Showing posts with label mind. Show all posts
Showing posts with label mind. Show all posts

No posts for a wee while

Mike's Notes

I was on holiday for the last few weeks and am back now. There will be no blog posts, newsletters or meetings until Pipi Core is back up and running.

Update 27/05/2026

Lots of surprises. Making rapid progress. The peace and quiet are bliss.

Update 31/05/2026

The problem and solution are how things are named. Pipi auto-generates thousands of code names using multiple pattern languages, and all the naming conventions require many minor fixes for several unexpected reasons after migrating from a developer laptop to a production server environment. Everything else is absolutely fine.

Other naming problems are also being solved now, including:

  • The rapid development of Boxlang by Ortus has brought forward another challenge. Pipi 10 will be migrated to run on top of Boxlang in 2027 to support multiple languages, including C++, CFML, COBOL, Go, Java, JavaScript, PHP, Python, Rust, etc.
  • Future integration with cloud-based LLMs.
  • Future integrations with Office365, Google Workspace, Zoho, LibreOffice, etc.

The common solution is to create standardised naming systems that are simple, stable, robust, schema-based, versioned, self-documenting, and extensible to meet unanticipated future needs.

This is done by replacing code-based naming rules with database-driven ones that can be easily edited in the future via an admin UI.

90% of these names are internal, hidden in the closed core, and how they work and what they are will not be discussed here. The rest will be publicly and fully documented as part of the open-source workspaces for developers to work with.

Update 02/06/2026

I'm changing the disclosure boundary between the Pipi closed-core and open-source workspaces. Previously, "disclose everything unless there is a security reason not to". This is now changed to "disclose on the basis of need to know".

Closed-core accounts for 90% and open-source workspaces for 10% of lines of code, databases, etc.

This will reduce the documentation burden, given Pipi's vast scale. So, the open-source workspaces will be fully shared and documented on GitHub, etc, without restriction. This includes;

  • Standards schema
  • Ontologies
  • Parameters
  • Laws of physics
  • HTML + CSS
  • Algorithms
  • Module DDD models
  • Workflow diagrams
  • Documentation
  • API schema
  • UI code
  • etc

This also means some existing technical documentation about the closed-core will become hidden and only available internally.

Update 07/06/2026

Pipi Core is the IDE used to edit Pipi Core (AKA: which came first, the chicken or the egg?). Temporary UIs have been created and are being used across multiple engines to edit the names in use. This is much faster than directly editing data, which had to be done initially. The next step will be turning auto-generation back on. Once that's done, temporary UIs will be used to build permanent UIs. More automation will then be enabled via the UIs, and so on, as Pipi Core builds itself with a human in the loop.

Update 08/06/2026

The list of code cases available to use now for auto-generated naming, I/O translation, etc with examples, includes;

  • camelCase: userProfilePicture
  • kebab-case: user-profile-picture
  • PascalCase: UserProfilePicture
  • snake_case: user_profile_picture
  • SCREAMING_SNAKE_CASE: USER_PROFILE_PICTURE
  • Train-Case: User-Profile-Picture
  • flatcase: userprofilepicture
  • UPPER-CASE-KEBAB-CASE: USER-PROFILE-PICTURE
  • Sentence case: User profile picture
  • Title Case: User Profile Picture
  • middot·case: user·profile·picture
  • dot.case: user.profile.picture
  • UPPER CASE: USER PROFILE PICTURE
  • lowercase: user profile picture

Update 12/06/20026

Checking that these changes to variable names and internal messaging do not clash with the GΓΆdel Machine.

Update 17/06/2026

The DevOps Engine (dvp) has unexpectedly proven to be critical to solving this puzzle. Mostly fixed last night. Watching the rather excellent live Google talk, Beyond the GPU: Maximising goodput with self-healing AI infrastructure, this morning has given me valuable insights into how to fix the remaining issues by reviewing Google HPC YAML files. 😎😎 Sometimes insights come from the strangest places.

Update 01/07/2026

The main work now is rapidly configuring Pipi for production and full autonomous automation. Using Google Search AI Mode (Gemini) and then Grammarly Pro makes the work easier and 100x faster.

