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What if the origin of life was not a one-in-a-zillion fluke, but a physical inevitability, solved by Earth’s crust acting as a quantum search engine?

Quantum annealing promises quantum advantage, courtesy of quantum tunnelling. But a single, elusive quantity, the spectral gap, determines whether it works. And in almost all interesting cases, that gap is either vanishingly small, unknowable in advance, or both.

Stare at a kettle in the quantum realm and it refuses to boil, as if your gaze alone could freeze time. This is the quantum Zeno effect, where how we define “observation” reshapes our view of reality.

El futuro no espera: lo moldean quienes actúan.

Argentina can lead Latin America into the quantum age, if it has the courage to invest.

The efficient-market hypothesis is dead, dead, deadski.

What happened to NVIDIA and quantum computing stocks recently proves the efficient-market hypothesis ought to be rejected.

What do the vendors’ quantum computer roadmaps look like at the end of 2024?

Which countries are investing the most private and public money into quantum computing?

What is the ‘killer app’ for noisy quantum computers in the near term?

Quantum computing is a furnace for burning venture capital money. Yes, it will be transformative once we have useful quantum machines. But we are still very far off from any utility.

Are you really honest about the risk in your product plans or do you have placebo confidence?

Superposition, quantum parallelism, and entanglement are the three key ingredients for qubits in quantum computers. But what is the recipe for mixing these ingredients to make quantum chips?

What does the architecture of a quantum computer look like? And how are you supposed to interact with a quantum chip?

What does a room-temperature superconductor mean for quantum computers?

Here is a guide for all my posts on quantum computing to help you figure out where to start!

Quantum computers of the next few years will be suitable for simulating quantum physics in chemistry and materials science, not for problems that require large amounts of data.

No idea where to begin with quantum computing? Here is a review of the various courses I have completed.

Recent surveys by Omdia and Q-CTRL point to an urgent need for product management in quantum computing: vendors and early adopters disagree on the top priorities for near-term use cases.

Fifteen questions (and answers) about quantum computing you always wanted but were afraid to ask.

What is the state of quantum technologies in the Fortune Global 500? What are the top use cases?

What are possible use cases for quantum computing in the current NISQ era? And who’s already using quantum technologies today?

Spin up Zoose in VSCode with only two clicks.

When can we expect quantum computers to be able to solve real problems more efficiently than classical computers?

No idea where to begin with quantum computing? Here is a curated list of articles, books, videos, courses, and online programmes to get ready for the revolution!

Whereas current computers use bits that are each either zero or one, quantum bits can be both zero and one. This quirky property of quantum physics can be used to our advantage in quantum computers.

The era of the quantum computer is nigh. Where are we today and how have we arrived here in terms of software for quantum computers?

Automating backwards-compatible schema evolution could recover almost $4 million in engineering capacity for a typical tech platform company.

What if Ralph Wiggum had friends?

What does it take to build an AI rehearsal system where the measure of success is that people stop using it?

What does benchmark supremacy have to do with operational reliability?

Ralph Wiggum works because it is a degenerate evolutionary search algorithm.

The hard part of machine learning is not serving features but deciding what they are.

A feature in machine learning is only defined once—until you demand it be correct.

It’s not a bad thing finding out that you don’t have all the answers. You start asking the right questions.

Product managers dream of shortcuts. Recruiting users can take weeks. Interviews are messy. Insights are fuzzy. So why not skip the hassle and just simulate the users?

What if you could manufacture “truth” through deception at scale to target your competitors?

LLMs make coding easy. That’s the pitch, and leadership is buying it. But we’ve seen this movie before; it doesn’t end well.

What happened to NVIDIA and quantum computing stocks recently proves the efficient-market hypothesis ought to be rejected.

What is the ‘killer app’ for noisy quantum computers in the near term?

How do LLMs behave when they are asked a question they cannot possibly answer?

Are you really honest about the risk in your product plans or do you have placebo confidence?

If a product works, do you care what’s inside? And if it does not work, should it matter what’s inside whether you have a right to a valid explanation?

Lately, I have been wondering why we seem to use AI to automate away the few things in life we as humans truly enjoy: the arts. Why have we not used automation to rid ourselves of menial tasks instead?

Trust, it is said, is hard to gain and easy to lose. But does trust matter to consumers?

