Is Particle Physics Dead, Dying, or Just Hard?

Mike's Notes

This Quanta Magazine article about the current state of Particle Physics got me thinking.

  • Particle physicists are very good at statistical physics and maths
  • They have to be very bright and well-trained
  • They like hard problems to solve
  • There are unemployed particle physicists
  • Ajabbi Research will need people in the future. Hmmm

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20/03/2026

Is Particle Physics Dead, Dying, or Just Hard?

By: Natalie Wolchover
Quanta Magazine: 26/01/2026

Natalie Wolchover is a columnist for Quanta Magazine, where she covered the physical sciences for more than a decade. Her writing has been featured in The Best American Science and Nature Writing, The Best American Magazine Writing, and The Best Writing on Mathematics, and has won several awards, including the 2022 Pulitzer Prize for Explanatory Reporting, the 2016 Evert Clark/Seth Payne Award, and the American Institute of Physics’ 2017 Science Communication Award. She was lead editor for the National Magazine Award–winning special issue, "The Unraveling of Space-Time." Her first book, The Question to Which the Universe Is the Answer, is scheduled for publication in 2027.

Columnist Natalie Wolchover checks in with particle physicists more than a decade after the field entered a profound crisis.

In July 2012, physicists at the Large Hadron Collider (LHC) in Europe triumphantly announced the discovery of the Higgs boson, the long-sought linchpin of the subatomic world. Interacting with Higgs bosons imbues other elementary particles with mass, making them slow down enough to assemble into atoms, which then clump together to make everything else.

A couple of months later, I took a job as the first staff reporter at the nascent science magazine that would become Quanta. Turns out I was starting on the physics beat just as the drama was picking up.

The drama wasn’t about the Higgs particle; by the time it materialized at the LHC there was already little doubt about its existence. The Higgs was the last piece of the Standard Model of particle physics, the 1970s-era set of equations governing the 25 known elementary particles and their interactions.

More striking was what did not emerge from the data.

Physicists had spent billions of euros building the 27-kilometer supercollider not only to confirm the Standard Model but also to supersede it by uncovering components of a more complete theory of nature. The Standard Model doesn’t include particles that could comprise dark matter, for instance. It doesn’t explain why matter dominates over antimatter in the universe, or why the Big Bang happened in the first place. Then there’s the inexplicably enormous disparity between the Higgs boson’s mass (which sets the physical scale of atoms) and the far higher mass-energy scale associated with quantum gravity, known as the Planck scale. The chasm between physical scales — atoms are vastly larger than the Planck scale — seems unstable and unnatural. In 1981, the great theorist Edward Witten thought of a solution(opens a new tab) for this “hierarchy problem”: Balance would be restored by the existence of additional elementary particles only slightly heavier than the Higgs boson. The LHC’s collisions should have been energetic enough to conjure them.

But when protons raced both ways around the tunnel and crashed head-on, spraying debris into surrounding detectors, only the 25 particles of the Standard Model were observed. Nothing else showed up.

In philosophy, “qualia” refers to the subjective qualities of our experience: what it’s like for Alice to see blue or for Bob to feel delighted. Qualia are “the ways things seem to us,” as the late philosopher Daniel Dennett put it. In these essays, our columnists follow their curiosity, and explore important but not necessarily answerable scientific questions.

The absence of any “new physics” — particles or forces beyond the known ones — fomented a crisis. “Of course, it is disappointing,” the particle physicist Mikhail Shifman told me that fall of 2012. “We’re not gods. We’re not prophets. In the absence of some guidance from experimental data, how do you guess something about nature?”

Once the standard reasoning about the hierarchy problem had been shown to be wrong, there was no telling where new physics might be found. It could easily lie beyond the reach of experiments. The particle physicist Adam Falkowski predicted to me at the time that, without a way to search for heavier particles, the field would undergo a slow decay: “The number of jobs in particle physics will steadily decrease, and particle physicists will die out naturally.”

The crisis and its fallout made for years of interesting reporting, but sure enough, the frequency of news stories related to particle physics diminished. I fell out of touch with sources. More than 13 years on, in this first column for Qualia, a new series of essays in Quanta Magazine, I’m taking stock. Is particle physics dying, as Falkowski predicted? Can new physics still be found? What’s the future for particle physicists? Will artificial intelligence help? How much hope is left in the search for answers to the many remaining mysteries of the universe?

Some particle physicists act as if there’s no crisis at all. The LHC is still running and will for at least another decade, and its operators are finding new sources of enthusiasm.

In the last couple of years, data handling at the collider has improved with the use of AI. Pattern recognizers can sort through the outgoing debris of proton collisions and classify collision events more accurately than human-made algorithms can. This helps the physicists to more accurately measure the “scattering amplitude,” essentially the probability that different particle interactions will occur. For instance, AI systems can determine more precisely how many top quarks arise in the aftermath of collisions versus the number of bottom quarks. Any statistical deviations from the predictions of the Standard Model could signify the involvement of unknown elementary particles.


A proton-proton collision documented by the Compact Muon Solenoid at CERN in 2012 shows evidence of the decay of the Higgs boson.

CMS Collaboration; Mc Cauley, Thomas

Novel particles as hefty as Higgs bosons would not be so subtle; they would have shown up already as pronounced bumps on data plots. But as Matt Strassler, a particle physicist affiliated with Harvard University, explained to me, the traces of lighter novel particles could still lie in so-called hidden valleys in the data. “There’s a huge amount of unexplored territory there,” he said. There might exist, for instance, an unstable type of dark matter particle that leaves its mark by occasionally arising and immediately decaying into an excessive number of muon-antimuon pairs. Detecting such an excess would point indirectly to the unstable particle’s existence. “For people who thought all the new physics is at high energies — they’re very disappointed right now,” Strassler said. “I don’t share that view. There are many opportunities for nature to provide clues at low energies.”

