> Hello
> My name is Julian
> This is my lifelog
>
and digital playground  

Quantified Quarantine Report

01 Intro

It has now been more than six weeks since I have last been to the office. 48 days to be exact. Like everyone else on this planet, I spent the majority of the last few weeks in the confines of my own four walls – with the occasional trip to the supermarket down the road.

Instead of writing yet another “Here are 8 tips to stay productive at home” article, giving you an update on my sourdough skills (hint: good), or my progress with Animal Crossing (hint: not so good), I thought I’d spend some time looking at my Quantified Self data and write up a Quarantine QS Report instead.

The graphs and statistics in this article look at the ten weeks between February 5, 2020 and April 13, 2020. They compare the first five weeks of quarantine with the five weeks before the lockdown.

If you have feedback or questions on anything in this report, give me a shout.

02 Mental Wellbeing

The good news first: So far, the quarantine hasn’t had any negative impact on my mental wellbeing. My reported daily happiness levels have on average remained almost the same (Average: 3.86 → 3.89).

fig 01  Perceived Happiness

My stress levels have decreased by almost 30% since the start of the lockdown (Average: 2.11 → 1.51). I assume that this is due to less in-person meetings.

fig 02  Perceived Stress
03 Physical Wellbeing

While the lockdown didn’t impact my mental wellbeing, it has had a severe impact on my physical wellbeing. My perceived backpain has increased by more than 37% in the last few weeks (Average: 2.69 → 3.69).

fig 03  Perceived Backpain

This is partly due to the lack of a proper desk setup at home – but probably also driven by less physical activity overall. My Fitbit reports a 22% decline in the average amount of steps since the start of quarantine (Daily Average: 10,964 → 8,510).

fig 04  Number of Steps

Even more importantly, covid-19 has killed my 180 weeks swimming streak. I can’t wait until swimming pools are allowed to open again.

fig 05  Swim Distance
04 Movement

Walking and swimming aren’t the only modes of transport impacted by the lockdown – my public transport consumption has also come to an almost complete halt (with the exception of a doctor visit that required it).

fig 06  Number of Rides by Mode of Transport

A positive side effect of spending more time at home is being exposed to less air pollution. The average AQI level of my first five weeks of quarantine was 21% lower than the average of the previous five weeks when things were still normal (Average AQI: 25.26 → 19.91).

fig 07  Average AQI Exposure
05 Media Consumption

Another positive side effect has been the additional time I get to spend reading. My book consumption has quadruplet since the start of the lockdown (Average Daily Reading Time: 15min → 77min).

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fig 08  Reading Time

Audio-based forms of media consumption, on the other hand, have seen a decline. My podcast listening time has gone down by almost 40% (Average Daily Listening Time: 23min → 14min).

fig 09  Podcast Listening Time

My music consumption has also decreased by 40% (Average Daily Number of Songs: 23 → 14).

fig 10  Number of Songs Listened To
06 Eating & Drinking

Since I don’t have a coffee machine at home, the number of espresso-based drinks I’ve been consuming has gone down dramatically. Instead, I finally get to use my Chemex and Aeropress more often – resulting in a 231% increase in filter coffee consumption.

fig 11  Coffee Consumption

Apart from the occasional pizza I pick-up at the Italian place in the neighborhood, my food consumption has also almost completely shifted to home-made meals.

fig 12  Number of Meals by Location

Relatedly, my number of Swarm check-ins has also decreased by 56%.

07 Screentime

Being confined to my own home has resulted in more phone screen time (especially social media apps). I now spend an average 151 minutes per day looking at my phone, compared to 131 minutes before the lockdown – a 15% increase.

fig 13  Phone Screentime
Apr 27, 2020  ×  Berlin, DE

AirPods as a Platform

01 Intro

One of the favorite activities of tech analysts, VCs and similar Twitter armchair experts is to predict what the next big technology platform might be.

The usual suspects that come up in these conversations are VR/AR, crypto, smart speakers and similar IoT devices. A new contestant that I’ve seen come up more frequently in these debates recently are Apple’s AirPods.

Calling AirPods “the next big platform” is interesting because at the moment, they are not even a small platform. They are no platform at all. They are just a piece of hardware.

But that doesn’t mean they can’t become platform.

02 What is a platform?

Let’s first take a look at what a platform actually is.

At its core, a platform is something that others can build on top of. A classic example would be an operating system like iOS: By providing a set of APIs, Apple created a playground for developers to build and run applications on. In fact, new input capabilities such as the touch interface, gyroscope sensor and camera allowed developers to create unique applications that weren’t possible before.

Platforms are subject to network effects: More applications attract more users to the platform, while more users in turn attract more developers who build more apps.

It’s a classic flywheel effect that creates powerful winner-takes-all dynamics. This explains why there are only two (meaningful) mobile operating systems – iOS and Android.

It also explains why everyone is so interested in upcoming platforms – and why Apple might be interested in making AirPods a platform.

03 Why AirPods aren’t a platform

In their current form, AirPods are not a platform. They don’t provide any unique input or output functionalities that developers could leverage. Active Noise Cancellation and Transparency Mode are neat but not new or Airpods-exclusive features – other headphones offer exactly the same. In either case, developers don’t have any control over them and thus can’t build applications that use these functionalities.

Some say that AirPods will give rise to more audio apps because they are “always in” which in turn will lead to more (and perhaps new forms of) audio content. That might be true – content providers are always looking for alternative routes to get consumers’ attention – but, again, it does not make AirPods a platform. You can use any other pair of headphones to use these audio apps as well.

If Apple wants to make AirPods a platform, it needs to open up some part of the AirPods experience to developers so that they can build new things on top of it.

04 On Siri & Voice Platforms

The most obvious choice here is Siri, which is already integrated into every pair of AirPods.

