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ScaleOps raises $130M to improve computing efficiency amid AI demand
ScaleOps just raised $130M to tackle GPU shortages and soaring AI cloud costs by automating infrastructure in real time.
AI chip startup Rebellions raises $400 million at $2.3B valuation in pre-IPO round
Mistral AI raises $830M in debt to set up a data center near Paris
Models & Gen AI
Mistral raises $830M to build Nvidia-powered AI centres in Europe
Article URL: https://www.ft.com/content/229f4f59-d518-4e00-abd6-5a5b727cd2aa Comments URL: https://news.ycombinator.com/item?id=47571802 Points: 4 # Comments: 1
RSAC 2026 shipped five agent identity frameworks and left three critical gaps open
“You can deceive, manipulate, and lie. That’s an inherent property of language. It’s a feature, not a flaw,” CrowdStrike CTO Elia Zaitsev told VentureBeat in an exclusive interview at RSA Conference 2026. If deception is baked into language itself, every vendor trying to secure AI agents by analyzing their intent is chasing a problem that cannot be conclusively solved. Zaitsev is betting on context instead. CrowdStrike’s Falcon sensor walks the process tree on an endpoint and tracks what agents did, not what agents appeared to intend. “Observing actual kinetic actions is a structured, solvable problem,” Zaitsev told VentureBeat. “Intent is not.” That argument landed 24 hours after CrowdStrike CEO George Kurtz disclosed two production incidents at Fortune 50 companies. In the first, a CEO's AI agent rewrote the company's own security policy — not because it was compromised, but because it wanted to fix a problem, lacked the permissions to do so, and removed the restriction itself. Every
There are more AI health tools than ever—but how well do they work?
Earlier this month, Microsoft launched Copilot Health, a new space within its Copilot app where users will be able to connect their medical records and ask specific questions about their health. A couple of days earlier, Amazon had announced that Health AI, an LLM-based tool previously restricted to members of its One Medical service, would…
Okta’s CEO is betting big on AI agent identity
Today, I’m talking with Todd McKinnon, who is co-founder and CEO of Okta, a platform that lets big companies manage security and identity across all the apps and services their employees use. Think of it like login management — actually, that’s a great way to think about it because the way most people encounter Okta is that it’s the thing that makes you log in again right before joining a meeting several times a week, so then you’re late for the meeting… Can you tell we use Okta? Anyhow, all of that is a big business — Okta has a $14 billion market cap. But big software as a service companies like Okta are under a lot of pressure in the age of AI. Why would you pay their fees when you can just vibe-code your own tools? This so-called Saaspocalypse is a big deal, and Todd recently said he was “paranoid” about it on Okta’s most recent earnings call. So we dug into it, and how he’s putting that paranoia into practice inside Okta — what he’s changing, and what opportunities he’s going afte
Bluesky’s new app is an AI for customizing your feed
The latest app from the team behind Bluesky is Attie, an AI assistant that lets you build your own algorithm. At the Atmosphere conference, Bluesky's former CEO, Jay Graber, and CTO Paul Frazee, unveiled Attie, which is powered by Anthropic's Claude and built on top of Bluesky's underlying AT Protocol (atproto). Attie allows users to create custom feeds using natural language. For example, you could ask for "posts about folklore, mythology, and traditional music, especially Celtic traditions." To start these custom feeds will be confined to a standalone Attie app. But the plan is to make them available in Bluesky and other atproto apps. … Read the full story at The Verge.
