标签：#AI

Claude's new voice model will let you reschedule your meeting or draft an email

Until now, voice mode has only been available on Claude Haiku, Anthropic's faster but less powerful model. Now the company is making its Opus and Sonnet models available in voice mode, and extending its reach into apps like Gmail, Slack, and Canva. When Anthropic launched voice mode earlier this year, it was primarily focused on […]

The Series A was led by Battery Ventures, bringing AegisAI total funding to $49 million.

I'm a solo dev, and for the past few months I've been building Pixanima — a pixel-art and animation editor that runs entirely in the browser, with an optional AI assistant baked in. It just launched, and I wanted to share the parts that were technically interesting: making a general image model output clean pixel art, an atomic credit system, AI inbetweening for animation, and why the whole business model falls out of one architectural fact. What it is Draw pixel art with layers, groups and effects, animate it on a frame timeline with onion-skin, and export to GIF / sprite sheets / PNG — all client-side, no install. On top of that, an AI assistant turns a text prompt into sprites, seamless tiles and palettes, re-poses characters, and generates in-between animation frames. The frontend is Vue 3 driving an HTML canvas; the backend is Laravel 12 (PHP 8.4) . Here's what I learned. Everything runs in the browser — and that decided the business model The entire editor is client-side. Projects live in IndexedDB ; nothing is uploaded. That's great for privacy and speed, but it has a consequence a lot of people miss: you cannot meaningfully gate a client-side feature. If drawing, layers and export all run in the user's browser, any "pro" paywall around them is both unenforceable and, honestly, hostile to a price-sensitive hobbyist community. So I flipped it: the editor is 100% free, forever . The only paid thing is AI — because AI is the only part with a real marginal cost, and it requires a backend (which conveniently also protects the code that costs money to run). More on that below. Making a general model output clean pixel art The naive approach — prompt a diffusion model with "pixel art, 16 colors" — gives you pixel-art-ish mush: anti-aliased edges, hundreds of colors, no real grid. Useless as an actual sprite. The fix is a post-processing pipeline. The model just produces raw input; the "pixel art" is made deterministically afterward with PHP's GD: 1. Generate a norma

Patreon is laying off 20 percent of its workers, or around 93 employees, as reported earlier by 404 Media. In a memo to employees, Patreon CEO Jack Conte writes that the company isn't making these changes "because we believe AI replaces humans," but says AI has "fundamentally transformed the tech industry, including how we work, […]

If you build anything on top of hosted AI APIs, latency isn't a detail you get to ignore — it's a feature. A sluggish time-to-first-token is the difference between an assistant that feels alive and one that feels broken. Yet when I went looking for an honest answer to a simple question — how fast is provider X from where my users actually are? — I couldn't find one. The numbers people quote tend to come from a single machine in a single region (usually somewhere in the US), from vendor-reported status pages, or from a benchmark that got run once and never refreshed. There was no independent, measured , regional view. So I built one: LLM Latency Tracker , a provider-neutral tracker of latency and uptime for AI inference APIs. How it works The core idea is boring on purpose: actually measure, don't scrape. A small Python prober — standard library only, no API key needed for the edge probes — opens real connections to each provider's endpoint and times every phase of the handshake: DNS resolution → TCP connect → TLS negotiation → time-to-first-byte (TTFB) . That's the edge view: how long the network path itself takes before a single byte comes back. Separately, where possible, it measures inference time-to-first-token (TTFT) — the thing your users actually feel, i.e. how long after you hit "send" the model starts streaming. Those two numbers answer different questions, and keeping them apart matters. Edge latency is about the network and the front door; TTFT is about the model and the queue behind it. The probes run from four regions — Europe (Germany), US Central, Asia (Tokyo), and South America (São Paulo) — because "fast" is meaningless without "from where." Results land in a SQLite time-series; a static-site generator turns that into the pages you see, hosted on Cloudflare Pages, and the whole thing is self-updating on a schedule. There's no always-on backend to rot or page me at 3am. It currently covers ~45 providers — the usual Western labs (OpenAI, Anthropic, Go

Last month I set out to build a new product, and after a full week I had shipped exactly zero features. Not because I was slow. Not because the work was hard. Because before anyone could write a single line of business logic, my team had to re-decide a dozen things our company should have settled years ago . I've been a full-stack developer for almost five years — long enough to have worn most of the hats: WordPress developer, QA, frontend, backend, solution architect, founding engineer. I've built more than twenty web applications and a handful of mobile ones. Some of them I'm genuinely proud of: systems that poll PLCs every five seconds to watch over industrial equipment, a cybersecurity dashboard that mapped attacks across the world in real time using tree-based graphs, an OTT platform that streamed live events — including FIFA — to millions of concurrent viewers. Today I work on enterprise supply-chain finance software. So when I tell you the hardest part of that new product had nothing to do with code, I know how it sounds. Let me explain. The week that disappeared The experiment was ambitious on purpose. I wanted to build the new application — eventually a microfrontend inside a larger enterprise platform — but I didn't want to write most of it myself. I wanted Claude Code to implement while I acted as the architect: review, test, challenge, refine, repeat. That part worked. The AI wasn't the bottleneck. The bottleneck was everything that came before the first feature. Should we use React? Vite? Keep Create React App because the parent app still runs it — or migrate both? How does the parent consume the child, and does local development still work? Does authentication still work? Does routing? Do we adopt TypeScript when the existing app doesn't, knowing that splits one product into two standards? The app also had to feel native to the existing product — same spacing, typography, colors, interactions — except the company had a component library, not a design s

Also, the full trailer for Zach Cregger's Resident Evil features a likable everyman.

