Ingredients for brilliance

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Some great practical advice. It's what I do.

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19/06/2025

Ingredients for brilliance

By: Julia F Christensen
Aeon: 09/06/2025

Julia F Christensen is a postdoctoral research fellow at the Warburg Institute, London. She is the co-author of Dancing Is the Best Medicine (2021) and the author of The Pathway to Flow (2025).

To tap into the flow state, your skill level and the challenge of the task you’re working on should be in perfect balance. This is one of the eight principles of flow, first described by the Hungarian scientist Mihaly Csikszentmihalyi. He coined the term ‘flow’ in 1990 after decades of scientific work about what surgeons, painters, dancers, writers, scientists, martial artists, musicians and other creatives have in common – a curious, all-absorbing state of mind where we feel amazing and are incredibly productive and creative at the same time.

Modern neuroscience distinguishes between two mental states: one of striving, where a surge of dopamine keeps us laser-focused on external goals like winning, perfection or achievement – and another of serene presence, where we hover in the moment, simply being. In this latter state, our neural chemistry shifts; endogenous opioids and endocannabinoids fill the brain, bringing feelings of deep satisfaction, fulfilment and joy in the now.

Motivation psychologists distinguish these two states as extrinsic and intrinsic motivation for what we’re doing. The former takes hard work and discipline to keep us going. The latter propels us forward, as by magic: flow. Research even shows that those more prone to enter the flow state might have lower risk of mental health problems and cardiovascular disease.

The more we read about flow or hear people describe what it feels like, the more we want to be in this state regularly. And we should – according to science! However, for most people, flow is something they might remember from childhood, when they were lost in play. Or it is something that may happen by chance but is incredibly hard to tap into at will.

The Romantic myth of the creatively engrossed genius also doesn’t help us. The human mind loves a hero’s story, and most of us seem to know what we were doing and why, in retrospect. Clearly, the thought leaders and architects of human history just ‘had it’, that creative ability to flow. Gutenberg’s printing press started off exponential literacy development. Electricity, vaccines and antibiotics brought about unparalleled social changes and health and wellbeing enhancements. The Lumière brothers moved people in 1895 with the first-ever movie. Fairy-tales, paintings, musical pieces and dances by known and unknown artists keep enthusing brains all over the world. Accounts of how one person’s creative flow leads to excellence, societal impact and Nobel prizes wow us.

It seems there’s nothing left for the rest of us mere mortals but to be bystanders to others’ brilliance. The more we read about the gifted, the more we feel blocked and barred from heightened creativity and the promise of the flow state ourselves. Who could ever keep up with Albert Einstein’s theory of relativity? Yet Einstein failed his university entrance exams in language and history, and he’d been broke and unemployed. The myth of the genius is that these individuals woke up one morning and excelled. As a result, too many people are convinced that either you’re creative and you just happen to be able to find flow, or you’re not and you don’t.

Once you grasp what sharpens our talent for brilliance, you’ll realise that flow is for everyone

What is never mentioned about the grand inventors, artists, scientists and doctors of our world who have done amazing deeds for humanity with their minds and hands is that they all failed in their attempts before they made it.

Besides failure, the second mysterious ingredient that made them brilliant in the first place and allowed them to wake up one morning and let their intuitive mind make ‘the splash’ is also never mentioned. Once you grasp what sharpens our talent for brilliance, and how to get it, you’ll realise that flow and creativity is for everyone.

But beware – the path to flow is paved with more bad advice.

‘You just have to feel it,’ our drawing teacher Carlo used to tell us, over and again.

It looked easy when he let his charcoal slide over the textured surface of the cold-pressed paper, his trace revealing shapes, intentions and emotions in 3D. It looked so effortless, and he looked so pretty, immersed as he was. Then he’d resurface and his facial expression would transform into an exhausted frown at our botched attempts to feel with a pen on paper. No matter how hard I’d tried, the feeling somehow didn’t stick to my pencil – and, after a while, I didn’t stick to the drawing classes either.

Flow is a fleeting, immersive state in which time and space seem to compress or expand, accompanied by a delicious fusion of movement and awareness – where you don’t just move: you are the movement. You have a very clear goal of what you’re trying to achieve. You know what you’re doing. You’re receiving clear feedback from the task itself about how it’s going, and you know when you’re doing it right. You’re also feeling intrinsically motivated to keep going, and the noise of uncertainty fades, leaving you feeling in control of your life and free from ruminative thought loops. All the while, Csikszentmihalyi’s core principle of matching the challenge to your skill makes you hover in this sweet spot, where what you’re doing is neither too hard nor too easy. Altogether, these dynamics form the eight core principles of flow.

Years ago, without any scientific training at all – when I was still a professional dancer, before the injury that ended it all – I knew this feeling well. I used to tap into it regularly. Especially when I was away from the competitive life of a professional dancer, far away from the classical music and the pointe shoes. At home in the kitchen dancing to Michael Jackson, or in some techno club at night, where I hid in a too-large hoodie and no one knew me. There, I could feel it and, like my drawing teacher Carlo, I couldn’t understand why others couldn’t just feel this way too.

It was so easy and it made life’s pressures recede into the shadows, letting me live.

Today, I’m a neuroscientist. I’ve since found flow in science, while writing fiction, dancing Argentine tango, belly dancing, reading – and one strange afternoon, I also finally found flow with drawing. Thanks to the knowledge about the brain that I have now, I know that just ‘feeling it’ is by no means enough to excel, be creative, nor to find flow. ‘Feel it!’ is well-meaning advice often given by artists, scientists and other professionals. I’m guilty of having shouted ‘Feel it!’ to bewildered dance students too.

The real control centre is in the brain. This is where movement begins

What we creatives are often unaware of is that talent isn’t everything. Sure, talent helps – but just as important is something else we rarely think about: repetition. The repeated movements of our craft – the physical routines we practise over and over – follow us everywhere. Whether we call it practice or technique, these repeated actions shape our brains in powerful ways, often without us even realising it.

They form unique connections in the brain – linking movement, memory and emotion. These connections stretch across the parts of the brain that control movement, wrap around the areas responsible for memory, and reach deep into the emotional core of the brain – the limbic system. That includes the insula, a region that helps manage both our physical health and our inner sense of self.

‘Muscle memory’ doesn’t live in our hands or legs. The real control centre is in the brain. This is where movement begins, guided by systems that plan and initiate what we do. From there, messages travel through long chains of nerve cells – from the brain down the spine and out to the rest of the body. Millions of tiny electrical signals, known as action potentials, move back and forth, telling our muscles, organs and even the tips of our fingers what to do next.