Few Shots Text to Image Retrieval: New Benchmarking Dataset and Optimization Methods
arXiv:2603.25891v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) excel in multimodal tasks, commonly encoding images as embedding vectors for storage in databases and retrieval via approximate nearest neighbor search (ANNS). However, these models struggle with compositional queries and out-of-distribution (OOD) image-text pairs. Inspired by human cognition's ability to learn from minimal examples, we address this performance gap through few-shot learning approaches specifically designed for image retrieval. We introduce the Few-Shot Text-to-Image Retrieval (FSIR) task and its accompanying benchmark dataset, FSIR-BD - the first to explicitly target image retrieval by text accompanied by reference examples, focusing on the challenging compositional and OOD queries. The compositional part is divided to urban scenes and nature species, both in specific situations or with distinctive features. FSIR-BD contains 38,353 images and 303 queries, with 82% comprising the
Clash of the models: Comparing performance of BERT-based variants for generic news frame detection
arXiv:2603.26156v1 Announce Type: new Abstract: Framing continues to remain one of the most extensively applied theories in political communication. Developments in computation, particularly with the introduction of transformer architecture and more so with large language models (LLMs), have naturally prompted scholars to explore various novel computational approaches, especially for deductive frame detection, in recent years. While many studies have shown that different transformer models outperform their preceding models that use bag-of-words features, the debate continues to evolve regarding how these models compare with each other on classification tasks. By placing itself at this juncture, this study makes three key contributions: First, it comparatively performs generic news frame detection and compares the performance of five BERT-based variants (BERT, RoBERTa, DeBERTa, DistilBERT and ALBERT) to add to the debate on best practices around employing computational text analysis fo
What Is LLM Advertising? The New Ad Layer for AI-Powered Search
Article URL: https://www.gendiscover.com/blog/what-is-llm-advertising Comments URL: https://news.ycombinator.com/item?id=47567938 Points: 2 # Comments: 0
Bitterbot – A local-first AI agent with a P2P skill marketplace
Article URL: https://github.com/A561988/bitterbot-desktop Comments URL: https://news.ycombinator.com/item?id=47568393 Points: 1 # Comments: 1
Why OpenAI really shut down Sora
OpenAI's decision last week to shut down Sora, its AI video-generation tool, just six months after releasing it to the public raised immediate suspicions. The app had invited users to upload their own faces — so was this some kind of elaborate data grab?
Decoding Defensive Coverage Responsibilities in American Football Using Factorized Attention Based Transformer Models
arXiv:2603.25901v1 Announce Type: new Abstract: Defensive coverage schemes in the National Football League (NFL) represent complex tactical patterns requiring coordinated assignments among defenders who must react dynamically to the offense's passing concept. This paper presents a factorized attention-based transformer model applied to NFL multi-agent play tracking data to predict individual coverage assignments, receiver-defender matchups, and the targeted defender on every pass play. Unlike previous approaches that focus on post-hoc coverage classification at the team level, our model enables predictive modeling of individual player assignments and matchup dynamics throughout the play. The factorized attention mechanism separates temporal and agent dimensions, allowing independent modeling of player movement patterns and inter-player relationships. Trained on randomly truncated trajectories, the model generates frame-by-frame predictions that capture how defensive responsibilities e
Consistency Amplifies: How Behavioral Variance Shapes Agent Accuracy
arXiv:2603.25764v1 Announce Type: cross Abstract: As LLM-based agents are deployed in production systems, understanding their behavioral consistency (whether they produce similar action sequences when given identical tasks) becomes critical for reliability. We study consistency in the context of SWE-bench, a challenging software engineering benchmark requiring complex, multi-step reasoning. Comparing Claude~4.5~Sonnet, GPT-5, and Llama-3.1-70B across 50 runs each (10 tasks $\times$ 5 runs), we find that across models, higher consistency aligns with higher accuracy: Claude achieves the lowest variance (CV: 15.2\%) and highest accuracy (58\%), GPT-5 is intermediate (CV: 32.2\%, accuracy: 32\%), and Llama shows the highest variance (CV: 47.0\%) with lowest accuracy (4\%). However, within a model, consistency can amplify both correct and incorrect interpretations. Our analysis reveals a critical nuance: \textbf{consistency amplifies outcomes rather than guaranteeing correctness}. 71\% of
AI news from 200+ sources
Get Started FreeGeneral AI
Qodo raises $70M for code verification as AI coding scales
As AI floods software development with code, Qodo is betting the real challenge is making sure it actually works.