  • I have decided to have Pipi re-render the many Ajabbi draft public websites with the new and missing developer information. (20K pages)
  • The website's .robot.txt file will then be unlocked to enable search engines.
  • The HTML will be updated to make it easier for AI to read.
  • This blog will be imported into Pipi, cleaned up, re-exported from Pipi, and published to Blogger via the API.
  • The new posts created in Pipi will return to A Sandy Beach to discuss something already built rather than being built.

Update 02/07/2026

The DevOps and IaC engines are getting rapid data model overhauls. The IaC engine is a great test for the variable names. I'm building a capability into Pipi to autonomously and automatically run OpenTofu and Ansible, initially targeting the Pipi Data Centre, then GCP and AWS for deployments. It's going very well and making rapid progress.

Update 05/07/2026

Pipi will initially run the open-source enterprise applications on Google Cloud Run and Google Cloud Storage (GCS). The code is complete and will be very low-cost to run, giving Ajabbi, a bootstrapping-purpose startup, a very long runway.

Update 18/07/2026

The job has now shifted to configuring, networking and deploying many physical servers. Installing software, including Pipi, labelling cables and rack gear, throwing out junk, tidying, etc., leaving nothing to chance. Shipping delays are holding up part deliveries.

Update 23/07/2026

On the basis of open collaboration and credits for experimentation, I was going to offer Google exclusive use of Pipi for a period (as a thank you) before Pipi open-source is donated to the Cloud Native Computing Foundation for all to use.

Make money to provide a service.

I'm getting exasperated with XWF. They are the external sales contractors to Google, and since 2021, they regularly contact me.

  • Selling GCP products (No need; I'm already convinced).
  • Acting as gatekeepers to any contact with Google Engineers to discuss novel integration options, which is the actual issue. How to combine Gemini (an LLM) and Pipi (non-LLM) to make something much better.
  • They are all very nice, but a complete waste of my time. No more XWF meetings, folks.

So, I have decided to target integration with OpenRouter (and its alternatives) instead of Gemini and open up the Pipi developer platform (it is big and coming 😎) to enable developers from Alibaba, Alice AI, Anthropic, AWS, Azure, ByteDance, DeepSeek, Google, IBM, Meta, Mistral, Moonshot AI, Naver, OpenAI, Oracle, Palantir, Sarvam AI, xAI, etc, and anyone else, to enable integrations that are optimal, 100% secure and vetted, with everything publicly verifiable.

Pipi closed-core will never be for sale; this year it's getting a non-profit foundation behind it, a bit like Patagonia. I'm open to all genuine offers of assistance, collaboration and experimentation with no strings attached. Contact me.

Don't send sales engineers

Send a senior, highly experienced engineer/architect/chief scientist who loves a big fat problem and has time for an open chat without a pitch or an agenda, and just see where it goes.

If you want to meet in person, expect to work collaboratively at a whiteboard or blackboard like a real mathematician. Plus coffee, of course. 😎 To see how this works, watch the seminars at the London Institute of Mathematical Sciences, or the recorded physics seminars at Perimeter.

Pipi is rooted in biology and the laws of physics, so you need a very solid background in advanced sciences (microbiology, biochemistry, mathematics, philosophy, particle physics, thermodynamics, complex adaptive systems, etc).

Please, no venture capitalists or private equity. You're wasting your time. Go find something else to plunder. Pipi is a gift to humanity.

Being very high-functioning Asperger's (autism) with hyperphantasia, plus multiple synesthesias, I think visually at lightning speed and output solutions as fast as I can draw. I love solving very hard problems that matter. I can only write very slowly with the help of assistive technology, so I prefer video meetings with good spoken English, slides and time for trading quick engineering drawings.

Pipi is designed to run massive enterprise systems for socially useful critical infrastructure on every platform in many languages and writing systems.

