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Article URL: https://www.npmjs.com/package/vibepad Comments URL: https://news.ycombinator.com/item?id=47600023 Points: 1 # Comments: 0



If you're tired of controlling Stream Deck devices by manually pushing buttons, then good news: Elgato will now let you delegate that task to a chatbot instead. The Stream Deck 7.4 software update released today introduces Model Context Protocol (MCP) support, allowing AI assistants like Claude, ChatGPT, and Nvidia G-Assist to find and activate Stream Deck actions on your behalf. "You still set up actions in Stream Deck app the same way you always have. MCP adds a new way to trigger them," Elgato said in its announcement. "Once everything is connected, you can type or speak requests and your AI tool will trigger the matching Stream Deck act … Read the full story at The Verge.

arXiv:2603.28929v1 Announce Type: new Abstract: Multi-intent detection papers usually ask whether a model can recover multiple intents from one utterance. We ask a harder and, for deployment, more useful question: can it recover new combinations of familiar intents? Existing benchmarks only weakly test this, because train and test often share the same broad co-occurrence patterns. We introduce CoMIX-Shift, a controlled benchmark built to stress compositional generalization in multi-intent detection through held-out intent pairs, discourse-pattern shift, longer and noisier wrappers, held-out clause templates, and zero-shot triples. We also present ClauseCompose, a lightweight decoder trained only on singleton intents, and compare it to whole-utterance baselines including a fine-tuned tiny BERT model. Across three random seeds, ClauseCompose reaches 95.7 exact match on unseen intent pairs, 93.9 on discourse-shifted pairs, 62.5 on longer/noisier pairs, 49.8 on held-out templates, and 91.

Deploying AI agents for repository-scale tasks like bug detection, patch verification, and code review requires overcoming significant technical hurdles. One major bottleneck: the need to set up dynamic execution sandboxes for every repository, which are expensive and computationally heavy. Using large language model (LLM) reasoning instead of executing the code is rising in popularity to bypass this overhead, yet it frequently leads to unsupported guesses and hallucinations. To improve execution-free reasoning, researchers at Meta introduce "semi-formal reasoning," a structured prompting technique. This method requires the AI agent to fill out a logical certificate by explicitly stating premises, tracing concrete execution paths, and deriving formal conclusions before providing an answer. The structured format forces the agent to systematically gather evidence and follow function calls before drawing conclusions. This increases the accuracy of LLMs in coding tasks and significantly

Article URL: https://openai.com/index/accelerating-the-next-phase-ai/ Comments URL: https://news.ycombinator.com/item?id=47593432 Points: 12 # Comments: 1

Runway is launching a $10 million fund and startup program to back companies building with its AI video models, as it pushes toward interactive, real-time “video intelligence” applications.

ThinkLabs AI, a startup building artificial intelligence models that simulate the behavior of the electric grid, announced today that it has closed a $28 million Series A financing round led by Energy Impact Partners (EIP), one of the largest energy transition investment firms in the world. Nvidia’s venture capital arm NVentures and Edison International, the parent company of Southern California Edison, also participated in the round. The funding marks a significant escalation in the race to apply AI not just to software and content generation, but to the physical infrastructure that powers modern life. While most AI investment headlines have centered on large language models and generative tools, ThinkLabs is pursuing a different and arguably more consequential application: using physics-informed AI to model the behavior of electrical grids in real time, compressing engineering studies that once took weeks or months into minutes. "We are dead focused on the grid," ThinkLabs CEO Josh W

Article URL: https://xenv.sh/ Comments URL: https://news.ycombinator.com/item?id=47593562 Points: 1 # Comments: 0

For three decades, the web has existed in a state of architectural denial. It is a platform originally conceived to share static physics papers, yet it is now tasked with rendering the most complex, interactive, and generative interfaces humanity has ever conceived. At the heart of this tension lies a single, invisible, and prohibitively expensive operation known as "layout reflow." Whenever a developer needs to know the height of a paragraph or the position of a line to build a modern interface, they must ask the browser’s Document Object Model (DOM), the standard by which developers can create and modify webpages. In response, the browser often has to recalculate the geometry of the entire page — a process akin to a city being forced to redraw its entire map every time a resident opens their front door. Last Friday, March 27, 2026, Cheng Lou — a prominent software engineer whose work on React, ReScript, and Midjourney has defined much of the modern frontend landscape — announced on

I've been building Sandflare for the past few months — it launches Firecracker microVMs for AI agents in ~300ms cold start. The idea came from running LLM-generated code in production. Docker felt too risky (shared kernel), full VMs too slow (5–10s). Firecracker hits the middle: real VM isolation, fast boot. I also added managed Postgres because almost every agent I built needed persistent state. One call wires a database into a sandbox. There are great tools in this space already (E2B, Modal, Daytona) — I wanted something with batteries-included Postgres, and simpler pricing What I'm trying to figure out: how do I get cold start below 100ms? Currently the bottleneck is the Firecracker API + network setup. Would love to hear from anyone who's pushed Firecracker further. https://sandflare.io Comments URL: https://news.ycombinator.com/item?id=47583255 Points: 2 # Comments: 3

arXiv:2603.29075v1 Announce Type: new Abstract: The way we're thinking about generative AI right now is fundamentally individual. We see this not just in how users interact with models but also in how models are built, how they're benchmarked, and how commercial and research strategies using AI are defined. We argue that we should abandon this approach if we're hoping for AI to support groundbreaking innovation and scientific discovery. Drawing on research and formal results in complex systems, organizational behavior, and philosophy of science, we show why we should expect deep intellectual breakthroughs to come from epistemically diverse groups of AI agents working together rather than singular superintelligent agents. Having a diverse team broadens the search for solutions, delays premature consensus, and allows for the pursuit of unconventional approaches. Developing diverse AI teams also addresses AI critics' concerns that current models are constrained by past data and lack the

Build production AI agents on MongoDB Atlas — with vector search, persistent memory, natural-language querying, and end-to-end observability built in.

