标签：#us

The US government is investigating whether the Cybercab, which lacks a steering wheel and pedals, meets vehicle safety standards.

While philosophers ponder AI consciousness, the models have ideas of their own.

Airbnb redesigned its authentication architecture around server driven flows and policy based challenge selection. The new Flexible Authentication system reduced authentication related code by 60%, cut the web client bundle by 100 KB, improved successful authentication by 2.6%, reduced duplicate account creation by 27%, and lowered OTP costs by 11%. By Leela Kumili

Elon Musk's gilded robot car barely made it on public roads before federal regulators launched an investigation into the vehicle's lack of traditional features - many of which are required by law. The National Highway Traffic Safety Administration said it was opening an audit query (AQ) into the Tesla Cybercab "to examine the process and […]

A senator's letter confirms the U.S. military moved to prevent the tracking after foreign adversaries used location data to target troops.

GitHub热门项目 | AI enabled pair programmer for Claude, GPT, O Series, Grok, Deepseek, Gemini and 300+ models | Stars: 7,609 | 40 stars this week | 语言: Rust

GitHub热门项目 | Rust GUI components for building fantastic cross-platform desktop application by using GPUI. | Stars: 13,942 | 199 stars today | 语言: Rust

The investigation was launched just a few hours after Tesla put the first production Cybercabs on the road in Austin.

I've been thinking about something that is becoming pretty common among developers.🤔 A lot of us...

We cannot forget that AI coding agents are not yet trustworthy : Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved. Anthropic, OpenAI, and Nous Research did not respond to requests for comment by the time of publication...

Homeland Security Investigations agents hit the outdoor retailer with a controversial subpoena as part of a dragnet search for the identities of protesters who entered a Minnesota church in March.

With the risk of infection rising, parents and young adults are facing agonizing choices about being out in public as school starts back up.

Microsoft opened GitHub Copilot code review for Azure Repos to all Azure DevOps customers, after acknowledging that many are not ready to migrate to GitHub. Reviews bill per use through the linked Azure subscription and appear in Cost Management 48 hours later. Budgets notify but do not stop reviews, and concurrency caps at five per organization. By Steef-Jan Wiggers

The "frequency healing" corner of the internet runs on faith. Apps ship MP3s labeled "40Hz gamma" and ask you to believe it. I'm a life scientist who builds web tools, and I couldn't ship that. So I built SereneSynth, a browser-based binaural beat and noise generator — and then I built a live spectrum analyzer into the page so anyone can audit the output in their own browser. This is the engineering write-up: the Web Audio graph, the FFT gotcha that almost made me publish wrong numbers, and how I cross-verified everything in Audacity. The honesty constraint first A binaural beat is not a tone in the air. Play 200 Hz into the left ear and 240 Hz into the right, and the listener's superior olivary complex computes the 40 Hz difference. A microphone — or a mono spectrum analyzer — will never show a 40 Hz peak. So the only honest thing a generator can prove is its carriers and its spectral slope. That is exactly what we measure. The synthesis graph Two sine oscillators, hard-panned with StereoPannerNode, summed into a master GainNode, tapped by an AnalyserNode before the destination — the analyzer observes exactly what the headphones receive. Settings that matter: fftSize 16384, smoothingTimeConstant 0.8. The FFT gotcha that almost made me ship garbage My first version used fftSize 1024: one fat bump near 220 Hz instead of two peaks. Bin width = 44100 / 1024 ≈ 43 Hz, and my carriers are 40 Hz apart — same bin, merged. At fftSize 16384 the bin width drops to ≈ 2.7 Hz and the carriers resolve as razor-sharp spikes at 200.0 and 240.0 Hz. Lesson: FFT size is the magnifying glass. If a "frequency proof" doesn't state its FFT size, ask. The widget renders a log axis (20–1000 Hz) because a linear axis wastes 90% of the canvas, and peak detection labels the top bins in the 100–500 Hz range live. Bit-exact, downloadable verification The page also renders 10-second stereo WAVs via OfflineAudioContext (16-bit PCM, 44.1 kHz): same graph, offline render, RIFF encode. No lossy compre