The idea is to ‘program’ the right moves in our brain so they become so automatic we can use them to, yes, feel, and to find flow.

One thing is for sure, if you keep chasing flow by some sort of celestial action, waiting for your inner genius to strike from nowhere, you’ll keep failing. Because that genius, apologies for being blunt, is, in fact, nowhere to be found. Genius is work.

Enter your new superpower: knowledge from neuroscience.

What may seem a strange, repetitive, even boring activity is in fact doing magic to their brains

The prefrontal cortex sits behind the forehead and is one of the youngest parts of the brain, in evolutionary terms. In other words, this is a system that evolved late in our species’ development and is thus fairly unique to humans. It also happens to mature last in our individual development, with restructuring continuing well into our 20s.

These parts of the brain are very ‘plastic’, meaning that they are easily shaped by experience and learning. So they are also key to the development of technique in our craft – be that in science, the arts or other fields – because they are suited to rule-based learning.

Neuroplasticity is our brain’s capacity to learn; to forge new connections between neural systems, as we practise something with our body. Professional singers and actors do daily vocal exercises, dancers do daily barre exercises – the same moves over and again – and musicians are known for their neverending scales practice that drives neighbours up the wall. What may seem a strange, repetitive, even boring activity that artists, scientists and other creatives engage in daily is in fact doing magic to their brains.

Repeating something consciously – in this context meaning exercising those prefrontal systems of the brain – is quite effortful, and it needs a lot of energy and attentional resources. Therefore, our brain starts to forge connections that let the movements we’re practising pass from explicit, effortful memory systems into implicit, almost automatic memory systems.

The Romantic painter J M W Turner, well known for his wild seascapes, continued attending life-drawing classes at the Royal Academy where he’d been a student, to practise the basic moves of his craft. In so doing, he kept exercising the fine motor skill needed to draw. Slowly, connections were made between different neural systems; Turner’s skill was powered not only by explicit, effortful connections of the prefrontal systems but also, ultimately, by implicit, procedural memory systems and enabled flow.

To investigate the contribution to creative expression of those rule-loving prefrontal systems and the deeper, feeling-based systems, a team of researchers from Drexel University in Philadelphia invited two groups of jazz musicians – one made up of novices, the other, of professional jazz musicians – to a brain-stimulation experiment. Jazz musicians are known to pour their heart into their strings in spectacular improv sessions, ‘feeling it’ and finding flow. In the experiment at Drexel University, transcranial direct current stimulation (tDCS) was used to introduce a little extra electric energy, via a coil held close to the brain, into the prefrontal systems of the musicians while they were playing.

Now – remember what you now know about the brains of experts. Regular technique practice allows us to tap into our skill, without having to think about it, because the skill has passed into implicit procedural memory systems in our brain. What do you think will happen if we now introduce extra energy into experts’ prefrontal systems?

Creatives who invite regular technique practice into their life will experience their art as second nature

Results showed that introducing extra energy into the rule-based systems pulled experts away from their intuitive expression. They performed worse. In contrast, the novices’ performance improved under this treatment. Clearly, the novices were still relying on those rule-based, logical brain systems to perform ‘correctly’ – therefore, introducing more energy into these systems helped them with their performance.

This works a bit like learning a new language. First, we learn the words, the basic grammar, and we make many mistakes. It is effortful and we have to think before uttering any sentence at all. But as we repeat the words, practise verbal tenses and vocabulary over and over, our brain realises the repetition and transports the skill of that new language from explicit to implicit memory systems. That’s when we start to express and create entire new sentences with that new language: one fine day, you may even understand a poem in that new language. Creatives who invite regular technique practice into their life will experience their art as second nature and a means to expression.

‘Talent’ is never enough for true brilliance. You do need technique practice to forge the right pathways in your brain.

That’s why the advice to ‘just let go’, ‘be in the present’ and ‘feel it’ are unhelpful to find flow. When flow happens to you, it may well feel magical, it might feel like you’re ‘letting go’. You feel a strange fusion of your movements and your awareness, and you’re somehow entirely enwrapped in the present. It’s still early days to say exactly how this works, but it has to do with those low-level, implicit memory systems that encode movements that we internalise with technique practice. Then, the prefrontal systems deactivate while we let the implicit motor memory systems do their job. That’s when you use that skill to express and find flow.

But this is a neural process that happens outside of your conscious awareness, you can’t do this at will.

If you’re able to write and read, you already have one potential flow tool at your disposal: you no longer have to think about writing a word, or deciphering my writing, letter by letter, as you read. Your writing and reading skills are firmly anchored in your implicit memory systems and you can effortlessly use them to express and to find flow. Many people experience flow while reading a book, as research led by Birte A K Thissen shows. And decades of biopsychological research by James Pennebaker and his team from the University of Texas at Austin has shown that expressive writing can lead to improvements in immune markers and wound healing, fewer doctor visits in a six-month follow-up period, and a lighter, happier mood overall. Expressive writing is a technique by which you write for some 15-20 minutes two to three times per week. While writing, you should focus on what you feel – and express that. Importantly, you should plan not to show what you write or create to anyone, due to the social injury risks of disclosure. Don’t, unless you know that the recipient of your vulnerable writing is worthy of your trust. Your flow-tool must be, and remain, your safe-space. Risk of hurt will root you firmly in the present and prevent you from finding flow.

How do we create a flow-tool for creative behaviours that we haven’t been using since mid-childhood, like reading and writing? Well, start with technique practice and copying. As scientists, we ask about the mechanism. Our brain creates habit-loops when it learns stuff. In neuroscientific terms, habits are action-based associations between a cue, an action and a reward.

The first step in achieving flow is understanding that the senses act as channels to the brain, then surrounding our senses with the right cues. For little time windows in our day, we should create cue-spaces that are conducive to flow. This means hearing, seeing, smelling, tasting and touching cues that will make our mind flow, including also maybe modifying the space we’re in (triggering our exteroception), the movements of our body (proprioception) and the feelings that rise to our awareness from within (eg, when what we eat, smell, etc trigger our interoception). As we repeat this experience, our brain forms conditioned neural links between these cues and the feeling of flow.

