标签：#astro

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

"Everyone wants to take that aircraft up now..."

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

Static Forms in Astro: Handling Submissions Without a Server with onsubmit.dev (form backend) Astro is a great fit for content-heavy sites that ship very little JavaScript, but that creates an interesting problem as soon as you add a contact form: where does the POST request go? With onsubmit.dev (form backend) , an Astro site can submit forms to an external endpoint instead of adding its own API route or server. This is particularly useful for Astro projects deployed as static files to a CDN, GitHub Pages, or another static host. You can keep the site static while still accepting contact requests, feedback, registrations, and similar submissions. Start with the zero-JavaScript pattern The simplest approach is also the most aligned with Astro's philosophy: use the browser's native form submission behavior. You don't need a hydrated component merely to collect a few fields. A regular HTML form can make a POST request directly to a form backend: --- // src/pages/contact.astro --- <form method="POST" action="https://onsubmit.dev/f/YOUR_FORM_ID"> <label> Name <input type="text" name="name" required /> </label> <label> Email <input type="email" name="email" required /> </label> <label> Message <textarea name="message" required></textarea> </label> <button type="submit">Send message</button> </form> Replace YOUR_FORM_ID with the endpoint supplied for your form. There is no client framework involved here. The browser serializes the named fields and sends them directly when the visitor clicks the button. That has several nice properties for an Astro project: No Astro server endpoint is required. No React, Vue, or other client runtime needs to be hydrated. The form still works when JavaScript is unavailable. Your static deployment remains static. It is worth remembering that native HTML already does a lot of work. required , type="email" , labels, and standard browser submission cover many simple forms without additional JavaScript. Where astro-onsubmit fits For Astro-specif

I built a calculator for BaZi — Chinese "Four Pillars" birth charts. Whatever you think of the interpretive tradition (and I'll get to that), the input math turned out to be a genuinely deep time-zone problem, and that's what this post is about. If you've ever thought "time zones, how hard can it be" — this is a tour of exactly how hard, with working TypeScript. The problem BaZi divides the day into twelve two-hour "branches", so your birth hour is one of the chart's four pillars. Get the hour wrong and you get a different chart — not slightly different, categorically different. Every calculator I could find feeds the system the wall-clock time from your birth certificate. But the tradition predates time zones by about two thousand years; it obviously means solar time — where the sun actually was over your birthplace. Clock time and solar time differ by more than most people think, and the difference decomposes into exactly three parts: 1. Daylight saving time — and it's historical. You need the DST rules in force on the birth date , not today's. China ran a now-forgotten DST experiment from 1986–91; Harbin kept its own zone before 1949. If you were born in Beijing in July 1988, your certificate is an hour ahead of standard time and no modern-day lookup will tell you that. 2. Longitude. Solar time shifts 4 minutes per degree from your zone's standard meridian. China spans five geographic zones but uses one clock — born in Ürümqi, your clock runs about two hours ahead of the sun. It's not just a China quirk: Vancouver sits at 123°W in a zone whose meridian is 120°W, so that's another 12 minutes, everywhere, always. 3. The equation of time. The sun itself runs up to ±16 minutes fast or slow over the year, thanks to orbital eccentricity and axial tilt. NOAA publishes an approximation that's accurate to under a minute: /** Equation of time (minutes), NOAA approximation */ export function equationOfTimeMinutes ( dayOfYear : number ): number { const b = ( 2 * Math . PI *