So far, though, no such indirect evidence of new physics has been detected. The more accurate the statistics have become at the LHC, the better they match the Standard Model. Michelangelo Mangano, a particle physicist at CERN, the laboratory that houses the LHC, said the collider today is like a tool for exploring the Standard Model’s predictions, and he considers this exploration worthwhile because not all consequences of the equations are easy to calculate. The search for new physics beyond the Standard Model is ongoing, Mangano said, but “the fact that it’s not giving positive results does not mean we are stuck, dead, or wasting our time.”

These questions are so fundamental that of course it’s worth nailing down every amplitude and checking every hidden valley, since we have the tool for the job. But for hunters of new physics, does the game end there?

The community wants to go bigger. CERN physicists want to build a Future Circular Collider, tripling the circumference of the LHC with a 91-kilometer tunnel beneath the Franco-Swiss border, to both probe higher energies and look for subtler signals. This FCC would initially collide electrons, which, unlike protons, are themselves elementary particles, with no substructure. Their clean collisions would allow more precise measurements of scattering amplitudes, making the FCC ultrasensitive to indirect signs of new physics. By the end of the century, the mega-collider would be upgraded to collide protons, as the LHC does now. Proton collisions are messier, but at the FCC they would achieve unprecedented energies — about seven times higher than the LHC can currently muster — so they have a chance, however slim, of revealing heavy particles beyond the LHC’s reach. (In theory, particle masses could range up to a million billion times greater than what the LHC energy scale can produce directly, so there’s no reason to expect them around the next bend.)

We’re not gods. We’re not prophets. In the absence of some guidance from experimental data, how do you guess something about nature? - Mikhail Shifman

As of now, the FCC’s fate is unknown; formal approval and funding commitments by member countries won’t come before 2028.

Meanwhile, U.S. particle physicists are aiming to complement the European strategy by constructing a brand-new type of machine: a muon collider. Muons are elementary like electrons, but they’re 200 times heavier, so their collisions would be both clean and energetic (albeit not reaching the collision energies of the LHC). Both the selling point and the challenge of this newfangled type of machine is that it will require major technical innovations (with all the spin-off potential that can bring), because muons are highly unstable. They must be accelerated and collided mere microseconds after they’re created.

Demonstrating the technology and then constructing the collider would take roughly 30 years, and that’s with federal funding. “We have to figure out how to do it in between 10 and 20 billion [dollars],” said Maria Spiropulu, a physics professor at the California Institute of Technology and co-chair of the committee behind a national report endorsing a muon collider program(opens a new tab) that came out in June 2025. Over the coming years, the Department of Energy will weigh whether to fund the proposal rather than competing science projects. What hurts its case is the lack of a “discovery guarantee,” which the LHC had with the Higgs boson.


Scientists and technicians inspected and upgraded systems at the Large Hadron Collider during the Long Shutdown 2, which began in 2018.

Maximilien Brice/CERN

Then again, as the mathematical physicist Peter Woit mused on his blog(opens a new tab), “Perhaps in our new world order where everything is controlled by trillionaire tech bros, the financing won’t be a problem.”

Deliberations about a Chinese supercollider have come to naught, I’m told. Instead, China has decided to pursue a “super-tau-charm facility”: a lower-energy particle scattering experiment that would cost mere hundreds of millions of dollars instead of tens of billions. The facility will produce a lot of tau particles and charm quarks, partly to study whether taus ever shape-shift into muons or electrons. This kind of switching isn’t predicted by the Standard Model, but it does happen in some theoretical extensions of it.

Okay, we might as well check. We’re desperate for new physics, and the price is good. But by definition it’s very difficult to know which shots in the dark are worth taking.

Adam Falkowski, who sounded the death knell for particle physics back in 2012, used to be known for the sharp commentary he supplied on his blog Résonaances(opens a new tab). But the Paris-based particle physicist hasn’t posted anything since 2022. He said that’s partly because he’s been tied up with fatherhood and partly because there hasn’t been much to say.

When we caught up on a video call, Falkowski told me, “I am very skeptical about future colliders. For me it’s very difficult to get excited about it.” He sees momentum behind CERN’s FCC campaign, but personally he worries about the huge costs and timescales, and the fact that “there are absolutely no hints that something is there within the reach of the next collider.”

For his part, Falkowski has turned to the theoretical study of scattering amplitudes, a growing research area focused on the geometric patterns underlying particle interaction statistics, patterns that could point toward a truer perspective on the quantum world. The field seeks to reformulate the equations of particle physics in a different mathematical language in hopes that this language might extend to quantum gravity. “There is a very vibrant program in trying to understand the structure of the physical theories,” Falkowski said. “The hope is that with the help of machine learning, that there can be very fast progress in the coming years. I think that’s where the best things have happened.”

But amplitudeology, as this field is known, is abstract — it’s no atom-smashing experiment. Falkowski said he does think experimental particle physics is dying. He has watched talented postdocs switch to other research areas or take data science jobs. “I’m not sure they are getting the best of the best as they used to,” he said, “because the prospects of returns are so distant. If you want to change the world now, you will do AI; you will do something different from particle physics.”


The ALICE (A Large Ion Collider Experiment) detector at the Large Hadron Collider was designed to study quark-gluon plasma.

CERN, Julien Marius Ordan/Science Source

This brain drain appears to be real. I spoke to Jared Kaplan, co-founder of Anthropic, the company behind the chatbot Claude. He was a physicist the last time we spoke. As a grad student at Harvard in the 2000s, he worked with the renowned theorist Nima Arkani-Hamed to open up the new directions in amplitude research that are being actively pursued today. But Kaplan left the field in 2019. “I started working on AI because it seemed plausible to me that … AI was going to make progress faster than almost any field in science historically,” he said. AI would be “the most important thing to happen while we’re alive, maybe one of the most important things to happen in the history of science. And so it seemed obvious that I should work on it.”

