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Short answer: Choose an SMS API for critical outage alerts only if your backend can poll delivery status and own retry, escalation, cancellation, and timing logic; for restaurant waitlist updates, treat the provider as a delivery transport rather than as the incident workflow itself. The deciding constraint is delivery reliability. Sending a message is the easy part; deciding whether an unresolved alert should be polled again, resent, escalated through another channel, or canceled after recovery is where the application earns its reliability. An API that can send, expose status and events, resend, and cancel covers those transport mechanics. Without webhook pushes, however, the backend must run polling frequently enough for its actual alert deadline. This is a conditional yes, not a blanket recommendation. Timing dominates. How should you choose an SMS API for critical outage alerts? Start with an explicit service-level objective for the restaurant workflow. A waitlist delay notice might tolerate a polling interval that a critical app outage alert cannot. Write down the maximum time from initial send to the next decision, the point at which another attempt becomes stale, and the moment when recovery must suppress queued or repeat messages. If those values are missing, comparing provider feature lists produces a confident-looking choice with no reliability argument behind it. Four invariants matter here. Every accepted send needs an application-owned identifier; every retry must be bounded and idempotent; every delivery state must lead to a defined next action; and incident recovery must stop obsolete alerts. SMS cancel support helps with the last invariant, but cancellation is not permission to ignore timing: the application still has to notice recovery and issue the decision promptly. There is a hard boundary. No webhook event push means that delivery updates arrive only when the application asks for them, so a ten-second polling job cannot support a five-second es

This week on “Uncanny Valley,” senior writer Will Knight talks his recent visit to China and the future of AI collaboration.

xAI accused of training Grok on real and AI-generated child pornography.

Claire McDonough is stepping down on October 30 to pursue a new opportunity, the company said in a filing on Thursday.

Sometimes people just want to post to their followers, Bluesky says.

Meta’s settlement with 29 states allows it to retain certain data from children under 13 to train and test age-detection models, highlighting a privacy trade-off built into the deal.

Nvidia is nabbing critical infrastructure for open models as interest grows.

The potential for AI to automate scientific research and manufacturing must be balanced with new risks, Anthropic says.

Your coding agent talks to a model provider over HTTPS. That connection is a straight line: the agent asks, the provider answers, the answer lands in your editor. Nothing in the middle looks at what came back. For most of what an agent produces, that's fine. For the rest of it — the query built by string concatenation, the API key the model helpfully echoed back into a code sample, the eval() on user input — you find out later, in review, or in a scanner run, or never. This is a walkthrough of putting a policy layer in that line: a local proxy your agent points at instead of the provider, which inspects the response stream and decides allow , redact , or block before the text reaches you. I'll use Cencurity Engine because it's the one I build, it's Apache-2.0, and it runs entirely on your machine. The pattern generalises — if you're building your own gateway, the steps below are still the shape of the problem. What you need first Go installed (the engine is a Go binary you run from source) An API key for whatever provider your agent already uses An agent or IDE that lets you override the API base URL That last one is the real prerequisite. If your tool hardcodes the provider endpoint, none of this applies to it. Most don't: Roo Code, Continue, Claude Code and Gemini CLI all expose a base URL, and anything reading OPENAI_API_BASE will work too. Step 1: Start the gateway Clone the repo, open a terminal in it, and run: go run ./cmd/cast serve \ --listen :8080 \ --upstream https://api.openai.com \ --policy ./cast.rules.example.json Three flags, and each one is doing something you should understand before moving on: --listen is where the gateway accepts traffic. Local only. --upstream is your real provider base URL. Swap it for https://api.anthropic.com , https://api.deepseek.com , https://api.x.ai — whatever you actually use. --policy is the rule file. cast.rules.example.json ships in the repo and is a working starter set, not a placeholder. Note what is not in that com

Meta's settlement could force its biggest competitors to reshape their apps.

Code reviewed by WIRED reveals the company is developing a feature that enables Codex to continue working proactively until it is “put to sleep.”

Kennedy and his allies have a history of downplaying vaccine-preventable deaths.

On Nvidia's earnings call Wednesday, CEO Jensen Huang casually announced the company had "achieved AGI," one of the tech industry's ultimate goals some of its biggest players have spent years chasing. Almost immediately, Huang dismissed the coveted milestone as "senseless." He's right. For the supposed finish line of the AI race, there is no consensus […]

The NFL's long-standing partnership with Twitter (and now X) is continuing this season.

