“She loves me,” says a man in love, picking off the first petal of a daisy. “She loves me not,” he says a moment later, picking off the next petal. And so, he continues removing the petals, until the last of the flower is gone and the results are in. The daisy helped the player determine whether or not he’s loved by the girl he desires.

But how did the daisy know that?

The daisy’s trick is that it gets a little help from the man himself. People play the “she-loves-me-she-loves-me-not” game with all sorts of questions, and the answer is not always 50-50. And sure enough, if we observe someone de-petaling a daisy in this way, we will usually see them adjust the rules midway through.

“I’m not even sure this is really a daisy,” a player might say as they get halfway through a flower and start to suspect that the result might be unfavorable in the end. They then go on to find another daisy to restart the game and discard the results of the first one. This improves their chances – now it’s only one in two flowers that has to have an even number of petals.

Our internal compass knows exactly how many times we are allowed to start again to balance out the game’s probabilities. If we give ourselves a 9-in-10 chance to be loved back, we might end up “cheating” three to four times, but not more. After that, if the last daisy has bad news, we’ll probably accept the verdict.

Rapid changes in technology make programming one of the fastest-moving careers. Avoiding burnout is the only way to have a long and sustainable career in tech.

Veteran software developers often recommend to:

1. Work at a place where you can grow. Constantly learning new things is a requirement in tech, but it’s only sustainable if you can do it as part of the job.
2. Build transferable skills. Many developers find it interesting to invest in learning leadership skills and explore technical management roles — those don’t change as often as programming languages do.
3. Have creative outlets and create a space to focus on yourself, to switch off and relax. Make sure you move enough, eat well, and spend quality time with friends and family.

Of course, there’s always the nuclear option: make your money and get out.

It’s often hard to take a step back and observe the situation when we’re in it, but the first step to recovery is always to recognize the problem. A good rule of thumb is that people need autonomy, competence and relatedness for happiness at the workplace. Programmers are no different.

We have a newborn at home, will be moving to a bigger apartment next week, and my projects are all going strong.

Moving places is a good reminder to automate and delegate everything we can, making time for the unpredictable.

Bridgewater founder Ray Dalio wrote that when you’re running a company, you’re essentially building a machine that creates a product.

The key for automation lies in this differentiation between machine and product. Whenever a problem comes up, it’s easy to remedy the immediate issue — the product –, but that doesn’t do anything for future-proofing the machine. You’d end up fixing the same problem over and over again.

Instead, improve the machine in a way it creates a better product, forever.

Part of the appeal of science is that it’s rigorous and methodical. We can assume that studies are generally right about what they claim. But, most studies focus on a narrow scope in order to control all input variables, and therefore the details matter a huge deal.

News, on the other hand, has to report an exaggerated version of reality because of its business model. Journalists are literally paid for capturing people’s attention, which is always easier with a bold claim. The bolder the claim, the more eyeballs the article will attract.

The problem with science is that it sounds wishy-washy. If you need to convince someone, “That thing MAY kill you” will beat “There’s evidence to support the opposite, but it can’t be ruled out that that thing kills you,” every time.

And then we have real life, which is complicated. It has variables that can never be controlled, and the topics that the public is interested in are way too broad for a single study. What’s left out from a study will be just as interesting as what it states.

Media actively exploits this phenomenon: advertisements and news outlets routinely cite scientists, studies and experiments to sound more credible. But if the decision-making part of our brain is switched off, who will fact check whether “4 out of 5 dentists” do in fact recommend a product?

To be a better reader of science journals, the solution is simple. Just keep your decision-making brain switched on.

Next time you read a study, keep asking questions such as:

1. Does it apply? Cars get their safety ratings through crash tests — a relatively small set of controlled “accidents” that manufacturers can design cars around. How much does a crash test score tell us about a real-life crash? As you might have guessed, most models don’t do well in accidents they weren’t optimized for.
   
2. How does it fit the bigger picture? We might be interested in the spread of a pandemic. This will encompass findings from all sorts of fields, from virology to network analysis to social sciences. A perfectly credible expert might tell us about whether a single virus can escape a face mask’s thin fabric without an issue — but we’d still need to know how many virus molecules are needed for an infection, or the size of the saliva droplets that virus molecules travel on.
   
3. A mathematical model is just an opinion with a spreadsheet. Whenever life gets too complicated, we use models to estimate different outcomes. A city’s mayor might ask, “If we improve our roads and lower bus prices, how many people move into the suburbs?” — and the computer will give its best answer.
   
   Prediction models are an amazing tool to discuss different options, but they’re more a visualization tool than a proven fact: they leave a lot to human judgment. Only believe the model as much as you believe the human presenting it.

Remember that experts can be wrong, science often changes its mind, and rational thinking is still the best we can all do. Being more critical about what you read will eventually help science journalism improve too.

This blog is a personal feed of magic, where everything happens organically. Sometimes useful, sometimes interesting, other times not so much. Random person on the internet writes stuff for other intelligent people, without much strategy or agenda.

(Maybe this will be one of those more useful posts actually, especially if you’re into content.)

On other magazines and blogs that we run, we think about content in a more strategic way.

That’s always an interesting creative challenge, because content is clay. You can use words and paragraphs to form a piece, and it also works the other way around. The overarching brand will somehow strengthen each individual article, making them easier to be discovered and read.

At my first job in 2000, at one of the first big Internet Portals, we used to have key pieces of content that we called a “Pusher”. A Pusher is an article that’s longer and better than the regular ones, and we knew that every time we posted one, the average number of visitors increased for forever.

The number of visitors on the site always fluctuates – we’ve seen more traffic on workdays and less on the weekends, more in the spring and less in the autumn.

Pushers triggered obvious spikes as people shared the articles organically, but the real cool thing was how they raised the baseline. People were introduced to the portal and kept coming back for more.

Of course content was totally different 20 years ago. There were more readers than blogs to read, so people were actively seeking out stuff. Today it’s the other way around.

Yet, Pushers somehow still work. As soon as you share something that’s really-really good, that will rise the baseline.

This works on blogs, Instagram, Twitter, or whatever drugs the new kids do these days.

Good content is still quite hard to write, so as a creative, you’ll often resort to cheat and steal. On the new blog that I mentioned, we’ve experimented a bit to keep up a weekly posting schedule.

1. I’ve hired people from Upwork to do research and write posts. (As far as the experiment goes, this was all garbage and we haven’t shared any of that. All money down the toilet.)
2. We reworked a workshop’s transcripts. (We’ve shared this at least, but it was a huge amount of work. Probably more than writing the article in the first place, and not having a huge backlog of events, this can’t scale either.)
3. We received a guest post offer (that lead nowhere)
4. I repurposed previous posts and articles that I wrote. (This worked well, but not extremely scalable for obvious reasons. I don’t write much.)
5. Hired a PR company to write posts. (This went well, the writing quality is brilliant, and they can work from a list of bullet points to make sure the content is correct. This spares us around 50% of the work. For price, it’s half of what random folks on Upwork charged.)
6. I wrote some posts as a future book’s chapters, following some of the instructions in the book “How not to suck at writing” – it works but it’s hard work.
7. We wrote guest posts for other blogs. (This is the best experiment so far actually and something we’ll likely keep up, because working with other editors raises the bar quite a lot.)

There you go, a useful post for Valentine’s Day.

Also, if you struggle with getting started, check out the Most Dangerous Writing App, which deletes everything unless you keep writing for 5 minutes straight.