It’s like switching on your brain’s energy-saving autopilot

Of course, it isn’t as easy as that from a neuroscientific point of view, and there is a lot that we still don’t know. But, for argument’s sake, let’s imagine this process like a golden thread between a cue and a memory stored in your memory systems that sit safely tucked away behind your temples. Now, each time this cue emerges before your senses, it swings a little lasso and, through receptors all over your body (in your eyes, nose, skin, etc) and long ganglia (nerve cells) – its lasso reaches into your brain and hooks on to its very special knob within your memory systems. Then, the cue pulls at the knob, and your mind follows in the direction of the memory encoded there, and off you go, back into flow. Because that feeling was encoded with the memory of that cue, your mind already knows the way. This happens each time a pianist touches the keys of their piano, a painter sees their pigment, or a ballet dancer hears their practice music.

The trick is to turn these cues into what I call ‘pathway prompts’ – little signals that help your brain slip into flow mode naturally, without needing to think about it. It’s like switching on your brain’s energy-saving autopilot.

For my own writing habit, I rely on cues that appeal to my senses and trigger familiar rhythms in my brain. I write in the mornings, when my body feels sensitive from just waking up. I drink coffee – the taste, smell, warmth and sound all tune me in. I sit in a café – the buzz of the place grounds me. I write on a laptop I use only for writing – it’s familiar, and signals ‘It’s time to focus.’

That’s my writing cue-scape – a set of sensory triggers that gently steer me into flow.

What’s yours?

A certain level of mastery makes it easier to find flow with your activity. In neural terms, ‘mastery’ is when the skill starts passing into the implicit, procedural memory systems. This will trigger the ‘skills-challenge principle’, where your chosen activity is neither too easy, nor too hard, all the better to absorb your attention.

‘Aren’t you done learning all those dance movements yet, Julia?’ one grumpy uncle of mine once said, while he loaded up a huge piece of cake onto his plate. I nibbled at my carrot and smiled at him. The ceiling is unlimited, and you can always keep learning and improving your artistic skill. The secret is, you’ll never be ‘done’ learning to dance, draw, write, play an instrument. And thankfully so – with art at hand, you’ll never be bored, you’ll always have something new to learn, discover and conquer: a new move, a new aesthetic. That movement on repeat, which has become so much you with time and repetition, will always bring you back to you, to your wonderful self.

Identify a flow-tool that matches your need for stimulation. Give your brain a respite from the unpredictability of life

Besides, repetitive movement practices have a wonderful side-effect if used well: they remove uncertainty from our brain. Uncertainty is part of all our lives to a larger or lesser extent; and it is among the chief killers of our calm. Csikszentmihalyi stated that being in flow makes us escape from the unpredictability of life. Technique practice offers the space to start on that journey, because repetitive movements – as when we practise an artistic skill like drawing, dancing, music-making or knitting – are washing machines for minds. Life is unpredictable, and our senses can’t always find something recognisable to cling to. When our ability to predict is weakened and our brain is put on alert, this mind-absorbing state can make us feel miserable. We can regain our footing by controlling our surroundings or other people, but if flow is what we seek, we’ll fail. What we need instead are routines in our day to create habits of wellbeing in our mind, because our brain will, during those periods of routine, know exactly what’s going to happen next.

After a while, of course, routines are boring. That’s why I suggest we all identify a flow-tool that matches our need for stimulation too. With a creative practice that is right for you, you’ll be building the right movement habits in your brain to make your art your second nature so you can find expression. At the same time, you’ll also be giving your brain a respite from the unpredictability of life.

Place those pathway prompts strategically in your surroundings – and off you go, flow.

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A timeline of Earth's average temperature

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This has to be one of the coolest visualisations.

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Last Updated

18/06/2025

A timeline of Earth's average temperature

By: Randall Munroe
xkcd: 12/09/2016




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What is Thermodynamic Computing and how does it help AI development?!

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The reasoning behind this chip is the same as behind Pipi 9. Pipi 9 runs on noise.

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17/06/2025

What is Thermodynamic Computing and how does it help AI development?!

By: Laszlo Fazekas
Medium: 05/04/2024

The foundation of modern computing is the transistor, a miniature electronic switch from which logic gates can be constructed, creating complex digital circuits like CPUs or GPUs. With the advancement of technology, transistors have become progressively smaller. According to Moore’s Law, the number of transistors in integrated circuits approximately doubles every 2 years. This exponential growth has enabled the exponential development of computing technology. However, there is a limit to how much the size of transistors can be reduced; we will soon reach a threshold below which transistors cannot function. Moreover, the advancement of AI has made the need for increased computational capacity more critical than ever before.


Transistor count per year from https://en.wikipedia.org/wiki/Moore%27s_law

The fundamental issue is that nature is stochastic (unpredictable). And here, I’m not just referring to quantum mechanical effects. Environmental influences, thermal noise, and other disruptive factors must be considered when designing a circuit. For a transistor, the expectation is that it operates deterministically (predictably). If I run an algorithm 100 times in succession, I must get the same result every time. Currently, transistors are large enough that these factors do not interfere with their operation, but as their size is reduced, these issues will become increasingly relevant. So, what direction can technology take from here? The “usual” answer: quantum computers.

An image of a quantum computer from https://www.flickr.com/photos/ibm_research_zurich/50252942522

In fact, with quantum computers, we encounter the same issue: the need to eliminate environmental effects and thermal noise. This is why quantum computers must be cooled to temperatures near absolute zero. These extreme conditions preclude quantum processors from replacing today’s CPUs. But what could be the solution? It appears that to move forward, we must abandon our deterministic computers and embrace the stochastic nature of the world. This idea is not new. It’s several billion years old.

Educational videos often depict the functioning of cells as little factories, where everything operates with the precision of clockwork. Enzymes, like tiny robots, cut up DNA, to which amino acids attach, leading to the production of proteins. These proteins neatly interlock and, during cell division, separate from the old cell to form a new one. However, this is a highly simplified model. In reality, particles move entirely at random, and when the right components happen to come together, they bind. While human-made structures operate under strict rules, here processes form spontaneously under the compelling influence of physical and chemical laws. Of course, from a bird’s-eye view, the system might appear to function with the precision of a clockwork.

DNA replication from https://en.wikipedia.org/wiki/DNA

A very simple example is when we mix cold water with hot water. It would be impossible to track the random motion of each particle. Some particles move faster, while others move slower. Occasionally, particles collide and exchange energy. The system is entirely chaotic, requiring immense computational capacity to simulate. Despite this, we can accurately predict that after a short period, the water will reach a uniform temperature. This is also a simple self-organizing system that is very complex at the particle level, yet entirely predictable due to the laws of physics and the rules of statistics. Similarly, cell division becomes predictable as a result of complex chemical processes and random motion. Of course, errors can occur. The DNA may not copy correctly, mutations may develop, or other errors may occur. That’s why the system is highly redundant. Several processes will destroy the cell in case of an error (apoptosis), thus preventing faulty units from causing problems (or only very rarely, which is how diseases like cancer can develop).