Quantum computers of the next few years will be suitable for simulating quantum physics in chemistry and materials science, not for problems that require large amounts of data.

While DevOps can be applied to machine learning, there are subtle differences that may not be obvious to the casual observer.

What are possible use cases for quantum computing in the current NISQ era? And who’s already using quantum technologies today?

Spin up Zoose in VSCode with only two clicks.

Learn how to run PyTorch in your browser in VSCode or JupyterLab. No installation required.

Machine learning without proper attention to code or data is futile. But how do you approach data and machine learning governance in practice? With cards!

The Riddler is back in town and no one can deploy a single machine learning model. Throwing more resources at the problem won’t solve anything and Commissioner Gordon knows it. There is only one man who can save Gotham from the pitfalls of perennial productionless prototypes: Batman!

When was the last time you read an article in the press on machine learning operations?

The last few years have seen a massive influx of “data scientists” who complete a few MOOCs, participate in a Kaggle competition or two, and think that experience is representative of life as a data scientist in the enterprise. If that mindset persists, and they show no desire to change it, they are what I call rogue data scientists.

Go distroless and reduce the image size as well as the number of CVEs for machine learning containers.

Check out the latest industry research for enterprise machine learning.

How many machine learning platforms run on Kubernetes? Which machine learning platforms can run in air-gapped environments? How common are feature stores in current machine learning platforms?

What is all the fuss about feature stores in machine learning?

Data and machine learning technologies for the big data era have been developed in the last twenty years. Let’s review these two decades in terms of data processing and machine learning frameworks as well as machine learning platforms that have been spotted in the wild.

Let’s take a closer look at the more popular open-source tools for data engineering, machine learning, and container orchestration.

Here’s my list of essential reading for people in the data and machine learning space.

Machine Learning (ML) is known as the high-interest credit card of technical debt. It is relatively easy to get started with a model that is good enough for a particular business problem, but to make that model work in a production environment that scales and can deal with messy, changing data semantics and relationships, and evolving schemas in an automated and reliable fashion, that is another matter altogether. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!

Lean Data and Machine Learning Operations (D/MLOps) is the adoption of the ‘lean’ philosophy from manufacturing. Its aim is to continuously improve the operation of data and machine learning pipelines.

TensorFlow is a free, open-source machine learning framework that’s geared towards deep learning. Optimization algorithms are at the heart of artificial neural networks. We can therefore let TensorFlow solve numerical optimization problems.

It has – perhaps somewhat prematurely – been called the sexiest job of the twenty-first century, but whether you buy into the Big Data hype or not, data science is here to stay.

In the previous post I talked about the details of the data set for the Kaggle challenge to build a model that predicts which New York Times blog articles become popular. In this post I shall discuss how I went about to discover and add features, and build a predictive model that landed me in the top 15% of all entries.

I completed the MITx course The Analytics Edge on edX, which I can wholeheartedly recommend to anyone who is interested in analytics. As a part of the MOOC, there was a competition on Kaggle to build a predictive model to answer the question, ‘What makes a New York Times blog article popular?’

What does it take to build an AI rehearsal system where the measure of success is that people stop using it?

Ask a product manager about PMF and they will almost certainly rattle off the Sean Ellis test with its 40% threshold. What few realize is that it’s mostly rubbish.

What if private health insurance in Argentina is broken on purpose? ReSalud is a blueprint for a buildable digital insurer: regulated infrastructure with a product surface, an operating model, and unit economics that work!

The hard part of machine learning is not serving features but deciding what they are.

A feature in machine learning is only defined once—until you demand it be correct.

Imagine a world that is less angry, less anxious, less stressed.

Stress, anger, and anxiety do not only fray lives, they cost the global economy on a colossal scale.

When product managers become customers themselves, they seem to forget their craft and instead fire off wish lists as if they were talking to Santa Claus.

At Spotify, we once chased a problem that sounded straightforward enough: how does a listener reach the moment where they press play?

What if you could pause before an emotional outburst?

We recite outcomes over output, yet we reward the reverse.

The Average Joe is anything but average! Digital products designed for such a character are exclusionary by design, as they do not represent 99% of the population.

Product managers dream of shortcuts. Recruiting users can take weeks. Interviews are messy. Insights are fuzzy. So why not skip the hassle and just simulate the users?

