标签：#ia

Will it be the VSS... Horizon , Explorer , Ascend or Apeiron ?

Ukrainian drone pilots teach the US military and NATO hard battlefield lessons.

Gemini 3.6 Flash debuted just 3 weeks ago, but Google says 3.7 has "substantial improvements."

X is expanding the open source code behind its 'For You' feed and launching new transparency tools that show users when its ranking systems have affected their accounts or posts.

A question I've been thinking about after discussing AI coding agents with several developers: Is passing the test suite enough to prove that an AI agent made the correct engineering decision? I don't think it is. And this isn't just a theoretical concern. Modern coding agents are increasingly working at the repository level rather than generating isolated code snippets. OpenAI's Codex documentation, for example, describes using repository-specific AGENTS.md instructions to tell the agent how to navigate a codebase, run tests, and follow project practices. Anthropic similarly describes Claude Code searching codebases, tracing dependencies, editing multiple files, and working with CI failures. ( OpenAI ) That changes what "correctness" means. Consider a simple scenario A project starts with: Architecture v1 API ↓ Service ↓ Database An AI agent learns this structure and implements a new feature correctly. The tests pass. Then the architecture changes: Architecture v2 API ↓ Event Bus ↓ Services ↓ Database The same task is requested again. If the agent continues following the old architecture, its code might still: compile, pass existing tests, satisfy the visible functional requirement, but still be wrong for the current system . This is the distinction I'm interested in: Code correctness ≠ Contextual correctness The Benchmark Problem Traditional coding benchmarks generally provide: Repository + Issue ↓ Agent ↓ Patch ↓ Tests / Evaluation This is valuable. SWE-bench, for example, was designed around real GitHub issues and repositories, and OpenAI created SWE-bench Verified with human validation because benchmark quality itself affects what we conclude about model capability. ( OpenAI ) But there is another dimension worth testing: What happens when the context changes? Recent research is already moving in this direction. SWE-ContextBench evaluates whether coding agents can reuse relevant experience across related tasks, while SWE-Explore focuses specifically on reposito

The social media giant says it was time for a sharper and more modern look after a decade.

Vercel has made the v0 API generally available, enabling developers and AI agents to programmatically generate, iterate on, preview, and deploy applications through API calls. By Daniel Dominguez

Perry Mason in: The Case of the Drifting Timer Opening Statement You need a reactive "current time" in your Vue 3 app. A schedule grid with a red line showing "now." A live clock. A dashboard that updates every minute. Every Vue developer reaches for setInterval first. It works. But "works" and "works well" are different things. This is the story of taking a naive timer from "it ticks" to production-grade — and the four iterations it took to get there. The prosecution calls four exhibits. Let's begin. Exhibit A: The Memory Leak const currentTime = ref ( new Date ()) onMounted (() => { setInterval (() => { currentTime . value = new Date () }, 60000 ) }) It works. Sort of. The defense rests — but the prosecution is just getting started. Exhibits of negligence: The interval is never cleared. When the component unmounts, the timer keeps firing every 60 seconds forever — updating a ref nothing reads anymore, and holding its closure (and everything the ref references) in memory for the lifetime of the page. Silent. Invisible. The kind of leak that shows up in production after a user navigates around your app for 20 minutes. Exhibit B: The Cleanup That Failed const currentTime = ref ( new Date ()) let timeInterval = null onMounted (() => { currentTime . value = new Date () timeInterval = setInterval (() => { currentTime . value = new Date () }, 60000 ) }) onUnmounted (() => { if ( timeInterval ) clearInterval ( timeInterval ) }) Now we clean up. The interval is stored in a variable, cleared on unmount. A step forward — but the prosecution has three more objections: Further evidence: This only works inside components. If someone calls this logic from a Pinia store or outside a component's setup() context, onUnmounted never fires. The timer leaks silently. (Composables called synchronously during setup() are fine — Vue's docs recommend exactly that. The problem is when there's no component instance at all.) The timer fires 60 seconds after load , not at the top of the minute

Cross-posting a technical tutorial is easy to start and surprisingly easy to get wrong. A URL import can leave code blocks split, headings as plain text, or metadata incomplete. The result may look acceptable at a glance while damaging the parts readers need most. This tutorial shows a reviewable DEV.to to Medium workflow using publish-agents , an open-source TypeScript project by Fernando Paladini. Its medium-publisher package imports a public article through Medium's import flow, checks the editor against the source Markdown, and can repair a small set of common formatting problems. TL;DR Checkout the stable v0.2.3 release, build the @paladini/medium-publisher-mcp package, log in once, and create a Medium draft with publish-devto . Keep the default draft behavior while you inspect the title, code blocks, headings, lists, and metadata. Prerequisites You need: Node.js 20 or newer. A published DEV.to article with a public URL. A Medium account that can create stories. A terminal that can run npm and the browser installation step. The package uses Patchright browser automation and a saved browser session. It does not use a Medium write API key. The project documents Medium UI changes as a compatibility risk, so treat the browser session and the resulting draft as reviewable state rather than an unattended guarantee. Install the released source The repository's v0.2.3 release is the stable reference for this walkthrough. Installing from that tag keeps the commands separate from later changes on the default branch. git clone https://github.com/paladini/publish-agents.git cd publish-agents git checkout v0.2.3 npm install npm run build -w @ paladini/medium-publisher-mcp npm link -w @ paladini/medium-publisher-mcp The build produces the CLI and MCP server from the package source. The package declares Node.js 20 or newer and uses patchright as its browser automation dependency. Its post-install step may install the bundled Chromium browser. If that step was skipped in your

Nvidia has a plan to make sure its GPUs won't lose value. It wants to convince a new crop of financiers to keep lending for AI buildouts.

