标签：#Law

Hachette, Cengage, Elsevier, and other publishers allege that Google trained its AI on copyrighted works without the necessary permissions.

Most automation advice assumes you're willing to pay for Zapier or spend weeks learning n8n. The business-automation-architect skill by @1kalin takes a different angle: your AI agent is already capable of running workflows on its own, using cron jobs, scripts, and built-in reasoning. No third-party automation platform required. The Core Premise Your agent has access to APIs, file systems, schedulers, messaging channels, and web tools. That's everything you need to automate business processes without installing anything else. The skill teaches you to think like an automation architect — finding the highest-value processes to automate, designing the workflow, implementing it with agent tools, and measuring the return. The philosophy is grounded: only automate processes that happen at least five times per week OR cost more than thirty minutes per occurrence. Below that threshold, the automation overhead rarely pays off. The 5x5 Automation Audit The first phase is a structured discovery process. The skill provides a scoring matrix across five dimensions — frequency, time cost, error impact, complexity, and number of systems involved. Each dimension is scored 0-3, giving a maximum score of 15. Processes scoring 12 or above are immediate candidates. Those between 8-11 go into the next sprint. Anything below 8 is left manual. The discovery questions are worth asking directly: what breaks when someone is sick? Where do things pile up waiting for a person? What data gets copied between systems every day? These are the real automation opportunities, and they rarely show up in generic automation advice. Designing the Workflow The skill defines a clear workflow architecture template covering triggers, inputs, steps, error handling, outputs, and monitoring. The trigger types supported are schedule (cron), webhook, event, manual, email, and file-based. Steps can be fetch, transform, send, decide, wait, or notify — each mapping directly to what an agent can actually do. Error hand

This essay was written with Nathan E. Sanders, and originally appeared in The Guardian . Opposition to AI data centers has emerged as a primary theme in US politics, one that—surprisingly—doesn’t fall along party lines. We applaud people coming together for constructive debate on any issue, and agree that communities need to evaluate whether any economic benefits these data centers bring is worth their costs. Still, we worry that a focus on data centers obscures the larger impacts of AI on people’s lives: the concentration of power of AI companies, and their widespread political and financial influence...

Broadcom accuses Allstate of dodging VMware audits.

Companies will once again be allowed to scan citizens’ personal texts, emails, and social media messages via the “Chat Control” bill to find child abuse material online.

After more than a decade of pushback, farmers and repair advocates have won access to equipment and services John Deere had long kept under its control.

OwO What's this? 💨✨ A tiny but mighty Mac mini M4 🍎⚡ with 16GB RAM, lots of local AI models 🤖🧠, and a BIG question… 🫣❓ -- an intro by Gemma 4. I have a Mac mini M4 with 16 GB of RAM, a pile of local models, and a very specific dream: Can I run a useful local AI agent that actually does things, but still feels nice to talk to? Not just "can it chat." Not just "can it write a haiku about Kubernetes." I mean: can it inspect the machine, patch files, search current information, use tools, avoid infinite loops, and still keep the cute assistant vibe? That last part turned out to matter more than I expected. My first round of testing was mostly about models. I compared gemma4:latest and ornith:9b inside OpenClaw, my local agent harness. Ornith won because it acted more like an agent. But after another day of testing, the story changed. The model still matters. Ornith is still the local model winner for me. But the harness matters just as much. And right now, my favorite setup is: Ornith + Hermes Agent The Original Question The original question was simple: Can a free local model behave like a useful agent on a small Mac? The machine is modest by AI workstation standards: Mac mini M4 16 GB RAM local model inference local agent harness Telegram or chat-style interface real files, real commands, real web/API checks This was never meant to be a scientific benchmark. No leaderboard. No synthetic score. No fake "reasoning" tasks. I tested practical things I actually care about: Find junk on disk and suggest what is safe to clean. Patch a Python script that fetches Bybit futures data. Search current web/API information and answer a crypto API question. My first conclusion was: Ornith beat Gemma. That is still true. But it was incomplete. The Thing I Missed: Gemma Had the Kawaii Soul ✨ I focused too much on tool use. That was fair, because agents need to act. But I missed something important: Gemma was much better at keeping the kawaii writing style ✨🌸. Gemma's messages were genu

T-Mobile wants Broadcom to keep supporting its VMware perpetual licenses.