If a static analyzer only walks .ts / .tsx / .js / .jsx , every other file type isn't scanned badly - it's not scanned at all. A .vue component, a .svelte widget, an .astro page: none of them exist to the tool. Not "low confidence." Not "partial support." Invisible, the same way an empty search result looks identical whether there's genuinely nothing to find or the search just never looked in the right place. That's exactly the gap Eventra's CLI had. It scans a codebase and tells you which tracked features are actually used - the whole pitch is "stop guessing which code is dead." Except if your team ships a Vue admin panel, a Svelte checkout widget, and an Astro marketing site around the same core app (which, if you've worked on more than one team, you've probably seen - nobody plans a multi-framework stack, it just accretes), the CLI would silently skip all three, report a clean scan, and never mention that it hadn't actually looked. The exact failure mode the product exists to prevent, happening inside the product itself. We'd already closed this gap once, for Vue. This month we closed it for Svelte and Astro too, and the interesting part isn't the frameworks - it's that adding two more meant touching exactly zero lines of the CLI's core analysis engine. The trick: don't teach the core anything The CLI's core is a TypeScript-compiler-API engine: it builds a real program, walks real ASTs, resolves real symbols across files, and figures out which .track("event_name") calls are statically reachable. It is, deliberately, framework-agnostic - it doesn't know what Vue is, and it shouldn't have to. So instead of teaching the core about .vue / .svelte / .astro , each framework gets a small, separate plugin whose only job is a translation: take the framework file, hand back one virtual TypeScript module. A Vue Checkout.vue becomes Checkout.vue.ts . A Svelte Cart.svelte becomes Cart.svelte.ts . The core never sees the original file - it sees TypeScript, because by the time

Star, moving at 8% the speed of light, may let us measure the black hole's rotation.

Hace unos meses empecé a construir Utiligo , una colección de calculadoras en español: horas extras, IVA, aguinaldo, préstamos, IMC. La premisa técnica era simple y me la tomé en serio: cero dependencias de JavaScript en el navegador . Sin React, sin frameworks de UI, sin librerías de gráficos. Ni una. Esto es lo que aprendí construyéndolo. Por qué cero dependencias La mayoría de estas herramientas hacen aritmética. Sumar horas, aplicar un porcentaje, dividir un salario entre 30. Enviar 40 KB de framework al navegador para calcular salario / 30 / 8 es desproporcionado, y en América Latina —donde está mi audiencia— buena parte del tráfico llega por móvil con conexiones irregulares. El stack quedó así: Astro en modo output: 'static' , que genera HTML puro <script is:inline> con JavaScript de toda la vida para la interactividad Cloudflare Pages para servirlo El resultado: páginas que funcionan antes de que termine de cargar cualquier cosa. Lo que sí duele de esta decisión Sería deshonesto contarlo como si no tuviera costes. Los gráficos hay que dibujarlos a mano. El gráfico de pastel del presupuesto mensual es SVG generado con trigonometría: var end = start + pct * 2 * Math . PI ; var x1 = cx + r * Math . cos ( start ), y1 = cy + r * Math . sin ( start ); var x2 = cx + r * Math . cos ( end ), y2 = cy + r * Math . sin ( end ); var large = pct > 0.5 ? 1 : 0 ; svg += ' <path d="M ' + cx + ' , ' + cy + ' L ' + x1 + ' , ' + y1 + ' A ' + r + ' , ' + r + ' 0 ' + large + ' ,1 ' + x2 + ' , ' + y2 + ' Z"/> ' ; Con una librería serían tres líneas. Aquí son treinta y hay que entender el arco elíptico de SVG. ¿Vale la pena? Para un gráfico, sí. Para un dashboard entero, probablemente no. No hay reactividad. Cada oninput actualiza el DOM a mano. Funciona bien con diez campos; con cien sería insostenible. El generador de QR tuve que escribirlo. Codificación Reed-Solomon incluida. Fue el fin de semana más educativo del proyecto y el que menos recomendaría repetir. El bug que me enseñó

The summer is officially over. I am staring at a remarkably clean Git branch, my laptop didn't literally take off into orbit (though the CPU fans certainly tried a few times during local CI builds), and I somehow know what git rebase -i does without having to Google it in a cold sweat. If you'd asked me back in May what I was going to be doing, I would have confidently told you I was going to "write tests for Astropy's C extensions." It sounded so neat. So contained. But open source doesn't really work like that. I came in thinking I was just going to write tests, and somewhere along the way, I ended up learning how the actual machinery underneath the Python abstraction works, how maintainers think about architecture, and how to safely catch C-level memory panics without taking down the entire interpreter. So, here is the real story of what I did for the last few months, what broke, how we fixed it, and where the project stands now. So, what was I actually supposed to do? Astropy is a beast of a library. The Python-facing API is incredibly robust and beautifully documented. But underneath all those pretty Python classes is a complex, mixed-language architecture. The library relies heavily on compiled C, and Cython extensions to handle the performance-critical hot-paths. The problem? That compiled layer was a massive testing blind spot. Before this summer, these performance-critical extensions were almost entirely tested indirectly, meaning they were only validated by calling the high-level Python wrappers. That is a risky abstraction. If a regression happens deep inside the C code, the Python layer sitting above it can accidentally mask it. You wouldn't know something was fundamentally broken until a downstream package started acting weird. My project goal was to build a dedicated, de novo test suite that bypassed the public API completely and exercised each compiled extension module directly. This wasn't just for code coverage. It was an absolute prerequisite for t

A device currently being used to help octogenarians do self-administered eye exams could be part of future space missions to monitor astronauts’ ocular health.

