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On Monday I published a piece admitting that my deploy verification tolerates sixty seconds of "not there yet" for a reason I couldn't defend. Three retries, twenty seconds apart. I picked twenty because it was the first interval where my false alarms stopped, my sample was about three deploys, and I had never once recorded how long propagation actually takes. I made three commitments in that piece. A birth certificate for the constant. A rule fixed before the run it judges. And the one that mattered most: emit the value, not just the verdict — a check that prints only pass or fail hides the exact signal that would tell me it's miscalibrated. I did the third one that afternoon. Every deploy since writes down how long it took to go green. Three samples in: Aug 03 ( 1.7, 21.7 ]s Aug 05 ( 0, 6.7 ]s Aug 05 ( 40, 70 ]s They're intervals rather than points because my poll spacing is twenty seconds. All I can honestly say is that green happened somewhere between the last failed check and the first successful one — a number I can't resolve finer than my own instrument. The third one failed Not the deploy. The check. I shipped a post, ran verification, and got a clean red: page 404, hero missing, sitemap entry absent. Three attempts, twenty seconds apart, exactly as designed. By its own rules the deploy had failed. Nothing was wrong. A longer script came back 200 on everything. Total elapsed: somewhere between forty and seventy seconds, against a tolerance of sixty. So the false alarm I widened the interval to eliminate returned on the third recorded sample, four days after I published the sentence "my sample was about three deploys." I'd like to say I predicted this. I predicted the category, not the timing, and the timing is the part that stings. The part I hadn't considered at all Here's what the red actually said, in order: attempt 1 article 404 · hero missing · list page MISSING · sitemap missing attempt 3 article 404 · hero missing · list page OK · sitemap missing The

I run a music metadata API as a solo developer. Under it sits a catalog of 90M+ recordings aggregated from six platforms: Spotify, Apple Music, Tidal, Beatport, Discogs, and MusicBrainz. The core job is cross-referencing: take whatever you know about a track (an ISRC, a platform ID, or a messy "artist + title" string from a DJ export) and resolve it to one canonical recording with everything else attached. When I started, I assumed this was mostly a plumbing problem. Every platform has an API, recordings have a standard identifier, join on it, done. Almost none of that survived contact with real data. This post is the parts I had to learn the hard way: why one song legitimately carries many ISRCs, how fuzzy matching on artist and title actually has to work, why recording-to-composition mapping is many-to-many in both directions, and the failure modes I now check for routinely. The ISRC almost solves it The ISRC (International Standard Recording Code) is a 12-character identifier for a specific recording. Daft Punk's "One More Time" is GBDUW0000053 : country prefix GB , registrant code DUW , year 00 , then a designation number. Every commercially released recording is supposed to have one, and most platforms expose it. So the naive architecture writes itself: one isrc column on the track table, join all six platforms on it, ship. That was my first schema, and it was wrong in a way that took a while to surface. Labels mint a fresh ISRC for every commercial variant of a recording. The radio edit gets one. The extended mix gets one. The 2001 release and the anniversary remaster get different ones. A reissue through a new distributor often gets one even when the audio is bit-identical. Regional releases sometimes get their own. None of this is an error; it is how the system is designed to work, because each of those is a distinct commercial product even when it is the same performance. The consequence: one canonical recording legitimately carries many ISRCs, and differen

We'll be getting an in-depth preview of Grand Theft Auto VI very soon: Rockstar announced this morning that it will be airing an "extended look" at the game on August 27th. The preview will stream on YouTube at 9PM ET, as per usual, but will also be shown earlier for Netflix subscribers, premiering at 3PM […]

A growing collection of pocket-size gadgets lets you easily make recordings and extract info from them. Here are our favorites.

The QS90H actually delivers on the all-in-one soundbar promise thanks to new woofers and a unique driver layout.

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

The Series B is the company's second fundraise since it last raised capital in 2020. In that time, it has increased its ARR by 10x to $60 million.

submitted by /u/donutloop [link] [留言]

I Replaced kube-proxy with eBPF in Production (And Why My Monitoring Went Blind for 6...

Zulfi Alam thinks his team doesn’t need to “prove” they engineered a new state of matter in their bid to reinvent computing. Science would disagree.

Samsung dared to bring 6K to the world of gaming monitors before our PCs were truly ready for it.

Nintendo has smashed its first-quarter earnings estimates for the fiscal year, bolstered by strong games sales and US tariff refunds, though its customers likely won't receive anything from the latter. In its latest financial results, Nintendo reports that operating profits soared to 142.5 billion yen (about $902 million) between April 1st and June 30th, compared […]

Chevron and Williams are big winners in the race to power artificial intelligence as they build out gas-fired power plants and pipelines.

Shark’s new PowerDetect Speed is lighter and more affordable than the original, but still managed to ace most of my vacuum tests.

