标签：#science

Astronomers will test equipment that, if it’s successful, will one day be crucial for discovering Earthlike planets.

I recently finished building a small suite of puzzle and game solvers that all run entirely in the browser — no backend, no API calls, no machine-learning models. You paste in a Sudoku, a chess position, or a crossword pattern, and the answer comes back instantly, computed on your own device. The fun part wasn't the UI. It was that each puzzle turned out to be a textbook excuse to reach for a different classic algorithm. Nine solvers, and I got to use constraint propagation, adversarial search, heuristic search, brute-force scanning, and plain old pattern matching — the stuff that shows up in an algorithms course and then, in most day jobs, never again. This is a tour of which algorithm fits which puzzle, and a few of the potholes I hit along the way. Everything here is vanilla JavaScript running in a Web Worker. The one design constraint: no server Before the algorithms, the rule that shaped all of them: it has to run client-side. That's a privacy choice (your puzzle never leaves the tab) and a cost choice (no compute bill), but it's also a fun forcing function. You can't lean on a beefy backend or a hosted model — you get one browser thread (well, a Worker thread) and whatever you can compute in a few hundred milliseconds. That budget is exactly why classic algorithms shine here. They're fast, deterministic, and small enough to ship as a script. Let's group the solvers by the technique each one leans on. Family 1: Constraint propagation Sudoku Sudoku is the poster child for constraint propagation. A cell that can only be one value forces that value; that in turn shrinks its neighbours' options, which forces more cells, and so on. Most "easy" and "medium" boards fall over from propagation alone (naked singles + hidden singles), and only the hard ones need a backtracking search on top. The nice property: the same engine that solves the board also powers the hint feature (find the next forced cell and explain why it's forced) and a uniqueness check — count solutions,

Psychedelics are often associated with disconnecting from reality, but a recent neuroimaging study found that psilocybin can actually make our brain activity more connected to the world around us.

Just as new data centers face growing backlash from neighboring communities, the US Environmental Protection Agency (EPA) is about to make it harder for people to weigh in on any pollution those centers create. The EPA plans to toss out a federal rule requiring public notice and an opportunity to comment when certain industrial sites […]

Influential right-wing conspiracy theorists and MAGA-aligned pundits are falsely claiming that the Covid vaccine killed Dolly Parton, the vaccination advocate and philanthropist.

Most "is this plugin safe?" advice is vibes. I wanted numbers, so I built a dataset. Here's what it found, and exactly how, so you can check my work or build your own. The finding first Of 8,010 WordPress plugins with a publicly documented vulnerability since 2023 (15,534 vulnerability records in total): 3,780 have been removed from the wordpress.org plugin directory. Removal stops updates but doesn't uninstall — affected sites keep running the code. 277 carried a critical (CVSS ≥ 9.0) flaw on record before removal. 2,115 are still installable today with a known vuln and no update in 12+ months — roughly 6.7M active installs combined. The part that surprised me most: "removed from the directory" is nearly invisible to a site owner. No dashboard warning, no email. The plugin just quietly stops getting fixes while sitting on the site. How I built it (no paid APIs) The whole thing runs on two public sources and no API keys. 1. Vulnerability data — the GitHub Advisory Database. It mirrors CVE records including the Patchstack and Wordfence CNA assignments that cover almost all WordPress plugin CVEs. It's a git repo, so a shallow, sparse clone of the advisories/unreviewed/{year} folders gets you the raw JSON: git clone --depth 1 --filter = blob:none --sparse \ https://github.com/github/advisory-database.git Each advisory carries the CVE ID, a CVSS vector string, CWE IDs, and reference URLs. The plugin slug isn't a first-class field — you recover it from the Patchstack/Wordfence reference URLs with a couple of regexes. That alone attributes the large majority of WordPress advisories to a specific plugin. 2. Maintenance signals — the wordpress.org plugin API. For each slug: https://api.wordpress.org/plugins/info/1.2/?action=plugin_information&request[slug]=SLUG That gives install count, last-updated date, tested-up-to version, and support-thread resolution ratio. A 404 (or an {error} body) means the plugin isn't in the directory — but that's ambiguous: it could be removed ,

Classic Machine Learning Through the Eyes of an SRE — Part 9: Isolation Forest The algorithm in one line: Isolation Forest scores how anomalous a point is by how few random cuts it takes to separate that point from everything else. No model of normal, no loss function, nothing optimized. ← Previous: Part 8 — Hierarchical Clustering Fails Beautifully · Next: this is the series finale — start at Part 1 . Every anomaly detector I had studied models what NORMAL looks like, then calls the leftovers outliers. K-Means: far from every centroid. DBSCAN: in the noise bucket. Sensible, and intuitive. Isolation Forest does not bother. It never models normal at all. It goes straight at the rare points with a single question: how few random cuts does it take to isolate you? Random cuts, literally. Pick a feature at random, pick a split value at random between that feature's min and max, repeat. A point that separates from the crowd in three cuts is anomalous. A point buried in the middle of a dense mass takes thirty. Grow hundreds of these random trees, average the isolation depth for each point, and you get an anomaly score. There is no loss function here. No optimization, not even the local kind that decision trees do at every split. Every cut is a coin flip, and the power comes entirely from averaging, which is the forest trick from the supervised half of this series now applied to pure randomness. Cheap randomness plus averaging beats careful modeling, as long as the target is something randomness naturally exposes. Rarity is exactly that. Sometimes the winning move is to optimize less. That sentence would have gotten me laughed out of my first ML study session. It is also this finale's thesis. The part I had completely backwards Here is the thing I did not know until I read the original paper properly, and it is the opposite of every instinct a decade of ops gave me. Isolation Forest deliberately trains each tree on a small subsample of your data, and this is not a performan

Study: Land use is the biggest factor in determining decomposition rates of organic cotton underpants.