As for the future of particle physics, AI makes worrying about it now rather pointless, in Kaplan’s view. “I think that it’s kind of irrelevant what we plan on a 10-year timescale, because if we’re building a collider in 10 years, AI will be building the collider; humans won’t be building it. I would give like a 50% chance that in two or three years, theoretical physicists will mostly be replaced with AI. Brilliant people like Nima Arkani-Hamed or Ed Witten, AI will be generating papers that are as good as their papers pretty autonomously. … So planning beyond this couple-year timescale isn’t really something I think about very much.”

Cari Cesarotti, a postdoctoral fellow in the theory group at CERN, is skeptical about that future. She notices chatbots’ mistakes, and how they’ve become too much of a crutch for physics students. “AI is making people worse at physics,” she said. “What we need is humans to read textbooks and sit down and think of new solutions to the hierarchy problem.”

Cesarotti was a high school junior when the Higgs boson was discovered. She grew up near Fermilab, the U.S. national lab in Illinois that houses the Tevatron, which was the world’s highest-energy particle collider before the LHC. (The top quark was discovered there in 1995.) This proximity taught her that a particle physicist was a thing you could be. Later, it turned out to be her thing. “What are the fundamental building blocks of the universe — those were the questions that I was most interested in knowing the answer to,” she told me. “But what people said was, ‘Particle physics is dead. Don’t do this.’”

It may have been a fair warning; Cesarotti has yet to land a permanent job as a rising particle physicist. The subfield has continued to shrink, she and others said, as faculty hiring committees and grad students go in other directions. “Definitely all this rhetoric that there was nothing to be found and you should give up on it — people listened,” she said. “And of course that means there are fewer people. It becomes a self-fulfilling prophecy. If you’re pushing all these talented people out of trying to solve these problems into a field that it’s easier to make an impact on, then you’re setting yourself up for failure.”

Cesarotti echoed a sentiment I’d heard from others, which sounds correct to me as well: “Particle physics isn’t dead; it’s just hard.” It’s hard to know what to think about or look for. But the most devoted particle physicists are thinking and looking all the same.

“It was easy for 125 years,” Strassler said. “One thing led to the next. That lucky century has, for now, at least in the medium term, come to an end. That could change tomorrow, or next century, or who knows.”

A hint of a new lightweight particle could, in theory, show up at the LHC, or in some other experiment. Strassler is particularly excited about the study of radioactive thorium-229 decay, which could reveal variations in the fundamental constants. I’m slightly partial to experiments looking for “axions,” dark matter candidates that are so lightweight that they can act a little like light itself.

On the theory side, an obvious solution to the hierarchy problem could drop naturally out of the geometry behind scattering amplitudes. Or, if Kaplan is right, AI systems might someday suggest powerful new ideas for how the 25 particles of the Standard Model fit into a more comprehensive pattern — a possibility I didn’t foresee back when the crisis began.

Clearly, further progress toward the truth remains possible in particle physics. But there’s no discovery guarantee. I’ve had more than 13 years to think about it, and it remains a disturbing prospect: All the empirical clues we can glean about nature’s fundamental laws and building blocks might already be in hand. The universe may plan on keeping the rest of its secrets.

These New AI Models Are Trained on Physics, Not Words, and They’re Driving Discovery

Mike's Notes

A fantastic use of AI. My instinct is to incorporate fluid-like systems into a future Pipi. That will require a real data centre.

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19/03/2026

These New AI Models Are Trained on Physics, Not Words, and They’re Driving Discovery

By: Elizabeth Fernandez
Fatiron Institute: 09/12/2025

Elizabeth Fernandez is a science writer specializing in science and society, science and philosophy, astronomy, physics, and geology.

While popular AI models such as ChatGPT are trained on language or photographs, new models created by researchers at the Flatiron Institute and other members of the Polymathic AI collaboration are trained using real scientific datasets. The models are already leveraging the knowledge they learn from one field to address seemingly completely different problems in another.

The Walrus AI model simulates fluid motion.

Walrus/Polymathic AI

While most AI models — including ChatGPT — are trained on text and images, a multidisciplinary team of scientists has something different in mind: AI trained on physics.

Recently, members of the Polymathic AI collaboration presented two new AI models trained using real scientific datasets to tackle problems in astronomy and fluidlike systems.

The models — called Walrus and AION-1 — are unique in that they can apply the knowledge they gain from one class of physical systems to seemingly completely different problems. For instance, Walrus can tackle systems ranging from exploding stars to Wi-Fi signals to the movement of bacteria.

That cross-disciplinary skill set is particularly exciting because it can accelerate scientific discovery and give researchers a leg up when faced with small samples or budgets, says Walrus lead developer Michael McCabe, a research scientist at Polymathic AI.

“Maybe you have new physics in your scenario that your field isn’t used to handling. Maybe you’re using experimental data, and you’re not quite sure what class it fits into. Maybe you’re just not a machine-learning researcher and just can’t burn the time working through all the possible models that might fit your scenario,” McCabe explains. “Our hope is that training on these broader classes makes something that is both easier to use and has a better chance of generalizing for those users, as the ‘new’ physics to them might be something another field has been handling for a while.”

Using cross-disciplinary models can also improve predictions when data is sparse or when studying rare events, says Liam Parker, a Ph.D. student at the University of California, Berkeley, and a lead researcher developing for AION-1.

The Polymathic AI team recently announced Walrus in a preprint on arXiv.org and presented AION-1 on Friday, December 5, at the NeurIPS conference in San Diego.

Walrus and AION-1 are ‘foundational models,’ meaning they’re trained on colossal sets of training data from different research areas or experiments. That’s unlike most AI models in science, which are trained with a particular subfield or problem in mind. Rather than learning the ins and outs of a particular situation or starting from a set of fundamental equations, foundational models instead learn the basis, or foundation, of the physical processes at work. Since these physical processes are universal, the knowledge that the AI learns can be applied to various fields or problems that share the same underlying physical principles. Foundational models have a host of benefits — from speeding up computations to performing well in low-data regimes to finding physics shared across different fields.