In contrast to other voice assistants like Alexa and Google Assistant, Apple has never really opened up Siri for 3rd-party developers. If they did, it would create a new platform that could have its own ecosystem of apps and developers.

But I’m not convinced that this is Apple’s best option.
Let me explain why.

Opening up Siri wouldn’t make AirPods a platform, it would make Siri a platform. This might sound like a technicality, but I think it’s an important difference. As Jan König brilliantly summarized in this article, voice isn’t an interface for one device – it’s an interface across devices. It’s more of a meta-layer that should tie different products together to enable multi-device experiences.

This means Apple has little interest in making Siri an AirPods-exclusive. Voice-based computing works best when it’s everywhere. It’s about reach, not exclusivity. This is part of the reason why Google and Amazon excel at it.

At the moment, Siri’s capabilities are considerably behind those of Google Assistant and Alexa. Again, this isn’t overly surprising: Google’s and Amazon’s main job is finding the right answers to users’ questions. The required ML capabilities for a smart assistant are among the core competencies of these two companies.

But even Amazon and Google haven’t really figured out the platform part yet, as indicated by the lack of breakout 3rd-party voice applications. It seems like the two platforms are still looking for their product-market-fit beyond being just cheap speakers that you can also control with your voice.

This is partly because the above-mentioned use case of voice as a cross-device layer isn’t something developers can build with the current set of APIs.

The other big reason I see is that people are mistaking voice as a replacement for other interfaces. Movies like Her paint a future where human-computer-interaction primarily occurs via voice-powered smart assistants, but in reality, voice isn’t great as a primary or stand-alone interface. It works best as an *additional* input/output channel that augments whatever else you are doing.

Let me give you an example: Saying “Hey Google, turn up the volume” takes 10x longer than simply pressing the volume-up button on your phone. It only makes sense when your hands are busy doing other things (kitchen work, for example).

The most convincing voice app I have seen to date was at a hackathon where a team used the StarCraft API to build voice-enabled game commands. Not to replace your mouse and keyboard but to give you an additional input mechanism. Actual multitasking.

05 What Apple Should Build

I’m not against Apple opening Siri for developers. On the contrary, given that AirPods are meant to be worn all the time, a voice interface for situations that require multitasking is actually a very good idea. But voice input should remain the exceptional case. And it shouldn’t be what makes AirPods a platform.

Instead of voice, I’d love to see other input mechanisms that allow developers to build new ways for users to interact with the audio content they consume.

Most headsets currently on the market offer the following actions with one (or multiple) clicks of a physical button:

These inputs were invented a long time ago and there has been almost zero innovation since. Why has no one thought about additional buttons or click mechanisms that allow users to interact with the actual content?

For example, when listening to podcasts I often find myself wanting to bookmark things that are being talked about. It would be amazing if I could simply tap a button on my headphones which would add a timestamp to a bookmarks section of my podcast app. (Or better even, a transcript of the ~15 seconds of content before I pressed the button, which are then also automatically added to my notes app via an Apple Shortcut.)

Yes, you could build the same with voice as the input mechanism, but as we discussed earlier, saying “Hey Siri, please bookmark this!” just doesn’t seem very convenient.

While podcast apps might use the additional button as a bookmarking feature, Spotify could make it a Like button to quickly add songs to your Favorites playlist. Other developers could build completely new applications: Think about interactive audiobooks or similar two-way audio experiences, for example.

This is the beauty of platforms: You just provide developers with a set of tools and they will come up with use cases you hadn’t even thought about. Crowdsourced value creation.

06 Closing Thoughts

(1) The input mechanism I describe doesn’t have to be a physical button. In fact, gesture-based inputs might be even more convenient. If AirPods had built-in accelerometers, users could interact with audio content by nodding or shaking their heads. Radar-based sensors like Google’s Motion Sense could also create an interesting new interaction language for audio content.

(2) You could also think about the Apple Watch as the main input device. In contrast to the AirPods, Apple opened the Watch for developers from the start, but it hasn’t really seen much success as a platform. Perhaps a combination of Watch and AirPods has a better chance of creating an ecosystem with its own unique applications?

(3) One thing to keep in mind is that Apple doesn’t really have an interest in making AirPods a standalone platform. The iPhone (or rather iOS) will always be the core platform that Apple cares about. Instead of separate iPhone, Watch and AirPods ecosystems, think about Apple’s strategy as more of a multi-platform bundle. Even as a platform, AirPods will remain more of an accessory that adds stickiness to the existing iPhone ecosystem.

Do you have thoughts or feedback on this post?
If so, I’d love to hear it!

Thanks to Jan König for reading drafts of this post.
Apr 19, 2020  ×  Berlin, DE

Media Consumption (Mar 2020)

>_ Summary
  • Read 6 books (2015 min, +540% MoM) and 32 long-form articles (+68%)
  • Listened to 483 songs (-26%) and 11 podcast episodes (732 min, +55%)
  • Watched 2 movies (+∞), 3 soccer games (290 min, -74%) and 28 TV episodes (1413 min, +9%)
  • Played 1 video game (35 min, -36%)
>_ Books

The Design of Everyday Things (Donald A. Norman)
░░░░░░░░░░░░░▓▓ Progress: 85-100%

Tools for Thought (Howard Rheingold)
▓▓▓▓▓▓▓▓▓▓▓░░░░ Progress: 0-73%

Designing Games (Tynan Sylvester)
▓▓░░░░░░░░░░░░░ Progress: 0-11%

Disunited Nations (Peter Zeihan)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

South of the Border, West of the Sun (Haruki Murakami)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

The Glass Hotel (Emily St. John Mandel)
▓▓▓▓▓▓▓▓▓▓▓▓░░░ Progress: 0-78%

>_ Recommended Articles

The Most Important Media Businesses of the (Past and) Future (Matthew Ball)

How Hip-Hop Helped Cash App Grow Faster (Trapital)

Cyberpunk: Then and now (The Dork Web)

>_ Recommended Podcasts

Ross Douthat on Decadence and Dynamism (Conversations with Tyler)

Building Prosumer Software with Rahul Vohra and Todd Goldberg (Venture Stories)

>_ Music

Top Artists: Tame Impala (58 plays), Endel (44), Trent Reznor and Atticus Ross (26), Cage the Elephant (21), Destroyer (21)

Apr 01, 2020  ×  Berlin, DE

Signaling as a Service

01 Intro

One of the best books I have read in the last few years is The Elephant in the Brain by Robin Hanson and Kevin Simler.