Watching Elon Musk fulminate at Bill Savitt during Musk v. Altman - the case in which Musk sued Sam Altman and OpenAI instead of seeing a therapist about his AI failures - was a bit like watching a toddler have a temper tantrum at his nursery school teacher. Savitt's questions were "designed to trick me," […]

Where AI incidents in legal actually come from, and what infrastructure (not policy) prevents them. Blake Aber · Predicate Ventures · 2026 The policy layer is table stakes. It isn't enough. When Sullivan & Cromwell apologized to a federal bankruptcy judge in April 2026 for AI hallucinations in a court filing, the firm's apology letter said the firm had policies. Safeguards existed. Those safeguards weren't followed. That framing, "the safeguard existed but wasn't followed," is how a policy failure gets described. But something more specific happened: a hallucination was generated, wasn't caught at generation time, wasn't caught at review time, and made it into a document that got filed. That's not a policy problem. It's an infrastructure problem. The distinction matters because it determines what you build next. What policy can and can't do Policy is a promise made before the event. A well-written AI acceptable-use policy says: don't submit output you haven't reviewed; verify citations before they go into a document; a human must approve anything client-facing. This works when the human executing the task has time, attention, and professional accountability in that moment. It fails when one of those is missing: a deadline, a junior practitioner, a late-night run. Policy can't: Verify a citation at the point of generation Flag output that has drifted below a confidence threshold Stop hallucinated text from appearing in a draft before a human ever sees it Detect when the underlying model is behaving differently than it was in testing Policy can: Set the expectation that review must happen Define who bears accountability when it doesn't Create a paper trail after the fact One of those is prevention. The other is compliance. What infrastructure does instead An AI harness layer operates at the point of generation, not at the point of review. This reflects a broader reality that production AI is mostly harness and very little model . For legal work specifically, three com

Jonathan Rinderknecht was facing arson charges for setting a fire on New Year's Day in 2025, which became one of the deadliest wildfires in LA history. To make their case, prosecutors turned to location data from his iPhone, security camera footage, and witness testimony. But they also turned to his ChatGPT logs. Prosecutors said that […]

Promo video comes as more US police departments fly drones as first responders.

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on aviation, air taxis, and Wi-Fi speeds at 30,000 feet, follow Andrew J. Hawkins. The Stepback arrives in our subscribers' inboxes on Sunday at 8AM ET. Opt in for The Stepback here. How it started Last year, […]

The Onion's InfoWars officially has a launch date: On July 2nd, the conspiracy network previously run by Alex Jones will return as a comedy and media platform. The reboot comes more than a year and a half after news broke that the satirical news site was working to acquire the property owned by Jones, a […]

A new FTC lawsuit reveals how sophisticated subscription app operators can allegedly use shell companies and payment infrastructure to stay active on app stores despite mounting consumer complaints.

Last month my OpenClaw agent kept making the same mistake: it would run a health check, the script would fail silently, and the agent would report "all systems operational" with total confidence. It wasn't broken. It was just doing what it was built to do — execute tasks — without any mechanism to learn from the outcome. So I built it a self-improvement loop. Every night at 2 AM, an isolated OpenClaw session wakes up, reads the previous day's execution logs, identifies patterns in what went wrong, and updates the agent's memory files. No human in the loop. No re-deployment. Just... learning. Here's what I built, what broke, and what actually works. Why Self-Improvement Is Hard for Personal Agents Enterprise AI labs solve this with massive infrastructure: reinforcement learning pipelines, full fine-tuning jobs, A/B testing frameworks that run for weeks. For a personal agent running on a cron job, that's not an option. The self-improvement loop for a personal OpenClaw setup has to be lightweight. It has to run in seconds, not hours. It has to write to plain text files that the next session will actually read. And critically, it has to avoid the feedback loop problem — an agent that rewrites its own improvement logic can spiral into nonsense if there's no anchor. The key architectural decision I made: separate the executor from the critic . Your main agent runs tasks. A separate isolated session reviews what happened and recommends changes. The main agent applies them on the next run. No single session is both judge and executioner. The Nightly Cron: What Actually Runs This is the cron I have running at 2 AM ET every morning: { "name" : "nightly-self-improvement" , "schedule" : { "kind" : "cron" , "expr" : "0 2 * * *" , "tz" : "America/New_York" }, "sessionTarget" : "isolated" , "payload" : { "kind" : "agentTurn" , "message" : "Review the last 24 hours of OpenClaw execution. Read memory/$(date +%Y-%m-%d).md and memory/yesterday.md. Identify 3 patterns where the agent u

On 14 April, the Trump administration quietly acknowledged the widespread use of AI to automate government processes. The office of management and budget (OMB) disclosed a staggering 3,611 active or planned use cases for AI across the federal government. The list has ballooned by 70% from the one published in the final year of the Biden administration, and includes many disturbing-seeming plans to hand over sensitive governmental functions to AI. Scanning this list, many readers may find many causes for alarm. It represents a transfer of decision processes from human to machine on a massive scale over matters of individual freedom, public health and well-being, nuclear reactor safety and more...

The Justice department says the Pentagon needs xAI to keep using its unpermitted gas turbines.

Did chatbot abandon mental health guardrails when a vulnerable user pushed back?

Under a new bipartisan bill, Americans could sue for damages if a government official illegally tries to coerce a social media, AI, or broadcasting company to remove their post - regardless of whether the platform actually does it. Senate Commerce Committee Chair Ted Cruz (R-TX) and Sen. Ron Wyden (D-OR) introduced the JAWBONE Act on […]