AION-1 is a foundational model for astronomy. It is trained on data from astronomical surveys that are already massive in their own right: the Legacy Survey, the Hyper Suprime-Cam (HSC), the Sloan Digital Sky Survey (SDSS), the Dark Energy Spectroscopic Instrument (DESI) and Gaia. All in all, that’s more than 200 million observations of stars, quasars and galaxies totaling around 100 terabytes of data. AION-1 uses images, spectra and a variety of other measurements to learn as much as it can about astronomical objects. Then, when a scientist obtains a low-resolution image of a galaxy, for example, AION-1 can extract more information about it, learned from the physics of millions of other galaxies.

Walrus’ domain is fluids and fluidlike systems. Walrus utilizes the Well — a massive dataset compiled by the Polymathic AI team. The Well’s data encompasses 19 different scenarios and 63 different fields in fluid dynamics. All in all, it contains 15 terabytes of data describing parameters such as density, velocity and pressure in physical systems as wide-ranging as merging neutron stars, acoustic waves and shifting layers in Earth’s atmosphere.

Such foundational models can be powerful. AION-1 and Walrus can utilize physics seen in a different case and apply it to learn about something new. It is similar to our senses. “Multiple senses together — rather than one at a time — gives you a fuller understanding of an experience,” the AION-1 team explained in a blog post about the project. “Over time, your brain learns associations between how things look, taste and smell, so if one sense is unavailable, you can often infer the missing information from the others.”

Then, when a scientist is performing a new experiment or observation, they have a starting point — a map of how physics behaves in other similar situations. “It’s like seeing many, many humans,” says Shirley Ho, Polymathic AI’s principal investigator and an astrophysicist and machine learning expert. Ho is a senior research scientist at the Flatiron Institute and a professor at New York University. When “you meet a new friend, because you’ve met so many people before now, you are able to map in your head … what this human is going to be like compared to all your friends before,” she says.

Foundational models make scientists’ lives easier by streamlining data processing. Scientists will no longer have to create a new framework from scratch for every project or task; instead, they can start with an already trained AI to use as a foundation. “I think our vision for some of this foundation model is that it enables anyone to start from a really powerful embedding of the data that they’re interested in … and still achieve state-of-the-art accuracy without having to build this whole pipeline from scratch,” says AION-1 lead researcher Parker.

Their goal is to make tools that scientists can use in their day-to-day research. “We want to bring all this AI intelligence” to the scientists who need it, Ho says.


Other Highlights From the NeurIPS 2025 Conference

CosmoBench: CosmoBench is a multiview, multiscale, multitask cosmology benchmark for geometric deep learning. Curated from the state-of-the-art cosmological simulations, CosmoBench is the largest benchmark of its kind, with over 34,000 point clouds and 25,000 directed trees. CosmoBench features challenging evaluation tasks from cosmology and diverse baselines, including cosmological methods, simple linear models and graph neural networks. This presentation will show how CosmoBench is pushing the frontiers of cosmology and geometric deep learning.

Lost in Latent Space: Physicists model and predict the behavior of physical systems using their understanding of the laws of physics. However, these calculations require significant computing power. Flatiron Institute scientists and other members of the Polymathic AI collaboration studied whether a less taxing form of computing can still yield accurate results. Known as ‘latent diffusion modeling,’ this computational model utilizes artificial intelligence to generate high-quality images at a lower computational cost while accurately capturing physical behavior.

Neurons as Detectors of Coherent Sets in Sensory Dynamics: Our perception of touch, taste, sight and pain is mediated by neurons that carry signals from peripheral receptors to the brain. This work shows that these neurons can be understood as detecting ‘coherent sets’ within the sensory stream — groups of stimulus trajectories that evolve together over time and therefore share a common past or a common future. By distinguishing these coherent sets, some neurons predominantly encode what has just occurred, while others reliably signal what is likely to happen next. Traditional classifications of sensory neurons can thus be reinterpreted as reflecting a division between past-focused and future-predictive processing. Understanding how the nervous system separates and transforms sensory input in this way may offer new routes for treating mental illness and may also guide the development of biologically inspired artificial intelligence.

Predicting Partially Observable Dynamical Systems: Scientists can predict the motion of a falling object or the evolution of fluids using deterministic models that compute a single future outcome from past observations. But this approach breaks down for physical systems where much of the state is hidden. A prominent example is the sun: We can observe the activity on its surface, but the processes deep inside remain largely invisible. Without access to those internal conditions, there isn’t enough information to forecast a single ‘correct’ future. Researchers at the Flatiron Institute, together with collaborators in the Polymathic AI project, have developed a probabilistic approach that can infer these hidden solar processes. By incorporating information from the distant past into a diffusion-based generative model, their method produces an ensemble of plausible futures, offering a clearer understanding of how past sunspot activity shapes its future evolution.

NVIDIA GTC Keynote 2026

Mike's Notes

I am attending NVIDIA GTC 2026 remotely. It is being held at the San Jose McEnery Convention Centre, in San Jose, California, USA, on March 16–19, 2026.

This is the Keynote by NVIDIA CEO Jensen Huang. The changes in technology were fascinating.

I joined the NVIDIA Developer Program to take a deep dive into algorithmic techniques by learning from some of the best.

Update 25/03/2026

Yesterday, Lex Fridman conducted an in-depth 2.5-hour interview with Jensen Huang, which follows up on his announcements at GTC. Available on YouTube and X. I discovered the interview through The Code newsletter.