Do PMs actually know the basics of product management?

The homeopathic product manager exists in many organizations. It is based on the mistaken belief that by diluting a product manager across multiple product teams their impact and influence increase disproportionately.

LLMs make coding easy. That’s the pitch, and leadership is buying it. But we’ve seen this movie before; it doesn’t end well.

Each year, three people die waiting for Cloudflare and Google to verify they are human.

Engagement is the vanity metric of all.

How can you figure out the probability of success of a product from various metrics? With Bayes’ rule, of course!

Given several correlated metrics, how do you calculate the probability of product/market fit? With sequential Bayes!

What does the Drake equation, which deals with estimating the number of extraterrestrial civilizations, have to do with product management? Quite a bit, actually!

Don’t be fooled by the calendar. There are only as many days in the year as you make use of.

Welcome to the cult of empty thinking! Where buzzwords stand in for substance, and progress is a PowerPoint slide.

How can systems thinking resolve the paradox of the Ship of Theseus?

The myth of Daedalus and Icarus is often held up as a warning against hubris. The real moral of the story is for product management and technology innovation, though.

I haven’t the foggiest. And that’s OK.

Are you spending time on the right tasks?

My favourite interview questions for (technical) product managers are not about case studies, technology, prioritization, metrics, or customer/market research, but about reading and thinking.

When product managers say “Maybe” they generally mean “No.” Which do you prefer as a customer: a convenient lie or the truth?

Are you really honest about the risk in your product plans or do you have placebo confidence?

You only need one question to ascertain whether a sudden customer problem is urgent or not. I call it the urgency test.

How do you manage feature ideas if you want to avoid product debt?

Product debt is like technical debt: today’s time saver becomes tomorrow’s burden and eventually dead weight. Unfortunately, product debt tends to turn into technical debt, so developers pay the interest on bad product decisions, too.

If a product works, do you care what’s inside? And if it does not work, should it matter what’s inside whether you have a right to a valid explanation?

A product team is a ship without a captain.

The prime directive of product discovery is to never assume anything.

The hardest lesson to learn for an engineer who turns into a (technical) product manager, especially in their own area of expertise, is to embrace being the dumbest motherfucker in the room.

A good product manager is the Pope of Nope.

Recent surveys by Omdia and Q-CTRL point to an urgent need for product management in quantum computing: vendors and early adopters disagree on the top priorities for near-term use cases.

The 70 + 3×10 model is a simple but effective model for DevOps teams to distribute their time among feature development, maintenance and support, operations, and learning.

ICE·T is a product prioritization model that works well for high-level prioritization and applies to internal products and platforms, too.

The Riddler is back in town and no one can deploy a single machine learning model. Throwing more resources at the problem won’t solve anything and Commissioner Gordon knows it. There is only one man who can save Gotham from the pitfalls of perennial productionless prototypes: Batman!

Over the years I have worked with some good product managers, heard of a few great ones, and endured others who were less than stellar. From these observations and my own experience, I have distilled a set of principles for product managers, a collection of dos and don’ts.

The behaviour of any bureaucratic organization can best be understood by assuming that it is controlled by a secret cabal of its enemies.

The cult of speed makes modern leadership sleepless and thoughtless.

No one tracks who built the foundation, only who cuts the ribbon.

I don’t want to belong to any club that will accept me as a member.

Do PMs actually know the basics of product management?

Ideological toilet paper is the scripted, power-preserving language leaders use to clean up a mess they created, only to flush it away once the performance ends.

Power is a poison: once leaders taste it, their voices become echoes of the system’s myths that bind and blind. In boardrooms and parliaments alike, idealistic visionaries slip into the role of mouthpieces for the very structures they vowed to challenge.

Ever noticed how tech executives sound eerily alike, repeating buzzwords and predictions with nearly identical phrasing? It is not your imagination: many leaders become deterministic parrots, a corporate ritual in which leaders mimic peers instead of daring to be original.

Everyone says they want to grow, until it involves actual effort.

Leadership courses celebrate vision, empowerment, and charisma. Yet history, psychology, and neuroscience paint a far less flattering picture. Power seduces, corrupts, and destroys.

Executives spout plenty of nonsense, but not all waffle is equal.

