今日已更新 35 条资讯 | 累计 37284 条内容
关于我们

标签:#Law

找到 75 篇相关文章

AI 资讯

How to Host OpenClaw for Multiple Clients in Production

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

2026-08-27 原文 →
AI 资讯

From kanban to harness: when the tracking tool becomes the orchestrator

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

2026-08-21 原文 →
AI 资讯

Police Are Hiding Their Use of Flock Surveillance Cameras

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...

2026-08-20 原文 →
AI 资讯

The Status Quo of AI in Software Development (2026)

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

2026-08-17 原文 →
产品设计

Buc-ee’s dodges John Oliver to sue another small business

Buc-ee's became something of a viral sensation during the World Cup, but it has a troubling history of suing small gas stations and convenience stores. On a recent episode of Last Week Tonight, John Oliver literally begged the company to sue him for selling merch featuring his squirrel mascot, Mr. Nutterbutter, with branding that reads […]

2026-08-08 原文 →