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25/03/2026

NVIDIA GTC Keynote 2026

By: Jensen Huang
YouTube: 17/03/2026

Jen-Hsun Huang, commonly anglicized as Jensen Huang, is a Taiwanese and American business executive, electrical engineer, and philanthropist who is the founder, president, and chief executive officer of Nvidia, the world's largest company by market capitalization. - Wikipedia

Watch NVIDIA Founder and CEO Jensen Huang’s GTC keynote as he unveils the latest breakthroughs in AI and accelerated computing. See how agentic AI, AI factories, and physical AI are powering the next generation of intelligent systems.


Jensen Huang: NVIDIA - The $4 Trillion Company & the AI Revolution | Lex Fridman Podcast #494

Reservoir computing bootcamp—From Python/NumPy tutorial for the complete beginners to cutting-edge research topics of reservoir computing

Mike's Notes

Looks very useful, as a way into using Reservoir Computing. The abstract is copied below. Follow the PubMed link to find the full paper.

I have long planned to add Reservoir Computing to Pipi.

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17/03/2026

Reservoir computing bootcamp—From Python/NumPy tutorial for the complete beginners to cutting-edge research topics of reservoir computing

By: Katsuma Inoue, T. Kubota, Quoc Hoan Tran, Nozomi Akashi, Ryo Terajima, Tempei Kabayama, JingChuan Guan, Kohei Nakajima
Chaos: 01/02/2026

.

Abstract

Reservoir computing (RC) is a machine learning framework that uses recurrent neural networks and is characterized by directly capitalizing on intrinsic dynamics instead of adjusting internal parameters. In particular, in the form of physical reservoir computing (PRC), recent studies have advanced by treating various physical systems as reservoirs and applying them to time-series data processing and quantifying information-processing properties. In this way, RC and PRC potentially have interdisciplinary impact, and as more researchers from diverse academic disciplines learn and utilize RC and PRC, there is potential for more creative research to emerge. In this paper, we introduce a Jupyter Notebook-based educational material called RC bootcamp for learning RC, being made publicly available under an open-source license (https://rc-bootcamp.github.io/). The RC bootcamp was originally developed and continuously updated within our research group to efficiently train our collaborators and new students, ultimately enabling them to conduct experiments by themselves. Considering the diverse backgrounds of learners, it starts with the basics of computer science and numerical computation using Python/NumPy, as well as fundamental implementations in RC, such as echo state networks and linear regression. Furthermore, it covers important analytical indicators based on dynamical systems theory, such as Lyapunov exponents, echo state property index, and information-processing capacity, as well as cutting-edge approaches utilizing chaos, including first-order, reduced and controlled error (FORCE) learning and innate training, and attractor design via bifurcation embedding. We expect that the RC bootcamp will become a useful educational material for learning RC and PRC and further invigorate research activities in the RC and PRC fields.

Design for the People: The US Web Design System and the Public Sans Typeface

Mike's Notes

The article reproduced below is from a fascinating website and is about another useful Design System.

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16/03/2026

Design for the People: The US Web Design System and the Public Sans Typeface

By: Jon Keegan
On a Sandy Beach: 02/07/2024

Jon Keegan is an investigative data journalist who covers technology. His work has appeared in The Wall Street Journal, The Markup and MIT Technology Review. Jon’s work has won several journalism awards, including the Loeb Award, the Society of Professional Journalists’ Excellence in Journalism Award and the Society of News Design’s Best of Digital Gold Award.

The United States has an official web design system and a custom typeface that belongs to the people. This thoughtful public design system aims to make government websites not only look good, but to make them accessible and functional for all.

Before the internet, Americans may have interacted with the federal government by stepping into grand buildings adorned with impressive stone columns and gleaming marble floors. Today, the neoclassical architecture of those physical spaces has been replaced by the digital architecture of website design – HTML code, tables, forms, and buttons. 

While people visiting a government website to apply for student loans, research veterans’ benefits, or enroll in Medicare may not notice these digital elements, they play a crucial role. If a website is buggy or doesn’t work on your phone, taxpayers cannot access the services they have paid for. This can feel like walking up to a boarded-up government building with broken windows, creating a negative impression of the government itself.  

 In the US, there are about 26,000 federal websites. Early on, each site had its own designs, fonts, and login systems, creating frustration for the public, and wasting government resources.

 A survey of the many different styles of buttons from government websites as of 2015. Source: 18F / GSA 

The troubled launch Healthcare.gov in 2013 highlighted the need for a better way to build government digital services. In 2014, President Obama created two new teams to help improve government tech.

Within the General Services Administration (GSA), a new team called 18F (named for their Washington, DC office at 1800 F Street) was created to “collaborate with other agencies to fix technical problems, build products, and improve public service through technology.” The team was built to move at the speed of tech start-ups rather than lumbering bureaucratic agencies. 

The U.S. Digital Service (USDS) was tasked “to deliver better government services to the American people through technology and design.” In 2015, the two teams collaborated to build the US Web Design System (USWDS)—a style guide and collection of user interface components and design patterns to ensure a consistent user experience across government websites. “Inconsistency is felt, even if not always precisely articulated in usability research findings,” said Dan Williams, the USWDS program lead, in an email. 

Some of the sample design elements for the USWDS. Source: https://designsystem.digital.gov/

Today, the system defines 47 user interface components such as buttons, alerts, search boxes and forms each with their own design examples, sample code and guidelines such as “Be polite” and “Don’t overdo it.” The USWDS is now in its third iteration, and is used in 160 government websites. “As of September 2023, 94 agencies use USWDS code, and it powers about 1.1 billion pageviews on federal websites,” said Williams.

USWDS design principles include focusing on real users’ needs, earning trust and embracing accessibility. The system requires websites to be optimized for all users, including people with disabilities such as those using screen readers or those with color blindness. Williams said accessibility is important to the team’s efforts, noting that they “prioritize any accessibility-related bug or improvement we find (or is contributed by our community).”






