标签：#m

Nothing will launch the entry-level Phone 4b on July 7.

YouTube TV and DirecTV Stream customers may be eligible for a cash payout, after Disney agreed to pay $50 million to settle claims that it forced the services to increase their subscription prices. Anyone who was subscribed to YouTube TV or DirecTV Stream between April 1st, 2019, and March 31st, 2026 is eligible to claim […]

The third day of Amazon Prime Day is for the real heads, and so is our live blog. We'll bring you deals, trends, and commentary until Amazon's annual summer sale finally hangs up its cleats.

The Moccamaster Prime Day deal is the best price I've seen this year, on a coffee maker that will probably outlive you.

Times are hard in 2026. These Amazon Prime Day deals under $100 on earbuds, Kindles, and other tested products should help make life just a little bit easier.

It’s day three of Prime Day, folks. We’re nearly 75 percent of the way through Amazon’s four-day sales event, and unsurprisingly, many deals are still sticking around. There have been all kinds of discounts on things like TVs, smart home gadgets, chargers, headphones, and more. Prime Day typically isn’t as big of a deal as […]

We’re finally getting Grand Theft Auto VI, poised to be one of the largest gaming releases in history.

Spencer Judge discusses the architectural pattern of building a shared core in Rust with language-specific layers on top. Drawing from his work on Temporal's SDKs, he shares lessons on navigating FFI boundaries, bridging async concepts, and managing memory safely. He explains the limitations of native extensions and how emerging tech like WebAssembly can streamline cross-language architecture. By Spencer Judge

Don't pay full price—snag one of these tasty Prime Day tech deals on some of our favorite WIRED-tested gadgets.

Most people assume Redis is fast because of some complex multi-threaded. Turns out it's the opposite — it runs on a single thread and still beats most databases. The reason comes down to how it handles I/O at the OS level and why memory access patterns matter more than thread count. Here's a animated breakdown of the internals if anyone's curious. submitted by /u/lonely_geek_ [link] [留言]

IBM has built a new prototype chip with around 100 billion transistors on an area the size of a fingernail, which is twice the density of the company’s previous state-of-the-art technology announced in 2021. The design could pave the way for faster and more energy efficient computers for years to come. For more than half…

It’s been hard to look away from headlines about the European heat wave this week. Temperatures are breaking records across the continent, and the weather is threatening lives, shutting down schools, and in one particularly ironic case, forcing the cancellation of a London Climate Action Week event about extreme heat. As the summer ramps up…

kreuzcrawl began as a Rust core with bindings for ten languages. v0.3.0 ships fourteen, adds a tiered WAF-aware dispatch engine, cuts peak streaming memory from ~2.5 GB to ~20 MB, and enables SSRF defense across every outbound call path by default. It is the first release we consider API-stable. This post covers what changed, why each decision was made, and what the harder engineering problems looked like from the inside. At a glance Area v0.2.0 v0.3.0 Language bindings 10 14 (+Dart, Kotlin/Android, Swift, Zig) Peak streaming memory ~2.5 GB ~20 MB SSRF protection opt-in on by default Dispatch model static HTTP / bypass / browser tiered, signal-driven escalation WAF fingerprints — 35 across 8 vendors Fingerprint hot-reload — lock-free ( ArcSwap ), 500 ms debounce MCP tools partial 1:1 with CLI, safety-annotated CLI subcommands scrape, crawl + batch-scrape, batch-crawl, download, citations Robots / sitemap parsers engine-internal public modules API stability preview stable Four new language bindings v0.2.0 shipped Rust, Python, Node.js, Ruby, Go, Java, C#, PHP, Elixir, and WebAssembly. v0.3.0 adds Dart , Kotlin/Android , Swift , and Zig — bringing the total to fourteen. None of the per-language glue is written by hand. Every binding is generated from the Rust core by alef , our polyglot binding generator. The Dart and Kotlin/Android packages bind through the C FFI layer ( kreuzcrawl-ffi ) via dart:ffi and JNI respectively. Swift binds through clang. Zig uses @cImport against the same C header. The generation pipeline also hardened in this release: the Docker publish matrix now builds each architecture natively rather than via QEMU emulation, the Dart build no longer requires the Flutter SDK for pub.dev publishes, Swift artifactbundle checksums are injected automatically, and the Elixir/PHP/Ruby releases preserve their lock files through the source-publish step. === "Python" ```sh pip install kreuzcrawl ``` === "Node.js" ```sh npm install @xberg/kreuzcrawl ``` === "Rus

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Xbox is testing new features, including support for 15-character gamertags.

Cloudflare released the Cloudflare One stack, an open-source library of agent skills for planning, deploying, and managing Zero Trust environments. The skills include automated migration logic for Zscaler and Palo Alto Networks, the same logic used in Cloudflare's Descaler program that has moved enterprise customers in hours rather than months. By Steef-Jan Wiggers

