标签：#chatbots

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

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.

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”

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.

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.”...

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

Novelists, journalists, and power LinkedIn posters are embracing first-person narratives and idiosyncrasies to avoid being mistaken for chat bots.

Companies’ increasing reliance on AI chatbots isn’t making the customer service experience smarter. It’s just making it more infuriating.

Hundreds of contractors working on a project for Meta pretended to be kids—and then prompted rival chatbots like Gemini and ChatGPT to discuss high-risk subjects.

Hackers are convincing Meta’s AI support chatbot to let them take over other peoples’ accounts: A video posted on X showed the step-by-step process to hack someone’s Instagram account. The hacker allegedly used a VPN to spoof the targets’ presumed location to avoid triggering Instagram’s automated account protections. Then, the hacker opened a chat with Meta AI Support Assistant and asked the bot to add a new email address to the target’s account. The chatbot can be seen sending a verification code to the email address provided by the hacker; the hacker then shares the verification code with the chatbot, which prompts the chatbot to show a button to “Reset Password.” The hacker enters a new password and takes over the victim’s account...