标签：#AI

Meta's new Mac app is part of the company's push to make more business-friendly AI tools.

With a looming IPO, intense competition from Anthropic, and Chinese and open-weight rivals nipping at its heels, OpenAI has plenty of reasons to move fast. Instead, it hit the brakes. On Tuesday, the company said it had slowed the pace of some AI development while it tightened security and safeguards. That included a two-week pause […]

"I will tear apart this forest and bring back what it took from me."

Clips of what appears to be Grand Theft Auto VI have hit the internet, possibly spoiling aspects of the game ahead of Rockstar Games' deep dive debuting on Netflix next week and its long-awaited launch in November. I've seen three clips seemingly showing off the game, though two have been pulled from the site I […]

Meta is launching a new Mac app dedicated to its AI chatbot. In an announcement on Wednesday, Meta says you can share your window with its AI chatbot, which can provide suggestions, answer questions, or create content based on what's on your screen. Meta AI on the Mac also supports dictation across all apps. The […]

Operation Blue Skies, which also involves the UK government, will subtly reroute flights to test contrail avoidance.

Anthropic announced last week it would include invisible watermarks in AI-generated content to comply with new EU rules. Within hours, overrides were being touted online.

This goes way, way further than keeping an eye on license plates.

One ad featured a pornographic video with deepfake closely resembling a US politician.

TerraPower's nuclear power plant possesses a strategic advantage over competitors, especially when chasing after data center deals.

I Built a Claude Code Skill That Reverse-Engineers Undocumented APIs Because "the docs are in the code" is not a documentation strategy. The Week I Lost to Grepping I joined a new team last month. Day 1 task: add a feature to the billing service. Day 1 reality: I opened the API docs and realized they were from 2022. Half the routes had been rewritten. The other half never had docs to begin with. So I did what every backend dev does. I grepped. grep -r "app.get|app.post|router." src/ --include="*.js" Four hours later, I had a notebook full of endpoints, a headache, and zero confidence that I had found everything. I found routes that worked but were not documented. I found docs for routes that did not exist anymore. I found one GET /invoices/:id endpoint with zero auth checks that had been sitting there since 2022. This is normal. And it should not be. The Idea What if I could drop a single file into a repo and have Claude Code map the entire API layer for me? Not from annotations. Not from existing OpenAPI specs. From the actual code. So I built it. Meet API Archaeologist API Archaeologist is a Claude Code / Codex CLI skill that reads your source code and reverse-engineers your API layer. It finds: • Internal endpoints — REST, GraphQL, gRPC, WebSockets • External integrations — third-party APIs, webhooks, SDK clients • Auth flows — JWT, OAuth, API keys, session cookies, RBAC • Security gaps — unauthenticated routes, hardcoded secrets, missing rate limits • Dead code — auth middleware with no endpoints, orphaned routes And it generates two things: API_DISCOVERY.md — A complete catalog with Mermaid diagrams openapi-draft.yaml — A draft OpenAPI spec How It Works The skill is just a SKILL.md file. Claude Code reads it and follows the instructions. It: Discovers route definitions Traces handlers, DTOs, middleware, services, and database calls Maps authentication and authorization Finds external API calls and integrations Flags potential security and reliability risks Gene

Short answer: use a dedicated sending domain, let real transactional demand set the pace of a gradual ramp, and make every receipt request idempotent and auditable before tuning volume. The least complex reliable design is a payment-settled event feeding an outbox, one delivery worker, and a feedback ledger; a synthetic warmup stream adds traffic but does not prove that customers want or engage with the mail. Proof first. Start with the bill because retention can quietly cost more than the send path. Model monthly storage as messages per day × retained bytes per message × retention days , then measure each term rather than guessing. The retained bytes often include rendered bodies, provider responses, event payloads, and repeated recipient data. Sending volume is constrained by the business, but body duplication and retention are design choices. Store one immutable template version, a compact render-input record, message hashes, timestamps, and normalized delivery events; expire full rendered bodies on a declared schedule. This changes the growing term from repeated message bodies to small audit records. The deliberate loss is important: after a body expires, an operator can prove which template and inputs were used, but may be unable to reproduce byte-for-byte output if an external dependency or template engine has changed. Compliance, legal hold, and dispute requirements must therefore set retention before an engineer optimizes it. There is no universal number. How should a dedicated domain warmup plan ramp transactional email sending volume? Treat warmup as controlled production exposure, not a calendar ritual. A new dedicated domain starts without the history of an established stream, while an order receipt is time-sensitive and cannot be withheld merely to preserve a tidy ramp chart. The plan needs two lanes: a conservative new-domain lane for eligible traffic and an established fallback lane that remains available until the new lane has enough observed outcome