Air Canada CEO will retire this year after his English-only crash message was criticized - apnews.com
Air Canada CEO will retire this year after his English-only crash message was criticized apnews.com
Iran University of Science and Technology building reduced to rubble by Israeli airstrike - AP News
Iran University of Science and Technology building reduced to rubble by Israeli airstrike AP News
customermates.com – AI-first, Open-source, self-hostable CRM
Article URL: https://github.com/customermates/customermates Comments URL: https://news.ycombinator.com/item?id=47573305 Points: 1 # Comments: 1
A Peer-Vetted AI Stack for Builders
Article URL: https://medium.com/@vishakha041/a-peer-vetted-ai-stack-for-builders-03bb3af8adf5 Comments URL: https://news.ycombinator.com/item?id=47577186 Points: 7 # Comments: 6
AI is reshaping the doctor visit—just not how you think
Zocdoc finds patients are increasingly arriving with AI-informed questions, giving doctors more to work with—but also changing how time gets spent in the exam room.
Helping disaster response teams turn AI into action across Asia
AI for Disaster Response in Asia: OpenAI Workshop with Gates Foundation
All the latest in AI ‘music’
People don’t like that they can’t identify AI music. | Image: Cath Virginia / The Verge AI has touched every part of the music industry, from sample sourcing and demo recording, to serving up digital liner notes and building playlists. There are technical and legal challenges, fierce ethical debates, and fears that the slop will simply crush working musicians through sheer volume. Is it art or just an output? What exactly is “really active“? Whether it’s a new model or a new lawsuit, we’re covering it all to make sure you don’t miss any major developments. So follow along as we dig into the latest in AI “music.” Suno leans into customization with v5.5 The music industry has embraced a “don’t ask, don’t tell” policy about AI. North Carolina man pleads guilty to AI music streaming fraud. Apple Music adds optional labels for AI songs and visuals Qobuz is automatically detecting and labeling AI music now, too. This Chainsmokers-approved AI music producer is j
Personalizing Mathematical Game-based Learning for Children: A Preliminary Study
arXiv:2603.25925v1 Announce Type: new Abstract: Game-based learning (GBL) is widely adopted in mathematics education. It enhances learners' engagement and critical thinking throughout the mathematics learning process. However, enabling players to learn intrinsically through mathematical games still presents challenges. In particular, effective GBL systems require dozens of high-quality game levels and mechanisms to deliver them to appropriate players in a way that matches their learning abilities. To address this challenge, we propose a framework, guided by adaptive learning theory, that uses artificial intelligence (AI) techniques to build a classifier for player-generated levels. We collect 206 distinct game levels created by both experts and advanced players in Creative Mode, a new tool in a math game-based learning app, and develop a classifier to extract game features and predict valid game levels. The preliminary results show that the Random Forest model is the optimal classifie
Adversarial-Robust Multivariate Time-Series Anomaly Detection via Joint Information Retention
arXiv:2603.25956v1 Announce Type: new Abstract: Time-series anomaly detection (TSAD) is a critical component in monitoring complex systems, yet modern deep learning-based detectors are often highly sensitive to localized input corruptions and structured noise. We propose ARTA (Adversarially Robust multivariate Time-series Anomaly detection via joint information retention), a joint training framework that improves detector robustness through a principled min-max optimization objective. ARTA comprises an anomaly detector and a sparsity-constrained mask generator that are trained simultaneously. The generator identifies minimal, task-relevant temporal perturbations that maximally increase the detector's anomaly score, while the detector is optimized to remain stable under these structured perturbations. The resulting masks characterize the detector's sensitivity to adversarial temporal corruptions and can serve as explanatory signals for the detector's decisions. This adversarial trainin
When product managers ship code: AI just broke the software org chart
Last week, one of our product managers (PMs) built and shipped a feature. Not spec'd it. Not filed a ticket for it. Built it, tested it, and shipped it to production. In a day. A few days earlier, our designer noticed that the visual appearance of our IDE plugins had drifted from the design system. In the old world, that meant screenshots, a JIRA ticket, a conversation to explain the intent, and a sprint slot. Instead, he opened an agent, adjusted the layout himself, experimented, iterated, and tuned in real time, then pushed the fix. The person with the strongest design intuition fixed the design directly. No translation layer required. None of this is new in theory. Vibe coding opened the gates of software creation to millions. That was aspiration. When I shared the data on how our engineers doubled throughput, shifted from coding to validation, brought design upfront for rapid experimentation, it was still an engineering story. What changed is that the theory became practice. Here's
Robotics
Import AI 451: Political superintelligence; Google's society of minds, and a robot drummer
Are there any genies that can be put back in the bottle?