"We built this city on good cacao" does have a nice ring to it.

You have questions about data centers, and WIRED has answers. Join our livestream on September 10 and our panel of experts will tell you everything you need to know.

How AI Helps Us Explore the Universe Modern telescopes and space missions generate more data in a single night than a team of human astronomers could review in a lifetime. The Vera C. Rubin Observatory in Chile, for instance, is expected to produce up to seven million alerts every night once it reaches full operational cadence, each one flagging something in the sky that changed since the last image. No group of humans can look at that stream and make sense of it in real time. Machine learning can, and increasingly does. This is the quiet story behind most recent breakthroughs in astronomy: it is not just bigger telescopes, but bigger telescopes paired with models that can filter, classify, reconstruct, and predict faster than any manual pipeline. Here is a tour of where AI is actually doing that work, and why it matters to anyone who writes code. The Data Problem Comes First Space science has quietly become a big data problem. The Rubin Observatory's ten-year Legacy Survey of Space and Time will produce roughly 60 petabytes of raw imagery and catalog around 20 billion galaxies and a similar number of stars. Every image the telescope takes is compared, pixel by pixel, against previous images of the same patch of sky, and any meaningful difference (a moving asteroid, a brightening supernova, a flaring galactic nucleus) triggers an alert within about two minutes of the exposure being taken. That alert stream is too large and too fast for manual triage. So astronomers built software "brokers": machine learning classifiers that sit between the telescope's raw output and the scientists, deciding in near real time which alerts are worth a second look. This is a pattern you will see across almost every domain of modern astronomy: instruments generate more signal than humans can parse, and a model is inserted into the pipeline to do the first pass of filtering. Finding Planets in a Sea of Noise Exoplanets are found mostly through the transit method: a planet passes in front

The eclipse will obscure about 93 percent of the moon’s surface. Here are the peak times and tips for the best viewing experience.

Climate change is causing glaciers to retreat and become less stable, upping the odds of catastrophic collapses.

It's 8:52 AM and 86 degrees Fahrenheit (30 Celsius) where I live in Southern California. My husband just came back from a morning outing with our four-month-old. "How was the botanic garden?" I ask him. "It was okay. It was just too hot," he tells me. I didn't expect us to spend so much of […]

Messing with the genetic code might have gotten a bit easier to do.

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. Oura's getting sued. A recently filed class-action complaint against the smart ring maker alleges that the company has duped customers […]

Olá, comunidade dev.to! Meu nome é Neto, tenho 17 anos e sou estudante de Ciência da Computação no UNIPÊ, em João Pessoa. Atualmente, estou cursando o segundo semestre da graduação e também estudando design profissional, área que considero importante para a criação de soluções digitais mais úteis, intuitivas e visualmente agradáveis. Minha trajetória na tecnologia ainda está no começo, mas já tem sido marcada por descobertas, aprendizados e desafios. Escolhi Ciência da Computação porque sempre tive curiosidade sobre como aplicativos, sites e sistemas funcionam. Quero aprender não apenas a programar, mas também a compreender todo o processo de desenvolvimento de um produto, desde a identificação de um problema até a construção de uma solução. Durante o curso, tive a oportunidade de desenvolver, com alguns colegas, um projeto relacionado à criação de um aplicativo. Essa experiência foi importante porque me mostrou que desenvolver um produto vai muito além de escrever código. Foi necessário discutir ideias, organizar tarefas, pensar nas necessidades dos usuários e encontrar soluções para os problemas que surgiram durante o processo. Mesmo enfrentando desafios simples, percebi como cada obstáculo pode contribuir para o nosso crescimento. Em alguns momentos, precisamos revisar decisões, corrigir erros e adaptar o projeto. Também aprendemos que uma equipe precisa manter uma boa comunicação, pois cada integrante possui habilidades, responsabilidades e pontos de vista diferentes. O estudo de design profissional complementa minha formação em computação. Estou aprendendo que uma aplicação não deve apenas funcionar corretamente: ela também precisa oferecer uma boa experiência ao usuário. Elementos como cores, tipografia, organização das informações, acessibilidade e facilidade de navegação influenciam a maneira como as pessoas utilizam um produto. Ainda tenho muito a aprender sobre programação, design e desenvolvimento de projetos. Porém, entendo que a evolução acontece aos po

New findings shed light on what’s making the parasite less treatable using the drug that’s considered a first line of defense.

Understanding unique photochemistry of such materials will help in conservation, storage, and exhibition.

The fatalities come amid declining vaccination rates and calls from President Donald Trump to split up the MMR vaccine.