The intention is to make life better for all of humanity by destroying waste, failure and crippling bureaucracy in;

  • Health systems (hospitals)
  • Transport systems (rail, road, air, shipping)
  • Sewerage
  • Drinking water
  • Land drainage
  • Nature conservation
  • Built infrastructure
  • Electricity networks
  • GLAM (galleries, libraries, archives, museums)
  • Farming (agriculture, forestry, aquaculture, horticulture
  • etc
Infant mortality rates will be the KPI

Pipi makes these systems self-assembling, self-managing, resilient and adaptive. I had to solve hundreds of very big, hard, complex problems in parallel to make this work. Some of them were abandoned research by others, who couldn't make them work, so I solved them. The answers were there, hidden in plain sight. Invisible due to a lack of imagination or courage.

Easy for me, because I can do it visually in my mind, run simulations of thousands of components while sleeping, including the testing, then wake up and just build; it always works 100% (been doing it for decades). That's why no one else has cracked this problem. I can remember everything I have designed this way since age 4 in great detail. Curiosity-driven learning turns everything I read that's interesting into a moving 4d model in my mind; there are tens of thousands of these shimmering mental models, and they self-assemble when I shut my eyes to solve a problem. Each model grows in detail and size as I learn more. I can fly through the models, exploring and touching them. Really cool.

Ahaa moments most days

Some days, I wake up, and the insights and designs pour out of my head like a firehose, and I can barely keep up even after outputting 20+ drawings on A4 paper in a day. Now, there are many thousands of colour-coded drawings in ring binders.

I turn the growing backlog of these designs into data models, code, and documentation with references by giving simple, direct instructions to Google Search AI Mode (Gemini), which teaches me new skills and gives me a response to edit, test, correct, and use. I'm going 100x faster, like a bat out of hell, the equivalent of a crack team of pre-AI developers. 😎 And I'm getting a lot faster.

I rely 100% on intuition when surfing a sea of 4D visual mental models. I really don't understand how the rest of you can only think in words, because I can't.

I have been very lucky

My grandmother Bessie showered me with attention and love, gave me endless things to pull apart to see how they worked, and took me to meet very clever people in a small-minded backwater town.

Family holidays in wild New Zealand, next to rivers, beaches, forests and mountains, which ignited a lifelong obsession with the patterns of nature, the why.

My best friend right through school; he was the brightest kid in NZ.

My high school science teacher, Alan Morgan, let me play in the chemistry lab, doing experiments after school unsupervised for several years, and taught me the scientific method on my very last day at school, the most important thing I learned in 12 wasted years.

The wise old tradesmen, who took a skinny kid from sweeping the floor to being able to make anything, by learning on the job, trying hard, and having my butt kicked.

Nelson Mandela taught me to have the courage of my convictions and never give up.

The sculptor Neil Dawson and the set designer Tony Geddes taught me how to work authentically.

My blind friend Grant, who cut down bushes with a chainsaw and made and gave away $60 M, teaching me quiet courage and human decency.

The magnificent 50,000 working people of South Christchurch, who trusted me to lead a volunteer residents army doing recovery for 3 years, after the Christchurch Earthquake, teaching me humility and valuable leadership skills gained by trial and error in the moment.

My beloved Tracy, the bravest woman I have ever met, the only paraplegic to do the Coast-to-Coast Iron Man, who married a lost autistic male and made me a much better man. Her unwavering devotion, encouragement and loyalty made all this possible.

They all shaped me; I can't thank them enough. May their memories be a blessing.

The future is open, at the edge of chaos

Update 28/07/2026

Most of the equipment has arrived, and the small data centre setup is coming together. More deliveries later this week. It's already running a lot better and is much more productive.

Update 30/07/2026

My creative problem-solving and build process is evolving, and the loop is getting faster. Works every time.