The energy consumption of a transistor can be comparable to the energy consumption of a cell, even though a cell is orders of magnitude more complex. Imagine the complex calculations we could perform with such low consumption if we carried them out in an analog manner, exploiting the laws of nature.

In biology, thermal noise is not only not a problem, but it is necessary. Below certain temperatures, biological systems are incapable of functioning. It is the random motion induced by heat that powers them.

The foundation of thermodynamic computing is similar. Instead of trying to eliminate the stochastic nature of physical processes, we utilize it. But what can be done with a computer whose operation is non-deterministic?

In fact, in the field of machine learning, there are many random components. For example, in the case of a neural network, the initial weights are randomly initialized. The dropout layer, which eliminates overfitting, also randomly discards inputs. But at a higher level, for instance, diffusion models also use random noise for their operation. In the case of Midjourney, for example, the model was trained to generate images from random noise, taking into account the given instructions.

Here, a bit of noise is added to the image at every step until the entire image becomes noise. The neural network is then trained to reverse this process, that is, to generate an image from noise based on the given text. If the system is trained with enough images and text, it will be capable of generating images from random noise based on text. This is how Midjourney operates.


Steps of Stable Diffusion from https://en.wikipedia.org/wiki/Stable_Diffusion

In current systems, we eliminate the random thermal noise to obtain deterministic transistors, and then on these deterministic transistors, we simulate randomness, which is necessary for the operation of neural networks. Instead of simulation, why not leverage nature’s randomness? The idea is similar to that of any analog computer. Instead of digitally simulating a given process, we should utilize the opportunities provided by nature and run it in an analog manner.

The startup Extrophic is working on the development of such a chip. Like Google, the company was founded by two guys: Guillaume Verdon and Trevor McCourt. Both worked in the field of quantum computing before founding the company, and their chip lies somewhere halfway between traditional integrated circuits and quantum computers.

Extropic’s circuit works in an analog manner. The starting state is completely random, normally distributed thermal noise. Through programming the circuit, this noise can be modified within each component. Instead of transistors, analog weights take their place, which are noisy, but the outcome can be determined through statistical analysis of the output. The guys call this probabilistic computing.

Microscope image of an Extropic chip from https://www.extropic.ai/future

These analog circuits are much faster and consume much less energy, and since the thermal noise is not only non-disruptive but an essential component of the operation, they do not require the special conditions needed by quantum computers. The chips can be manufactured with existing production technology, so they could enter the commercial market within a few years.

As we have seen from the above, Extropic’s technology is very promising. However, what personally piqued my interest is that it is more biologically plausible. Of course, I don’t think that the neurons in artificial neural networks have anything to do with human brain neurons. These are two very different systems. However, the human brain does not learn through gradient descent. Biological learning is something entirely different, and randomness certainly plays a significant role in it.

As I mentioned, in biology and nature, everything operates randomly. What we see as deterministic at a high level is just what statistically stands out from many random events. This is how, for example, many living beings (including us humans) came to be through completely random evolution yet are built with almost engineering precision. I suspect that the human brain operates in a similar way to evolution. A multitude of random events within a suitably directed system, which we perceive from the outside as consistent thinking. This is why genetic algorithms were so intriguing to me, and now I see the same principle in Extropic’s chip.

If you are interested, check the company homepage or this interview with the founder guys.

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It Was the Damn Phones

Mike's Notes

I discovered this searing poem on the After Babel substack. It says everything about the disgusting misuse of technology by major tech companies and the effects on people, especially the mental health of the young.

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21/11/2025

It Was the Damn Phones

A Gen Z poet conveys the effects of the phone-based childhood

By: Kori Jane Spaulding
After Babel: 12/06/2025

A poet from Houston, Texas, Kori is just 21 years old and has already published three books of poems and one novel.

Below is a transcription of the spoken word poem shared in the video. A slightly different version of the poem can be found in two of Kori’s books: Books Close (pg. 92-93), and Ajar (pg. 270-271).

I think our parents were right.

It was the damn phones.

We laughed as children, hearing, “It’s that Snapgram and Instachat and Facetok”.

They didn’t understand. They couldn’t even say it right. We thought we knew better than them.

They didn’t know what it was like, having the world at the tip of our fingers.

We scroll through the trash so much, we have news headlines tattooed on our skin.

Wires for veins. AI for a brain. And they may not have understood. But they were right.

It was the damn phones.

I prided myself on sobriety, on being drunk with only propriety. I was above addiction.

A hypocritical notion. For am I not addicted to my own anxiety?

Brought on by a need for constant stimulation. A drug in our pockets.

But who can blame us? We were but children when they were given.

We didn’t know how to stop it. If I added up all the hours I spent on a screen,

existential dread and regret would creep in. So I ignore this fact by opening my phone.

And it’s not like I can throw it away. It’s how we communicate. It’s how we relate.

It’s a medicine that is surely making our souls die.

I used to say I was born in the wrong generation, but I was mistaken.

For I do everything I say I hate. Exchanging hobbies for Hinge,

truth with TikTok, intimacy with Instagram, sanity with Snapchat.

I have become self-aware. Almost worse than being naive. I know it’s poison, but I drink away.

The character behind the phone screen has become self-aware.

We used to be scared of robots gaining consciousness, a lie by the media companies.

To keep us distracted enough, so not to become conscious of the mess they created.

We are the robots. We are the product. And so I sit and I scroll and I rot on repeat.

Sit and scroll and rot.

Until my thoughts are what is being fed to me on TV,

until my feelings are wrapped up in celebrities,

until my body is a tool of my political identity.

I sit and I scroll and I rot.

And I post on the internet how the internet has failed us

so that I may not fail my internet presence. I think our parents were right.

It was the damn phones.

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Google’s “What’s New in Web UI” Talk: Less Custom Component JavaScript, More Web Standards

Mike's Notes

In the long term, this could help resolve UI issues.

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Last Updated

15/06/2025

Google’s “What’s New in Web UI” Talk: Less Custom Component JavaScript, More Web Standards

By: Bruno Couriol
InfoQ: 08/06/2025

Una Kravets recently presented in a talk recent developments in Web UI supported by the Chrome team. Some common UI patterns that currently require a significant amount of JavaScript may soon be implemented in a declarative manner with new features of HTML and CSS, with less custom JavaScript, and with built-in accessibility.

