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"Auto-provisioning" is one of those features that sounds boring on a spec sheet and turns out to be doing a surprising amount of work once you look underneath it. The pitch is simple: drop in a list of users and the system spins up hundreds of configured softphones across devices in minutes, no manual setup per user. The pitch is easy. The plumbing behind it is where the interesting problems live. I've been picking this apart lately and wanted to write down what actually has to happen for "provision 500 users in minutes" to be true. What manual provisioning looks like (so you can see what's being automated away) Setting up one softphone by hand is a checklist. You create the user on the platform, assign an extension and credentials, point the client at the right SIP server, configure the transport (TLS, ports), set codecs, wire up voicemail, maybe push notification tokens, contacts, feature flags. Then you do it on their device. For one user, fine, twenty minutes. For five hundred, that's a full-time job for a week, and every manual step is a chance to fat-finger a config and generate a support ticket later. Auto-provisioning exists to make that whole checklist happen without a human running it each time. The core problem: getting config to a device that isn't configured yet The central puzzle of provisioning is a chicken-and-egg one. You want to hand a device its configuration, but the device doesn't yet know who it is or where to get that config. So the whole flow is about bootstrapping identity and config onto a blank client safely. A few common approaches: Provisioning URL + credentials: The user (or an admin) enters a provisioning username/password, or the client is pointed at a provisioning server URL. The client authenticates, the server looks up who this is, and returns the full config bundle. Simple, works, but needs the initial credential to get entered somehow. QR / activation code: Instead of typing SIP settings, the user scans a code or enters a short a

TechCrunch talks agents, UX, and reporting to Greg Brockman with OpenAI's head of product.

Illinois prosecutors shared defendants’ personal data with federal immigration agents without criminal warrants, public disclosure, or legislative oversight.

Welcome to this week's Top 7, where the DEV editorial team handpicks their favorite posts from the...

Thanks to a dwindling supply of open roles, “one-click” applications, and the rise of artificial intelligence, it’s easier than ever to apply for a job. We’re all paying the price.

AI coding agents leave you with 5-to-20-minute gaps all day. Five real ways I fill them — and the one "quick" habit that makes you the bottleneck.

5:00 PM. Laptop closed. Slack closed. Workday over. Except my brain didn't get the memo. I'm making...

There’s no clearer sign of the data center boom than rampant gas projects that have been proposed or that are already under construction.

Airbound's ultra-lightweight approach to drone delivery has attracted backing from Greenoaks, DoorDash, and Silicon Valley investor Lachy Groom.

Originally published at ai.bedvibe.studio . I built a real-time tracker in Rust — about two thousand lines — that reads a live ADS-B feed, keeps a Kalman-filtered track per aircraft, and screens every pair for closest approach against separation minima. Roughly 150 aircraft, a full cycle in under a millisecond. It ran clean. Tests passed, the picture looked right, the numbers were plausible. It was refusing about one measurement in nine , and the only reason I ever found out is that the rejections went to a counter instead of a log line. The gate has a sub-second tolerance for clock error The tracker runs an innovation gate: when a position arrives, the filter predicts where the aircraft should be, and if the measurement is too far from that prediction it is rejected as physically impossible rather than believed. Once a track converges the innovation standard deviation settles around 36 m, so a five-sigma gate sits at roughly 180 m. An airliner at 250 m/s covers 180 m in 0.7 seconds . So the gate's entire tolerance for a wrong timestamp is under one second. Any pipeline that mis-times its measurements by more than that will have them rejected — correctly, and invisibly. The feed reports its own staleness. The pipeline dropped it. Every ADS-B record carries a field saying how old that position already was when the response was generated. In the original build it was parsed into the contact struct and never read again — the only other place that field appeared in the entire codebase was as 0.0 in test fixtures. Every measurement was therefore stamped with the tracker's own cycle clock, as though it had been observed at the instant it landed. This is the common case, not an exotic one. A field that is decoded and then unused looks identical to a field that is decoded and used , right up until you go looking for its second reference. Here is what that field actually contains, sampled across two consecutive polls of the live feed: reported age of position median 0.31 s p

The president bought when the stock was in the mid-$150 range. SpaceX finished trading on Monday back at its IPO price of $135.