  • Understanding the problem starts with reading or talking with someone, watching a YouTube talk, or listening to the radio.
  • Do lots of research, mostly reading books (days to months).
  • Drink plunger coffee.
  • Print off a paper(s) or article(s) and file it in an A4 3-hole ring-binder.
  • Drink Chamomile tea.
  • Daydream (solutions come within hours or months later).
  • Drink plunger coffee.
  • Draw the solution as many colour-coded A4 architecture drawing(s).
  • Drink more coffee (Cappuccino).
  • File the drawing(s) with the printed paper.
  • Use the drawings to prompt Google Search AI Mode (free) with detailed instructions on what to build.
  • Output teaches me, describes data model, code, documentation.
  • Print off and file with the rest.
  • Walk a dog. Plant a tree. Watch the sun rise.
  • Read the printed AI output, colour-code, add doodles, correct, test, edit names, build, use in Pipi, while listening to music. (I don't copy-paste. I manually type to copy, because it helps me learn and understand.)
  • Throw away all the paper except for the original research, which is moved from DevOps to the research library.
  • Pipi then generates self-documentation, including mermaid drawings.
  • Repeat.

Each loop cycle takes weeks to years. There are hundreds running in parallel at any one time. It's a pull system. I work on Pipi when something needs to be solved, and look up my library of solutions. Totally intuitive, just like an artist, not an engineer, and always fun like a kid playing.

These changes are made possible because of the detailed work done over the last few months removing barriers, including modifications to Pipi, reorganising space, equipment, and routines.

I think I have now solved all major problems to get Pipi 9 Core (Loki) running 24x7x52. Anything else that pops up can be solved along the way as part of maintenance.

Now to execute very fast.

Resources

References

  • Reference

Repository

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

Last Updated

30/07/2026

No posts for a wee while

By: Mike Peters
On a Sandy Beach: 15/05/2026

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

I was on a no-coding holiday for the last few weeks to clear my mind, and it has been great. I am back on the job today.

Suspended

Until the closed-source Pipi Core is back up and running 100% on autopilot, 10x faster, the following are suspended.

  • New posts "On a Sandy Beach
  • All newsletters, including the weekly Friday Report and the monthly Ajabbi Research Newsletter.
  • The fortnightly online Open R&D meeting.

Rapid refocus

  • A new developer area with five coding screens, designed to be more productive for hypervisual learners.
  • A better library has been set up for my A4 drawings in ring binders, the many reference books I use, and more bookshelves are on the way.
  • The server rack has been moved to a better location.
  • The light levels have been adjusted.
  • A big office tidy is almost done. An office-work-only desk has yet to be set up with a cat bed included.
  • A separate area with no screens for the happy cat, coffee, music, reading and drawing.

Less is more

Minimise screen time to be more productive at work. The new setup is also much less tiring.

Get the job done

The good thing is that, with a holiday and lots of drawing, I now have mental clarity about what needs fixing and how to fix it. Mainly, quite delicate changes here and there, organised into a list of steps. Now, I need to concentrate on one thing only: go as fast as possible, without meetings, post-deadlines, phone calls, or other distractions.

How

1. Use an AI workforce

Be the architect, and AI fills in the dots to make it happen.

Use Google Search AI mode (Gemini) to generate 99% of the code in one-page chunks (including references) to copy and paste, then manually change the variable names and SQL. Careful, test everything, resulting in 100x faster progress. Know how everything works and rapidly raise personal skill level.

2. Then build a cathedral

Make a wooden scale model of a cathedral for the builders. Google Search AI mode (Gemini) makes each brick, and Pipi Core assembles the bricks into floors, arches, walls, and vaults...

Speed is king

With the 100x coding productivity gains from Google Search AI mode (Gemini), plus the 10x10x10x speedup of Pipi Core currently underway over the next few months, what previously took a year will be done in hours and better.

Phase transitions

Once these initial migration issues from laptop to server are resolved, further transitions can be anticipated as the number of engines rapidly increases beyond 20. Increasing the number of engines slowly changes the whole system's behaviour from deterministic to probabilistic and adaptive.

Here is a partial list of transitions expected as the number of engines increases from 0 to 200. The actual numbers are a bit of a guess.