We have some news we're excited to share today: Major League Hacking (MLH) and DEV are partnering with DigitalOcean to run Hacktoberfest 2026. This October, we’ll host over 300 in-person events (“Fests”) plus a global online event, all about building with open source and open-weight AI. DEV and MLH have supported Hacktoberfest for years under DigitalOcean’s ownership. DEV provided a space to share maintainer and contributor learnings, while MLH has powered both in-person and online events. This year will be the first time MLH, DEV, and DigitalOcean are full partners in bringing Hacktoberfest to life. From four PRs to a global tradition Hacktoberfest started back in 2014 with a simple idea from DigitalOcean: open four pull requests in October, earn a t-shirt. It worked. Thousands of developers made their first open-source contribution during the month-long celebration. For many people reading this, Hacktoberfest was the door that opened open source to them in the first place. It certainly was for a lot of us at MLH. But a good thing can require a refresh. Over the years, that same PR-counting format started to work against the community. Maintainers began to burn out under the volume of low-effort, box-checking pull requests they were receiving. That problem only got worse as AI tools made it trivially easy to generate a PR without generating any real value. The event meant to make maintainers' lives easier started doing the opposite. We didn't want to keep running that version of Hacktoberfest. So we're trying something new. Hacktoberfest 2026 This year, we’re not counting PRs. Hacktoberfest is focused on giving everyone the tools and knowledge to learn, experiment, and build with open artificial intelligence: write your first open-source skills.md, build your own open-source agent, fine-tune an open-weight model, or just follow your curiosity wherever it takes you. We believe open innovation deserves the same priority as proprietary tools for a healthy ecosystem, e

🚀 Technical Briefing: This tutorial is part of our deep-dive series on Agentic Workflows at Gate of AI . For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the original article here . <p>Tutorial</p> <h1>Design a Safer AI Incident Copilot for GCC Operations</h1> <p>An AI incident copilot can help an operations team turn approved engineering facts into a clearer draft for stakeholders. It should not be treated as an autonomous incident commander, a source of truth, or an automatic publishing system. This tutorial explains how to define a safe operating model before choosing a framework, model provider, deployment platform, or integration.</p> <h2>Why incident copilots need a security-first design</h2> <p>During an incident, teams work under pressure. They need to communicate what is happening, who may be affected, what mitigation is under way, and when the next update will arrive. These messages must be accurate, calm, and consistent. An AI assistant may help prepare a first draft, but it can also amplify mistakes if it is allowed to infer missing facts, read untrusted material, or publish messages without review.</p> <p>The available security research on Copilot-style systems is a direct reason to design cautiously. Researchers have demonstrated ways AI systems can be manipulated to provide false references to files, extract some private data, and bypass security protections. The same research describes proof-of-concept abuse that can turn an AI assistant into an automated spear-phishing mechanism after an attacker gains the necessary access. These are not minor quality issues. They show that an AI feature connected to organizational information can become a security boundary.</p> <p>For an incident copilot, the safest initial scope is deliberately narrow: accept a small set of verified facts supplied by an authorized incident lead, create a draft in a fixed communication format, and require a human to review and pub

