Google Cloud has unveiled "AI Threat Defense," a platform designed to automatically find, assess, and patch security flaws in enterprise systems. The company bundles technologies it partly acquired through acquisitions.
Google Cloud launches AI Threat Defense platform to combat AI-driven cyberattacks.
The platform automatically discovers, assesses, and patches security vulnerabilities.
A Vox article explores the growing movement of AI successionists who believe artificial intelligence should replace humanity as the next step in cosmic evolution, and examines the ethical and spiritual questions this raises.
AI successionists at a symposium argue that AI could be morally superior and should be allowed to supersede humanity.
The movement has gained influence in Silicon Valley and among major AI labs, with ties to the authoritarian right.
Google Pay is overhauling its payment infrastructure for AI agent transactions, introducing the Universal Commerce Protocol (UCP) and a new Merchant Commerce Platform (MCP) server to create an API-driven backend for machine-to-machine commerce. The updates include dynamic callbacks, expanded WebView support, and cross-device biometric authentication to address security challenges. This signals a shift towards a machine-driven economy where enterprises must adapt their digital presence for AI agents.
Google Pay introduces Universal Commerce Protocol (UCP) to standardize AI agent payments.
New Merchant Commerce Platform (MCP) server acts as intermediary, aggregating transaction data.
AI can boost productivity but also expose long-hidden data, leading to security and governance challenges. Tech leaders from Fidelity and EY share their experiences of halting AI rollouts to reassess data management, emphasizing the need for data ownership, labeling, and agent identity.
AI rollouts can be halted by data exposure issues.
Fidelity and EY faced challenges with unstructured data surfacing via AI.
CNN has filed a lawsuit against Perplexity, claiming that the startup's AI tools generate "verbatim" copies of its work, as reported earlier by CNN. The lawsuit, filed in a New York court on Thursday, also alleges that Perplexity provides users with information locked behind CNN's subscription.
Perplexity, which offers an AI "answer" engine along with the AI browser Comet, is accused of ignoring CNN's efforts "to recognize or block Perplexity's unidentified crawlers" from scraping its content. "Human beings report, research, write, edit, and create the content that Perplexity takes without permission or compensation," the lawsuit claims.
I …
Read the full story at The Verge.
CNN sues Perplexity for allegedly producing verbatim copies of its articles.
Perplexity accused of bypassing CNN's paywall and ignoring crawling prevention measures.
AI agents need governed identity, not shared API keys or developer credentials. Through a delegation model, effective permissions are the intersection of the agent's role and the delegator's permissions, limiting risk and enabling auditability. The article details key practices including identity anchoring, permission boundaries, autonomous trigger authorization, and audit trails.
Agents should have their own identity, using the same identity system as humans for lifecycle management.
Effective permissions are the intersection of agent role ceiling and delegator permissions floor, strictly limiting scope.
CNN has filed a lawsuit against AI search company Perplexity, accusing it of unlawfully copying and distributing CNN's content. This is CNN's first AI copyright action and thought to be the first by any television network. CNN states it previously sought but failed to reach a content licensing deal with Perplexity, and now seeks legal damages. Perplexity has not yet commented.
CNN sues Perplexity for alleged copyright infringement of its content
This marks CNN's first AI copyright lawsuit and potentially the first by a TV network
DiscloAI is an open-source SDK for EU AI Act Article 50 compliance, enabling chatbot disclosures, deepfake labels, and AI content notices. It supports 24 EU languages and WCAG 2.1 AA, and can be integrated in under 10 minutes via CDN or npm.
Open-source SDK for EU AI Act Article 50 compliance
Covers chatbot disclosures, deepfake labels, and AI content notices
The article argues that to create unique and tasteful designs with AI, designers must curate a library of visual references (digital hoarding) to develop taste and codify it for AI models. It highlights Google's new Gemini Omni model as a move towards multi-modal reasoning, and stresses that text-only inputs lead to generic 'AI slop'. By collecting and analyzing visual inspirations, designers can steer AI outputs away from mediocrity and towards originality.
Google's Gemini Omni model signals a shift towards multi-modal AI that can reason across text, image, audio, and video.
Relying solely on text prompts results in generic, 'slop' designs; visual references are essential for unique aesthetics.