A production postmortem. We shipped browser-side image compression (Rust → WASM + WebGPU), turned on cross-origin isolation for speed, and watched every format crash with compression worker crashed . Here's the root cause and the fix. The setup We built an image compressor that runs 100% in the browser — Rust compiled to WASM for the codec work, WebGPU for the heavy ML passes (background removal, denoise, watermark). No upload, so users' pixels never leave the device. Privacy is the whole selling point. For the multi-threaded code paths we rely on shared memory + atomics , which in the browser requires crossOriginIsolated . So we served the document with: Cross-Origin-Embedder-Policy: require-corp Cross-Origin-Opener-Policy: same-origin That gives us crossOriginIsolated === true , unlocks SharedArrayBuffer , and lets the *‑threaded WASM builds actually spawn workers. The build uses a nightly toolchain ( nightly-2025-06-01 + -Z build-std ) with: RUSTFLAGS = "--cfg=... +atomics,+bulk-memory --shared-memory --import-memory" and a custom rayon handle pool ( with_turbo_pool ) instead of build_global , so we control worker lifecycle and can abort/self-heal. The incident After flipping COEP to require-corp in production, every format started crashing with the same message: compression worker crashed Not one codec — JPG, PNG, WebP, AVIF, all of them. It was a P0: the core feature was dead for every user. What made it nasty: it only reproduced under real cross-origin isolation . Local dev without COEP was fine. Staging without the header was fine. So the bug hid until it hit production traffic. Root cause The *‑threaded WASM packages spin up nested rayon workers to parallelize the codec. Under COI + COEP require-corp , those nested workers get blocked by Cross-Origin-Resource-Policy / COEP — the spawned worker script is treated as a cross-origin response without the right CORP header, so the browser refuses it. No worker → the rayon pool never initializes → the compression c

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

Intro There's a pitch behind every "AI writes your tests too" workflow: more coverage, less manual toil, a safety net that used to take a sprint now takes minutes. The pitch skips over what that safety net is actually made of. When the same model writes the implementation and the test suite, you haven't added a second, independent check. You've asked one reviewer to grade its own homework and handed you the green checkmark as if someone else had signed off. The blind spot loop A model reasons about a function once, forms an implicit set of assumptions (input shapes, timezone handling, what counts as "empty"), and writes the implementation against those assumptions. Ask the same model to write tests for that function, and it doesn't re-derive correct behavior from scratch. It writes tests against the same assumptions it just used to write the code. If it assumed dates always arrive as ISO strings in UTC, the implementation assumes that, and the tests assume it too. The suite goes green. The assumption is still wrong. Tests that pass for the wrong reason (Illustrative, not a specific case, but recognizable to anyone who's shipped an AI-generated suite.) Picture a discount-calculation function where the model assumes quantities are always positive integers. The implementation skips a negative-quantity check. The generated tests exercise 1, 5, and 100, because those are the "normal" values a model reaching for plausible test data will reach for. Nothing ever asks what happens at -1 or 0, because neither pass, the code or the tests, ever considered them worth asking about. Coverage tooling reports 100% on this function. The bug ships anyway. Coverage becomes a false signal High line or branch coverage from an AI-authored suite tells you the code paths were exercised, not that the right inputs exercised them. A suite can hit every line of a function and still never send it a null, an empty array, a duplicate key, or a value at a type boundary, if the author, human or mode

RustDesk is gaining attention today with +84 GitHub stars , and the reason is straightforward: it provides an open-source remote desktop experience while allowing teams to control the infrastructure behind it. Unlike a client-only tool, RustDesk is built around a self-hosting model. The desktop client connects through a RustDesk ID server ( hbbs ) for rendezvous and a relay server ( hbbr ) when direct peer-to-peer connectivity is unavailable. This separation makes the architecture easier to reason about and gives operators more control over traffic and metadata. A quick server experiment can start with Docker: docker run -d \ --name rustdesk-hbbs \ --network host \ -v " $PWD /rustdesk-data:/root" \ rustdesk/rustdesk-server:latest \ hbbs docker run -d \ --name rustdesk-hbbr \ --network host \ -v " $PWD /rustdesk-data:/root" \ rustdesk/rustdesk-server:latest \ hbbr For production, configure the client with your server’s public key and hostname rather than relying on default discovery. Keep the relay and rendezvous ports documented, restrict administrative access, and store the generated keys in a protected location. The Rust implementation is a practical fit for a latency-sensitive desktop application: native binaries, low runtime overhead, and broad platform support. The trade-off is operational complexity. Self-hosting means handling updates, firewall rules, TLS or tunnel termination, backups, and monitoring yourself. Things to watch before production: Network design: Direct connections may fail behind strict NAT, forcing traffic through the relay and increasing bandwidth usage. Security controls: Treat the server key, access credentials, and client distribution process as sensitive infrastructure. Upgrade testing: Validate client/server compatibility in a staging environment before rolling out updates widely. For developers who want remote support without surrendering control of the entire connection path, RustDesk is a compelling open-source project to test in a p

While restaurant owners might look to generative AI as a shortcut to sprucing up their menu, customers can viscerally sense that something is wrong with the food.

An invite-only event in Austin, Texas, kept online fans in the dark for hours.