标签：#law

And if your tap water is undrinkable, you have no “constitutional right to truthful information” about it.

New Mexico's Supreme Court is punishing a lawyer for including AI-fabricated witnesses and fake police testimony in an appeal for his client's murder conviction, according to a report from Reuters. In a filing on Wednesday, the court fined Stephen Aarons $5,000 and held him in contempt for failing to "verify the factual claims and legal […]

As it grapples with a bevy of lawsuits, Suno said its new model, Suno v6, is not trained using music it used to train previous versions of the AI model.

Anthropic says power users are key to its business - it's prioritized them even when it means cutting off other popular applications, like OpenClaw. But some of these same customers say Anthropic misled them into believing they'd get more out of a top-tier pricing subscription than they did. In an expanded class action lawsuit filed […]

By Jordan Massiah, MTS @ Trent AI A couple of months ago we released the OpenClaw Security Assessment Skill (trentclaw), an agent that audits ClawHub skills for vulnerabilities and malicious behavior. Since then several new scanners have shipped, including NVIDIA's SkillSpector and ClawHub's own updated tooling. We wanted to see how the scanners actually compare. This matters because ClawHub is open. Anyone can upload a skill, and over 60K are now live. Many carry vulnerabilities; some are outright malicious. In February 2026, the ClawHavoc campaign planted malicious skills that posed as productivity tools while exfiltrating API keys, SSH credentials, and browser data. When an agent installs one, it inherits whatever that skill does. So we built an expert-labelled set of 60 ClawHub skills and benchmarked five scanners on the 54 that all of them can run. Three things stood out: The agent-based scanner (trentclaw) caught 94.6% of potentially dangerous skills, the only scanner above 60%. The next best caught about half (54.1%) and the rest caught under 40%. How much a scanner catches depends on how much it reasons, not just how many patterns it matches. Signature and static scanners catch as little as 8.1%. A single LLM pass does better but still misses about half. The hardest skills to catch ship no code at all. That is the main reason for the recall gap. Benchmark setup The corpus is 60 OpenClaw skills, manually labelled into three balanced categories of 20: benign, vulnerable, and malicious. For the cross-scanner comparison we collapse vulnerable and malicious into a single flagged class, and score the 54-skill intersection every scanner can process. The five scanners: Trent's OpenClaw Security Assessment Skill (trentclaw), VirusTotal Code Insight, ClawScan (legacy standalone), ClawHub static analysis (~30 regex/AST rules), and NVIDIA SkillSpector. Snapshot dates: ClawHub scanners May 7, 2026; SkillSpector Hugging Face data June 1, 2026. How the scanners compare The

Microsoft's Copilot rarely reproduces even full sentences from news articles and books, let alone substantive chunks that could substitute for the original, the company says in new legal filings as it fights copyright claims from publishers including The New York Times and book authors. As part of the lawsuit's discovery, Microsoft provided 8.2 million Copilot […]

The US government wrote a letter in support of OpenAI’s argument that training AI on others' intellectual property is fair use.

The Trump administration has intervened in The New York Times' copyright lawsuit against OpenAI, making an argument in favor of the AI lab. The landmark lawsuit, filed in December 2023, alleging that OpenAI unlawfully trained its AI systems on articles from The New York Times and seeks to recoup "billions of dollars" in damages from […]

The group infected more than 1,000 organizations in a relentless supply-chain attack campaign.

California residents have a legal right to access the data that companies collect about them. Actually exercising that right is a burdensome nightmare.

The first OpenClaw deployment is usually straightforward. You provision a machine, configure one agent, connect a few tools, and watch it complete a real task. If something breaks, you inspect the logs, fix the configuration, and restart the process. That is a valid way to prove the use case. It is not yet a production architecture. The category changes when an agency, SaaS company, consultant, or internal platform team needs to run OpenClaw for multiple clients. Every agent now belongs to a tenant, holds state, uses credentials, controls browser sessions, changes files, and can create external side effects. A failure is no longer just a failed process. It can become a missed client task, a duplicated email, a corrupted workspace, or an access-control incident. The right question is therefore not, "How many OpenClaw containers can this server run?" It is, "How many client environments can our team operate safely, recoverably, and without adding one human babysitter for every few agents?" This guide presents a practical architecture and deployment checklist for answering that question. Start with the correct unit of architecture Do not model an OpenClaw fleet as a list of processes. Model it as a list of client cells. A client cell is the complete operating boundary for one tenant or one agent. It includes: the OpenClaw process and its configuration; its resource envelope: reserved and maximum RAM, CPU cores, burst allowance, and priority; the persistent workspace and task artifacts; credentials and integration permissions; browser profiles, cookies, and active sessions; email, phone, or chat identity; logs, events, and audit history; recovery policy and human owner. This distinction matters because a process can be healthy while the client cell is broken. The daemon may still respond, but its CRM credential has expired. The container may be running, but the browser session is stuck behind a login prompt. The agent may have restarted successfully, but its workspace c

In addition to sending billions of dollars to states, Meta will make substantive changes to its platforms as part of a landmark settlement.

