标签：#ChatGPT

OpenAI has more than 100 million weekly active ChatGPT users in India, a huge chunk of whom are on the free or the lower-priced Go tiers.

Open ChatGPT this week and, without any announcement in the chat window, it may already have formed an opinion about how old you are. From 18 August, OpenAI began rolling out “age prediction” on its consumer plans: a system that guesses whether your account belongs to someone under 18 and, if it decides you’re a minor, quietly switches you into a restricted version called ChatGPT for Teens. You are not asked. If the guess lands on “teenager,” the guardrails go up by default. Answer first, because the mechanism matters more than the alarm: the guess is behavioural, and it is admittedly imperfect. By OpenAI’s own account the system reads “general topics you talk about, the times of day you use ChatGPT, how and when your account is used, and how long your account has existed.” And the way to make it stop guessing is not a toggle. It is to prove your age to a third-party verifier called Persona, with a live selfie, a government ID, or both. The choice on offer isn’t whether to be identified. It’s how. None of this arrives from nowhere, and we’ll be fair about why in a moment. But a change that infers a protected characteristic from the content of your conversations, applies real restrictions on the strength of a guess, and offers identity verification as the only exit is worth reading slowly — especially for the adults who will be misclassified, because OpenAI says plainly that some will be. What OpenAI actually switched on The launch has two parts. The visible one is ChatGPT for Teens , announced on 18 August: a version with study-focused features and stronger safety defaults for under-18s. The consequential one is age prediction , the system that decides who gets dropped into it. In OpenAI’s words, “If our system estimates someone is under 18 or they state their age is between 13 and 17, they are automatically placed into ChatGPT for Teens.” It is rolling out globally, with the EU following “in the coming weeks” to fit regional rules. What does the teen experience act

Ever wanted someone else to do your texting for you? ChatGPT is being offered up as an automated text scribe via a new Apple Messages integration.

OpenAI is quietly testing a feature that lets ChatGPT users pay to undo their own usage limits. Hit the weekly cap on a $20 Plus plan and, for some users, a prompt now appears offering to restore the allowance to full for roughly $8. On the $200 Pro plan, the equivalent reset is reported to run up to about $80. The company never announced it; it was discovered by a subscriber who ran into it at the point of being locked out, and an OpenAI spokesperson later confirmed the company is exploring ways for capped users to buy more usage . The answer-first version: your flat monthly subscription now has a pay-to-continue button, and it shows up at the worst possible moment. The reset restores your usage to 100% and pushes the next weekly renewal about seven days out. It is cheaper than upgrading, which is the point — but it is also a new charge that did not exist a month ago, applied to a limit most users cannot see coming, offered at the instant they are least able to say no. What OpenAI is actually testing The mechanics, as reported, are straightforward. When a ChatGPT Plus subscriber exhausts their weekly message allowance, instead of only being told to wait, some accounts now see an option to pay to reset. Redeeming it restores usage to full and resets the weekly clock. The price sits at around $8 for Plus; on Pro it scales up to roughly $80, still framed as a stopgap against a full plan change. The feature was first surfaced by a Reddit user on the $20 plan who described a black prompt appearing at login once their allowance ran dry — not a setting they went looking for, but one that found them. That detail — a user, not a press release, breaking the news of a paid feature — is itself worth noting: the first public account of how OpenAI plans to charge for extra usage came from someone who had already been charged the inconvenience of being locked out. OpenAI has not disputed the reports. A spokesperson described the effort as exploring ways for people who exhaust the

ChatGPT and other AI models are now authoring and editing much of the new web.

“Memory” is the feature everyone asked for and few thought through. The pitch is lovely: the assistant remembers your preferences, your projects, your writing style , the fact that you are vegetarian and allergic to long emails, so you never have to repeat yourself. In practice, it is one of the most consequential privacy decisions in consumer AI, dressed up as a convenience toggle, and most people flipped it on without reading past the word “remember.” Convenience and surveillance are the same feature The uncomfortable truth is that there is no version of persistent memory that is not also a growing personal record. For the assistant to remember your details, it has to store your details. The thing that makes it feel like it knows you is a file — structured or otherwise — accumulating what you have told it, and inferring more from what you did not. The warmth and the dossier are not two features. They are one feature seen from two angles. A system that remembers everything you tell it is, definitionally, a system that keeps a record of everything you tell it. The friendliness is the interface; the record is the substance. The profile that shapes what you are shown A stored model of who you are does not sit there inertly; it starts to shape the responses you get. That is the entire selling point — a memory-enabled assistant tailors its answers to what it believes about you. But tailoring cuts both ways. Once the system has decided you are a particular sort of person, with particular views and particular tastes, it begins to give you the version of the world it thinks you want, and you lose the ability to know what it would have said to someone it had profiled differently. The personalisation that feels like being understood is also, quietly, a narrowing. We have seen this film before, with recommendation feeds that learned our preferences and then fed them back to us until the preferences hardened into a cage. A memory-driven assistant risks the same dynamic applied

A guide on how to check if hackers have broken into your accounts on the most popular AI platforms.