Bartesian's cocktail makers would be best described like a Keurig or Nespresso machine, but for alcoholic drinks.

Pricing hasn't changed, and now you can get captain's chairs for the R1S.

Let's shield the astronauts instead of the spacecraft.

The police-tech giant Flock is announcing today that it will change officers’ access to its nationwide network of license plate readers, in an apparent effort to quell a growing backlash and win back contracts lost amid concerns about mass surveillance and police abuse. Several changes aim directly at a problem that has made recent headlines:…

Bluesky CEO Toni Schneider is joined by COO Rose Wang for a Disrupt Stage session on whether social media can start over and where Bluesky fits into that potential reboot.

Comfy MCP is Comfy's first-party local Model Context Protocol server. It lets an MCP-capable coding agent inspect the models and nodes in your ComfyUI installation, validate workflows, run them, and retrieve the outputs. The detail that prevents the most confusion is that two processes are involved : comfy launch starts ComfyUI. Your AI client starts comfy-mcp as a local stdio server. If you run comfy-mcp directly and it appears to do nothing, it is probably waiting for an MCP client. That is normal for a stdio server. Disclosure and verification scope: AI tools assisted with drafting and editing this adaptation. I reviewed the finished article and checked the commands and material claims against Comfy's official documentation, repository, and PyPI pages on 13 August 2026. I have not run a generation on my own hardware for this article, so this is a documentation-verified setup guide, not a hands-on performance test. Comfy's documentation currently labels the MCP offering a public beta, so tools and behaviour may change. What you need Before starting, have: Python 3.10 or newer. The examples below use Python 3.11. comfy-cli 1.14.0 or newer. A ComfyUI workspace, either created with comfy install or selected with comfy set-default . An MCP client that can start a local stdio server, such as Claude Code, Cursor, or Claude Desktop. The models and custom nodes required by the workflow you want to run. The MCP bridge is not what determines the hardware requirement; the selected ComfyUI workflow does. A small image workflow and a large video workflow can have very different memory needs. 1. Install comfy-cli and comfy-mcp I prefer a dedicated virtual environment. It keeps the executables in a predictable place and avoids mixing these packages with unrelated Python projects. Windows PowerShell mkdir comfy-mcp-guide cd comfy-mcp-guide py -3 . 11 -m venv . venv . \.venv\Scripts\Activate.ps1 python -m pip install --upgrade pip python -m pip install "comfy-cli>=1.14.0" comfy-mc

AI is disrupting career progression by eliminating the learning opportunities at each rung while simultaneously enabling people to perform above their experience level, Alasdair Allan explained in his talk Engineering Progression When AI Ate the Middle at QCon London. Fewer junior developers join the industry, and AI slows hiring at the entry level. By Ben Linders

Over a billion people worldwide have livers with excess fat, which can lead to a host of medical problems. Researchers think AI tools can spot the condition—and help stop it—early enough to save lives.

When we set out to talk to kids about artificial intelligence, we thought we knew what we’d hear. We expected some to tell us they were using it to cheat a little, the way Millennials and Gen Xers opened up CliffsNotes or programmed formulas into their TI-82s, and others to share inspiring ways they were…

You put your app behind a tunnel (or any reverse proxy) to test webhooks. Everything works. Then you notice something odd in your logs: every single request comes from the same IP address. Congratulations, you've met the X-Forwarded-For problem. What actually happens When a request flows through a tunnel, the TCP connection to your app comes from the relay, not the real client. So request.remote_addr — the value your framework uses for rate limiting, IP logging, geo-blocking, brute-force detection — is the relay's address. For every request. From every user. The consequences are quiet and nasty: Your rate limiter now rate-limits the relay, not the client. One aggressive user trips the limit and everyone gets blocked. Or worse, the limit is per-IP and effectively unlimited, because each relay node looks like one "user." * Your access logs are fiction. Security review of an incident? Every entry says the same address. * IP allowlists silently break. "Only allow my office IP" now allows nothing, or everything, depending on how it's wired. The fix (and its trap) The proxy already tells you the real client IP — in the X-Forwarded-For header. Every framework has a setting to trust it. Flask: ProxyFix . Express: app.set('trust proxy', ...) . Rails, Django, Laravel: equivalents exist. Here's the trap: trust that header blindly and anyone can spoof it. A client can send X-Forwarded-For: 1.2.3.4 directly, and if your app believes headers from anyone, your rate limiter is bypassed with a curl flag. The correct setup has two halves: 1. Trust `X-Forwarded-For` only when the immediate connection comes from a proxy you control (your tunnel relay, your load balancer). 2. Strip or ignore the header on direct connections. Most frameworks express this as "trusted proxies" — a list of proxy IPs whose forwarded headers you believe. Set it. It's five minutes of config that determines whether your security features are real or decorative. Why this matters more in the tunnel era Tunnels us