The talk focuses on three particularly tricky UI patterns: customizable select menus, carousels, and hover cards. All three UI patterns are commonly found in design systems, with many lines of JavaScript to implement custom styling, presentation, layout, interaction, or accessibility patterns. With browser vendors evolving web standards to incorporate those patterns away from userland into the browsers themselves, developers may have less work to do in the future and simply rely on the platform. Less custom JavaScript also benefits users in the shape of increased performance. The proposed declarative APIs have already shipped in at least one stable browser engine.

The first pattern discussed is the customizable select menu. The native <select> element’s internal structure has been historically difficult to style consistently across browsers:

A common frustration for developers who try to work with the browser’s built-in form controls (<select> and various <input> types) is that they cannot customize the appearance of these controls to fit their site’s design or user experience. In a survey of web developers about form controls and components, the top reason that devs rewrite their own versions of these controls is the inability to sufficiently customize the appearance of the native controls.

The building blocks for a customizable select are the Popover API and Anchor Positioning.

The Popover API handles the floating list of options, ensuring it appears above other UI elements, is easy to dismiss, and manages focus. Popover has reached baseline status and is now available in all browsers.

Command invokers (command and commandfor attributes) provide a declarative HTML solution similar to popovertarget for connecting button clicks to actions (e.g., opening a dialog), reducing the need for boilerplate JavaScript.

Anchor Positioning is a CSS API that lets developers position elements relative to other elements, known as anchors. This API simplifies complex layout requirements for many interface features like menus and submenus, tooltips, selects, labels, cards, settings dialogs, and many more. Anchor Positioning is part of Interop 2025, meaning that it should land in all browsers by the end of the year.

The improved select element anatomy showcases two parts, a button, and a popover anchored to that button, all with corresponding selectors for targeting and styling:

Styles can be applied to the popover through the selector ::picker(select). An example of custom styling is as follows:

/* enter custom mode */
select,
::picker(select) {
  appearance: base-select;
}
/* style the button */
::select-fallback-button {
  background: gold;
  font-family: fantasy;
  font-size: 1.2rem;
}
/* style the picker dropdown */
::picker(select) {
  border-radius: 1rem;
}
/* style the options */
option {
  font-family: monospace;
  padding: 0.5rem 1rem 0.5rem 0;
  font-size: 1.2rem;
}
/* style selected option in the dropdown */
option:checked {
  background: powderblue;
}
/* style the option on hover or focus */
option:hover,
option:focus-visible {
  background-color: pink;
}
/* style the active option indicator */
option::before {
  content: '';
  font-size: 80%;
  margin: 0.5rem;
}
/* etc. */
body {
  padding: 2rem;
}

Developers are encouraged to review the full talk for additional technical details, demos, and explanations. The talk additionally explains how recent features from the CSS Overflow 5 specification, namely scroll buttons and scroll markers, enable scroll-driven animations (e.g., carousels) purely in CSS.

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A knockout blow for LLMs?

Mike's Notes

Some criticisms by Gary Marcus of the fundamental weaknesses of LLMs. The Tower of Hanoi looks fun. I must make one to try.

Resources

References

  • The Algebraic Mind (2021) MIT Press, by Gary Marcus.

Repository

  • Home > Ajabbi Research > Library > Authors > Gary Marcus
  • Home > Ajabbi Research > Library > Subscriptions > Marcus on AI
  • Home > Handbook > 

Last Updated

14/06/2025

A knockout blow for LLMs?

By: Gary Marcus
Marcus on AI: 07/06/2025

LLM “reasoning” is so cooked they turned my name into a verb

Quoth Josh Wolfe, well-respected venture capitalist at Lux Capital:

Ha ha ha. But What’s the fuss about?

Apple has a new paper; it’s pretty devastating to LLMs, a powerful followup to one from many of the same authors last year.

There’s actually an interesting weakness in the new argument—which I will get to below—but the overall force of the argument is undeniably powerful. So much so that LLM advocates are already partly conceding the blow while hinting at, or at least hoping for, happier futures ahead.

Wolfe lays out the essentials in a thread:

In fairness, the paper both GaryMarcus’d and Subbarao (Rao) Kambhampati’d LLMs.

On the one hand, it echoes and amplifies the training distribution argument that I have been making since 1998: neural networks of various kinds can generalize within a training distribution of data they are exposed to, but their generalizations tend to break down outside that distribution. That was the crux of my 1998 paper skewering multilayer perceptrons, the ancestors of current LLM, by showing out-of-distribution failures on simple math and sentence prediction tasks, and the crux in 2001 of my first book (The Algebraic Mind) which did the same, in a broader way, and central to my first Science paper (a 1999 experiment which demonstrated that seven-month-old infants could extrapolate in a way that then-standard neural networks could not). It was also the central motivation of my 2018 Deep Learning: Critical Appraisal, and my 2022 Deep Learning is Hitting a Wall. I singled it out here last year as the single most important — and important to understand — weakness in LLMs. (As you can see, I have been at this for a while.)

On the other hand it also echoes and amplifies a bunch of arguments that Arizona State University computer scientist Subbarao (Rao) Kambhampati has been making for a few years about so-called “chain of thought” and “reasoning models” and their “reasoning traces” being less than they are cracked up to be. For those not familiar a “chain of thought” is (roughly) the stuff a system says it “reasons” its way to answer, in cases where the system takes multiple steps; “reasoning models” are the latest generation of attempts to rescue the inherent limitations of LLMs, by forcing them to “reason” over time, with a technique called “inference-time compute”. (Regular readers will remember that when Satya Nadella waved the flag of concession in November on pure pretraining scaling - the hypothesis that my deep learning is a hitting a wall paper critique addressed - he suggested we might find a new set of scaling laws for inference time compute.)

Rao, as everyone calls him, has been having none of it, writing a clever series of papers that show, among other things that the chains of thoughts that LLMs produce don’t always correspond to what they actually do. Recently, for example, he observed that people tend to overanthromorphize the reasoning traces of LLMs, calling it “thinking” when it perhaps doesn’t deserve that name. Another of his recent papers showed that even when reasoning traces appear to be correct, final answers sometimes aren’t. Rao was also perhaps the first to show that a “reasoning model”, namely o1, had the kind of problem that Apple documents, ultimately publishing his initial work online here, with followup work here.

The new Apple paper adds to the force of Rao’s critique (and my own) by showing that even the latest of these new-fangled “reasoning models” still —even having scaled beyond o1 — fail to reason beyond the distribution reliably, on a whole bunch of classic problems, like the Tower of Hanoi. For anyone hoping that “reasoning” or “inference time compute” would get LLMs back on track, and take away the pain of m mutiple failures at getting pure scaling to yield something worthy of the name GPT-5, this is bad news.