Fitted with telescopes, the plane did our longest imaging of the Sun's corona.

We expected them to be there, but they had previously been too small to see.

Here’s when some of this year’s most anticipated astronomical events will happen and the best way to observe them.

Also: Cyborg diving suits for cockroaches, why sleepy sperm whales blow bubbles, Betelgeuse's companion star.

From how galaxies form to the beginnings of the universe, the images from the space telescope are giving cosmologists new data to explore—and theories to build out.

A discovery in a solar system 73 light-years from Earth is challenging definitions and “blurring the lines between stars, planets, and moons.”

I run a fact-check site for an unreleased game. That genre is a swamp: half the pages you find are somebody's guess reprinted six times until it reads like news. I wanted the opposite, so I made provenance a schema requirement instead of an editorial habit. If a claim has no source, the build fails. Here is how that works in Astro, and what it cost me. Sources live in the content schema, not in the prose Every entity on the site is a YAML file validated by a Zod schema. The interesting part is that sources is not optional: const sourceSchema = z . object ({ url : z . string (). url (), date : z . string (), // when the source said it, not when I read it }); const base = { status : z . enum ([ ' confirmed ' , ' trailer-spotted ' , ' rumor ' , ' debunked ' ]), updated : z . string (), sources : z . array ( sourceSchema ). min ( 1 ), }; export const entitySchema = z . object ({ name : z . string (), description : z . string (), sections : z . array ( z . object ({ heading : z . string (), text : z . string (), status : z . enum ([ ' confirmed ' , ' trailer-spotted ' , ' rumor ' , ' debunked ' ]), sources : z . array ( sourceSchema ). min ( 1 ), // per section, not per page })). optional (), ... base , }); Two decisions in there matter more than they look. Sources are per section, not per page. A page usually mixes a confirmed fact with a plausible reading of a trailer. One source list at the bottom lets those blur together. Per-section sources force me to say which sentence rests on what. Status is a required enum, not a boolean. rumor and debunked are first-class. The page renders a badge from the same field, so the reader sees the confidence level next to the claim instead of a disclaimer nobody scrolls to. The cost is real: adding a paragraph means finding a citable source for it. Several times I have deleted a nice sentence because I could not back it. That is the feature working. Seven locales, and the empty ones stay invisible The site ships in seven languages, a

A portfolio for thoughtful, reliable learning technology I am a senior full-stack software engineer with 20 years of experience building scalable web applications, primarily for learning and education. I recently published a focused portfolio to share the products, technologies, and engineering work behind that experience. Diogo Bastos | Senior Full-Stack Software Engineer Professional portfolio of Diogo Bastos, a senior full-stack software engineer. diogobastos.pages.dev The site is intentionally straightforward: a clear overview of my background, selected professional work, personal projects, certifications, and a public résumé. What you will find Learning technology work : projects across Pearson eDynamic Learning, HMH, and Neovation Learning Solutions. Full-stack engineering : React and TypeScript on the frontend; Node.js, Java, APIs, SQL, and cloud delivery practices on the backend. Recent personal projects : experiments in Python, FastAPI, React, AWS, and Java/Spring Boot. A concise, accessible build : the portfolio is a static site built with Astro, with attention to responsive design and usability. I care about turning complex product needs into dependable experiences for the people who use them. If you work in software engineering, learning technology, or product development, I would be glad to connect. Explore the portfolio: diogobastos.pages.dev Thanks for stopping by.

Scientists have long assumed that there’s an upper limit to the intensity of the solar winds that reach Earth. New research suggests there’s not—and the implications are alarming.

Black holes eject powerful energy jets that blow away the surrounding gas to great distances. So how can they continue to grow?