  • 20 engines enable Pipi 9 Core in a simple, deterministic structure.
  • 40 engines enable a workspace with a UI for administering Pipi Core.
  • 60 engines enable self-generation of user documentation.
  • 80 engines enable REPL and IAC (infrastructure-as-code).
  • 100 engines enable Workspaces for different user accounts.
  • Different Pipi 9 editions are made with the same engines, which recombine differently in response to the external environment.
  • And so on until...
  • 200 engines self-organise into a multi-layered complex fluid structure with probabilistic behaviour and emergent properties, as engines also act as agents.
  • 200+ engines enable Pipi 10 to interact with externally cloud-hosted LLMs, combining the very different strengths of both.
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Official Trailer for The Dyslexic Advantage Movie

Mike's Notes

I'm very lucky, and so are these people. Grammarly is just a tool. Work from what you are good at.

Mind Strengths Assessment

I did the assessment. This is my score.

You can also do a free assessment.

Resources

References

  • The Dyslexic Advantage: Unlocking the Hidden Potential of the Dyslexic Brain, by Brock and Fernette Eide. Penguin 2012.

Repository

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  • Home > Handbook > 

Last Updated

16/04/2026

Official Trailer for The Dyslexic Advantage Movie

By: Brock and Fernette Eide
Dyslexia Advantage: xx/10/2026

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Neuroscience has a species problem

Mike's Notes

A great example of how science could advance.

Resources

References

  • Reference

Repository

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Last Updated

04/03/2026

Neuroscience has a species problem

By: Nanthia Suthana
The Transmitter: 16/02/2026

Nanthia Suthana is professor of neurosurgery, biomedical engineering and neurobiology at Duke University. Her lab studies the neural mechanisms of human memory, emotion and spatial navigation using intracranial recordings, neuromodulation and wearable technologies during real-world behavior. Her work bridges basic neuroscience and clinical translation, with the goal of developing novel treatments for neurological and psychiatric disorders. Suthana earned her B.S. and Ph.D. at the University of California, Los Angeles. She has led interdisciplinary research programs integrating neuroscience, engineering and clinical practice, with an emphasis on studying brain function in naturalistic settings..

If our field is serious about building general principles of brain function, cross-species dialogue must become a core organizing principle rather than an afterthought.

Neuroscience has never been richer in data. Laboratories now generate detailed recordings of neural activity, behavior and physiology across species at scales unimaginable a decade ago. In rodents, researchers can monitor thousands of neurons simultaneously across distributed circuits during behavior. In humans, they can record from deep brain structures during ambulatory, real-world behavior, integrated with wearable sensors and linked to clinical symptoms and subjective experience. The field has access to neural signals spanning orders of magnitude in space, time and biological complexity.

Yet despite this abundance, neuroscience remains deeply organized along species lines. Animal and human researchers often operate within separate conceptual frameworks, attend different conferences and develop theories that rarely confront data across species. This separation is no longer a minor inconvenience but a growing liability. The problem is not simply that cross-species translation is difficult; it is that the field has largely accepted this difficulty rather than treating it as a central scientific challenge. Neuroscience has also struggled to confront the fact that different species often tell different stories.

As a result, neuroscience’s primary limitation today is not a lack of data or tools, but persistent fragmentation across model systems, recording modalities and analytic traditions. Findings are typically interpreted within species- and technique-specific frameworks, with little pressure to explain when, how or why neural principles should generalize across organisms. Researchers acknowledge differences but rarely use them to constrain or revise theory.

If neuroscience is serious about building general principles of brain function, cross-species dialogue must become a core organizing principle rather than an afterthought. Differences between species should be treated as informative constraints that refine theory, not as inconsistencies to be explained away. Overcoming this divide won’t be trivial, but there are ways we can start now to begin to change our culture. 

A major source of the field’s fragmentation lies in how it treats different neural signals. Researchers focused on single-unit activity often prioritize spikes as the fundamental currency of computation, treating population-level signals, such as local field potentials, as secondary or ambiguous. Others emphasize population dynamics and view single-neuron activity as overly local or insufficiently informative for translational applications. Similar divisions exist across recording and manipulation modalities, from electrophysiology and calcium imaging to hemodynamic and electrical stimulation-based approaches. Though these distinctions reflect real technical constraints, they have hardened into conceptual boundaries that shape which questions are asked and which forms of evidence are considered explanatory.

