LAsteNERD

In 1955, Mary Tsingou helped run a computational experiment at Los Alamos that revealed unexpected behavior in nonlinear systems—work that later became foundational to chaos theory and computational science.

This piece traces that history from early supercomputers and the Fermi-Pasta-Ulam-Tsingou problem to modern AI systems used to explore uncertainty in scientific research.

Trinity: https://www.lanl.gov/media/publications/1663/0922-revising-t...

Story about how Los Alamos is reinventing neutron imaging — think “x-rays with neutrons” — using new event-mode cameras that record each neutron interaction with nanosecond-level precision.

Traditional neutron imaging works like a long-exposure photo: useful, but blurry. The new system (based on a camera called LumaCam) timestamps every photon from neutron events and uses techniques like event centroiding and pulse-shape discrimination to clean up noise and sharpen images dramatically.

Why neutrons? Because they reveal things x-rays can’t — like light elements, isotopic composition, and crystal structure. This is especially powerful for materials science and nuclear diagnostics, where understanding what something is made of (not just where it is) really matters.

Article from Los Alamos is about the ICE House, a facility where they test electronics by blasting them with neutrons that mimic what you’d get flying at 35,000 feet for decades. One hour of testing = 100 years of cosmic radiation.

It’s part of a larger effort to make electronics rad-hard — so that microchips don’t randomly glitch or die in flight (or in orbit). Especially relevant as chips shrink and transistor counts hit hundreds of billions (i.e. more chances for failure).

Some highlights:

Neutrons from space can flip bits or cause “latch-ups” (think: permanent short circuits).

These upsets can lead to weird bugs, BSODs, or worse — especially at altitude.

The ICE House runs ~24/7 and still can’t keep up with demand from avionics and chip companies.

They’re now planning a third beamline to expand testing capacity, and even working on proton-based testing for space use cases.

If you’re into hardware reliability, aerospace, or just cosmic-ray horror stories for computers, this is worth the read.

LANL researchers are cataloguing the molecules in healthy human breath to create a baseline profile that could enable new non-invasive diagnostics.

Some details:

Method: Using tandem mass spectrometry, the team has identified 227 distinct compounds across 31 volunteers, narrowing to 48 common features that may define “healthy breath.”

Patterns: Certain metabolites correlated with sex and time of day; others trace back to environmental contaminants or microbiome interactions.

Partnerships: Collaborating with the University of New Mexico to expand sampling (including both breath and blood data).

Goal: Easy-to-use diagnostic tests where a patient might one day “just breathe” to screen for illness, fatigue, or impairment.

The approach echoes the original breathalyzer’s leap in the 1950s but applies it to a far wider range of health conditions.

Los Alamos's proton radiography (pRad) facility uses beams of high-energy protons to capture images of dynamic, high-density events — think explosions. A clear, animated explainer of how it works.

Chaos theory gave us the butterfly effect: tiny changes that balloon into massive consequences (Lorenz’s weather simulations, or Bradbury’s time-travel butterfly). But Los Alamos researchers just showed that quantum systems don’t always play by those rules.

Using theory, simulations, and IBM’s quantum processors, physicists explored whether small quantum-level disruptions would spiral out of control over time. The result? At the quantum scale, entanglement actually heals damage. A particle “sent back in time” and deliberately altered can return to the present nearly unchanged.

In other words:

Lorenz-style chaos does exist at the quantum level (slight variations can diverge wildly). But there’s also a quantum anti-butterfly effect: in sufficiently entangled systems, information “damaged” in the past can be restored in the present. This has direct implications for quantum computing (a new way to measure “how quantum” a computer really is) and potential applications in information security and error correction. As lead scientist Bin Yan put it: “At the quantum scale, reality is self-healing.”

If the writing does the job it needs to do--in this case, a deft summary of an article--why is it better if it comes from a human vs. AI? Analysis, sure. But summary? This is the whole point of the article...do you actually prefer to read bad writing because it was written by a real person?

The class of 2026 has had generative AI for their entire college career. What started as a novelty in 2022 has become second nature: surveys show >90% of undergrads now use AI for schoolwork, from drafting essays to summarizing readings.

For students, the motivation is pragmatic: AI saves time, reduces stress, and helps balance overwhelming academic and extracurricular demands. It’s less about “cheating” and more about survival in a system that prizes productivity and credentials. Professors, meanwhile, are scrambling—reverting to handwritten exams, shifting grading toward tests, or trying moral appeals. Yet many remain unaware of just how normalized AI has become on campus.

The result: higher ed has been fundamentally reshaped in just three years. Students expect project-based, real-world assignments that resist AI shortcuts. But with faculty stretched thin by budget cuts, research demands, and political headwinds, systemic redesign feels unlikely. For now, both students and professors face the same reality: a college education is what you make of it—AI included.

If you're wondering--yes, I used AI for the synopsis. Big question for me, is what does the future of education look like? How do kids get the skills they need to use AI, while still getting the skills they need to be skeptical of it?

Original discussion here: https://news.ycombinator.com/item?id=44798695

Discussion continues, with more on dynamic imaging and the labs, here: https://news.ycombinator.com/item?id=44876919