图表不会画？跟AI说句话就行 写代码画图这件事，劝退过不少人。编辑器打开了，文件是空的，脑子里想的是"画个折线图"，手却不知道往哪放。查文档、调参数、处理日期格式、挪图例……折腾半小时，数据还没理清楚。我太懂了，因为我自己就是这么过来的。 现在情况不一样了。ChatGPT、Claude、Copilot，随便挑一个，你跟它说"我想看什么样的图表"，它就把代码给你写好了，而且一次跑通的概率不低。这不是什么科幻设定，我天天这么干活。 AI到底在干嘛？ 说白了，AI不负责画图，它负责把你想画的东西翻译成代码。你说人话，它出Python、R或者JavaScript，然后你的数据就变成图了。常用的搭配大概是这些： Python配Matplotlib或Seaborn，做静态图、论文插图，稳 Python配Plotly，要交互、能缩放能悬停的那种 R配ggplot2，学术界和统计人的心头好 JavaScript配ECharts或D3，网页上动的图 老板只要Excel图？AI也能写VBA宏 API不用背，你直接说："我有个CSV，两列，一列日期一列销售额，帮我画折线图加趋势线，中文标题，X轴标签转45度，存成PNG。"代码马上给你。 关键是说人话 AI给不给力，很大程度看你问得清不清楚。我总结了一个公式，特别好用： 要干什么 + 数据啥样 + 用啥工具 + 图长啥样 + 存成啥格式 举个例子。你手上有个sales.csv，里面是月份和金额，想做柱状图，红色边框，高清图。 你要是说"帮我画个图"，谁也帮不了你。 但你要是说："用Python读sales.csv，第一列是月份，第二列是销售额。用matplotlib画柱状图，柱子天蓝色，边框红色，柱子上方标数值，标题写'月度销售额'，保存成300dpi的png。"AI给的代码基本就是能直接跑的。 一个真实的操作过程 拿Python举例，整个过程就三步。 第一步，让AI先读数据。"我有个data.csv，三列：date、product、revenue，格式是2024-01-01这种，写段Python代码读进来并打印前五行。" import pandas as pd df = pd . read_csv ( ' data.csv ' , parse_dates = [ ' date ' ]) print ( df . head ()) 跑一下，确认数据没错。 第二步，画图。"用Seaborn画每个月的总收入折线图，x轴月份，y轴收入，带数据点标记，标题'月度收入趋势'，图例去掉。"AI会一次性给你把分组聚合和绘图代码都写好。 第三步，跑，报错就贴给AI，改完再跑。五分钟搞定。 别把脑子扔了 AI写代码，你得把关。至少得看得懂它在干嘛：数据读得对不对，列名有没有搞错；按月求和还是逐条画，逻辑对不对；坐标轴排序有没有问题，标签重没重复；输出路径能不能找到。 AI有时候会一本正经地胡说八道——编个不存在的参数，或者用了个过时的库版本。这时候你得会看报错，会去查文档。说到底，AI是加速器，不是方向盘。 还能让AI干点别的 有时候图难看，真不怪画图代码，是数据太脏。你可以直接让AI洗数据："revenue列空值填0，删掉product列里带'测试'的行，日期列转成标准格式。"一行提示词，pandas代码给你安排好。 你也可以把一部分数据贴给AI，问它："这是我用户的年龄分布，你觉得用啥图展示比较合适？"它会跟你推荐直方图、箱线图或者小提琴图，还会解释为什么——等于白嫖一个懂行的顾问。 各家AI怎么选 ChatGPT（GPT-4系列），综合能力强，中文对话舒服，解释得很细，适合从零开始 Claude（Opus或者Sonnet），写代码比较稳，长代码上下文不容易丢，调试的时候给力 GitHub Copilot，装在编辑器里，你写它补，适合本来就在写项目的人 通义灵码、文心快码，国产的，中文响应快，跟国内开发环境更搭 哪个最强不好说，顺手最重要。我自己习惯先用ChatGPT把框架搭出来，再用Copilot改细节。 几个常见的坑 Matplotlib默认不认识中文，画出来全是方块，得加 plt.rcParams['font.sans-serif'] = ['SimHei'] 和 axes.unicode_minus = False 。 Windows路径里反斜杠容易转义出错，让AI用正斜杠，或者加个 r 前缀，再不行就上 pathlib 。 列名里带空格或特殊字符的话，AI可能写成 df.Revenue ，其实得 df['Revenue (M)'] 。把列名原样粘给AI，别让它猜。 Plotly在Jupyter里死活不显示？那是renderer没设置。 批量出图，才是真省事 文件多的时候，让AI写个循环。比如"文件夹里一

OpenAI is launching a preview of a sped up version of its latest, most powerful model, in an effort to court enterprise users.

Martin Spier explains how agentic workflows dramatically increase code change volume at OpenAI. He discusses the hidden systemic performance costs of rapid shipping beyond GPUs, and shares how deploying always-on AI agents automates profiling, regression detection, and continuous optimization to maintain product speed and scalability at massive global scale. By Martin Spier

Clinicians and researchers say AI companies need to open up their safety data.

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.

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.

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

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

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