these mistakes looks like failure. In fact, they can look like progress. The danger is that you become more efficient without becoming more certain that you’re heading in the right direction.

Sometimes the better move is to step back: care less about controlling the result, leave more room for variance, or change the question entirely.

The real skill is knowing when to stop optimizing and ask whether you’re still optimizing for the right thing.

Optimization can become a trap when you forget what you're optimizing for.

Until next week
-GH

Thanks for reading Verstreuen! 👋