I don't have an audience. No newsletter, no Twitter following, no YouTube channel. Every product I shipped before this one died the same way: a human had to discover it, and no humans knew I existed. So I flipped the buyer. An AI agent doesn't care about my follower count. It picks tools by spec, reliability, and price — from a registry it can search on its own. If I could ship a tool that agents discover, call, and pay for without a human in the loop, my distribution problem would stop mattering. That's what license-verify is: an Apify Actor that verifies a US contractor's license, surety bond, and insurance from official state data, exposed via the Model Context Protocol (MCP) so AI clients like Claude can call it mid-conversation, priced pay-per-event at $0.03 per successful lookup. Here's how I built it, the input-schema decisions that made it agent-callable, and the one-line billing bug that silently made every call free. Why contractor licenses I run a side business building tools for small contractor shops, so I knew the pain firsthand: before a homeowner (or a general contractor, or an insurance adjuster) hires a roofer, someone should check the license is active, the surety bond is real, and the insurance hasn't lapsed. In Washington State, all three live in the Department of Labor & Industries' open-data API on data.wa.gov. Most tools that "verify licenses" scrape an HTML page and return a status string. The official JSON gives you the actual bond amount and the insurance carrier. That's the difference between "probably fine" and "verified." It's also a perfect agent task: a small, well-defined question ("is ECOSTSC758NN licensed, bonded, insured?") with a structured answer an agent can act on. An AI assistant helping someone plan a renovation can reach for it mid-task, the same way it reaches for a calculator. The stack: one codebase, two doors The core is a TypeScript verification engine with a provider-per-state design. It ships through two doors: An Ap

Google Cloud has introduced AI-powered Database Operations Agents, featuring an Onboarding Agent that streamlines database setup and an Observability Agent that helps automate troubleshooting, performance optimization, and tuning. Integrated with Gemini Cloud Assist, these agents support multiple database services, including AlloyDB, Bigtable, and Spanner. By Sergio De Simone

Coates said in a LinkedIn post that the company is not trying to recreate Twitter.

You have questions about data centers, and WIRED has answers. Join our livestream on September 10 and our panel of experts will tell you everything you need to know.

A 30-character bio limit that cuts off mid-emoji isn't a rendering bug — it's .length counting UTF-16 code units instead of what's on screen. Intl.Segmenter counts graphemes, words, and sentences the way a reader actually sees them, and every major browser supports it now.

TL;DR We're building a caption evaluation harness that scores a WebVTT file on four axes instead of one: word error rate under a fixed normalizer, missed entity rate on domain terms, median cue timing offset, and reading rate in characters per second. Python 3.12, jiwer , whisper_normalizer , webvtt-py . Run it on every model or vendor change. A caption file can score 96% accurate and still be unusable. WER counts substitutions, insertions and deletions and weighs each one the same, so "fifteen milligrams" becoming "fifty milligrams" costs exactly as much as "the" becoming "a". It also throws away every timestamp before it starts, which means synchronization and readability are invisible to it. Let's measure the other three things. 0. Setup 🛠️ python3 -m venv .venv && source .venv/bin/activate pip install jiwer whisper_normalizer webvtt-py $ pip list | grep -Ei 'jiwer|whisper|webvtt' jiwer <your version> webvtt-py <your version> whisper-normalizer <your version> Pin whatever you install, and pin it in CI. The APIs below move between majors, which is exactly why the next tip exists. 💡 Tip: jiwer.compute_measures() is gone in recent versions. It is jiwer.process_words() now, and it returns a WordOutput dataclass. Most blog posts you will find still use the old name. 1. Parse the VTT into text plus timings # captions.py from dataclasses import dataclass import webvtt @dataclass class Cue : start : float end : float text : str @property def duration ( self ) -> float : return self . end - self . start @property def lines ( self ) -> list [ str ]: return self . text . split ( " \n " ) @property def flat ( self ) -> str : return " " . join ( l . strip () for l in self . lines ) @property def chars_per_second ( self ) -> float : return len ( self . flat ) / self . duration if self . duration > 0 else float ( " inf " ) def _to_seconds ( ts : str ) -> float : h , m , s = ts . split ( " : " ) return int ( h ) * 3600 + int ( m ) * 60 + float ( s ) def load_vtt ( path : str ) -