Some federal websites that use the USWDS. Clockwise from top left: Va.gov, Medicaid.gov, Worker.gov, Supremecourt.gov

To ensure clear and consistent typography, the free and open-source typeface Public Sans was created for the US government. “It started as a design experiment,” said Williams, who designed the typeface, which was released in 2019. “We were interested in trying to establish an open source solution space for a typeface, just like we had for the other design elements in the design system,” said Williams. Based on the Libre Franklin typeface, Public Sans is described as “a strong, neutral, principles-driven, open-source typeface for text or display.” 


Both Public Sans and the USWDS embrace transparency and collaboration with government agencies and the public, inviting contributions to their development via the projects’ GitHub pages. 

To ensure that the hard-learned lessons of improving public technology aren’t forgotten, the projects embrace continuous improvement. One of Public Sans’ design principles offers key guidance in this area: “Strive to be better, not necessarily perfect.”

How your brain can be trained like a muscle

Mike's Notes

Some good tips.

  • Stop working when brain fade sets in.

Resources

References

  • Reference

Repository

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

Last Updated

15/03/2026

How your brain can be trained like a muscle

By: Joanna Fong-Isariyawongse
RNZ: 1/02/2026

Joanna Fong-Isariyawongse is associate professor of Neurology, University of Pittsburgh.

When the brain is asked to stretch beyond routine, that slight mental discomfort is often the sign that the brain is being trained, a neurologist says.

If you have ever lifted a weight, you know the routine: challenge the muscle, give it rest, feed it and repeat. Over time, it grows stronger.

Of course, muscles only grow when the challenge increases over time. Continually lifting the same weight the same way stops working.

It might come as a surprise to learn that the brain responds to training in much the same way as our muscles, even though most of us never think about it that way. Clear thinking, focus, creativity and good judgment are built through challenge, when the brain is asked to stretch beyond routine rather than run on autopilot. That slight mental discomfort is often the sign that the brain is actually being trained, a lot like that good workout burn in your muscles.

Tasks that stretch your brain just beyond its comfort zone, such as knitting and crocheting, can improve cognitive abilities over your lifespan. Unsplash

Think about walking the same loop through a local park every day. At first, your senses are alert. You notice the hills, the trees, the changing light. But after a few loops, your brain checks out. You start planning dinner, replaying emails or running through your to-do list. The walk still feels good, but your brain is no longer being challenged.

Routine feels comfortable, but comfort and familiarity alone do not build new brain connections.

As a neurologist who studies brain activity, I use electroencephalograms, or EEGs, to record the brain’s electrical patterns.

Research in humans shows that these rhythms are remarkably dynamic. When someone learns a new skill, EEG rhythms often become more organized and coordinated. This reflects the brain’s attempt to strengthen pathways needed for that skill.

Your brain trains in zones too

For decades, scientists believed that the brain’s ability to grow and reorganize, called neuroplasticity, was largely limited to childhood. Once the brain matured, its wiring was thought to be largely fixed.

But that idea has been overturned. Decades of research show that adult brains can form new connections and reorganize existing networks, under the right conditions, throughout life.

Some of the most influential work in this field comes from enriched environment studies in animals. Rats housed in stimulating environments filled with toys, running wheels and social interaction developed larger, more complex brains than rats kept in standard cages. Their brains adapted because they were regularly exposed to novelty and challenge.

Human studies find similar results. Adults who take on genuinely new challenges, such as learning a language, dancing or practicing a musical instrument, show measurable increases in brain volume and connectivity on MRI scans.

The takeaway is simple: Repetition keeps the brain running, but novelty pushes the brain to adapt, forcing it to pay attention, learn and problem-solve in new ways. Neuroplasticity thrives when the brain is nudged just beyond its comfort zone.

The reality of neural fatigue

Just like muscles, the brain has limits. It does not get stronger from endless strain. Real growth comes from the right balance of challenge and recovery.

When the brain is pushed for too long without a break – whether that means long work hours, staying locked onto the same task or making nonstop decisions under pressure – performance starts to slip. Focus fades. Mistakes increase. To keep you going, the brain shifts how different regions work together, asking some areas to carry more of the load. But that extra effort can still make the whole network run less smoothly.

Neural fatigue is more than feeling tired. Brain imaging studies show that during prolonged mental work, the networks responsible for attention and decision-making begin to slow down, while regions that promote rest and reward-seeking take over. This shift helps explain why mental exhaustion often comes with stronger cravings for quick rewards, like sugary snacks, comfort foods or mindless scrolling. The result is familiar: slower thinking, more mistakes, irritability and mental fog.

This is where the muscle analogy becomes especially useful. You wouldn’t do squats for six hours straight, because your leg muscles would eventually give out. As they work, they build up byproducts that make each contraction a little less effective until you finally have to stop. Your brain behaves in a similar way.

Likewise, in the brain, when the same cognitive circuits are overused, chemical signals build up, communication slows and learning stalls.

But rest allows those strained circuits to reset and function more smoothly over time. And taking breaks from a taxing activity does not interrupt learning. In fact, breaks are critical for efficient learning.

The crucial importance of rest

Among all forms of rest, sleep is the most powerful.

Sleep is the brain’s night shift. While you rest, the brain takes out the trash through a special cleanup system called the glymphatic system that clears away waste and harmful proteins. Sleep also restores glycogen, a critical fuel source for brain cells.

And importantly, sleep is when essential repair work happens. Growth hormone surges during deep sleep, supporting tissue repair. Immune cells regroup and strengthen their activity.

During REM sleep, the stage of sleep linked to dreaming, the brain replays patterns from the day to consolidate memories. This process is critical not only for cognitive skills like learning an instrument but also for physical skills like mastering a move in sports.

On the other hand, chronic sleep deprivation impairs attention, disrupts decision-making and alters the hormones that regulate appetite and metabolism. This is why fatigue drives sugar cravings and late-night snacking.

Sleep is not an optional wellness practice. It is a biological requirement for brain performance.