When a coding agent changes a systems program, a source diff is only the beginning of the question. The more useful question is: what exact machine-facing artifacts would this semantic change produce, and can another process independently verify that relationship? That is one of the research problems we are exploring in SEMAPRAX , an Apache-2.0 agent-native systems programming language built at Wavect GmbH. SEMAPRAX is currently v0.2 pre-alpha experimental research software . It is not production-ready. The narrow mechanism described here is useful precisely because its claims are bounded. From a patch to target projections SEMAPRAX has a read-only command: semaprax target-evidence <file> <patch.spatch> The command takes a verified source snapshot and a semantic patch. It independently rebuilds both the base program and the patched candidate, then derives several deterministic compiler-owned projections: semantic Graph JSON an explicit capability manifest Native C11 source a structurally validated WebAssembly Core module For every projection, the report records a domain-separated digest and byte length. It also classifies the projection as changed or unchanged. That sounds simple, but the distinction matters. A source edit can leave one projection unchanged while altering another. A documentation-level identity change, a capability change, and a runtime-behavior change should not all be flattened into the same “some bytes changed” signal. The target report therefore binds the proposed semantic change to the compiler artifacts it actually affects. Why deterministic output is the prerequisite Evidence over compiler output is only useful when the output is reproducible. SEMAPRAX treats source formatting, semantic graph data, diagnostics, semantic patches, and target artifacts as deterministic projections. The same admitted input must produce the same bytes. Otherwise a digest says little: a second verifier could not distinguish a meaningful change from nondeterministic

We built a plugin for the GitHub Accessibility Scanner to make sure your alt text is actually accessible. Here's how it works. The post Your alt text passes automated checks. That doesn’t mean it’s any good. appeared first on The GitHub Blog .

Proteger datos que deben seguir siendo secretos dentro de diez años es un problema de hoy : un adversario puede capturar tu tráfico cifrado ahora y descifrarlo cuando exista la capacidad cuántica ( harvest now, decrypt later ). Quipu es una librería libre de cifrado híbrido post-cuántico para datos en reposo: combina criptografía clásica probada con la nueva, de modo que solo se rompe si ambas caen a la vez. Rust puro, y por qué Quipu nació apuntando a varios lenguajes: un núcleo en Rust con una C ABI encima y bindings para Python, Node y Go. Funcionaba, pero la lección fue clara: mantener una interfaz de C estable más cuatro bindings, cada uno con su empaquetado y sus pruebas de interoperabilidad, era complejidad que no pagaba para el objetivo real —proteger datos en reposo— y ampliaba la superficie de ataque con unsafe que no queríamos. Hoy Quipu es Rust puro : memoria segura, sin garbage collector , sin unsafe de primera parte . Y para quien no programa en Rust, se distribuye como rueda nativa de Python (vía PyO3) — que es la superficie que el cliente que no es de Rust de verdad necesita. Una sola base de código, una sola cosa que auditar. Es la misma filosofía que guía el resto: donde hay buena criptografía, se reutiliza; la simplicidad es una decisión de seguridad, no una comodidad. Instalación cargo add quipu # Rust pip install quipu-crypto # Python (rueda nativa, PyO3) Cifrar y descifrar en Python import quipu # Simétrico con contraseña blob = quipu . encrypt_stream ( b " datos sensibles " , " mi-passphrase " ) assert quipu . decrypt_stream ( blob , " mi-passphrase " ) == b " datos sensibles " # Post-cuántico para un destinatario pub , sec = quipu . generate_keypair () # X25519 + ML-KEM-1024 c = quipu . encode_to_recipient ( b " secreto " , pub ) assert quipu . decode_as_recipient ( c , sec ) == b " secreto " Qué hay debajo Cifrado: XChaCha20-Poly1305 (AEAD autenticado). Derivación de claves: Argon2id (resistente a fuerza bruta) + HKDF. Post-cuántico: X25519

The FTC and Zillow have announced a settlement that ends the case alleging that a 2025 "partnership" between Zillow and Redfin violated antitrust laws. The FTC had alleged Zillow agreed to pay Redfin to syndicate its listings, while Redfin would end its own advertising contracts and promise not to compete with Zillow for multifamily listings. […]

Robotaxis are expanding. So is the fight over the rules governing them. In New York, Gov. Kathy Hochul withdrew a proposal earlier this year that would have opened the door to driverless robotaxis outside New York City after taxi drivers, unions, and state lawmakers opposed it. Six months later, commercial driverless service remains illegal in […]

General Intuition, the startup building a foundation model that trains generalized AI agents how to move through space and time, is in talks to raise at a $6 billion pre-money valuation from new investors including Valor Ventures, Point72 Ventures, Seven Seven Six.

I wanted to disagree with 'AI made me a worse reviewer' from Michael Amachree (@dev_michael) ....

Inside the frontier lab’s push to bring AI agents from software engineers to the masses.

Today on Decoder, I’m talking with Tim Cadogan, the CEO of GoFundMe. You know GoFundMe — it’s a major fundraising platform where you can donate to help people with everything from medical expenses to little league uniforms to starting a new business. Tim took over as CEO in March 2020 — a fascinating and chaotic […]