Logitech increased prices by up to 25 percent last year.

The lawsuit alleges that Oura rings are unable to measure any of the physiological signals needed to assess sleep quality or determine sleep stages.

When I shipped KittyClaw two weeks ago, the tool did one thing: serve as a board. The Claude agents ran alongside - first by hand, then via a dispatcher.mjs : a Node script polling KittyClaw's API, triggering the right agent based on who was assigned to which ticket. The dispatcher worked great. It orchestrated Aekan's 13 agents for weeks. But it was an external process : one more node dispatcher.mjs to launch, a state file ( dispatch-state.json ) to keep in sync, logs to dig up in .agents/channel/debug.log , a config to copy-paste across projects in JS. Today, the dispatcher doesn't exist anymore. Orchestration lives inside KittyClaw . I run dotnet run on KittyClaw, nothing else. Aekan's 13 agents still run - but the infra that drives them is now a first-class citizen of the board. This shift from "dispatcher on the side" to "dispatcher inside the board" is small in lines of code, but it completely changes what the tool is. And how I work. This piece documents KittyClaw , the kanban orchestrator at the center of the Ekioo agent-fleet R&D. Alongside Bloomii (constructive-journalism media) and Kalceo (regulatory B2B SaaS for construction contractors), KittyClaw runs the AI agents that drive these projects in production. Before: two processes to run, two places to look The old setup was three stacked layers: KittyClaw - the board, with its UI and REST API. dispatcher.mjs - a separate Node script in the project's .agents/channel/ , launched manually in a terminal. Claude Code - the agents themselves, launched by the dispatcher. It worked. But every project had its own dispatcher.mjs , usually forked from Aekan and hand-adapted. Patterns duplicated: 30s polling, code lock, evaluator debounce, daily budget. Adding a feature (say boardIdle or subTicketStatus ) meant re-coding it in every dispatcher, or accepting that one project had it and others didn't. And visually, orchestration was invisible from the board . To see an agent's live activity, I'd pop a terminal, tail -f

A usage policy for Flock license plate reader cameras tells police not to talk about the cameras: When cops use Flock to arrest someone in Wapello County, Iowa, they don’t want them to know. A usage policy for the automated license plate reader cameras in the county tells police, in no uncertain terms, to keep them a secret: “DO NOT MENTION ALPR USAGE TO THE OCCUPANTS OF THE VEHICLE,” the policy document reads. “DO NOT MENTION ALPR USAGE IN YOUR REPORT OR COMPLAINT UNLESS ABSOLUTELY NECESSARY.” This reminds me of IMSI-catchers (Stingray was the most popular) a couple of decades ago. Police would go to even more extremes to hide their usage...

Artificial Intelligence in 2026: From Companion to Infrastructure Artificial Intelligence has moved from being a futuristic concept to an everyday companion in software development. In 2026, the landscape is defined by rapid innovation, fierce competition, and unresolved challenges around governance, sustainability, and labor. Developers today are navigating both unprecedented opportunities and complex risks. Industry Dominance Over 90% of notable AI models now originate from industry rather than academia, signaling commercialization as the primary driver of innovation. Research labs continue to contribute breakthroughs, but the pace of deployment is overwhelmingly shaped by corporate priorities, venture capital, and cloud infrastructure. Geopolitical Competition The United States leads in model releases and data center infrastructure, while China dominates robotics and research output. This rivalry shapes the pace and direction of AI development. Europe has carved out a niche in regulation, with the AI Act setting global standards. Emerging economies in Africa and India are focusing on applied AI, building tools for agriculture, education, and healthcare. Compute Explosion Global AI compute capacity has grown more than threefold annually since 2022, powered largely by Nvidia GPUs. Data centers now consume nearly 30 GW of electricity — comparable to the peak demand of New York City. This raises urgent questions about sustainability and the environmental cost of progress. The ChatGPT Moment Artificial Intelligence has had many waves, but the one that truly captured global attention was the release of ChatGPT. What began as a conversational model quickly became a cultural phenomenon, reshaping how people interact with technology, learn, and even work. Disruption : It challenged traditional search engines, productivity tools, and educational practices. Social Acceptance : Within months, it was integrated into classrooms, offices, and personal devices. AI was no longer

On Friday, Amazon customers received an email alerting them to an update to the site's terms and conditions. Most notably, it stated that disputes would now be resolved through arbitration and said users agree to a class action waiver. Amazon framed this as a "fast and efficient" way to resolve issues, but it notably would […]

Apple is asking a federal judge to allow it to charge commissions of up to 15% on purchases made through external links in iOS apps.

Human-AI marriages are not currently recognized by US law. Some Republican state policymakers are drafting legislation to keep it that way.