Overdoing any task, whether it be weight training or sitting at the computer for too long, can overtax the muscles as well as the brain. Unsplash

Exercise feeds the brain too

Exercise strengthens the brain as well as the body.

Physical activity increases levels of brain-derived neurotrophic factor, or BDNF, a protein that acts like fertilizer for neurons. It promotes the growth of new connections, increases blood flow, reduces inflammation and helps the brain remain adaptable across one’s lifespan.

This is why exercise is one of the strongest lifestyle tools for protecting cognitive health.

Train, recover, repeat

The most important lesson from this science is simple. Your brain is not passively wearing down with age. It is constantly remodeling itself in response to how you use it. Every new challenge and skill you try, every real break, every good night of sleep sends a signal that growth is still expected.

You do not need expensive brain training programs or radical lifestyle changes. Small, consistent habits matter more. Try something unfamiliar. Vary your routines. Take breaks before exhaustion sets in. Move your body. Treat sleep as nonnegotiable.

So the next time you lace up your shoes for a familiar walk, consider taking a different path. The scenery may change only slightly, but your brain will notice. That small detour is often all it takes to turn routine into training.

The brain stays adaptable throughout life. Cognitive resilience is not fixed at birth or locked in early adulthood. It is something you can shape.

If you want a sharper, more creative, more resilient brain, you do not need to wait for a breakthrough drug or a perfect moment. You can start now, with choices that tell your brain that growth is still the plan.

Cascade of permissions on Pipi

Mike's Notes

Making visible how each account type has access to Pipi.

Resources

  • Resource

References

  • Reference

Repository

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

Last Updated

17/03/2026

Cascade of permissions on Pipi

By: Mike Peters
On a Sandy Beach: 14/03/2026

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

Think of Pipi as an iceberg. Most of it is hidden underwater. 

This is the matrix of Pipi account permissions. Each account has a different role, creating opportunities for the account above and dependency on the one below.

Pipi currently has about 3,000,000 lines of code. Most of it is self-generated, so the code volume is a bit of a guess at the moment, based on sampling. (There is semantic versioning as a record of change, not version control. 😊 )

Code % Role Account License
0.001 User Personal Open-source
1 System Enterprise
1 System Configuration Developer
1 Developer Rules Researcher
1 Pipi Admin Agent Closed-core
96 Self-organising Pipi

Personal Account

  • Everyone gets one and uses it to do work
  • User interface personalisation
    • language
    • accessibility
    • preferences
    • privacy

Enterprise Account

  • Run the system
  • Make the rules for users
    • Security
    • Roles and permissions.

Developer Account

  • Make the rules for the enterprise system
  • Modules
  • API
  • Integrations
  • IaC
  • Provide support

Researcher Account

  • Make the rules the developers use
  • Create constraints
    • Import the ontologies
    • Add the laws of physics

Agent Account

  • Interact with Pipi
  • Creates the rules for the researchers.

Pipi

  • Autonomous
  • Self-organising
  • World-model
  • Path taken
  • Evolving
  • Replicating
  • Digital twin
  • Swarm
  • Learns from how Pipi is being used by users

AWS and Google Cloud Preview Secure Multicloud Networking

Mike's Notes

The Connection Coordinator API Specification may be essential to use if it becomes widely adopted. It depends a bit on the pricing model.

Resources

References

  • Connection Coordinator API Specification

Repository

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

Last Updated

13/03/2026

AWS and Google Cloud Preview Secure Multicloud Networking

By: Renato Losio
InfoQ: 25/12/2025

Renato has extensive experience as a cloud architect, tech lead, and cloud services specialist. Currently, he lives in Berlin and works remotely as a principal cloud architect. His primary areas of interest include cloud services and relational databases. He is an editor at InfoQ and a recognized AWS Data Hero. You can connect with him on LinkedIn.

In a surprising move, AWS and Google Cloud have recently partnered to simplify multicloud networking, introducing a common standard and leveraging "AWS Interconnect - Multicloud" and "Google Cloud's Cross-Cloud Interconnect". The new option makes it easier for organizations to manage and secure workloads across both clouds, with Azure expected to join in 2026.

Currently in preview, the solution combines AWS Interconnect – Multicloud with Google Cloud’s Cross-Cloud Interconnect and defines an open interoperability specification that other cloud providers can also adopt. Designed to avoid managing circuits, routers, and routing configurations, the new option is intended to simplify the deployment of secure multicloud workloads, enabling customers to establish private, high-speed connectivity between Google Cloud and AWS.

Available on GitHub, the Connection Coordinator API Specification describes the OpenAPI 3.0 specification for the symmetric API used to coordinate managed L3 connectivity. Rob Enns, VP of cloud networking at Google Cloud, and Robert Kennedy, VP of network services at AWS, write:

"Previously, to connect cloud service providers, customers had to manually set up complex networking components including physical connections and equipment (...) This could take weeks or even months. AWS had a vision for developing this capability as a unified specification that could be adopted by any cloud service provider, and collaborated with Google Cloud to bring it to market."

The new solution targets customers who want to run workloads across distributed regions, have low-bandwidth needs, and want cross-cloud connectivity without managing physical infrastructure. One of the common concerns among practitioners is the pricing of the solution, which has not been disclosed yet, with Corey Quinn, chief cloud economist at The Duckbill Group, writing:

"This is either transformative or a waste of everyone's time, and it's impossible to tell which because the one thing that matters most to settling that question is "what's the price." They aren't disclosing it yet, so at the moment it occupies a superposition of "excellent/crap." Please collapse the waveform so we know which one it is."

According to AWS documentation, the managed private connectivity service enables customers to define direct 1 Gbps connections between AWS VPCs and Google Cloud VPCs at no cost during the preview. Tyler Batts, senior customer ops engineer at Second Front, comments:

"It’s not in GovCloud yet, but the direction is obvious: AWS is baking multicloud into the platform instead of leaving teams to piece it together themselves (...) If you run serious workloads in the cloud, this is one of those updates worth paying attention to!"