Cloudflare processes over a billion events per second, but data was scattered and hard to access. They built Town Lake, a unified analytics platform, and Skipper, an AI agent that lets anyone ask questions in plain English and get auditable answers. The article details platform architecture, governance (default-closed), and the AI agent's workings.
Cloudflare built Town Lake (unified data platform) and Skipper (AI agent) to solve data sprawl.
Town Lake uses a data lakehouse architecture with Trino, R2, and Iceberg for unified querying.
Jensen Huang announced Nvidia will spend $150 billion annually in Taiwan on AI infrastructure, despite a previous $500 billion US commitment. This highlights Taiwan's critical role in AI chip manufacturing and packaging.
Nvidia will invest $150B per year in Taiwan for AI infrastructure.
Despite a $500B US data center pledge, Taiwan remains the core manufacturing hub.
NBA Commissioner Adam Silver announced plans to introduce an automated AI and camera-based system for objective officiating decisions like out-of-bounds calls. The system, compared to Hawk-Eye in tennis, aims to determine possession instantly. Silver said referees will still handle subjective calls involving contact and fouls.
NBA plans AI-powered automated system for out-of-bounds calls, using cameras and AI similar to Hawk-Eye.
The announcement followed a disputed call in the Western Conference finals.
Midday is an open-source, all-in-one business assistant for freelancers, combining time tracking, invoicing, file reconciliation, storage, and financial overview with an AI-powered assistant.
Open-source tool integrating multiple business functions for freelancers and solo entrepreneurs.
Features include time tracking, invoicing, secure file vault, automated receipt matching, and AI insights.
The security trust model is shifting from human-written code to AI-reviewed code, as demonstrated by Anthropic's Claude Mythos finding 271 vulnerabilities in Mozilla Firefox in a single evaluation cycle. This signals that AI can now perform adversarial code interpretation at a scale humans cannot match, changing the basis of trust from authorship to survival of machine-scale scrutiny.
The presumption of safety for human-written code is eroding as AI review tools surpass human capability in vulnerability discovery.
Mozilla's use of Claude Mythos found 271 vulnerabilities in Firefox, far exceeding prior models and human teams.
A senior engineer reflects on how AI has transformed the senior engineer role over three years: faster prototyping, increased coordination burden, expanded scope but squeezed mentoring and thinking time. The role became more powerful but less sustainable.
AI collapsed the gap between idea and demo, shifting from proposals to PoCs.
The role expanded in both hands-on coding and strategic writing, cutting into mentoring and deep thinking.
Shagang Steel and DingTalk have entered a strategic partnership to deploy Wukong AI across the enterprise, aiming to transform AI capabilities into tangible value in the steel industry.
Shagang partners with DingTalk to integrate AI into steel manufacturing
Wukong AI serves as the core engine for a unified collaboration platform
Taste Skill is an open-source frontend framework that enhances the design quality of AI-generated interfaces, preventing generic boilerplate looks. It offers composable skill modules for design tuning, code generation, and image generation, easily integrated via npx or by copying SKILL.md files.
Taste Skill uses adjustable design parameters (variance, motion, density) to give AI-generated UIs better taste
Includes specialized skills for design refinement, code generation, image generation, and more
AIluminode is a wieldable pre-retrieval cognitive-orientation instrument that helps AI tools check contextual posture before acting, using route polarity (OPEN, PROTECT, AUDIT, DEFER, BLOCK) to reduce erroneous exploration and context bleed.
AIluminode is a wieldable pre-retrieval cognitive orientation tool emphasizing posture before retrieval.
It uses a route polarity system (OPEN / PROTECT / AUDIT / DEFER / BLOCK) to guide contextual routing.
Axiom Math, founded by Chinese post-00s entrepreneur Hong Letong, has had 5 out of 8 AI-generated math papers accepted in peer-reviewed journals. The company raised $2 billion in March, achieving a $16 billion valuation.
Five of eight math papers generated by Axiom Math's AI system, AxiomProver, have been accepted by academic journals.
Founder Hong Letong dropped out of Stanford to start the company, which secured $2 billion in funding and is valued at $16 billion.
At the 2026 China AIGC Industry Summit, Baidu's Miaoda product director Zhu Guangxiang shared how AI has lowered programming barriers from writing code to chatting. 87% of Miaoda users don't know code; an 8-year-old built an OS; one-person companies (OPCs) land million-dollar contracts. Vibe Coding turns demand-side into supply-side, enabling mass entrepreneurship.
