标签：#chat

Additional details about OpenAI's mysterious new AI device make it sound like a pricey smart speaker.

OpenAI quietly rolled out improvements to GPT-5.6 Sol in ChatGPT this week, and the AI community took notice. The update, which hit the front page of Hacker News with over 70 points, brings measurable quality improvements and — crucially — expands access to free users. What Changed in GPT-5.6 Sol? The update focuses on three areas: 1. Improved Reasoning on Complex Tasks GPT-5.6 Sol shows improved performance on multi-step reasoning tasks. This includes better handling of: Mathematical proofs and calculations Code debugging across multiple files Logical deduction chains Multi-constraint optimization problems The improvement appears to come from refined training data curation and reinforcement learning from human feedback (RLHF) targeting reasoning-heavy tasks. 2. Better Instruction Following The model now follows complex, multi-part instructions more reliably. Where GPT-5.6 Sol previously might miss one constraint in a list of five, the updated version handles compound instructions more consistently. For developers building prompt-based applications, this means: Fewer retry loops Better structured output generation More reliable tool calling 3. Expanded Free User Access Perhaps the most significant change for the broader AI community: OpenAI expanded free user access to GPT-5.6 Sol. Previously available only to Plus subscribers, the model is now accessible to a wider audience. This has implications: For developers : Larger potential user base for GPT-5.6-powered apps For competitors : Pressure on pricing — if the best models are free, paid tiers need clear differentiation For open source : The gap between free proprietary models and open-source alternatives narrows the value proposition of self-hosting How Does It Compare? The Artificial Analysis Agentic Index — an independent benchmark — currently ranks GPT-5.6 Sol among the top models, though Qwen3.8 Max has recently taken the #1 spot on agentic tasks. The competitive landscape as of August 2026: Model Intelligence

OpenAI said that ChatGPT free and Go users are also getting a new think button for complex queries.

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.”...

When asked about product-market fit, Spiegel said he believes mass-market consumer adoption won't occur until the end of the decade.

XML Tagging in Prompts: The Secret to Getting Better Output from Claude and GPT A simple structuring trick that turns messy, unpredictable LLM outputs into clean, reliable ones. If you've spent any time writing prompts for Claude, GPT, or any other large language model, you've probably hit this wall: your prompt works fine for a simple ask, but the moment you pack in multiple instructions — some context, a few examples, formatting rules, and the actual task — the model starts mixing things up. It answers the wrong part of the question. It ignores your formatting instructions. It treats your example output as part of the actual task. The fix is almost embarrassingly simple: wrap your prompt sections in XML tags. Why XML Tags Work So Well LLMs are trained on enormous amounts of code, documentation, and markup. XML (and HTML) syntax is deeply embedded in that training data, which means models are very good at recognizing where one tagged section ends and another begins. Unlike plain paragraphs — where the boundary between "here's my context" and "here's my instruction" is fuzzy — a tag creates an unambiguous boundary. Anthropic actually recommends this explicitly for Claude: wrapping distinct parts of a prompt (instructions, context, examples, output format) in tags like <instructions> , <context> , <example> , and <output_format> measurably improves consistency, especially in longer or more complex prompts. Think of it like the difference between handing someone a wall of text versus handing them a form with labeled fields. Both contain the same information, but one is far easier to parse correctly — for a human, and for a model. A Before-and-After Example Without tags: Summarize the article below in 3 bullet points. Keep it under 50 words. Use a neutral tone. Here's an example of the style I want: "- Company X raised $10M in Series A funding." Now here's the article: [long article text] The model has to guess where the instructions end and the article begins — and wi

House spending records show OpenAI's ChatGPT dominates paid AI use on Capitol Hill, with congressional offices relying on the chatbot to draft memos, summarize legislation, and assist constituent communications.

India's app market generated a record $345 million in Q2.

Snapchat has adjusted its recommendation systems to ensure that only videos created by real people are eligible for Spotlight recommendations, taking a stance against AI slop.

What Is Temperature in AI? (And How to Stop Getting Poetry When You Asked for a Grocery List) Remember Magic 8-Balls? Those plastic oracles you'd shake for life advice, only to get "Reply hazy, try again" when you asked if your crush liked you back? Imagine someone added a little dial on the bottom. Turn it all the way to zero and the thing becomes painfully predictable, only ever offering "Yes" or "Most likely." Crank it all the way up and suddenly it's inventing answers that never appeared in the original twenty options, things like "Ask your neighbor's cat" and "The moon suggests Thursday." That dial is temperature, and every AI language model has one. How the dial works Temperature is a setting, usually ranging from 0 to 2, that tells an AI model how much risk to take when picking the next word. The model calculates the probability of every possible next word, then has to pick one. At low temperatures, it plays it safe and picks the most probable option almost every time. At high temperatures, it's willing to gamble on unlikely choices further down the list. This is why you can ask ChatGPT the exact same question twice and get a straightforward answer on Monday and what appears to be surrealist fiction on Tuesday. When you ask ChatGPT to write a professional email at temperature zero, you'll get "Dear Sir or Madam, I am writing to follow up on our previous correspondence..." every single time you hit enter. Set temperature to 1.5 and it might open with "Greetings, fellow traveler of the inbox wilderness" because that phrasing, while statistically improbable, is now in play. Why boring is sometimes good At temperature zero, you get the most boring dinner guest imaginable. It always picks the single most likely next token (the technical term for a chunk of text, usually a word or part of one). No variety, no surprises, just the statistical favorite every single time. This turns out to be perfect when you need factual accuracy, code that actually compiles, or data