Hanoi is a classic game with three pegs and multiple discs in which you need to move all the discs on the left peg to the right peg, never stacking a larger disc on top of a smaller one.

(You can try a digital version at mathisfun.com.)

If you have never seen it before, it takes a moment or to get the hang of it. (Hint, start with just a few discs).

With practice, a bright (and patient) seven-year-old can do it. And it’s trivial for a computer. Here’s a computer solving the seven-disc version, using an algorithm that any intro computer science student should be able to write:

[VIDEO unable to copy]

Claude, on the other hand, can barely do 7 discs, getting less than 80% accuracy, left bottom panel below, and pretty much can’t get 8 correct at all.

Apple found that the widely praised o3-min (high) was no better (see accuracy, top left panel, legend at bottom), and they found similar results for multiple tasks:

It is truly embarrassing that LLMs cannot reliably solve Hanoi. (Even with many libraries of source code to do it freely available on the web!)

An, as the paper’s co-lead-author Iman Mirzadeh told me via DM,

it's not just about "solving" the puzzle. In section 4.4 of the paper, we have an experiment where we give the solution algorithm to the model, and all it has to do is follow the steps. Yet, this is not helping their performance at all.

So, our argument is NOT "humans don't have any limits, but LRMs do, and that's why they aren't intelligent". But based on what we observe from their thoughts, their process is not logical and intelligent.

If you can’t use a billion dollar AI system to solve a problem that Herb Simon one of the actual “godfathers of AI”, current hype aside) solved with AI in 1957, and that first semester AI students solve routinely, the chances that models like Claude or o3 are going to reach AGI seem truly remote.

That said, I warned you that there was a weakness in the new paper’s argument. Let’s discuss.

The weakness, which was well-laid out by anonymous account on X (usually not the source of good arguments) was this: (ordinary) humans actually have a bunch of (well-known) limits that parallel what the Apple team discovered. Many (not all) humans screw up on versions of the Tower of Hanoi with 8 discs.

But look, that’s why we invented computers and for that matter calculators: to compute solutions large, tedious problems reliably. AGI shouldn’t be about perfectly replicating a human, it should (as I have often said) be about combining the best of both worlds, human adaptiveness with computational brute force and reliability. We don’t want an “AGI” that fails to “carry the one” in basic arithmetic just because sometimes humans do. And good luck getting to “alignment” or “safety” without reliabilty.

The vision of AGI I have always had is one that combines the strengths of humans with the strength of machines, overcoming the weaknesses of humans. I am not interested in a “AGI” that can’t do arithmetic, and I certainly wouldn’t want to entrust global infrastructure or the future of humanity to such a system.

Whenever people ask me why I (contrary to widespread myth) actually like AI, and think that AI (though not GenAI) may ultimately be of great benefit to humanity, I invariably point to the advances in science and technology we might make if we could combine the causal reasoning abilities of our best scientists with the sheer compute power of modern digital computers.

We are not going to be “extract the light cone” of the earth or “solve physics” [whatever those Altman claims even mean] with systems that can’t play Tower of Hanoi on a tower of 8 pegs. [Aside from this, models like o3 actually hallucinate a bunch more than attentive humans, struggle heavily with drawing reliable diagrams, etc; they happen to share a few weakness with humans, but on a bunch of dimensions they actually fall short.]

And humans, to the extent that they fail, often fail because of a lack of memory; LLMs, with gigabytes of memory, shouldn’t have the same excuse.

What the Apple paper shows, most fundamentally, regardless of how you define AGI, is that LLMs are no substitute for good well-specified conventional algorithms. (They also can’t play chess as well as conventional algorithms, can’t fold proteins like special-purpose neurosymbolic hybrids, can’t run databases as well as conventional databases, etc.)

In the best case (not always reached) they can write python code, supplementing their own weaknesses with outside symbolic code, but even this is not reliable. What this means for business and society is that you can’t simply drop o3 or Claude into some complex problem and expect it to work reliably.

Worse, as the latest Apple papers shows, LLMs may well work on your easy test set (like Hanoi with 4 discs) and seduce you into thinking it has built a proper, generalizable solution when it does not.

At least for the next decade, LLMs (with and without inference time “reasoning”) will continue have their uses, especially for coding and brainstorming and writing. And as Rao told me in a message this morning, “the fact that LLMs/LRMs don't reliably learn any single underlying algorithm is not a complete deal killer on their use. I think of LRMs basically making learning to approximate the unfolding of an algorithm over increasing inference lengths.” In some contexts that will be perfectly fine (in others not so much).

But anybody who thinks LLMs are a direct route to the sort AGI that could fundamentally transform society for the good is kidding themselves. This does not mean that the field of neural networks is dead, or that deep learning is dead. LLMs are just one form of deep learning, and maybe others — especially those that play nicer with symbols – will eventually thrive. Time will tell. But this particular approach has limits that are clearer by the day.

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Supercharge Your BoxLang Applications with Maven Integration

Mike's Notes

This is an excellent addition to BoxLang. Pipi 9 is capable of generating these code samples. It could also edit the pom.xml file.

The question remains how to get this code from Pipi into BoxLang, so Pipi can run BoxLang autonomously.

Resources

References

  • Reference

Repository

  • Home > Ajabbi Research > Library >
  • Home > Handbook > 

Last Updated

13/06/2025

Supercharge Your BoxLang Applications with Maven Integration

By: Luis Majeno
Ortus Solutions: 06/06/2025

Luis is the CEO of Ortus.

We're excited to announce a supercharged feature for BoxLang developers: Maven Integration! This powerful addition opens the door to the entire Java ecosystem, allowing you to seamlessly incorporate thousands of Java libraries into your BoxLang applications with just a few simple commands.

Why Maven Integration Matters

BoxLang has always been about combining the best of both worlds - the simplicity of dynamic languages with the power of the JVM. With Maven integration, we're taking this philosophy to the next level by giving you instant access to:

Thousands of Java libraries from Maven Central

  • Automatic dependency management - no need to manage it manually or copy jars around
  • Zero configuration - it just works out of the box
  • Clean management - add and remove dependencies with simple commands

This integration that we ship with BoxLang is at the runtime home level. However, it can easily be adapted to individual applications if needed, in case you are in shared environments.