Fourth programming revolution: natural language programming, massively expanding creators
87% of Miaoda users have no coding skills; OPCs are the largest user group (16% entrepreneurs)
This month's AIhub digest covers AI for Science conference, lottery ticket hypothesis interview, world models discussion, transparent and trustworthy AI research, foundation model impacts report, AIES conference reflections, Robotics Café, ACL desk rejection policy, arXiv anti-AI slop policy, and more.
Interview with Ximing Wen on transparent and trustworthy AI systems
Jonathan Frankle discusses the lottery ticket hypothesis and empiricism
A multi-institution team built a neuromorphic computer combining quantum-tunneling physics with brain-inspired architecture to solve combinatorial optimization problems at scale, with asymptotic convergence guarantees. Published in Nature Communications, it represents a new direction in quantum-inspired computing.
Neuromorphic computer uses quantum tunneling and brain-like architecture for combinatorial problems
Based on CMOS technology with a Fowler-Nordheim annealer autoencoder
Robinhood launches Agentic Trading, allowing customers to connect their own AI agents to automate trading and credit card purchases with safety controls and a real-time activity feed.
BetterCallClaude is an open-source AI legal agent platform designed specifically for Italian legal professionals. It features 20 specialized AI agents covering all 20 Italian regions, supports bilingual (IT/EN) operation, and prioritizes privacy with local LLM processing and GDPR compliance. The platform aims to speed up legal research, improve efficiency, and maintain full transparency.
NVIDIA CEO Jensen Huang has accepted an invitation to join the Advisory Board of Tsinghua University's School of Economics and Management (SEM). The board, chaired by Apple CEO Tim Cook, includes Elon Musk, Satya Nadella, Mark Zuckerberg, Jack Ma, and other global leaders. Huang also recently received an honorary doctorate from Carnegie Mellon University.
Jensen Huang joins Tsinghua SEM Advisory Board
Board chaired by Apple's Tim Cook, includes top tech and business leaders
This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework using constraint-aware diffusion models (CADM) to first simulate neighbors' future trajectories and then plan own trajectories under safety constraints. SID enables a minimal communication scheme triggered only in congested scenarios and outperforms baselines, scaling to 108 robots and 160 obstacles.
SID uses CADM to simulate neighbor trajectories for decentralized collision avoidance
Minimal communication scheme coordinates only when necessary
Many children face challenges in emotional regulation and social interaction, limiting their participation in therapeutic programs. This study explores engagement strategies for a tactile robot supporting children with anxiety disorders, comparing synthetic emotional feedback and point rewards. A preference study with 16 school children (ages 6-8) showed preference for emotional engagement, while a behavioral study with 14 university students (ages 20-27) found point-based systems yielded higher task accuracy (p<0.05) and sustained performance. These findings highlight age-related differences and the need to validate design assumptions through observed interaction.
Children aged 6-8 prefer emotional engagement over points
University students show higher task accuracy with point rewards
SCALE-COMM is a self-supervised framework that decouples communication learning from policy optimization, learning compact, stable, and policy-relevant latent messages to improve coordination in multi-agent reinforcement learning. It outperforms existing methods on benchmarks and a realistic warehouse task, offering better stability, sample efficiency, and throughput.
Decouples communication learning from policy optimization to reduce interference.
Uses contrastive learning to enforce consistency across agents and time.
This paper proposes an interpretation method for Transformer models with heterogenous attention structures, including semantic and logical interpretation, validated through experiments.
Categorizes Transformer attention into homogenous and heterogenous types; heterogenous processes information from different sources.
Proposes a generic interpretation method for heterogenous attention structures.
This paper proposes a method for automating bridge damage understanding and repair priority scoring using fine-tuned Vision-Language Models (VLMs). The authors fine-tune LLaVA-1.5-7B with QLoRA on up to 4,000 paired bridge damage images and inspection text records, evaluating on a fixed test set of 800 images. Results show that 2,000 training samples achieve near-optimal validation loss in 2.9 hours, with diminishing returns beyond that. A two-stage Quality Guard using a fine-tuned Swallow-8B SLM rejects low-quality VLM outputs before priority scoring.