The book makes two main arguments:

a) Most of our everyday actions can be traced back to some form of signaling or status seeking

b) Our brains deliberately hide this fact from us and others (self deception)

So we think and say that we do something for a specific reason, but in reality, there’s a hidden, selfish motive: to show off and increase our social status.

You may have heard about a similar concept before called conspicuous consumption. Conspicuous consumption describes the practice of purchasing luxury goods (or services) for the sake of signaling the buyer’s wealth in order to attain or maintain a certain social status.

A classic example of this would be a luxury watch: A Rolex isn’t better at telling the time than a cheap Casio – but a Rolex signals something about its owner’s economic power and thus their social standing.

This is not a new theory, but Simler and Hanson argue that a lot more human behavior can be explained by signaling. Here are a few examples from the book:

  • Consumption
    Signaling does not only explain luxury purchases but also consumption of all sorts of other goods: “Green products” are more about signaling a prosocial attitude than actually helping the environment. Other consumption signals include loyalty to a specific subculture (e.g. band t-shirts), athleticism & health consciousness (athleisure clothing) or intelligence (e.g. Rubik’s Cube).

  • Charity
    There are several indicators that suggest that giving to charity isn’t really about improving the well-being of others: The lack of effective altruism demonstrates that we don’t care much about the actual outcome of our donations and studies show that our charitable behavior is heavily driven by visibility (hardly any donations are anonymous), peer pressure (95% of donations are solicited) and mating motives (donations are higher and more likely when observed by a member of the opposite sex). Charity is about appearing generous rather than actually doing good.

  • Education
    You would think that going to school is about learning and acquiring skills, but then why do students pay tens of thousands of dollars for Ivy League schools when all of the learning material is effectively available online for free? Why do we use grading systems when we know that students learn worse when being graded? The answer, again, is signaling: Education helps with credentialing and signaling to potential employers.

There are many more examples in the book (and I recommend reading the whole thing), the point the authors are trying to make is clear: Almost everything has a signaling component – we are just not aware of it. In fact, Hanson believes that “well over 90 percent” of human behavior can be explained by signaling.

Whether or not you agree with that exact number, I think it’s an interesting thought experiment to look at a specific behavior and think about what the hidden signaling subtext of that behavior might be.

Ever since finishing the book, the signaling behavior I’ve been thinking about the most is the adoption – and more importantly the monetization – of software products and services.

This is what this blog post is about.

02 Components of Signaling

Let’s take a closer look at signaling first.

The way I see it, signaling can be broken down into different components:

  • Signal Message
  • Signal Distribution
  • Signal Amplification

To better illustrate what I mean let’s take a pair of sneakers as an example.

The first component is what I call the signal message. This is whatever (hidden) subtext you are trying to convey. In the case of our sneakers this is probably something along the lines of “I can afford to spend $100 on a pair of shoes” and “I live an active, healthy lifestyle”.


In order to get your signal message across to other people you need some form of signal distribution. This is the second component of my signaling taxonomy.

So how are you going to distribute the signal message of your sneakers? You simply wear them where other people can see them. The obvious constraint here is that your signal distribution is limited to things you can display in public. This is why people are willing to spend hundreds of dollars on shoes but not on socks.

The third component is signal amplification: If everyone is wearing cool sneakers .. how do you make sure yours stand out? You could buy the pair with the most noticeable design or the one with the flashiest colors, for example. These signal amplifiers help you to better compete against status rivals.


Let’s recap: Signaling can be broken down into signal message, distribution and amplification. “Real world” products are great at visualising a signal message due to their physical nature. However, as a consequence there are also physical boundaries to distribution because there are only so many people you can signal to at once.

But what about software?

03 Software’s Signaling Limitations

Digital products have one crucial disadvantage over atom-based products and services: Intangibility. Apps live on your phone or computer. No one can see them except for you.

The signal message of a fitness app is the same as that of a gym membership or athletic wear (strength & fitness display), but the signal is much weaker because you can’t distribute it to anyone.

I believe that this is the main reason why consumer software companies have a harder time monetizing than their physical counterparts.

Here’s another example: eBooks have never caught up with paper books despite being more convenient. On the contrary, physical book sales have remained stable (and in some markets even increased) in recent years. Interestingly though, people spend less time reading them. Their value seems to stem from lying around the house to impress visitors (see also coffee table books) – a benefit digital books simply can’t offer.

Another point of evidence is the lack of luxury software products. People spend absurd amounts of money on jewellery, handbags and cars, but I can’t think of a piece of software with an even remotely similar price tag. Sure, people have tried to sell $999 apps but those never took off.

The app that comes closest to a luxury service that I can think of is Superhuman, which charges its users $30 a month for an email client (which you could also get for free by just using Gmail).

But there’s a difference to other software products: Superhuman has signal distribution built in. Every time you send an email via Superhuman, your recipient will notice a little “Sent via Superhuman” in your signature.