LLMs make coding easy. That’s the pitch, and leadership is buying it. But we’ve seen this movie before; it doesn’t end well.

What do jazz, improvisation theatre, and Japanese philosophy teach us about leadership?

Don’t be fooled by the calendar. There are only as many days in the year as you make use of.

Welcome to the cult of empty thinking! Where buzzwords stand in for substance, and progress is a PowerPoint slide.

A village idiot is generally harmless. The townspeople mostly ignore or humour that person. When such a person suddenly obtains power, it becomes a serious problem.

Leaders who hire people smarter than they cause what I call the organizational IQ gradient.

Are you spending time on the right tasks?

Are you really honest about the risk in your product plans or do you have placebo confidence?

A product team is a ship without a captain.

A good product manager is the Pope of Nope.

The 70 + 3×10 model is a simple but effective model for DevOps teams to distribute their time among feature development, maintenance and support, operations, and learning.

Here’s my list of essential reading for people in the data and machine learning space.

A common practice in hiring in the tech industry is the take-home interview challenge, which, despite its neutral appearance, is implicitly biased and can actually decrease diversity in an industry that is already predominantly young, white, and male.

There can be many reasons why employees quit. Many people leave for jobs with better remuneration at other companies, yet the companies they leave behind hire people who do the same. Therein lies a paradox: why are fewer people promoted than hired from outside, especially since those who quit obtain pay rises significantly larger than those who stay at the same company?

Lean Data and Machine Learning Operations (D/MLOps) is the adoption of the ‘lean’ philosophy from manufacturing. Its aim is to continuously improve the operation of data and machine learning pipelines.

Needless complexity in production systems is contrary to best practices and common sense. Yet quite a few developers relish beautifully crafted solutions that, in theory, can cater to everyone’s taste, but in practice serve no one. Stability and predictability are paramount to operations, yet the desire to experiment with technologies and frameworks often outweighs such considerations. To that end I’d like to present a simple principle for DevOps: 2BIG.

Over the years I have worked with some good product managers, heard of a few great ones, and endured others who were less than stellar. From these observations and my own experience, I have distilled a set of principles for product managers, a collection of dos and don’ts.

It’s not a bad thing finding out that you don’t have all the answers. You start asking the right questions.

Our professional networks are sacrosanct. Every person you once sat next to is a long-term asset. Let any connection lapse and you are supposedly sabotaging your future.

The cult of speed makes modern leadership sleepless and thoughtless.

No one tracks who built the foundation, only who cuts the ribbon.

I don’t want to belong to any club that will accept me as a member.

Do PMs actually know the basics of product management?

Ideological toilet paper is the scripted, power-preserving language leaders use to clean up a mess they created, only to flush it away once the performance ends.

Welcome to a guided tour of the human brain!

Humans are lousy at judging risk. Why is that? Let’s dive into the psychology and neuroscience of risk.

Power is a poison: once leaders taste it, their voices become echoes of the system’s myths that bind and blind. In boardrooms and parliaments alike, idealistic visionaries slip into the role of mouthpieces for the very structures they vowed to challenge.

Ever noticed how tech executives sound eerily alike, repeating buzzwords and predictions with nearly identical phrasing? It is not your imagination: many leaders become deterministic parrots, a corporate ritual in which leaders mimic peers instead of daring to be original.

Everyone says they want to grow, until it involves actual effort.

The brain is an organ for learning. If you do not feed it new ideas, it will atrophy.

Leadership courses celebrate vision, empowerment, and charisma. Yet history, psychology, and neuroscience paint a far less flattering picture. Power seduces, corrupts, and destroys.

Twenty billion years, longer than the universe itself has existed. That is how much time humanity has collectively spent on religion: praying to gods that never answered, wars fought in their names, or monuments built to myths that turned to dust when empires crumbled.

Economic control is a fantasy politicians and economists sell between crashes.

Argentina’s president, Javier Milei, has launched one of the most radical economic experiments in modern history. His “shock therapy” or “chainsaw plan” promises to break decades of crises. But is it a viable plan for prosperity?

From agriculture to AI, each major economic revolution is driven by a new kind of value conversion: a way of transforming one type of value into another.

Countries that innovate early in transformative technologies tend to capture a disproportionate share of the economic value, making it exceptionally difficult for latecomers to catch up.