Adding an AI feature looks deceptively easy. You sign up for an API key, paste in a prompt, and within an hour you've got a working demo that makes the whole team lean over your shoulder. Then you ship it, traffic arrives, and two things happen at once: your latency graph develops a long, ugly tail, and your monthly bill arrives with a number that makes finance schedule a meeting. The gap between "impressive demo" and "production feature" is almost entirely about cost and latency engineering. The model is the easy part. Here's how to cross that gap. First, understand what you're actually paying for Most LLM APIs bill by tokens — roughly ¾ of a word each — and they bill both directions: the tokens you send (input) and the tokens the model generates (output). Output tokens are usually several times more expensive than input tokens, which has a non-obvious consequence: a verbose prompt is cheaper than a verbose answer. This reframes optimization. People obsess over trimming their prompts while letting the model ramble for 800 tokens when 80 would do. If you want to cut cost, the highest-leverage move is almost always constraining the output : ask for JSON, ask for a single sentence, set a max_tokens ceiling, and tell the model explicitly to be terse. Latency follows the same logic. Generation is sequential — the model produces one token at a time — so output length is the single biggest driver of how long a request takes. A 50-token answer is fast almost regardless of model. A 2,000-token answer is slow even on the fastest infrastructure. Lever 1: Don't call the model when you don't have to The cheapest, fastest LLM call is the one you never make. Two techniques eliminate a startling share of traffic. Caching identical and near-identical requests. Many real-world prompts repeat — the same FAQ-style question, the same document summarized twice, the same classification of similar inputs. A cache keyed on the normalized prompt turns a repeat request into a sub-millisecond

Dai metadati in una lingua a 20 localizzazioni senza impazzire tra click e schermate: un flusso pratico per indie e piccoli team. Localizzare un’app non significa solo tradurre le stringhe dell’interfaccia. Una buona parte dell’acquisizione organica passa dai metadati su App Store Connect : titolo, sottotitolo, descrizione e keyword. Il problema è che, quando provi a farlo “a mano” dal pannello web, diventa subito un lavoro di pura resistenza: apri la scheda, cambi lingua, compili i campi, salvi, ripeti. Ora moltiplica per 10–20 lingue. Per molti indie (e in generale per chi ha poco tempo e zero voglia di click ripetitivi) il punto di svolta è usare ASC CLI per rendere questa attività automatizzabile, ripetibile e verificabile . Perché la localizzazione dei metadati è un caso d’uso perfetto per una CLI Dal punto di vista del flusso di lavoro, i metadati App Store hanno tre caratteristiche che li rendono ideali per l’automazione: Sono campi strutturati (title, subtitle, description, keywords): non stai “inventando” contenuti ogni volta, stai trasformando contenuti. Sono ripetitivi per lingua : la sequenza di operazioni è identica, cambia solo la locale. Sono tanti : più lingue aggiungi, più l’approccio manuale scala male (tempo, errori, incoerenze). Con una CLI, invece, il lavoro si sposta dal “fare cose” al definire un processo : prendi i metadati di partenza, generi le varianti linguistiche, applichi l’update in batch. Cosa conviene localizzare (e cosa no) In genere ha senso includere in un passaggio di localizzazione “massiva”: App name / title (attenzione ai limiti e ai trademark) Subtitle (spesso è la parte più ASO-oriented) Description (qui conta più la leggibilità che la traduzione letterale) Keywords (campo delicato: va adattato, non tradotto alla cieca) Al contrario, è meglio trattare con più cautela: Claim e frasi marketing molto creative : in alcune lingue risultano innaturali se tradotte letteralmente Keyword strategy : la ricerca utenti cambia per mercat

n8n is the kind of tool you start using lightly and then quietly route half your operations through. At which point "it's running on someone's cloud seat, metered per execution, with my API keys living on their servers" starts to feel less great. Self-hosting fixes all three — flat cost, no execution cap, and your keys stay on a box you own. With Docker it's a fifteen-minute job. How much server it actually needs Honest numbers first, so you don't over- or under-buy: ~2 GB RAM is the sweet spot — n8n plus its Postgres database plus normal workflows sit comfortably here. 1 GB works if your workflows are light, but you'll notice it on bigger runs. 4 GB if you do heavy parallel executions or push large payloads through. n8n isn't CPU-hungry at rest; it spikes during runs. A 2-core box is fine for most setups. (More on matching specs to workload in the sizing guide .) The Docker setup On a fresh Ubuntu/Debian box, install Docker: curl -fsSL https://get.docker.com | sudo sh Make a folder and a docker-compose.yml — n8n with a persistent volume and Postgres: services : n8n : image : docker.n8n.io/n8nio/n8n restart : always ports : - " 127.0.0.1:5678:5678" environment : - N8N_HOST=n8n.yourdomain.com - N8N_PROTOCOL=https - WEBHOOK_URL=https://n8n.yourdomain.com/ - DB_TYPE=postgresdb - DB_POSTGRESDB_HOST=db - DB_POSTGRESDB_PASSWORD=change-me volumes : - ./n8n-data:/home/node/.n8n depends_on : [ db ] db : image : postgres:16 restart : always environment : - POSTGRES_PASSWORD=change-me - POSTGRES_DB=n8n volumes : - ./db-data:/var/lib/postgresql/data sudo docker compose up -d Two things worth pointing out: the volumes ( n8n-data , db-data ) are what keep your workflows alive across restarts and upgrades — don't skip them. And n8n is bound to 127.0.0.1 , not 0.0.0.0 — it's not exposed to the internet directly. That's deliberate; the next step handles access safely. Access: HTTPS or a tunnel Public URL (needed for OAuth nodes and webhooks): point a subdomain at the server and run

Meta is bringing back the Facebook Creator Studio page manager, now "reimagined" as a standalone AI companion app. The new app aims to make it easier for creators to connect with their audiences and show them "exactly how to grow on Facebook," according to Meta's announcement. Meta's AI Creator Assistant is a central focus of […]