In a similar fashion, apps like Strava use their built in social networks as a signal distribution channel for their premium subscriptions. Users who have upgraded get a little premium badge and appear in exclusive premium leaderboards.

Another interesting way to solve the signal distribution problem is to add a physical product to a software’s premium offering, which allows signaling via casual contact (like fashion products).

Neobanks such as N26 or Revolut reward their premium users with a fancy metal card which doesn’t just look nice but is also noticeably heavier than normal credit cards. There aren’t a lot of other benefits that justify the hefty €17/month price tag these banks charge for their premium tiers – clear evidence that the primary monetization driver is in fact signaling.

04 Signal Distribution as a Service

While many digital products struggle to monetize as well as their real-word counterparts, the Internet has created a whole new kind of signaling utility: Distribution as a service.

Physical products are limited to the amount of people you see in public – but the Internet allows you to reach a virtually infinite number of people at once.

This is the primary value that social networks like Facebook, Snapchat and Instagram provide. These services don’t contain a hidden signal message. All they do is provide signal distribution at scale. Want to increase the number of people who can see your sneakers? Just take a photo and post it on Instagram.

A positive feedback loop of views, likes and comments helps you to quantify how successful your signal distribution has been.


Eugene Wei calls this Status as a Service:

By merging all updates from all the accounts you followed into a single continuous surface and having that serve as the default screen, Facebook News Feed simultaneously increased the efficiency of distribution of new posts and pitted all such posts against each other in what was effectively a single giant attention arena, complete with live updating scoreboards on each post. It was as if the panopticon inverted itself overnight, as if a giant spotlight turned on and suddenly all of us performing on Facebook for approval realized we were all in the same auditorium, on one large, connected infinite stage, singing karaoke to the same audience at the same time.

It’s difficult to overstate what a momentous sea change it was for hundreds of millions, and eventually billions, of humans who had grown up competing for status in small tribes, to suddenly be dropped into a talent show competing against EVERY PERSON THEY HAD EVER MET.

Social networks are subject to network effects: The more users join a network, the more valuable the network becomes. Your incentive to use Facebook increases with the number of people you can distribute your signal message to. This is why social networks are free to use – in order to maximize their signaling potential they need to acquire as many users as possible.

A social network like Path attempted to limit your social graph size to the Dunbar number, capping your social capital accumulation potential and capping the distribution of your posts. The exchange, they hoped, was some greater transparency, more genuine self-expression. The anti-Facebook. Unfortunately, as social capital theory might predict, Path did indeed succeed in becoming the anti-Facebook: a network without enough users. Some businesses work best at scale, and if you believe that people want to accumulate social capital as efficiently as possible, putting a bound on how much they can earn is a challenging business model, as dark as that may be.

While Path did indeed fail as a distribution provider, I would argue that keeping the network’s size small can still have benefits in line with my signaling theory: Deliberately limiting the number of people who can join a network (e.g. by charging a membership fee) creates scarcity which in turns makes the network more interesting. Network membership becomes the signal message. Users pay a membership fee to signal to other members that they are part of the tribe.

Some examples:

These social networks still rely on some critical mass and network effects, but need to set an artificial limit to the amount of people who can join. If membership isn’t scarce, the membership loses its signal message. The same applies to physical products: Apple will never offer a cheap iPhone to compete with low-end Android devices – it would destroy the company’s signal message that the iPhone is a luxury product.

In contrast to iPhones, there is another limitation that social networks with this strategy face: Like in the before mentioned software examples, signal distribution is limited to the in-group. Signaling however is strongest when you can signal tribe affiliation to both in-group members as well as outsiders. This is also the reason why luxury car manufacturers don’t limit their advertising campaigns to potential buyers but deliberately extend it to people who will never be able to afford the car.

But as we’ve discussed earlier, the intangibility of software makes signaling to the out-group difficult: You would instagram a photo from your Equinox gym, but would you share a screenshot of your MyFitnessPal Premium subscription?

Instead of monetizing network membership, the software products that monetize most successfully have chosen another strategy: Make memberships free and monetize signal amplification instead.

05 Monetizing Signal Amplification

Earlier, we defined signal amplification as product features that help users to increase the strength of the signals they want to convey to stand out of the crowd. In the example of our aforementioned sneakers, flashy colors help to amplify our signal message.

Similar amplifiers exist in the software world, but they often come in the form of a set of tools. Take the Instagram photo editor for example: Applying filters to your photos makes them look nicer and hopefully more noticeable in the app’s status arena – aka the newsfeed.

Eugene Wei calls these amplifiers Proof of Work:

Almost every social network of note had an early signature proof of work hurdle. For Facebook it was posting some witty text-based status update. For Instagram, it was posting an interesting square photo. For Vine, an entertaining 6-second video. For Twitter, it was writing an amusing bit of text of 140 characters or fewer. Pinterest? Pinning a compelling photo. You can likely derive the proof of work for other networks like Quora and Reddit and Twitch and so on. Successful social networks don’t pose trick questions at the start, it’s usually clear what they want from you.

While Instagram, Twitter and the other above-mentioned social networks are free to use, other companies have figured out a clever way to monetize their signal amplifiers. The two companies who have done this most successfully are Tinder and Fortnite.

Let’s start with Tinder.

06 How Tinder Monetizes Signal Amplification

Tinder is a social network for dating – or in other words, a signal distribution network to display your mating worthiness. Like other social networks, Tinder is subject to network effects: The value of the network increases with its size. The obvious strategy therefore is to make memberships free so that as many people as possible can join. (Technically, dating apps are two-sided networks. The value for female members increases with the number of male members and vice versa.)

Tinder’s primary proof-of-work mechanism is to optimize one’s profile picture for a maximum number of swipe rights. But with millions of rivals on the same platform, competing for status with just a few pixels of profile picture real estate becomes a really hard task.