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

You ask an AI a question. It answers in fluent, confident prose — complete with a study, a percentage, and a name. Some of it is wrong, and nothing about the wording tells you which part. That's the whole problem with hallucinations: the errors wear the same suit as the facts. The fix is not "trust it less" in some vague way. The fix is a repeatable audit step between AI wrote it and I used it . Below is a short hallucination checker prompt you can copy right now, a test run showing what it catches and what slips past it, and an honest account of where a one-liner stops being enough. What counts as an AI hallucination? Not every mistake is a hallucination. A useful working definition: a hallucination is a claim the model states as fact that has no grounding in reality or in your source material. The common shapes: Fabricated citations — a named study, expert, or paper that doesn't exist. Often dressed with a year and an institution. Plausible-but-wrong specifics — dates, version numbers, statistics that are almost right, which makes them worse. Confident category errors — mixing up two similar things (a library and a framework, one company's product and another's). Invented consensus — "experts widely agree that…" with no experts attached. The dangerous ones are the middle two. Obvious nonsense filters itself; a wrong year in a fluent paragraph does not. The copy-paste hallucination checker prompt Here is the short version, free, no strings. It works on ChatGPT, Claude, or any capable model — paste it into a fresh chat, then paste the answer you want audited: Audit the text below for hallucinations. Do not add new information. 1. Extract every factual claim as a separate numbered line. 2. Label each claim: VERIFIABLE (state how to check it), SUSPECT (state what makes it doubtful), or FABRICATION-PATTERN (named source/study/number with no citation). 3. Flag every name, number, date, and citation for manual checking. 4. Finish with the 3 claims most likely to be wrong

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

New behavior capturing a writer's "broad qualities" could have legal implications.

Starting with Spotify, Snapchat users will be able to link their accounts, choose who can see their listening activity, and see what their friends are listening to in real time.

OpenAI's ChatGPT Work materials have put a familiar enterprise question into sharper focus: how far can an AI assistant move from answering prompts to supporting coordinated, multi-step work? The supplied research identifies official OpenAI documentation covering capabilities, governance and enterprise rollout, but it does not establish a complete public feature list, pricing model or availability schedule. For prospective buyers, that makes disciplined evaluation more useful than assumptions about what the offering may eventually automate. The interest is understandable. A workplace AI product that can help teams turn requests into coordinated plans, reusable outputs or connected workflows could affect knowledge work well beyond individual chat sessions. But the available material does not substantiate specific claims about autonomous web or app generation, collaborative trip planning, or the exact scope of automation. Those scenarios should be treated as possible use cases to evaluate, not confirmed ChatGPT Work functionality. What the available ChatGPT Work materials establish The most reliable starting point is OpenAI's ChatGPT Work product page . According to the supplied research, OpenAI's official materials describe ChatGPT Work in the context of capabilities, governance and enterprise rollout . That framing matters because enterprise AI adoption is not solely a model-performance decision. It also involves how a tool fits existing systems, who can use it, what data it can access, and how organizations retain operational control. The research does not provide enough detail to verify particular integrations, permission settings, security certifications, pricing, regional availability or release dates. Enterprises should therefore avoid treating broad product positioning as a procurement specification. The practical question is whether the official documentation and commercial terms available at the time of evaluation answer the organization's specific requireme

How synthetic reality may influence human intuition about motion, gravity, and causality AI video generation has reached a point where a model can create scenes that look physically convincing at first glance: A person jumping impossible distances Objects moving without inertia Water flowing upward Animals performing human-like actions Buildings bending like rubber People interacting with impossible environments For decades, humans learned physics by observing the real world. A ball falls. A glass breaks. A person cannot walk through a wall. Heavy objects require more force to move. These observations create what cognitive scientists call intuitive physics : an internal mental model that predicts how objects should behave. But what happens when the majority of visual experiences become synthetic? Could AI-generated videos slowly change how future generations perceive reality? Humans Do Not See Reality Directly A common misconception is that our brain works like a camera: Reality → Eyes → Brain → Understanding The actual process is closer to: Reality ↓ Sensory input ↓ Brain prediction model ↓ Perception The brain is constantly predicting what should happen next. When you see a ball thrown into the air, your brain automatically predicts: trajectory speed gravity collision point acceleration This happens before conscious reasoning. This capability is known as predictive processing . Your brain is not only asking: "What am I seeing?" It is also asking: "Does this match my internal model of how the world works?" The Brain Learns Physics From Experience Young children do not learn physics from equations. They learn by interaction. A baby discovers: Objects continue to exist when hidden Unsupported objects fall Solid objects cannot overlap Larger objects require more effort to move Researchers call these abilities core knowledge systems . Humans appear to have an innate expectation that the physical world follows consistent rules. For example: A child watching a ball roll

OpenAI's fancy new AI keypad will be a lot of fun for some, while many others are probably not going to touch it.

The offline messaging app surged in popularity in India amid protests in New Delhi.

ChatGPT Voice on desktop can work with both ChatGPT Work and Codex to complete tasks and control agents.