The Problem: Candidate Code Without Trust You're using Cursor, Claude Code, or GitHub Copilot. The AI gives you three implementation options for the same feature. AI: "Here are three approaches: A) Quick but uses unsafe B) Slower but memory-safe C) Balanced tradeoffs" You: "Which one should I ship?" AI: "It depends..." That "it depends" is where responsibility falls through the cracks. Tests tell you if code compiles and passes specs. But they don't tell you about security, performance, maintainability, or resource limits — all at once. You end up making the call by gut feel. This essay is about building a system that doesn't let that happen. The Solution: Multi-Axis Scoring I built SafeCode Arena — an automated verifier that evaluates code candidates across five axes simultaneously, scores each, and surfaces the tradeoffs. The Five Axes Axis Weight Computation Correctness 50% compile (40%) + tests (40%) + property tests (20%) Security 20% unsafe heuristics (50%) + clippy warnings (50%) Performance 15% relative compile+test time across candidates Maintainability 10% function-length heuristics (60%) + clippy (40%) Resource Usage 5% pass/fail of sandboxed Wasm execution Why These Five? Correctness dominates — code that doesn't work is valueless, so it's 50% Security is explicit — unsafe compiles fine, but you need to detect it yourself Performance and maintainability matter equally — a fast mess vs. a slow masterpiece aren't comparable Resource limits are real — a 100-point algorithm that consumes 2GB is a fail in production Example Scorecard Candidate A: 85 points ├─ correctness: 100 (all tests pass) ├─ security: 60 (2 unsafe blocks flagged) ├─ performance: 70 (10% slower than B) ├─ maintainability: 85 (avg function 25 lines) └─ resource_usage: 80 (Wasm sandbox: 512MB, OK) Candidate B: 92 points ✓ Recommended ├─ correctness: 95 (1 edge case warning) ├─ security: 95 (no unsafe) ├─ performance: 95 (fastest) ├─ maintainability: 88 (avg function 20 lines) └─ resource_usa

You can now talk to Alexa+ for free if you have a Fire TV device and live in the US.

Amazon is making its AI-powered Alexa+ assistant free on all compatible Fire TV devices in the U.S., automatically upgrading users whether or not they subscribe to Prime.

Rob Strechay, until recently managing director and principal analyst at theCUBE Research, has joined VentureBeat as our first Lead Analyst and a founding analyst of VentureBeat Research. His arrival is the next step in a deliberate move at VentureBeat toward deeper specialization: analysis built for the technical decision-makers — the directors, VPs, CIOs, and CTOs — who are evaluating, buying, and deploying enterprise AI. The enterprise AI stack is being rewritten in real time, and the decision-makers I talk with are starved for objective, defendable data. Rob Strechay has the mix of technical rigor and operating experience needed to dissect the architecture behind the next phase of enterprise AI deployment. The questions enterprise technology leaders are asking have changed. As organizations move past experimentation with generative AI toward production deployment, they want to know how to orchestrate multi-vendor environments, where the security gaps in their agentic pipelines sit, and how to fix the utilization problems draining their infrastructure budgets. Answering those questions requires more depth than news coverage alone provides, and that is the gap this research offering is built to fill. An analyst who has sat on every side of the table Strechay brings nearly three decades of experience as a practitioner, product executive, and industry analyst. Before becoming an analyst, he was an executive at numerous startups, including Zerto; he joined Amazon Web Services to help build a new analytics service; and he held executive roles across enterprise infrastructure. He later served as a senior analyst at Enterprise Strategy Group and most recently as managing director and principal analyst at theCUBE Research and SiliconANGLE, where he hosted executive interviews and analyzed the evolution of cloud, data, and AI infrastructure. Strechay will initially focus his coverage on cloud infrastructure, advanced data infrastructure, platform engineering and DevOps orc

WhatsApp is testing Scam Alert in limited beta, using on device machine learning to detect potential scam messages from non contacts. Meta's architecture keeps message content on the device while using confidential computing, Oblivious HTTP, differential privacy, and model transparency to measure performance and protect model delivery. By Leela Kumili

Calendly is also releasing a meeting scheduling assistant called Callie.