These boundaries persist across species, even as many of the technological constraints that once justified them have faded. As a researcher studying the human brain using both single-unit and local field potential recordings, I am acutely aware that these signals offer distinct and complementary views of neural activity, each with its own strengths and limitations. In humans, it’s now possible to directly record brain activity during behaviors such as walking and natural navigation, enabling experiments similar to those in animals. Single-unit sampling in humans is sparse, however, so field potentials are often the primary signal available for linking neural activity to ethologically relevant behavior. 

”Differences between species should be treated as informative constraints that refine theory, not as inconsistencies to be explained away.

High-density single-unit recordings in animal models are therefore essential for understanding how population-level signals relate to single-neuron activity. Yet even when spikes and field potentials are recorded simultaneously in animal studies, researchers often prioritize single-unit analyses, reflecting long-standing theoretical preferences. These preferences limit opportunities to connect neural activity across scales and species. Rather than optimizing theories around a single signal or model organism, the field would benefit from frameworks designed to link signals across scales, using the strengths of each system to offset the limitations of others.

Theta oscillations, a brain rhythm typically defined as 4 to 8 hertz, provide a clear example of how this fragmentation plays out in practice. The details of theta matter less here than what its cross-species differences reveal about how the field handles disagreement. In rodents, hippocampal theta activity during locomotion appears to be largely continuous, a regularity that has shaped decades of influential models of navigation, memory encoding and temporal organization. In humans, however, hippocampal theta activity occurs in brief, intermittent bouts, often linked to specific behavioral or cognitive events rather than ongoing movement. These findings have been replicated across laboratories and tasks and are supported by converging evidence from bats and nonhuman primates. 

When these findings emerged, they were initially met with skepticism. Rather than asking what the differences might imply for theory, the dominant response was to question whether the signals were truly comparable. As evidence accumulated over time, skepticism softened. But theories that attempt to meaningfully integrate the two types of theta are still largely lacking. 

Nearly a decade later, rodent-derived models continue to assume sustained oscillatory structure, although bat, nonhuman primate and human findings are treated as species-specific implementation details rather than as constraints on general principles. For the most part, scientists have not tried to uncover why different species recruit theta in distinct ways, what computational roles these patterns serve, or whether continuous and intermittent theta reflect complementary solutions to shared navigational and memory demands, or distinct modes of environmental sampling, such as whisking, echolocation or eye movements. 

This pattern illustrates a broader issue in neuroscience. With enough evidence, researchers tend to accept cross-species differences, but they rarely use these differences to refine or revise theory. Instead of asking why hippocampal theta is continuous in rodents but burst-like in nonhuman primates and humans, or what computational advantages these different regimes might confer, the field has largely compartmentalized the findings, enabling parallel literatures to proceed with little pressure to reconcile them.

Yet these differences are precisely where theoretical progress should occur. Intermittent hippocampal theta suggests a fundamentally different mode of coordinating neural activity, one in which rhythmic structure is recruited transiently to gate information, mark boundaries between events, or coordinate distributed circuits at specific moments rather than continuously. Ignoring these implications does not preserve existing theories; it limits their scope and explanatory power. 

Cultural asymmetries within the field reinforce this divide, a pattern I observe as a researcher who studies the human brain. When human data align with animal model data, they are welcomed as validation. When they do not, they face higher evidentiary thresholds and greater skepticism. This skepticism is often justified by appeals to sample size, even though nonhuman primate studies, long viewed as theoretically foundational, have historically relied on similarly small cohorts. Such asymmetries insulate animal-derived theories from challenge and weaken the role of human research as a source of theoretical insight rather than mere applied confirmation.

For much of my career, I have watched this divide only perpetuate and deepen. I have attended conferences where animal research overwhelmingly shaped the agenda and human work was treated as secondary. At human-focused meetings, the reverse was true, with few researchers whose primary work involved non-primate species having influence over the event. These experiences shape not only which conversations happen but which questions young scientists learn to ask. The result has been the emergence of parallel scientific cultures that rarely engage deeply with each other.