Gradient Labs uses GPT-4.1 and GPT-5.4 mini and nano to power AI agents that automate banking support workflows with low latency and high reliability.

After Anthropic released Claude Code's 2.1.88 update, users quickly discovered that it contained a package with a source map file containing its TypeScript codebase, with one person on X calling attention to the leak and posting a file containing the code. The leaked data reportedly contains more than 512,000 lines of code and provides a look into the inner workings of the AI-powered coding tool, as reported earlier by Ars Technica and VentureBeat. Users who have dug into the code claim to have uncovered upcoming features, Anthropic's instructions for the AI bot, and insight into its "memory" architecture. Some things spotted by users inclu … Read the full story at The Verge.

ChatGPT is now accessible from your CarPlay dashboard if you have iOS 26.4 or newer and the latest version of the ChatGPT app, 9to5Mac reports. Apple's recently-launched iOS 26.4 update added support for "voice-based conversational apps" in CarPlay, opening the door to let you use AI chatbots with voice features through Apple's in-car platform. When using ChatGPT through CarPlay, the app doesn't show text conversations, according to 9to5Mac - instead, you can only have conversations with the app using your voice. (Apple's developer guidelines ask that apps don't show text or imagery as responses.) The CarPlay app isn't completely devoid of … Read the full story at The Verge.

This just showed up a couple of days ago on GitHub. Note that ANE is the NPU in all Apple Silicon, not the new 'Neural Accelerator' GPU cores that are only in M5. (ggml-org/llama.cpp#10453) - Comment by arozanov Built a working ggml ANE backend. Dispatches MUL_MAT to ANE via private API. M4 Pro results: 4.0 TFLOPS peak at N=256, 16.8x faster than CPU MIL-side transpose, kernel cache, quantized weight support ANE for prefill (N>=64), Metal/CPU for decode Code: https://github.com/arozanov/ggml-ane Based on maderix/ANE bridge. submitted by /u/PracticlySpeaking [link] [comments]
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OpenAI's latest funding round, led by Amazon, Nvidia, and SoftBank, values the AI lab at $852 billion as it nears an IPO.

I'm excited to announce that AWS Security Agent on-demand penetration testing and AWS DevOps Agent are now generally available, representing a new class of AI capabilities we announced at re:Invent called frontier agents. These autonomous systems work independently to achieve goals, scale massively to tackle concurrent tasks, and run persistently for hours or days without constant human oversight. Together, these agents are changing the way we secure and operate software. In preview, customers and partners report that AWS Security Agent compresses penetration testing timelines from weeks to hours and the AWS DevOps Agent supports 3–5x faster incident resolution.

Slack today announced more than 30 new capabilities for Slackbot, its AI-powered personal agent, in what amounts to the most sweeping overhaul of the workplace messaging platform since Salesforce acquired it for $27.7 billion in 2021. The update transforms Slackbot from a simple conversational assistant into a full-spectrum enterprise agent that can take meeting notes across any video provider, operate outside the Slack application on users' desktops, execute tasks through third-party tools via the Model Context Protocol (MCP), and even serve as a lightweight CRM for small businesses — all without requiring users to install anything new. The announcement, timed to a keynote event that Salesforce CEO Marc Benioff is headlining Tuesday morning, arrives less than three months after Slackbot first became generally available on January 13 to Business+ and Enterprise+ subscribers. In that short window, Slack says the feature is on track to become the fastest-adopted product in Salesforce's 2

Softr, the Berlin-based no-code platform used by more than one million builders and 7,000 organizations including Netflix, Google, and Stripe, today launched what it calls an AI-native platform — a bet that the explosive growth of AI-powered app creation tools has produced a market full of impressive demos but very little production-ready business software. The company's new AI Co-Builder lets non-technical users describe in plain language the software they need, and the platform generates a fully integrated system — database, user interface, permissions, and business logic included — connected and ready for real-world deployment immediately. The move marks a fundamental evolution for a company that spent five years building a no-code business before layering AI on top of what it describes as a proven infrastructure of constrained, pre-built building blocks. "Most AI app-builders stop at the shiny demo stage," Softr Co-Founder and CEO Mariam Hakobyan told VentureBeat in an exclusive in

The former BP chief is entering the AI age with American data center projects.

Ring's app store will allow the company to target broader use cases beyond security, like elder care or business needs.

The round reflects growing investor interest in AI‑native platforms to modernize legacy outsourced IT.

Emerald AI touts new fundraising success and partnerships with utilities and power generators.

Love to see the BACKSST BOYS at the Sphere. The Google Pixel 9 walked so that the Samsung Galaxy S26 could run. Google introduced AI editing tools to Photos slowly. It started with changes to the background - make the sky more blue, or remove crowds of tourists. Things got weird once the company added natural language requests and let you ask for basically any change. There were some guardrails, but in many cases it was easy to prompt your way around them into creating a potentially harmful image of something that never happened - helicopter crashes, smoking bombs on street corners, that kind of thing. That's the world Samsung's updated Photo Assist steps into. At Unpacked in Febru … Read the full story at The Verge.

Article URL: https://www.omgubuntu.co.uk/2026/03/firefox-smart-window-hands-on Comments URL: https://news.ycombinator.com/item?id=47585853 Points: 1 # Comments: 0

Feds probe whether NYC Council member, Hochul aide took bribes to help migrant shelter provider AP News

The company turns footage from robots into structured, searchable datasets with a deep learning model.