Luckily, Tinder offers a variety of additional signal amplifiers that help you to stand out. The sole purpose of features like Tinder Boost and Super Likes is to outcompete status rivals by giving you preferential signaling treatment. And guess what – they come with a price tag.

Tinder’s entire business model is built on the assumption that people are willing to spend money on signaling. That assumption seems to be correct: Tinder made a staggering $1.2 billion in revenue last year making it one of the most successful apps world wide.

07 Fortnite – The Ultimate Status Battleground

Fortnite has seen even greater levels of financial success: In the last two years combined, the game has brought in more than $4 billion in revenue – and like Tinder, it too monetizes primarily with signal amplification.

For the longest time, the monetization model of games used to be – and for many still is – one-time upfront payments which then allowed you to play the game for as long as you wanted to.

That business model changed with the emergence of mobile games on iOS and Android. Instead of charging players upfront for access, mobile games are typically free to play. However, in order to progress faster and win the game, users will eventually have to pay for small upgrades with in-app purchases.

Similar to these traditional mobile games, Fortnite is also free to play. As a multiplayer game that many play with their real-life friends, this strategy makes a lot of sense – the network becomes more valuable the more people join.

In contrast to mobile games however, Fortnite is also free to win. None of the in-app purchases available impact the core gameplay. You can’t buy more powerful weapons or stronger armor that give you an advantage over other players.

That’s because the core gameplay isn’t the core signaling layer – and thus also doesn’t offer the greatest monetization potential.


Fortnite is more than just a game. It’s more like a giant virtual theme park, or the closest thing we have to a metaverse even. People don’t just come for the battle royale game – they come to meet and hangout with friends.

But if The Elephant in the Brain has taught us anything it’s that you don’t just meet people for fun. You are engaging in a constant battle for attention and status. Signaling is the meta game that Fortnite provides – and monetizes.

Fortnite’s monetization model is based on cosmetics: The skin your character wears; the looks of your glider and the tools you use; the way your character dances (emotes) – all of these are signaling amplifiers with different signal messages to uniquely express yourself in the game. And you have to purchase them.

Fortnite has pulled off what so many other software products have been struggling with – monetizing a purely digital product whose value is not based on utility or entertainment but solely on the one thing we all secretly care so much about: Signaling.

08 Summary

While the physical nature of material goods and services is perfect to visualize hidden signaling messages, there are natural limitations to distribution and amplification.

Software perfectly complements physical goods by distributing their signal messages at scale. Maximizing scale, however, prevents it from monetizing said distribution. This is why social media services are free to use. The added signaling value is solely captured by the physical products that are being shared.

The intangibility and fungibility of software also makes it difficult to create and share (and monetize) software products that contain hidden signal messages of their own. This explains why there are no software equivalents of luxury products such as Rolex watches or Louis Vuitton handbags.

The financially most lucrative strategy for software companies is to provide distribution for free and instead monetize users who want to stand out of the crowd with paid signal amplification.

A closing thought: I tell myself that I write these blog posts “just for fun”, but let’s be honest … all I really want is to signal how smart I am. So if you could head over to Twitter and give me a Like or a Follow, that’d be great. Thanks!

Thanks to Gonz Sanchez, Jan König, Max Cutler and Robin Dechant for reading drafts of this post.
Mar 28, 2020  ×  Berlin, DE

Music for Productivity

As part of my ongoing work on a personal operating manual, I’ve been thinking a lot about what Tyler Cowen would call the “Julian Production Function”. Namely, a list of all the habits, routines and things that make me more productive.

One element of my personal production function I find particularly interesting is the impact of music (or audio more general) on my productivity.

Based on almost 15 years of Last.fm data, I know that music consumption overall is a pretty good proxy to measure my productivity. The graph above shows the number of songs I listened to on a monthly basis (the gray bars) as well as the monthly average per year (the black line).

My music consumption peaked in years where I did lots of deep work such as studying, writing or coding (2008, 2010, 2013, 2017-2018). Years with less screen time correlate with a lower number of songs I listened to (2014-2016, 2019).

Of course not every type of music is necessarily great for productivity. When I dug a little deeper into the data I found another interesting trend: Over the last ~10 years, I have increasingly listened to ambient music and soundtracks. These two graphs essentially represent my efforts to optimize my music consumption for further productivity improvement.

The problem is that I haven’t found a good way to measure if (or which of) those attempts to improve my productivity have actually paid off. More music consumption tells me that I worked more – but it doesn’t tell me if I worked better or more efficiently.

It’s hard to quantify actual productivity or focus work: I’ve tried using productivity proxies such as RescueTime, number of emails sent and GitHub contributions – but all of those are pretty vague. Rating each day with a “perceived productivity”-score also hasn’t really produced any meaningful data.

Nevertheless, I will continue to experiment with different music to improve my focus. Here’s a list of music apps and playlists I’m currently using:

AMBIENT MUSIC
Brian Eno’s Music for Airports is great for productivity – I especially like this 6 hour time-stretched version. Someone put together a fantastic Spotify playlist of similar airport music. Max Richter’s Sleepis meant to be listened to at night” but I find it perfect for focus work. (I also tried listening to it at night to see if it would improve my sleep. It didn’t.) If you like Richter’s work, you might also enjoy Nils Frahm and Jóhann Jóhannsson.

SOUNDTRACKS
I can recommend pretty much everything by Trent Reznor and Atticus Ross (the new Watchmen OST is particularly great). The Black Mirror soundtracks have a similar dystopian feel – Jason Kottke compiled all of the songs in a playlist here. Hans Zimmer’s Interstellar and Dunkirk soundtracks are also great for productive work.