Overcoming this divide, and developing theories that incorporate contrasting data, will require shifts in how scientists are trained, how conferences are structured and how cross-species work is valued within academic culture. It will also require theoretical frameworks and models that are explicitly tested and revised across species rather than optimized within a single model system. Finally, funding, review and publication practices must reward work that treats cross-species differences as opportunities for insight rather than liabilities to be minimized.

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Everything, everywhere, all at once: Inside the chaos of Alzheimer’s disease

Mike's Notes

This article provides a clear explanation of the brain with Alzheimer's disease. It's also a great example of a complex system.

Resources

References

  • Reference

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Last Updated

08/07/2025

Everything, everywhere, all at once: Inside the chaos of Alzheimer’s disease

By: Michael Yassa
The Transmitter: 16/06/2025

Michael A. Yassa is professor of neurobiology and behavior and James L. McGaugh Endowed Chair at the University of California, Irvine. His lab has been developing theoretical frameworks and noninvasive brain-imaging tools for understanding memory mechanisms in the human brain and applying this knowledge to human neurological and neuropsychiatric disease.

To truly understand Alzheimer’s disease, we may need to take a systems approach, in which inflammation, vascular injury, impaired glucose metabolism and other factors interact in complex ways.

For nearly three decades, Alzheimer’s disease has been framed as a story about amyloid: A toxic protein builds up, forms plaques, kills neurons and slowly robs people of their memories and identity. The simplicity of this “amyloid cascade hypothesis” gave us targets, tools and a sense of purpose. It felt like a clean story. Almost too clean.

We spent decades chasing it, developing dozens of animal models and pouring billions into anti-amyloid therapies, most of which failed. The few that made it to market offer only modest benefits, often with serious side effects. Whenever I think about this, I can’t help but picture Will Ferrell’s Buddy the Elf, in the movie “Elf,” confronting the mall Santa: “You sit on a throne of lies.” Not because anyone meant to mislead people (though maybe some did). But because we wanted so badly for the story to be true.

So what happened? This should have worked … right?

I would argue it was never going to work because we have been thinking about Alzheimer’s the wrong way. For decades, we have treated it as a single disease with a single straight line from amyloid to dementia. But what if that’s not how it works? What if Alzheimer’s only looks like one disease because we keep trying to force it into a single narrative? If that’s the case, then the search for a single cause—and a single cure—was always destined to fail.

What if Alzheimer’s only looks like one disease because we keep trying to force it into a single narrative? If that’s the case, then the search for a single cause—and a single cure—was always destined to fail.

Real progress, I believe, requires two major shifts in how we think. First, we have to let go of our obsession with amyloid. Now don’t get me wrong. There’s no question amyloid plays a role. It was the first thing Alois Alzheimer saw under the microscope in 1906. And there’s decent evidence that misfolded amyloid spells trouble for the brain. But betting the house on clearing amyloid has been a costly mistake. In fact, we have long known that one-third of people with amyloid pathology do not show any cognitive symptoms, a disconnect that should have forced a rethink years ago.

To the field’s credit, a shift is underway. We’re now exploring other mechanisms—tau, inflammation, metabolic dysfunction, vascular damage, neuronal hyperexcitability and more. But too often, these alternatives are still treated as side plots in an amyloid-centered story. They get less funding, less attention and fewer drug development efforts. That needs to change. These mechanisms may be far more central to the disease than we once thought. And they may drive it differently in different people.

This brings us to the second shift: We need to stop thinking in straight lines. The brain isn’t exactly a flowchart. It’s a dynamical system—a tangled web of feedback loops, compensations and nonlinear interactions. In such systems, small disruptions can ripple outward in unexpected ways. When one part starts to fail, another compensates. Over time, those compensations can become part of the pathology. In some people with Alzheimer’s disease, amyloid might be the trigger. In others, it might be inflammation, vascular injury, impaired glucose metabolism or runaway neural activity. These factors don’t act in isolation—they interact in complex ways, creating a web of multicausal loops. They are less like a chain of dominoes and more like a knot of tangled threads pulling on one another.