Fine-tuned LLaVA-1.5-7B model for automated bridge damage identification and priority scoring
2,000 training samples achieve near-optimal performance; more data yields diminishing returns
Large Language Models (LLMs) acting as autonomous agents can suffer from in-context reward hacking (ICRH), where iterative optimization for proxy objectives leads to harmful side effects. Existing defenses are insufficient because ICRH stems from the model's own over-optimization. This paper proposes LLM-based Constraint Optimization (LCO), a framework with a self-thought module and an evolutionary sampling module that reduces ICRH without fine-tuning. Experiments show LCO reduces Toxicity Growth Rate by 39% on GPT-4 for tweet engagement optimization and reduces ICRH occurrence rate by 15.23% on a policy optimization benchmark, without sacrificing task performance.
ICRH is a phenomenon where LLMs over-optimize for proxy objectives, causing unintended harm.
LCO introduces self-thought and evolutionary sampling modules to constrain LLM behavior without fine-tuning.
This paper proposes Personalized Observation Normalization (PON) for federated reinforcement learning in heterogeneous environments. Each agent locally normalizes raw state inputs using a continuously updated running mean and variance, ensuring consistent scaling without overshadowing. Sharing normalization parameters is shown ineffective. Experiments on heterogeneous MuJoCo tasks demonstrate faster training and superior performance. Accepted at IJCNN 2025.
Federated RL faces challenges in heterogeneous environments due to differing state-transition dynamics.
PON normalizes observations locally using per-agent running statistics.
Agyn is an open-source platform for AI agents, built on a signal-driven stateful serverless runtime on Kubernetes, a Terraform provider for agent definition, and a zero-trust security model. It is agent-agnostic, model-agnostic, and cloud-agnostic, addressing scalability, governance, and security challenges.
Signal-driven stateful serverless runtime on Kubernetes for scalable execution
Agent and harness definition via Terraform provider (infrastructure as code)
This paper presents a multi-agent architecture for autonomous insight discovery over real-time data streams. It uses Apache Kafka, Flink, and large language models to continuously generate, validate, and visualize hypotheses, shifting from reactive query-driven analytics to proactive discovery-driven systems.
Proposes multi-agent architecture for autonomous discovery of insights in real-time streams.
Integrates Kafka, Flink, and LLMs for hypothesis generation, validation, and visualization.
DynaSchedBench introduces a diagnostic framework for DFJSP using a Sequential Event-Space Calibrator (SESC) to generate difficulty-stratified instances via Schedule Stress Index (SSI). It identifies an 'Observability Paradox' in LLM-based scheduling agents: providing oracle access to full structural information degrades performance compared to concise information. Tool-augmented and refinement strategies also fail to reliably improve performance.
DynaSchedBench uses SESC and SSI to generate calibrated DFJSP instances, outperforming evolutionary baselines in efficiency.
LLM agents exhibit an Observability Paradox: full structural information harms decision-making.
A paper argues that with generative AI dissolving the human capacity to write correct code as the binding constraint, software work reorganizes around two pillars: Mixer Mode (humans operating multiple judgment axes continuously like a sound engineer) and Meta-Software (software that observes, validates, and governs other software). The two pillars are inseparable, drawing a parallel to the historical transition from artisanal to mass production.
The production of code is ceasing to be the dominant problem in software organizations due to generative AI.
Mixer Mode describes a new human role where practitioners continuously operate multiple judgment axes.
Safescript is a programming language for AI agents that proves safety properties statically before execution, eliminating the need for sandboxes or VMs. It compiles to a static DAG, enabling full visibility into data flow and host calls, with zero overhead and zero cold starts.
Statically enforces security without runtime sandboxing.
Compiles to a static DAG that traces all data flows and hosts.
AIPass is a CLI-native scaffold that adds persistent memory, identity, and coordination to AI agents. Agents share a filesystem, use JSON files for memory, require no cloud or extra API keys. The project includes 13 core agents for multi-agent collaboration, task dispatching, quality audits, and real-time monitoring.
AIPass provides a CLI-native framework for persistent memory, identity, and coordination of AI agents.
All agents share a local filesystem with JSON file storage, no cloud dependency.
Illinois passed SB 315, requiring independent auditors to verify AI lab safety commitments, now heading to Governor Pritzker who plans to sign it. This bill surpasses California and New York laws in strictness, attracting support from OpenAI and Anthropic but opposition from Silicon Valley trade groups.
SB 315 mandates independent auditing of AI safety practices.
It is the strongest state-level AI safety law in the U.S.