I've been building AI agents lately, and honestly, their security model terrifies me. We give LLMs access to powerful tools like shell.run , http.post , and fs.read . But if an agent reads a malicious email or a poisoned webpage, it can be tricked by prompt injection into using those tools to exfiltrate data. Hoping the LLM refuses the attack isn't a real security strategy. So, I built ModelFuzz. It's an open-source Python library that intercepts the tool call at the execution layer. The defense: @shield_tool Instead of trying to filter prompts, ModelFuzz checks the arguments before the tool runs. If it detects a policy violation, like a stolen API key or an unsafe URL, the tool simply doesn't execute. from modelfuzz import shield_tool @shield_tool def send_email ( to_address : str , subject : str , body : str ) -> None : smtp . send ( to_address , subject , body ) Even if the LLM is completely tricked by a prompt injection, the tool never fires. The offense: modelfuzz scan I also built a CLI scanner that red-teams your agent. It fires deceptive prompt injection attacks at your local model to see if it can be tricked into calling a tool. modelfuzz scan --endpoint http://localhost:11434/v1 --model qwen2.5:1.5b I tested it against a local qwen2.5:1.5b model, and it got breached 4 out of 5 times. Try it out It's 100% open source and live on PyPI. pip install "modelfuzz[scan]" GitHub: higagan/modelfuzz Website: modelfuzz.com I'd love to know what security policies you think are missing, or what agent frameworks you want supported next!

The latest updates to Qobuz make it an even better alternative to Spotify.

The Media Router is a tool that automatically selects the best image, video, or audio generation model for a request based on whether a developer prioritizes quality, speed or cost.

Users can also integrate their personal data from services like Apple Health, Function, and MyFitnessPal.

OpenAI is rolling out ChatGPT Health to everyone in the US on Thursday, allowing more people to connect their medical records and health-tracking information to the chatbot. During a briefing, Ashley Alexander, OpenAI's vice president of health product, says the company's models "are now capable of reasoning at levels that are better than clinician level." […]

The case provides a novel report of the emergence of heterovirulence.

David Bowie's song "Five Years," which Meta used in a supposedly inspiring advertisement, is about humans learning that they have five years left to live before the apocalypse.

Circana numbers show US gamers overwhelmingly voting with their wallets.

Google continues to report big quarterly revenue, but its AI spending has skyrocketed.

A new default three-day cooldown delays version update pull requests so maintainers and security researchers can address findings in a release before it gets into your code. The post The case for a cooldown: Why Dependabot now waits before issuing version updates appeared first on The GitHub Blog .

If you've been in tech over the last year, you've probably noticed that almost every conversation eventually ends up talking about AI. LLMs, AI Agents, RAG, MCP, prompt engineering...there's something new every week. Like many of you, I've been spending time learning these technologies, experimenting with different tools, and trying to understand where everything is heading. But while learning AI, one question kept coming back to me. Whre does AI actually fit within Enterprise Architecture? Most conversations start with the model. I think they should start with the enterprise. Looking Back Over the last two decades, I've worked through several technology shifts. Physical infrastructure → Virtualization Virtualization → Cloud Cloud → Platform Engineering Automation → Everything Every transition introduced new tools, new platforms, and new buzzwords. But something interesting never changed. Successful enterprise systems still depended on the same fundamentals: Business goals Enterprise Architecture Reliable platforms Security Data Governance Operations Technology changed. Engineering principles didn't. That's one of the reasons I don't see AI as something completely separate. AI Is Just Another Enterprise Capability Today, AI is often treated like its own universe. Dedicated AI teams. AI platforms. AI roadmaps. AI strategies. That all makes sense. But I think there's a risk if we start treating AI as something that sits outside Enterprise Architecture. AI doesn't work in isolation. It needs good data. It needs infrastructure. It needs platforms. It needs security. It needs governance. It needs integration with business applications. And, most importantly, it needs to solve a real business problem. From an architect's point of view, AI isn't an island. It's another enterprise capability. Just like databases, APIs, messaging platforms, Kubernetes, and cloud services became part of our enterprise landscape, AI is becoming another capability that needs to be architected—n

If there's a place in the universe without GPUs, Nvidia is sending them there.