What if you could manufacture “truth” through deception at scale to target your competitors?

To some crypto is the second coming. To others it’s an environmental disaster designed for scams and illicit activity. Let’s look at what the data says.

Who actually benefits from Argentina’s removal of exchange rate restrictions (a.k.a. “cepo cambiario”)?

The world is sprinting towards something—no one is sure what exactly, but we’re damn sure to go there as fast as possible. The economy demands growth, and if that costs our sanity, our freedom, or our planet, so be it. There are always more planets, right?

The efficient-market hypothesis is dead, dead, deadski.

What happened to NVIDIA and quantum computing stocks recently proves the efficient-market hypothesis ought to be rejected.

What if private health insurance in Argentina is broken on purpose? ReSalud is a blueprint for a buildable digital insurer: regulated infrastructure with a product surface, an operating model, and unit economics that work!

Imagine a world that is less angry, less anxious, less stressed.

Stress, anger, and anxiety do not only fray lives, they cost the global economy on a colossal scale.

What if you could pause before an emotional outburst?

Collapse, in the context of civilizations, is the “significant loss of an established level of sociopolitical complexity.” But what causes societies to collapse?

I present a timeline for the Aegean Bronze Age based on the high chronology.

While you can take great stereoscopic pictures with a smartphone using sequential shots, as I described in the first and second parts of this three-part series on smartphone stereo photography, sometimes you want to capture a three-dimensional scene in motion. Even that’s possible but you do need additional gear, for instance the Kúla Bebe.

Stereoscopic photography is possible with very simple tools: a viewer and a camera. In this post we’ll take a look at common mistakes beginners make, how to spot them, and how to fix these problems, so your stereo images look great.

3D and VR may sound like recent inventions, but the former has been around since the mid-1800s. Stereoscopic cards were extremely popular in Paris in the 1860s (e.g. Les Diableries). In a series of posts I’ll focus on how stereoscopic imaging works, how to create stereoscopic images with any camera or smartphone, and what mistakes beginners tend to make and how to avoid them.

What if the origin of life was not a one-in-a-zillion fluke, but a physical inevitability, solved by Earth’s crust acting as a quantum search engine?

Consciousness is best understood as a system, not a single entity.

A lament for the gentle, and a reckoning with the rain that soaked them.

I don’t want to belong to any club that will accept me as a member.

Twenty billion years, longer than the universe itself has existed. That is how much time humanity has collectively spent on religion: praying to gods that never answered, wars fought in their names, or monuments built to myths that turned to dust when empires crumbled.

From agriculture to AI, each major economic revolution is driven by a new kind of value conversion: a way of transforming one type of value into another.

Countries that innovate early in transformative technologies tend to capture a disproportionate share of the economic value, making it exceptionally difficult for latecomers to catch up.

The world is sprinting towards something—no one is sure what exactly, but we’re damn sure to go there as fast as possible. The economy demands growth, and if that costs our sanity, our freedom, or our planet, so be it. There are always more planets, right?

What do jazz, improvisation theatre, and Japanese philosophy teach us about leadership?

What is the best way to learn audio engineering for the (wannabe) home studio owner in 2021?

There are two crucial pieces of information for each song: who performed it (performing rights) and who owns the rights to it (copyrights). As of today, there is no central system that maintains this information. Is this really such a problem? Well, Spotify had to settle a $30m lawsuit a while ago because they had no idea whom to pay. There is also an infamous case in which 107% of the rights to a single song were sold. So, yes, it’s a pretty big deal. That’s why I want to take a look at blockchains as a possible solution.

For most of human history, music has been composed and performed by amateurs, enslaved individuals, and professionals in direct employment of the nobility. Music as a full-time career option without some form of servitude has only been a fairly recent phenomenon. Before we shall dive into the complexities of the music industry and how musicians make money now, let’s go back in time and see how we got here.

2016 was a good year for the music business: revenue from streaming and digital downloads exceeded that of physical sales for the first time in the history of the industry. On the one hand, companies such as Spotify and Apple have made digital music easily accessible and legal. On the other hand, each stream only pays artists fractions of a cent, which means that all but the most popular artists make very little money from streaming. Fair compensation for artists in a digital world is a recurring topic in media and industry. In this article I want to look at the ways professional musicians make money now.