All connections between the AWS and Google Cloud network devices are encrypted by default, and hardware is configured to transmit customer traffic only when the encryption session is active. Enns and Kennedy add:

Both providers engage in continuous monitoring to proactively detect and resolve issues. And this solution is built on a foundation of trust, utilizing MACsec encryption between the Google Cloud and AWS edge routers.

The preview is currently free and supports five AWS and Google Cloud regions in the US and Europe, including Northern Virginia, Oregon, and Frankfurt.

Design in code, get praise

Mike's Notes

Using this method for the upcoming workspace testing. Design is done in Pipi, not Figma. IMO, Figma is over-hyped.

Resources

References

  • Reference

Repository

  • Home > Ajabbi Research > Library > Subscriptions > Adam Silver
  • Home > Handbook > 

Last Updated

12/03/2026

Design in code, get praise

By: Adam Silver
Adam Silver: 19/01/2026

Adam Silver is an interaction designer with over 15 years experience working on the web for a range of companies including Tesco, BBC, Just Eat, Financial Times, the Department for Work and Pensions and others.

He’s particularly interested in inclusive design and design systems and writes about this on his blog and popular design publications such as A List Apart. This isn’t his first book either: he previously wrote Maintainable CSS, a book about crafting maintainable UIs with CSS.

This week I demoed some flows I’d been redesigning to a room full of product managers and stakeholders.

The programme I’m on is huge. We’re redesigning a highly complex, enterprise-grade, case-working system.

There are many feature teams, each with their own product manager. I’ve been on the programme for 6 months now but it’s so big I’ve not met many of them.

The meeting was meant to present the before/after of the redesigns to show what it looks like to just use the simple and accessible patterns from the GOV.UK Design System and a few of my own, encouraging other feature teams to reuse them.

It did that.

But it also did something else, something I didn’t really expect:

Toward the end of the meeting, the conversation veered off.

There were comments and questions about the tool I'd used to create and demo the designs:

“I love the way this demos new concepts.”

“The prototype really helps you to understand the user journey”

“It’s been so helpful for our developers to show them how something actually works”

Most designers use Figma, but I had created an HTML prototype using the GOV.UK Prototype Kit.

Don’t get me wrong, Figma has its place but at the end of the day:

Figma can only produce pictures of software.

Not actual software.

Actual software is alive.

  • It moves
  • It adapts
  • It errors
  • It loads
  • It responds

Figma might be useful to design in.

But it’s not good to design “out”.

By that I mean:

When you present your designs with Figma, you’re not interacting with the product you’re designing.

You’re interacting with the software you used to design.

As a result:

  1. It’s slow and jarring. Your audience has to watch you stop, scroll, pan and zoom around Figma between each step.
  2. It may not do your design justice. Your audience is focused on you navigating Figma rather than experiencing your design.
  3. It may hide problems. You’ll probably jump between screens and miss important details - realistic data, micro interactions, transitions, loading states, error states and edge cases.

Designing in code and demoing in the browser forces you - or at least encourages you - to confront these things.

For my demo, instead of just sharing screens:

  1. I opened the browser and typed in the URL
  2. I signed into the case-working system
  3. I landed on the overview page which showed me my priority tasks
  4. I made the screen smaller to show two instances of the app side by side
  5. I clicked “Cases” in the primary menu to go to the case list
  6. I searched, filtered and sorted the list to find a particular case
  7. I filled out a complex multi-step form flow with conditional logic

In other words, I went through the entire end-to-end journey and interacted with the prototype just like real users would.

This allowed my audience of product managers to free up their mental energy and instead focus on understanding the design intention and potential gaps.

That’s 100x harder to do with Figma.

After the meeting, I received a lot of positive feedback which is great because I love praise.

One product manager actually suggested I help train some of the other designers on the programme who are less familiar with the Prototype Kit.

But she also pointed out that it would probably take up too much of my time.

Luckily, I’ve been preparing for this moment for 2 years. I told her:

I have a course that teaches designers how to use the GOV.UK Prototype Kit to unlock the many benefits of prototyping in code.

If you’d like to learn how to use the Prototype Kit and unlock those benefits — including a little unexpected praise when you present at your next show and tell:

​https://prototypekitcourse.com​

Cheers,

Adam

10 Must-read books and surveys about AI and Machine Learning

Mike's Notes

Alyona Vert from Turing Post compiled this fantastic list of free resources on AI and Machine Learning. Turing Post is excellent and worth subscribing to.

Resources

References

  • Reference

Repository

  • Home > Ajabbi Research > Library > Subscriptions > Turing Post
  • Home > Handbook > 

Last Updated

11/03/2026

10 Must-read books and surveys about AI and Machine Learning

By: Alyona Vert
Turing Post: 16/02/2026

Joined Turing Post in April 2024. Studied control systems of aircrafts at BMSTU (Moscow, Russia), where conducted several researchers on helicopter models. Now is more into AI and writing.

Deep Learning, context engineering, LLMs, multimodal models and agents – all the basics together for your convenience

Sharing some free, useful resources for you. In this collection, we’ve gathered books and surveys that can be your perfect guides to the major fields and techniques. Hope this really helps you master AI and machine learning and fill in any gaps in your knowledge!

  1. Machine Learning Systems by Vijay Janapa Reddi
  2. Understanding Deep Learning by Simon J.D. Prince
  3. Interpretable Machine Learning by Christoph Molnar
  4. Foundations of Large Language Models by Tong Xiao and Jingbo Zhu
  5. A Survey on Post-training of Large Language Models
  6. A Survey of Generative Categories and Techniques in Multimodal Generative Models
  7. Context Engineering 2.0: The Context of Context Engineering
  8. Agentic Large Language Models, a survey
  9. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
  10. Mathematical Foundations of Geometric Deep Learning by Haitz Saez de Ocariz Borde and Michael Bronstein