How It Works

The magic happens through BoxLang's pre-configured pom.xml file located in your BoxLang home directory (~/.boxlang). The workflow is simple:

Add dependencies to the pom.xml file

Run mvn install to download libraries

Start using Java libraries immediately in your BoxLang code

That's it! BoxLang automatically loads all JARs from the lib/ folder, making them available throughout your runtime. For our full documentation please visit our book: https://boxlang.ortusbooks.com/getting-started/configuration/maven-integration

Real-World Examples

Let's see this in action with some practical examples that showcase the power of this integration.

Generate QR Codes in Seconds

Need to create QR codes? Just add the ZXing dependency:

<dependency>
    <groupId>com.google.zxing</groupId>
    <artifactId>core</artifactId>
    <version>3.5.2</version>
</dependency>

Then use it in your BoxLang code:

function createQRCodeGenerator() {
    return {
        "saveToFile": ( text, filePath, size = 300 ) => {
            var writer = new com.google.zxing.qrcode.QRCodeWriter()
            var bitMatrix = writer.encode( 
                text, 
                new com.google.zxing.BarcodeFormat().QR_CODE, 
                size, 
                size 
            )
            
            var image = new com.google.zxing.client.j2se.MatrixToImageWriter()
                .toBufferedImage( bitMatrix )
            var file = new java.io.File( filePath )
            
            new javax.imageio.ImageIO().write( image, "PNG", file )
            return filePath
        }
    }
}
// Generate QR code for your website
qrGenerator = createQRCodeGenerator()
qrFile = qrGenerator.saveToFile( 
    "https://boxlang.ortussolutions.com", 
    "/tmp/boxlang-qr.png", 
    400 
)
println( "QR code saved to: " & qrFile )
Create Professional PDFs
Want to generate dynamic PDFs? Add iText and you're ready to go:
<dependency>
    <groupId>com.itextpdf</groupId>
    <artifactId>itext7-core</artifactId>
    <version>8.0.2</version>
    <type>pom</type>
</dependency>

Now create beautiful PDFs programmatically:

function createStyledPDF( filePath, title, content ) {
    var writer = new com.itextpdf.kernel.pdf.PdfWriter( filePath )
    var pdf = new com.itextpdf.kernel.pdf.PdfDocument( writer )
    var document = new com.itextpdf.layout.Document( pdf )
    
    // Add styled title
    var titleParagraph = new com.itextpdf.layout.element.Paragraph( title )
        .setFontSize( 20 )
        .setBold()
    document.add( titleParagraph )
    
    // Add content
    var contentParagraph = new com.itextpdf.layout.element.Paragraph( content )
        .setFontSize( 12 )
    document.add( contentParagraph )
    
    document.close()
    return filePath
}
// Generate a professional report
reportPDF = createStyledPDF(
    "/tmp/quarterly-report.pdf",
    "Q4 2024 Business Report",
    "BoxLang continues to revolutionize dynamic programming on the JVM."
)

Getting Started

Getting started with Maven integration is incredibly simple:

1. Install Maven

macOS (Homebrew):

brew install maven

Windows (Chocolatey):

choco install maven

Linux (Ubuntu/Debian):

sudo apt install maven

2. Navigate to BoxLang Home

cd ~/.boxlang

3. Add Dependencies

Edit the pom.xml file and add your desired dependencies. Search Maven Central for libraries.

4. Install Dependencies

mvn install

5. Start Coding!

All dependencies are now available in your BoxLang applications immediately.

Why This Changes Everything

Maven integration fundamentally transforms what's possible with BoxLang:

Instant Access to Specialized Libraries

Need machine learning? Add Weka or DL4J. Want advanced image processing? Add ImageIO extensions. Need specialized data formats? There's probably a Java library for that.

Dependency Management Made Simple

Gone are the days of manually downloading JARs and managing versions. Maven handles transitive dependencies, version conflicts, and updates automatically.

Enterprise-Ready from Day One

Access to mature, battle-tested Java libraries means your BoxLang applications can handle enterprise requirements without reinventing the wheel.

Easy Experimentation

Want to try a new library? Add it to your pom.xml, run mvn install, and start experimenting. Don't like it? Run mvn clean and it's gone.

What's Next?

This is just the beginning! Maven integration opens up a world of possibilities for BoxLang developers. We're excited to see what amazing applications you'll build with access to the entire Java ecosystem.

Some areas we're particularly excited about:

  • Machine Learning: Integrate Weka, DL4J, or other ML libraries
  • Scientific Computing: Use Apache Commons Math for statistical operations
  • Data Formats: Work with Excel files, XML processing, and specialized formats
  • External Integrations: Connect to cloud services, databases, and APIs with dedicated clients

Try It Today!

Maven integration is available now in the latest version of BoxLang. Here's how to get started:

  • Update BoxLang to the latest version
  • Navigate to your BoxLang home (cd ~/.boxlang)
  • Edit the pom.xml file to add dependencies
  • Run mvn install
  • Start building amazing applications!

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Technological Jerk: Why Users Resist Your New Features (And What to Do About It)

Mike's Notes

Looks great. I must read this book and then write a review afterwards.

Resources

References

  • Progressive Delivery: Build The Right Thing For The Right People At The Right Time (IT Revolution Press, November 2025), By James Governor, Kim Harrison, Heidi Waterhouse, and Adam Zimman.

Repository

  • Home > Ajabbi Research > Library > Subscriptions > IT Revolution
  • Home > Ajabbi Research > Library > Publisher > IT Revolution Press
  • Home > Handbook > 

Last Updated

12/06/2025

Technological Jerk: Why Users Resist Your New Features (And What to Do About It)

By: Leah Brown
IT Revolution: 03/06/2025

Leah Brown is Managing Editor at IT Revolution working on publishing books and guidance papers for the modern business leader. I also oversee the production of the IT Revolution blog, combining the best of responsible, human-centered content with the assistance of AI tools.

It’s Friday at 9:52 p.m. You open the app on your phone to adjust the alarm on your smart speakers. You need to ensure you’re up early to make a flight. When the app opens, it’s different. “Oh, cool, a new update,” you think at first. But after twenty minutes of fruitlessly tapping around the screen, you discover through Reddit that the new app update has completely removed the ability to control alarms.

You spend the next hour setting up physical alarm clocks while trying not to wake your family.

Sound familiar?

This scenario isn’t just frustrating—it represents a fundamental disconnect in delivering software. Software developers are often proud of their innovations, and businesses are eager to ship them. But users are increasingly exhausted by the constant technological churn disrupting daily lives and workflows.