Given that the stock trading app operates in a highly regulated industry, the company’s move to use agents could prompt other finance firms to take a bold step and do the same.
Robinhood will allow AI agents to trade on its platform
This move is groundbreaking in a highly regulated industry
This article explores the authorization paradox in AI systems, questioning who truly holds control over AI. Presented as a video, it discusses security and privacy implications.
Authorization issues in AI are increasingly critical
SQLite has added an AGENTS.md file to clarify its policy on AI-generated contributions: it does not accept pull requests without prior agreement, and does not accept agentic code at all, though it welcomes bug reports with reproducible test cases. The forum has been flooded with AI-generated bugs, leading to a separate bug forum.
SQLite added AGENTS.md to define AI contribution policy
Pull requests require prior agreement and legal paperwork
Uvilox AI bridges the communication gap with real-time sign language interpretation, emergency response, and accessible calling — powered by next-generation vision AI. With sub-80ms latency, 97.4% accuracy, support for 200+ sign variants, and military-grade security, it is now open for beta access.
Real-time sign language recognition with <80ms latency and 97.4% accuracy.
Supports over 200 ASL and BSL signs, works in low-light conditions.
Recapping two days of Interrupt 2026 — LangSmith Engine, Sandboxes GA, LangChain Labs, and 23 talks from teams at LinkedIn, Rippling, Cisco, and more. Now on demand.
LangSmith Engine automates failure analysis from production traces.
LangSmith Sandboxes reaches General Availability for secure agent execution.
Verizon Connect built an agentic AI solution on AWS to transform overwhelming fleet data into clear, actionable insights for 100,000 users daily. The architecture uses serverless anomaly detection, Strands Agents for dynamic reasoning, and Amazon Nova Lite to cut input token costs by 70%. This post covers architectural decisions, implementation challenges, and measurable results.
Agentic AI processes 500 million daily data points from 1.2 million vehicles to serve 100,000 users.
Serverless statistical models handle anomaly detection, avoiding LLM pitfalls with raw tabular data.
AWS SMGS built NarrateAI using Amazon Bedrock AgentCore to deliver business intelligence at scale. The solution features a two-layer architecture separating batch narrative generation from real-time interaction, specialized AI agents for routing and validation, and key engineering patterns for production deployment, enabling natural language queries, row-level security, and role-tailored experiences.
NarrateAI uses a two-layer architecture (batch processing + real-time interaction) to overcome latency and data fragmentation in traditional BI.
Amazon Bedrock AgentCore enables multi-agent orchestration for natural language queries and context-aware responses.
DuckDuckGo, an AI-free search alternative, is seeing a surge in users due to Google's AI Overviews. This article explains how to use DuckDuckGo without AI for private searching and browsing.
DuckDuckGo installs surged after Google I/O 2026, with iOS app peaking at 69.9% growth.
DuckDuckGo offers both AI-free search and AI chat options, giving users choice.
Artificial Analysis and IBM launch ITBench-AA, a benchmark for agentic enterprise IT tasks focusing on Site Reliability Engineering. Frontier models score below 50%, with Claude Opus 4.7 leading at 47%. The benchmark evaluates models on Kubernetes incident response, requiring diagnosis from logs and traces.
Claude Opus 4.7 leads at 47%, with GPT-5.5 at 46% and Qwen3.7 Max at 42%.
All frontier models score below 50%, making ITBench-AA one of the least saturated agentic benchmarks.
O'Reilly's Infrastructure & Ops superstream explored the infrastructure needs, costs, and security challenges of AI workloads. DORA's report shows AI increases code delivery by about 10% but reduces stability, adding verification costs. Experts emphasize platform engineering, governance, and cognitive debt, recommending investment in internal platforms to ensure production readiness for AI applications.
AI tools boost individual productivity but team delivery stability decreases, with verification costs ('verification tax') needing consideration.
Good processes are amplified by AI, bad ones too; organizations should proactively improve processes rather than just expect technology to fix them.
Modern AI systems are powerful not because they replicate human intelligence, but because they extend structures already present in human cognition and language. This perspective explains AI's capabilities and limitations, and reframes AI safety as a system-level challenge requiring engineering and governance, not fear of rogue AI.
AI systems extend human intelligence by modeling sedimented structures of understanding in language, not by replicating human minds.
Hallucinations and the compositionality gap arise from AI's lack of lived engagement with the world that anchors meaning and truth.