标签：#bots

Installing a large language model on your personal computer gives you a handy digital assistant that won’t compromise your data privacy.

The company is testing robots that can swap cables, reset servers, and take on other tasks performed by technicians, fueling concerns among some workers that their jobs could be at risk.

The potential for AI to automate scientific research and manufacturing must be balanced with new risks, Anthropic says.

Beijing’s endlessly delightful Robot Games featured tons of impressive stunts. But the most mind-blowing tricks challenged the humanoid’s brain, not its brawn.

Record-breaking robot races are less substantial than household chore challenges.

FCC ban on foreign-made robots accelerated RoboStore’s US manufacturing plans.

“We're not necessarily building in a dogmatic fashion towards full autonomy.”

The Unitree G1 has found online fame as a relatively affordable robot that can charm a crowd. But can it ever hold down a real job?

Joi AI hired 10 people to masturbate using AI companions as part of a monthlong “wellness” study. The company claims the practice could help “solve male loneliness.”

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

Bartesian's cocktail makers would be best described like a Keurig or Nespresso machine, but for alcoholic drinks.

Short answer: the least risky alternative to a single-provider OpenAI-compatible API is a thin routing layer with one internal contract, a small Python adapter, and an eval set that measures answer quality before price. Treat “cheapest” as a workload result, not a label. A US/EU chatbot also needs a deliberate data-residency decision before a key or SDK enters production. The attractive story is easy: one API key, one SDK, and a familiar chat-completions shape. Measure it. The production story has more edges. Provider-specific tool calls, token accounting, streaming events, retention settings, and regional routing can differ while the first text response still looks fine. That is how an in-app chatbot passes a demo and fails an eval. Consider a support bot that retrieves three passages, answers in a stream, and offers an escalation tool. A compatibility test that checks only the final sentence can miss an empty retrieval marker, a tool argument that is valid text but invalid JSON, a stream terminator that the client never handles, and a fallback that sends the same user request to a second region. The transcript still looks plausible in a screenshot. The trace tells a different story. I've learned to make those states explicit in the adapter before tuning a model. I build RAG and agent features in Python, so my first question is not “which model wins?” It is “which contract can I test?” The app should own that contract. A provider adapter should translate it at the boundary, and the rest of the application should never know whether the request went to an OpenAI-compatible endpoint, a Claude-style API, a Gemini-style API, or a local service. How can an app chatbot compare compatible API alternatives across US and EU? Start with the request that matters to the user: a message plus retrieved context, a latency budget, a maximum output, and a trace ID. Record the selected region and provider in server-side metadata, but don't send a secret to the browser. “One API key”

Cloudflare recently introduced Precursor, a client-side behavioral analysis engine that continuously evaluates session interactions, such as mouse movements and keyboard timing, to improve detection of sophisticated bots and AI agents without relying solely on one-time challenges like CAPTCHAs. By Renato Losio

WIRED spoke with Tucker Bryant, an artist and former Google employee who created ChatTJB to get people to reflect on the “strange moment” we’re in.

Ati Robotics assembles its robots in India and uses just a few Chinese parts—a strategy that could pay off as the Trump administration cracks down on Chinese humanoids.

And it’s personal information (alternate link ): The exposed data includes an AI-powered therapy app that someone appears to have vibe-coded, notes on meetings, and a dashboard someone made apparently to analyze medical billing data. Exposed chats reportedly include private cryptocurrency wallet keys and personal information like peoples’ addresses. What seems to be the issue is a user setting about data sharing. Anthropic’s position is that it’s not their problem : “We give people control over sharing their Claude conversations publicly, and in keeping with our privacy principles, we do not share chat directories or sitemaps with search engines like Google,” the company said in a statement. “These shareable links are not guessable or discoverable unless people choose to share them themselves. When someone shares a conversation, they are making that content publicly accessible, and like other public web content, it may be archived by third-party services.”...

July 2026 is over. The month that opened with AUTONOMOUS 2026 and WAIC 2026 running simultaneously on opposite sides of the Pacific closed with the sector tallying what it built. The number that defines the period is $55.8 billion in robotics funding across H1 - nearly double the prior full-year record. But the more durable signal from this week is operational rather than financial: Neura Robotics has a confirmed deployment date at a Schaeffler facility in December, NVIDIA's simulation-to-real pipeline is now functional at production scale, and five simultaneous shifts are reshaping factory floors right now, not in 2027. The questions that drove the first half of 2026 - does Physical AI work, is the funding real, will the robots actually arrive - are no longer interesting. H2 starts with harder ones. Stats: Value Description $55.8B Robotics funding raised in H1 2026, nearly double the prior annual record $8.6B Humanoid startup funding in H1 2026 alone, 1.8x all of 2025 December 2026 Confirmed first deployment of Neura Robotics humanoids at Schaeffler's German facilities 5 Simultaneous operational shifts reshaping factory floors identified in the mid-2026 analysis Neura Robotics Has a Deployment Date: December 2026 in a Schaeffler Factory Most Physical AI deployment announcements are directional. "We are partnering with X to explore robotics in our facilities" is a press release. A confirmed month and a specific facility is a contract. Neura Robotics confirmed that Schaeffler - one of the key investors in its $1.4 billion Series C alongside Amazon, Nvidia, Qualcomm, and the European Investment Bank - plans to deploy Neura's humanoids in its German facilities in December 2026 . Schaeffler manufactures precision bearings and components for electric vehicles, operating in environments where dimensional tolerances are measured in micrometers. Deploying a humanoid robot in that context is a fundamentally different challenge than warehouse pick-and-place or automotive sequ

What Is Retrieval Augmented Generation (RAG), and Why Does It Make AI So Much Less Confidently Wrong? You know that game show contestant who buzzes in before the host finishes reading the question, shouts "MOUNT EVEREST!" with absolute certainty, and then looks genuinely confused when the correct answer turns out to be "the Treaty of Westphalia"? That's been AI for most of its existence. Supremely confident, occasionally correct, and deeply committed to whatever pops into its head first. Now imagine that same contestant gets a new rule: before answering, they can phone a friend who has the exact relevant textbook already open to the right page. The friend reads them the actual answer, word for word, and then the contestant puts it in their own words for the judges. Suddenly, our buzzer-happy friend is getting questions right. That phone call is Retrieval Augmented Generation, and it's the reason AI chatbots have gotten weirdly more useful in the past year. The Old Way: Confidently Wrong at 200 Miles Per Hour Traditional large language models (big AI systems trained on tons of text) get trained on enormous dumps of text scraped from the internet, books, and whatever else researchers can feed them. Then the training ends. The model gets sealed off, frozen in time with whatever it learned. When you ask a question, these models generate answers by predicting the most plausible-sounding next words based on patterns they memorized during training. It's essentially very sophisticated autocomplete. The AI has no fact-checking mechanism. It doesn't "know" anything in the way you know your own phone number. It just knows what words tend to follow other words. This leads to what researchers politely call hallucinations, which is a fancy term for "making stuff up with tremendous confidence." The AI generates text that sounds authoritative and well-structured because it's learned the pattern of how authoritative text sounds. But the actual facts? Those might be completely invent

The latest version of Google DeepMind's AI model includes a significant jump into “physical AGI.” But plopping AI into the real world comes with risks.

A German startup sent a camera-wearing chef to my apartment. In exchange for a free lunch, I let them record every chop and stir to train future humanoids.