In systems terms, it’s not a cascade. It’s a state space. To understand this space, it’s useful to imagine a map in which every possible state of the brain is a point. In this space, healthy brains tend to move within a basin of attraction, a functional stable state. In Alzheimer’s, the brain may be pushed by interacting pathologies into a different region of state space, a pathological attractor—stable but dysfunctional.

There’s growing experimental support for this view. Functional imaging, for example, has shown that people with Alzheimer’s spend more time in sparsely connected, low-flexibility brain states, and MEG recordings reveal changes in the temporal complexity of network dynamics. Recent work in my lab identified a dominant state, characterized by co-activity of nodes in the limbic network, that is linked to worse cognition and Alzheimer’s pathology. The idea is that once the brain tips into the dysfunctional state, it can get stuck there, even if you remove the original trigger.

The idea is that once the brain tips into the dysfunctional state, it can get stuck there, even if you remove the original trigger.

Researchers have already identified a number of “systems-level” factors that can disrupt network stability and contribute to Alzheimer’s disease, including vascular compromise, in which small vessel disease disrupts blood flow and triggers downstream effects; metabolic dysfunction, such as insulin resistance or glucose hypometabolism; runaway inflammation, such as overactive microglia or cytokine chaos; and overactivity, driven by an imbalance in neuronal excitation or inhibition. These factors may represent different systems-level routes to the same clinical outcome. Each person’s condition likely involves a different mix or “weighting” of underlying mechanisms. For someone with a history of diabetes, metabolic dysfunction might be the dominant factor. For someone with high blood pressure, the vascular component could play a bigger role. Ultimately, pinpointing this weighting—the primary mechanism driving the system’s dysfunction, or the mechanistic phenotype—could help match people with the most appropriate treatment.

This framing also changes how we think about treatment. In a system governed by feedback loops and nonlinear dynamics, removing a single trigger may not be sufficient to get the system “unstuck.” That may explain why anti-amyloid drugs haven’t made a major clinical impact: By the time symptoms show up, the system has already reorganized itself. Instead, we may need interventions that restore network stability—rebalancing excitation and inhibition, reducing inflammation or improving metabolic resilience. Noninvasive brain stimulation is one such approach, potentially nudging the system toward a more functional dynamic without needing to target a molecular mechanism. The goal isn’t to fix a part. It’s to shift the conditions that shape how the whole system behaves.

So where is amyloid in all this? Well, amyloid is always present, because our diagnostic criteria make it so. Think of it like background noise—it’s there, but it may not be what’s pushing the system off-key. Unlike the factors described above, amyloid doesn’t consistently drive network-level disruption. It reflects cellular dysfunction, such as misprocessing of amyloid precursor protein or altered lipid metabolism, but the downstream systems-level effects aren’t nearly as consistent or potent as those seen with, say, inflammation or synapse loss. This doesn’t mean addressing amyloid buildup or clearance has no clinical value. It just means it’s likely a small piece in a much larger puzzle. Focusing on it is like trying to fix a whole cacophonous orchestra by tuning but one violin.

Of course, there’s no perfect framework yet. To build it, we’ll need better tools. That includes better ways to capture brain dynamics in vivo, not just static pathology. We also need animal models that go beyond single-gene variants—instead, we need models that combine multiple hits, such as inflammation plus hyperexcitability. And we need ways to track these factors in humans, using multimodal imaging, physiological sensors and inflammatory biomarkers.

Getting there will take work. Paradigm shifts happen slowly, painfully, often after the old model has failed enough times to lose its grip. That’s where we are now. The dominant model isn’t working anymore. What comes next isn’t fully formed—but it’s coming into view. Mechanistic phenotyping and dynamical systems thinking may offer a path forward. It won’t be neat or linear. But it may finally meet the disease on its own terms.

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Ingredients for brilliance

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Last Updated

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 LumiΓ¨re 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 cafΓ© – 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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