Parte 13 de la serie Fitz . Se abre el capítulo del frontend: Fitz compila componentes .fitzv a WebAssembly, y este es el dev loop que hace que editarlos se sienta instantáneo — la misma experiencia "guardar y verlo" que te da Vite, sobre un lenguaje que compila a binario nativo. El setup: un lenguaje compilado con frontend Fitz es un lenguaje compilado — HTTP, async, Postgres, JWT viven en la sintaxis y emite un binario nativo vía Rust. La historia del frontend es un formato de componentes single-file, .fitzv (state + events + <template> , al estilo Vue/Svelte), que compila a WebAssembly : fitz build --bin web --target wasm-client # → target/wasm/web/{web.js, web_bg.wasm} Sin npm install , sin config de bundler, sin framework externo — el componente se vuelve un bundle WASM autocontenido (el demo del contador pesa 11.4 KB gzipped). Acá viene la objeción refleja: compilado = feedback lento . Editás, esperás una compilación entera, refrescás el browser a mano. Es lo opuesto a lo que un loop de frontend debería sentirse. Por eso Fitz tiene fitz dev . El loop Apuntá fitz dev a un bin wasm-client y deja de ser un compilador para ser un dev server: fitz dev # sirve en http://127.0.0.1:1234/ Qué hace: Rebuild incremental con wasm-pack --dev (sin wasm-opt ), reusando un crate estable así la cache de cargo queda caliente — el primer build compila las deps, cada save siguiente es de ~1-2 segundos . Un dev server que sirve el root de tu proyecto como python -m http.server : tu index.html , tu CSS, el bundle en target/wasm/<bin>/ . ¿Sin index.html ? Genera uno mínimo en el punto de mount . Auto-refresh del browser por WebSocket : guardás un .fitzv / .fitz / fitz.toml y la página se recarga sola. Sin F5 a mano. Guardás, y ~2 segundos después el browser muestra el cambio. En un lenguaje compilado. El detalle que importa: el state sobrevive el reload La mayoría de los hot-reload pierden tu estado en un reload completo — ibas tres clicks adentro de un contador, editás el template,

Cuando el objetivo es algo tan delicado como un diagnóstico asistido, confiar en un único modelo es arriesgado. FeliniAI usa tres pipelines complementarios que se refuerzan entre sí, igual que un veterinario combina lo que ve, lo que mide y lo que sabe. Pipeline 1 — Visión: MobileNetV2 Una CNN MobileNetV2 (PyTorch, transfer learning) clasifica imágenes de la piel/pelaje del gato en categorías visuales. Elegí MobileNetV2 por su equilibrio entre precisión y ligereza: corre rápido en CPU, lo que mantiene la inferencia por debajo de 1 segundo. Alcanza un 93,4% de accuracy visual . Pipeline 2 — Clínico: XGBoost + ICADA El núcleo del sistema es un clasificador XGBoost que trabaja sobre 33 features clínicas derivadas de los criterios ICADA (los criterios estandarizados de dermatitis atópica felina): estacionalidad, distribución de las lesiones, prurito, respuesta a tratamientos previos. Sobre un dataset de 8.000 casos , este módulo logra un F1 macro de 0.9675 en validación cruzada 5-fold. La búsqueda de hiperparámetros se hizo con Optuna y la explicabilidad con SHAP. Pipeline 3 — LLM: la síntesis Finalmente, un LLM ( Llama 3.3 70B vía Groq ) integra las salidas de los dos modelos anteriores y las traduce en una recomendación legible: qué tipo de alergia es más probable, con qué confianza y qué pasos sugerir. El LLM no diagnostica solo: orquesta y comunica lo que han calculado los modelos especializados. Por qué tres pipelines y no uno Porque cada uno cubre el punto ciego del otro. La visión capta lo que una foto muestra pero un cuestionario no; el modelo clínico capta el historial que una foto no puede mostrar; el LLM convierte ambos en algo accionable. Es un patrón de ensemble heterogéneo aplicado a datos de naturaleza distinta. Resultados F1 macro (clínico): 0.9675 , accuracy 0.9909. Accuracy visual: 93,4%. 4 tipos de alergia, 33 features clínicas, <1s de inferencia. Qué aprendí Que en dominios sensibles, la arquitectura correcta no es "el modelo más grande", sino varios

submitted by /u/r_retrohacking_mod2 [link] [留言]

I was optimizing the Lua transpiler for my modding language grug and ran into a really weird LuaJIT performance bug. The same benchmark could randomly run 20× slower, and it turned out a LuaJIT NYI could silently blacklist an unrelated hot loop. I wrote up the investigation here: The LuaJIT NYI That Silently Poisoned an Unrelated Hot Loop It goes from the benchmark mystery through LuaJIT's trace recorder internals, and ends with a PR to get unpack off LuaJIT's NYI list. Feedback is very welcome! :) submitted by /u/MyNameIsTrez [link] [留言]

submitted by /u/mariuz [link] [留言]

Most mobile apps ship a privacy policy as a link that opens Safari. A smaller number ship an in-app rendered policy. A tiny minority ship what I think is the single highest-leverage privacy screen: a summary that mirrors your Nutrition Label in plain language, designed to be read. Why it's high-leverage The privacy summary sits at the intersection of three concerns: Users read it before granting sensitive permissions. Especially for camera, contacts, location, and health data. App Store reviewers check it exists and matches your store listing. Regulators (GDPR, CCPA) reward it. Clarity is a compliance signal, not a legal defense — but it's what an investigator asks for first. Sixty minutes of design work. Meaningful trust dividends. Often the difference between first-submission approval and a rejection loop. What to include The privacy summary should mirror your Privacy Nutrition Label but with real language humans can parse: Each data category you collect — displayed as a card or a row, with a clear icon. Why you collect it — one line, plain language. "So you can log in on another device," not "for authentication purposes." Where it's used — is it stored on our servers, shared with third parties, only on your device? Be specific. How to opt out or delete it — a link or a button to the settings page where the user can act on that category. Six to eight cards, one per data category. No more. Visual patterns that read as trustworthy Design choices that consistently score high in user-testing for trust signal: Real language, not legalese. "We store your email address so you can log in" beats "Personal identifiers are retained for authentication purposes." Muted, confident colors. No warning reds, no compliance yellows. A neutral surface with soft accent for the data-category icons. Readable typography. 16pt body, generous line-height (1.5x), enough paragraph spacing that scanning is easy. Icons per category (not just text). A camera icon for camera data, a location pin