NATURE SOUNDS
You can find endless nature sound playlists on Spotify and YouTube. I prefer thunderstorm and rainforest sounds. Noisli is great to mix and match different sounds.

LO-FI ANIME BEATS / CHILLHOP
I’m subscribed to ChilledCow and Chillhop Music on YouTube, but to be honest this type of music has never really worked for me.

FOCUS MUSIC APPS
My go-to productivity app these days is a service called Endel which “creates personalized, sound-based, adaptive environments that help people focus and relax” based on a variety of inputs (including time of day, weather, heart rate and location). I have also heard good things about Brain.fm and focus@will.

Last but not least, Flow State is an excellent newsletter that sends you a daily focus music recommendation.

Do you have any other recommendations?
Please let me know on Twitter!

Mar 16, 2020  ×  Berlin, DE

Media Consumption (Feb 2020)

>_ Summary
  • Read 2 books (315 min, -73% MoM) and 19 long-form articles (-44%)
  • Listened to 656 songs (+34%) and 9 podcast episodes (472 min, +6%)
  • Watched 0 movies (-100%), 11 soccer games (1125 min, +389%) and 22 TV episodes (1298 min, +127%)
  • Played 4 video games (55 min, +∞%)
>_ Books

The Design of Everyday Things (Donald A. Norman)
░░░░░▓▓▓▓▓▓▓▓░░ Progress: 31-85%

Ra (Sam Hughes)
░░░░▓▓░░░░░░░░░ Progress: 25-35%

>_ Recommended Articles

Spotify: The Ambient Media Company (Brett Bivens)

Reddit’s Profane, Greedy Traders Are Shaking Up the Stock Market (Bloomberg)

Yuval Noah Harari’s History of Everyone, Ever (The New Yorker)

>_ Recommended Podcasts

Sriram Krishnan on Building Consumer Social Platforms (Venture Stories)

World After Capital, with Albert Wenger (Invest Like the Best)

>_ TV Shows

Chernobyl (Season 1)
░░░▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 20-100%

Narcos: Mexico (Season 2)
▓▓▓▓▓▓░░░░░░░░░ Progress: 0-40%

Succession (Season 2)
░░░▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 20-100%

Watchmen (Season 1)
▓▓▓▓▓▓▓▓░░░░░░░ Progress: 0-56%

>_ Music

Top Artists: Endel (110 plays), Zoot Woman (80), Trent Reznor and Atticus Ross (68), Abel Korzeniowski (35), Tame Impala (25)

Mar 01, 2020  ×  Berlin, DE

My Quantified Self Setup

The number one question I get asked on Twitter these days is how I get the data for my media consumption posts and quantified self reports. So I thought I’d dedicate this week’s post to explain my tracking setup.

I got into self tracking in the early 2000s when I first discovered HistoryStats – a Miranda plugin that gave me interesting statistics about my messaging behavior (who I talked most often to, what words I used the most, etc). A few years later, Audioscrobbler (which is now last.fm) allowed me to get similar statistics about my music habits. I also started to keep logs of books and movies that I had read and seen.

Inspired by the work of Nicholas Felton, I later began to quantify all sorts of other activities. My 2013 Quantified Self report – my most comprehensive tracking project so far – included data such as: How much I walked, drank, ate and slept. How many people I talked to. How many buildings I entered. I even tracked how much time I spent in the shower and brushing my teeth.

At the beginning, I always carried a little notebook around with me to write down things as they happened, but over time I got into the habit of simply being more present and remembering the core activities I wanted to track, so I could digitize them later. Even activities such as reading books I now consciously quantify and memorize while I’m doing them.

I currently use an Airtable spreadsheet for the vast majority of my QS activities, which I update about once or twice a day.

My 2020 tracking sheet looks like this: I have 10 spreadsheet tabs – one for each tracking category. These include Sleep, Wellbeing, Fitness, Work, Media, Drink, Food, Social, Travel and Screen Time.

Each tab then has a variety of metrics that I update on a daily basis. This is my current structure:

Sleep 🌙
  ↳ Bed Time (hh:mm)
  ↳ Wake Up Time (hh:mm)
  ↳ Perceived Sleep Quality (1-5 rating)
  ↳ Sleep Location
     ↳ City (single select)
     ↳ Location type (single select: home/hotel/…)
  ↳ Nightmares (y/n)
  ↳ ██████ ██

Wellbeing 🙏
  ↳ Perceived Happiness (1-5 rating)
  ↳ Perceived Stress (1-5 rating)
  ↳ Perceived Overall Healthiness (1-5 rating)
  ↳ How Worried am I About the Future? (1-5 rating)
  ↳ Perceived Backpain (1-10 rating)
  ↳ Illnesses (multi-select)
  ↳ ██████ ██
  ↳ ███ ███

Fitness 💪
  ↳ Meditation (hh:mm)
  ↳ Back Exercise (y/n)
  ↳ Stretching (y/n)
  ↳ Swimming
     ↳ Swim distance (meters)
     ↳ Swim time (hh:mm)
  ↳ Push-ups (number)
  ↳ Plank (duration in minutes)
  ↳ Other Sports
     ↳ Sport (single select)
     ↳ Duration (hh:mm)

Work 💼
  ↳ Start Time (hh:mm)
  ↳ End Time (hh:mm)
  ↳ Work Type (single select: work/writing/…)
  ↳ Work Location (single select: home/office/coworking/…)
  ↳ Perceived Productivity (1-5 rating)
  ↳ Perceived Job Happiness (1-5 rating)