In their upcoming book, Progressive Delivery: Build The Right Thing For The Right People At The Right Time (IT Revolution Press, November 2025), authors James Governor, Kim Harrison, Heidi Waterhouse, and Adam Zimman call this phenomenon technological jerk.

The Physics of Disruption

In physics, “jerk” isn’t just someone cutting you off in traffic—it’s the rate at which acceleration changes. It’s the feeling that makes you grab for the subway pole when the train lurches or brace yourself during an elevator’s sudden start.

As the authors explain in the book, “Just as physical jerk throws our bodies off balance, technological jerk throws our mental models and established workflows into disarray when software changes too abruptly or without proper preparation.”

This isn’t about resistance to change itself. It’s about our human capacity to absorb the rate of change. And in today’s software environment, that rate is accelerating far beyond what many users can comfortably process.

The Business Impact of Technological Jerk

When users experience technological jerk, they don’t typically blame themselves—they blame your product. This manifests in ways that directly impact your business:

  • Decreased engagement: Users avoid using features they’re not confident navigating.
  • Rising support costs: Every abrupt change creates a flood of inquiries and complaints.
  • Negative reviews: More than ever, users vocalize their frustration publicly.
  • Increased churn: At its worst, users switch to competitive products that feel more stable.
  • Feature abandonment: New capabilities that cost thousands of development hours go unused.

In 2019, Slack faced significant backlash after releasing a major UI redesign that disrupted established workflows. Despite the company’s belief that the new interface would ultimately improve productivity, users revolted against the change. Some organizations even delayed upgrading to maintain productivity.

Even worse, in January 2025, Sonos CEO Patrick Spence was forced to resign after an app update broke core functionality. The cost of failing to manage technological jerk isn’t just customer dissatisfaction—it can be existential.

Why We Create Technological Jerk

If the effects are so damaging, why do we keep creating software experiences that jar our users? Several factors are at play:

1. The Curse of Knowledge

When you’ve spent months designing and building a feature, the change seems intuitive, even obvious. You can’t un-see what you know. This cognitive bias makes it nearly impossible to accurately predict how disruptive a change will feel to someone encountering it for the first time.

2. Deployment ≠ Release ≠ Adoption

Many organizations have embraced CI/CD to optimize their deployment pipelines, shipping code dozens or hundreds of times daily. But we haven’t created equivalent sophistication around how we release those changes to users and support their adoption journey.

The software industry conflates three distinct processes:

  • Deployment: Getting code to production environments
  • Release: Making features available to users
  • Adoption: Users successfully incorporating features into workflows

While optimizing for deployment speed, we’ve neglected the human-centered processes of release and adoption.

3. The “User Knows Best” Fallacy

“But users asked for this!” is a common defense when pushback occurs. This ignores a crucial reality: users typically ask for outcomes, not specific implementations.

When a user says, “I want a faster search function,” what they’re really asking for is, “I need to find critical information during customer calls without losing the customer’s attention.” Your implementation of a “faster search” might actually disrupt the workflow they’ve optimized around the current search.

4. The False “Everyone” Narrative

Product teams often speak of “our users” as a monolithic entity. “Our users want this.” “Our users will love this.” This ignores the reality that your user base contains multiple personas with dramatically different needs, technical sophistication levels, and change tolerance thresholds.

What delights your early adopters may alienate your steady mainstream users. Assuming “everyone” will react similarly to change is a recipe for creating technological jerk.

Early Signs of a Better Approach

Some organizations have begun exploring solutions to this problem:

Feature Flagging Beyond A/B Testing

Companies like GitHub use sophisticated feature flagging not just for testing but as a fundamental control mechanism that separates deployment from release. Rather than abruptly pushing changes to all users simultaneously, they create control points that allow for gradual, deliberate exposure of new capabilities.

Ring Deployments

Microsoft has pioneered the concept of “ring deployments,” where changes progress through increasingly larger circles of users, starting with internal teams and expanding gradually to early adopters before reaching the general population. This creates a progressive exposure pattern that catches issues early while allowing most users to avoid the earliest, most disruptive moments of a new feature.

User-Controlled Release Cadence

Some products now offer explicit user choice in when and how they adopt new features. Google Workspace, for instance, allows administrators to choose between “Rapid Release” and “Scheduled Release” tracks, acknowledging that different organizations have different change absorption capacities.

The Rise of Product Operations

Just as DevOps emerged to bridge the gap between development and operations, a new discipline—Product Operations—is forming to manage the increasingly complex interface between product teams and users. This emerging function explicitly owns the user transition experience, much as DevOps owns the code transition experience.

Beyond Adhoc Solutions

These approaches represent important first steps, but they remain fragmented and inconsistent across the industry. What’s needed is a comprehensive framework that systematically addresses technological jerk by reconceptualizing how we deliver software.

Such a framework would need to:

  • Recognize different user segments’ varying capacities for change absorption
  • Provide mechanisms to measure and manage the rate of change
  • Create feedback loops that detect when change is happening too rapidly
  • Delegate control to those closest to the impact
  • Balance the innovation needs of development teams with the stability needs of users

This isn’t about slowing innovation—it’s about enabling sustainable innovation that users can absorb and benefit from. It’s about finding the sweet spot between technological stagnation and technological whiplash.

What’s Next?

Progressive Delivery introduces a comprehensive framework to do just this—a systematic approach to managing technological jerk while maintaining innovation velocity.

Drawing on their extensive combined experience in the industry, as well as case studies from companies like GitHub, Disney, Adobe, and AWS, the authors demonstrate how organizations of various sizes and industries have addressed this challenge through a combination of cultural, procedural, and technical practices.

Until the book comes out, you can start by recognizing when you’re creating technological jerk in your own products:

  • Are users complaining about the pace of change rather than the changes themselves?
  • Do support tickets spike after every release?
  • Do you have features with mysteriously low adoption despite obvious benefits?
  • Have users created workarounds to avoid using your latest capabilities?

These are all signs that your delivery approach may be creating more friction than function. By recognizing the problem, you’ve taken the first step toward building better relationships with your users through more thoughtful software delivery.

Because at the end of the day, what matters isn’t just what we build—it’s how we deliver it, to whom, and at what pace. Get that right, and both your users and your business will thrive.

This post explores concepts from the upcoming book Progressive Delivery: Build The Right Thing For The Right People At The Right Time by James Governor, Kim Harrison, Heidi Waterhouse, and Adam Zimman (IT Revolution Press, November 2025), which introduces a comprehensive framework for delivering software in ways that respect both innovation needs and user adoption capacities.

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