Media Consumption 📚
  ↳ Kindle Books
     ↳ Book Title (single select)
     ↳ Reading Time (hh:mm)
  ↳ Physical Books
     ↳ Book Title (single select)
     ↳ Reading Time (hh:mm)
  ↳ Audio Books
     ↳ Book Title (single select)
     ↳ Reading Time (hh:mm)
  ↳ Podcasts
     ↳ Podcast Show (single select)
     ↳ Podcast Episode (single select)
     ↳ Listening Time (hh:mm)
  ↳ TV
     ↳ TV Show (single select)
     ↳ TV Episode (single select)
     ↳ Watch Time (hh:mm)
  ↳ Soccer
     ↳ Teams (multi-select)
     ↳ Goals (number)
     ↳ Watch Time (hh:mm)
  ↳ Movies
     ↳ Movie (single select)
     ↳ Watch Time (hh:mm)
  ↳ Games
     ↳ Game (single select)
     ↳ Play Time (hh:mm)

Drinks ☕️
  ↳ Filter-based Coffee (number)
  ↳ Espresso-based Coffee (number)
  ↳ Beer (ml)
  ↳ Wine (number of glasses)
  ↳ Non-alcoholic Drinks
     ↳ Drink (single select)
     ↳ Amount (ml)
  ↳ Alcoholic Drinks
     ↳ Drink (single select)
     ↳ Amount (ml)

Food 🍕
  ↳ Breakfast
     ↳ Dish (multi-select)
     ↳ Location (single select)
  ↳ Lunch
     ↳ Dish (multi-select)
     ↳ Location (single select)
  ↳ Dinner
     ↳ Dish (multi-select)
     ↳ Location (single select)
  ↳ Pizza (number of slices)
  ↳ Meat Consumption (multi-select: beef/pork/…)
  ↳ Fish Consumption (multi-select: salmon/tuna/…)
  ↳ Perceived Healthiness (1-5 rating)

Social 👫
  ↳ In-person Conversations
     ↳ Partner (y/n)
     ↳ Close Friends (y/n)
     ↳ Friends / Acquaintances (y/n)
     ↳ Co-worker (y/n)
     ↳ Other (y/n)

Travel 🚗
  ↳ Did I use this mode of transport today?
     ↳ Plane (y/n)
     ↳ Car (y/n)
     ↳ Subway (y/n)
     ↳ Taxi (y/n)
     ↳ Tram (y/n)
     ↳ Train (y/n)
     ↳ Bus (y/n)
     ↳ Scooter (y/n)
     ↳ Boat (y/n)
     ↳ Other (y/n)

Screen Time 📱
  ↳ Total Phone Screen Time (hh:mm)
  ↳ Time Spent in Social Apps (hh:mm)
  ↳ Phone Pickups (number)
  ↳ Notifications Received (number)

While this might sound like a lot of work, it doesn’t actually take a lot of time to fill out the sheet each day. I probably spend less than 5 minutes per day in my Airtable spreadsheet.

In addition to my tracking sheet, there are a few metrics that are calculated (semi) automatically:

Number of Articles Read
Whenever I finish reading an article I add it to Pocket with a specific tag. Every article (title, URL) then automatically gets added to another Airtable spreadsheet via IFTTT.

Music Behavior
Last.fm tracks every song I listen to.

Fitness and Sleep Data
I have a Fitness Alta that tracks the number of steps I take and how active I am. It also tells me how long and well I slept.

Location Data
I check into every single place I visit with Swarm. I also have constant location tracking turned on in Google Maps, Gyroscope and Zenly. None of these always-on services is particularly great – I really miss Moves App.

Screen Time
I use Screen Time on my iPhone and RescueTime on my laptop. I wish Apple would release a Screen Time API so that I didn’t have to manually copy data from the app to Airtable.

Air Quality
The Plume Flow 2 is the latest addition to my quantified self setup. This is a mobile air pollution sensor that measures Particulate Matter (PM1, PM2.5, PM10), Volatile Organic Compounds (VOCs) and nitrous oxides (NO2).

I’m thinking about upgrading to a newer fitness tracker with a heart rate monitor or a more sophisticated sleep tracker like the Oura Ring. It would also be interesting to move to a sort of tracking bot that randomly asks me questions through-out the day (see Felix Krause’s LifeSheet or Reporter app) to avoid peak-end bias.

Eventually, I’d also like to build a dashboard that combines my Airtable spreadsheet with my yearly goals and gives me real-time reminders and statistics about my routines and habits – but this is for another article.

If you have thoughts or questions about my setup, I’d love hear them.

Feb 23, 2020  ×  Berlin, DE

Inventory Update (Q1/20)

This is a quarterly update and review of new tools and products that I recently added to my personal productivity stack.

Spoonbill
I’ve been looking for a product like this for a while: Spoonbill connects with your Twitter (and GitHub) account and sends you diff-updates on the bios of the people you follow. You can receive updates via email and RSS. Someone should build this for LinkedIn.

Noto
Noto is an app to send email notes to yourself. The app opens directly to the input screen – a simple swipe then sends the note to a pre-defined email address. This is ideal for people like me who use their inbox as their primary productivity control center and to-do list. You can add up to six different email addresses which becomes pretty powerful in combination with Superhuman’s split inbox feature. I wish a note functionality like this was built directly into the iOS lock screen.

Zenly
This is one of the most interesting apps I’ve played around with lately. Zenly is essentially the Gen Z version of Foursquare: A location-first social network, but instead of manually checking into places, users constantly share their live location (as well as other data such as your current battery status). What I find most interesting though, is the app’s fog of war-like map that shows you exactly which areas you’ve already explored (plus the exact discovery percentage number per city). This is a great way to quantify my movement patterns and set monthly or yearly discovery goals (I currently do this with Swarm).

Feb 06, 2020  ×  Dublin, IE

Media Consumption (Jan 2020)

>_ Summary
  • Read 5 books (1160 min, -19% MoM) and 34 long-form articles (+36%)
  • Listened to 488 songs (-21%) and 8 podcast episodes (444 min, +26%)
  • Watched 3 movies (-40%), 2 soccer games (230 min, +5%) and 10 TV episodes (571 min, -43%)
  • Played 0 video games (0 min, -100%)
>_ Books

A Little Life (Hanya Yanagihara)
░░░░░░░░░░▓▓▓▓▓ Progress: 66-100%

The Design of Everyday Things (Donald A. Norman)
░▓▓▓▓░░░░░░░░░░ Progress: 3-31%

Designing Design (Kenya Hara)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

Ra (Sam Hughes)
▓▓▓▓░░░░░░░░░░░ Progress: 0-25%

Hackers & Painters (Paul Graham)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

>_ Recommended Articles

2019 Letter (Dan Wang)

Products I Wish Existed (2020 Edition) (Elad Gil)

Products I’d Pay For (2020 Edition) (Dominic Monn)

Underutilized Fixed Assets (Kevin Kwok)

Two Hands Are A Lot (Dominic Cummings)

Shopify: A Starcraft Inspired Business Strategy (Non-GAAP Thoughts)

>_ Recommended Podcasts

Matt Clifford on Investing Pre-Company (Invest Like The Best)

Reid Hoffman on Systems, Levers, and Quixotic Quests (Conversations with Tyler)

>_ Music

Top Artists: Trent Reznor & Atticus Ross (201), Woodkid (36), Auf Der Maur (35), 坂本龍一 (35), Abel Korzeniowski (33)

Feb 01, 2020  ×  Berlin, DE

Newsletters and Alternative Trade Routes

01 Newsletters & Blogs

Newsletters are growing like mushrooms these days.

It feels like there’s not a single day without someone in my Twitter or LinkedIn network announcing the start of his or her new personal newsletter (or podcast, for that matter).

Newsletters have been called “the future” and “the next generation” of media, “a more attractive medium than the newsfeed” and people’s “favorite new social network”.

To me, newsletters feel more like a rebirth of blogs and RSS: Both typically have long-form, high quality content and they are distributed via an open standard.

Substack and Revue are essentially trying to become the WordPress of newsletters, while Stoop is trying to build a Google Reader equivalent to capture the demand side.

What’s interesting about newsletters is that consumers are willing to pay for them. While blogs have never really figured out monetization (apart from ads), Substack alone claims more than 50,000 paying subscribers.

This might partly be a timing thing (blogs were popular during a time when people weren’t used to the concept of paying for digital content yet), but I wonder if it’s also driven by the nature of how newsletters work: You have to wait to receive them – like an Amazon package. Maybe that makes the medium feel more tangible and thus worth paying for?

Some have argued that newsletters create a more intimate relationship between writers and readers and that it’s this intimacy that consumers are willing to pay for. I don’t disagree with that theory but would argue that blogs are even more intimate than newsletters. If a newsletter is a personal message from a writer, a blog is the writer’s personal home the reader gets invited to.

What’s special about personal blogs is not just the actual writing, it’s also the design the content is presented in. Newsletters lack the unique design aspect that blogs have.

Side Note: I firmly believe that the lack of design customization options is one of the main reasons Medium has never lived up to its potential.

It also seems like newsletters are less discoverable than blogs. Blog posts would often reference other blogs (remember blog rolls and trackbacks?), which is how readers would discover new content. Newsletters feel more isolated. (Someone should start a newsletter-recommendation-newsletter)

02 Alternative Trade Routes

So what then explains the newsletter hype?
Simple: Distribution.

Every medium is essentially a two-sided market that needs to solve a chicken-and-egg-problem in order to take off. You need content suppliers to attract consumers and vice-versa.

While blogs could in theory be read by anyone with a browser, the technology that really mattered on the consumer side were RSS readers – and those were never adopted en masse.

Social networks on the other hand have become a victim of their own success: The amount of consumers has attracted so many players on the supply side that platforms needed to introduce algorithmic feeds to handle the abundance of content.

This is why writers like newsletters so much. As other distribution channels are becoming increasingly crowded, email provides an alternative trade route.

That problem is that the nature of cross-side network effects will ultimately lead to newsletters facing the same dilemma: As long as the number of subscribers increases, so will the number of newsletter publishers. Users’ email inboxes – already full with non-newsletter-emails – will get as crowded as social media newsfeeds. Just wait until Gmail introduces an algorithmic feed for your newsletter inbox.

Perhaps a more interesting question, then, is to ask what’s the alternative trade route to email?

03 Alternative Alternative Trade Routes

I’ve been fascinated by the rise of Telegram blogs in recent months.

Tucked between personal messages from friends, Telegram blogs are read in an even more intimate setting than email. In contrast to newsletters, readers can see the entire history at once (less ephemeral) and engage with the author and/or other readers via built-in community features.

Here are a few channels I enjoy:

At the moment, most of these are just link lists and RSS-like notification channels, but I wonder if they’ll evolve into a unique Telegram blog format over time.

Do you have any other Telegram recommendations?
I’d love to hear about them!

04 Appendix

(1) Some argue that email (like blogs) are better for content creators than social networks because the medium’s open standard means that they “own their audience”. What people seem to fail to see is that an open standard doesn’t matter much when the demand side is controlled by an aggregator. Bloggers lost thousands of subscribers when Google Reader shut down. Gmail has more than a billion users.

(2) This post might sound like I dislike newsletters. That’s not the case – I just really miss personal blogs 🙂 Here are a few newsletters I love (and which you should subscribe to): Monday Musings, Alex Danco (+ Scarcity in the Software Century), Stratechery, Matt’s Thoughts In Between, Super Organizers, Johannes Klingebiel’s Zine, Seedtable.

Jan 26, 2020  ×  Neuruppin, DE
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