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Forget email surveys or long calls spent on hold. Voicebox lets people send customer feedback by recording a voice note on their phone.

Introdução A Anthropic anunciou recentemente a inclusão de uma marca d'água nos textos gerados pelos modelos Claude. O objetivo é distinguir conteúdos gerados por humanos daqueles criados por IA generativas. Funcionamento O prompt enviado é convertido em tokens que são usados para calcular a probabilidade do próximo token , se repetindo até uma resposta que faça sentido seja retornada para o usuário. Esse é o processo padrão utilizado pela maioria dos modelos de IA Generativa. Agora a Anthropic adotou uma abordagem determinística para selecionar o próximo token a partir da lista de prováveis candidatos, que continua sendo gerada aleatoriamente. Esta nova abordagem usa uma chave privada e parte do contexto já gerado para selecionar o próximo token, sucessivamente até a geração total do texto que o usuário recebe como resposta ao prompt inserido. De acordo com a Anthropic, a adição desse identificador não consumirá tokens adicionais nem tornará o modelos mais lento para responder. De forma resumida, o diagrama a seguir mostra o funcionamento dessa abordagem. O que motivou esta ação Em 2024 a União Europeia aprovou Lei de Inteligência Artificial , primeira lei criada para regulamentar a inteligência artificial, prevista para entrar em vigor a partir de 02/08/2026. O Artigo 50 n.º 2 determina que: "..., incluindo sistemas de IA de finalidade geral, que geram conteúdos sintéticos de áudio, imagem, vídeo ou texto, devem assegurar que os resultados do sistema de IA sejam marcados num formato legível por máquina e detectáveis como tendo sido artificialmente gerados ou manipulados." . 1 Algumas das Big Techs assinaram o pacto de adesão e estão implementando a parte técnica de acordo com cronogramas próprios. Entretanto uma rápida pesquisa mostra que a aplicação prática por parte das empresas proprietárias de modelos LLM ainda é pequena. Entre os motivos citados estão: pode tornar os textos rastreáveis; perda estimada de até 30% dos clientes da plataforma fragilidade técnica

You probably don't need one expensive AI subscription. You need the right AI stack. AI subscriptions have quietly become another monthly expense. One tool for writing. Another for research. Another for coding. Another for image generation. Another for PDFs. Before you realize it, you're paying for several AI services every month — even though you use only a fraction of their capabilities. ChatGPT Plus alone is currently $20/month. That's $240 a year before adding anything else. But here's the interesting part: Do you actually need to pay for all of it? I decided to approach the problem differently. Instead of looking for one “best” free AI tool, I built a free AI stack where different tools handle different jobs. The goal isn't to prove that free AI is better than paid AI. The goal is much more practical: How much of a paid AI workflow can you realistically replace with free tools? The Biggest Mistake: Looking for One AI to Do Everything This is how most people use AI: Open ChatGPT → ask everything → hit usage limits → consider upgrading. But AI tools are increasingly specialized. A research engine doesn't need to be your coding assistant. A coding model doesn't need to be your web-search engine. A writing assistant doesn't need to be your data-analysis environment. Instead of asking: “Which free AI is the best?” Ask: “Which free AI is best for this particular task?” That simple change makes the free ecosystem much more powerful. My Free AI Stack Here's the architecture I would use for a zero-subscription workflow. Task Free Option Why General AI assistant ChatGPT Free Everyday questions and writing Web research Perplexity Free Search + citations Coding Gemini / free coding tools Code generation and debugging Research & experimentation Google AI Studio Model experimentation Microsoft workflow Copilot Free Web-based assistance Private/offline AI Ollama / LM Studio Local inference The important point is that these tools don't have identical capabilities or limits. For

The famous American philosopher Smashmouth once said that the years start coming and they do not stop coming. If you follow the artificial intelligence industry, you have never felt the weight of those words more deeply than right now. For years, science fiction fans have watched Tony Stark talk to Jarvis, his brilliant, autonomous, and conversational AI assistant. We watched Jarvis build 3D models, hack into secure mainframes, and seamlessly manage Stark's entire life. We all thought that level of technology was decades away. But over a span of just three days in September 2026, the entire landscape of technology completely shifted. Anthropic launched two massive models. Meta dropped a frontier model so cheap it is practically free. And OpenAI released GPT 6 Astra in an event so chaotic it literally took down the internet. Grab a coffee and buckle up. We are going to break down this insane week of AI news, dive into the real engineering breakthroughs, and figure out if we just witnessed the birth of the real Jarvis. The Day The Internet Went Dark Usually, the first week of September is quiet in the tech world. But this year, AGI apparently waits for no one. Right before OpenAI was scheduled to announce GPT 6 Astra, something bizarre happened. ChatGPT, Claude, Grok, and Cursor all went completely dark at the exact same time. The most logical explanation is a massive Azure cloud infrastructure outage. But the timing was so perfectly cinematic that people immediately started joking that Astra's first act as a public model was to assassinate its competitors. When the lights finally came back on, the OpenAI rollout was spectacularly messy. They published their launch page, major news outlets released their carefully written embargoed stories, and then, for reasons nobody fully understands, OpenAI ripped the page down for 90 minutes. Tech influencers immediately began playing the ultimate status game, flexing about how long they secretly had early access to Astra. Meanwh

Every small business has one person who is the office search engine. Where is the refund form. What goes in the Friday report. Which supplier do we use for rush jobs. The answers are written down somewhere, but asking that person is faster than finding them, so the questions keep coming and that person never gets a full hour of their own work. An SOP chatbot fixes exactly that. Staff type the question, and the bot answers with the steps from the procedures you already wrote, and shows which document it took them from. Nothing else. This article explains how that works using one picture, shows two bots we actually run, and is honest about what the bot will never do. The box Picture the AI as a very capable temp worker who shows up every morning with no memory of your business at all. Not the address, not the prices, not how you handle a late delivery. Smart, fast, and completely blank. Before you ask them anything, you hand them a box. In the box are your documents. The rule, taped to the lid, says: answer only from what is in the box, and if the answer is not in the box, say so. That box is what people in the AI world call the context. Everything the bot knows about you at the moment it answers is what you put in the box for that one question. It does not learn your business over time. It reads the box, answers, and forgets. Next question, new box. Two things follow from this picture, and they explain almost everything about SOP chatbots. The box has a size. Anthropic, the company behind the Claude models, says in its engineering write-up on contextual retrieval that a knowledge base under about 200,000 tokens, roughly 500 pages, can simply be included with every question, with no extra machinery. Most small businesses have far less than 500 pages of procedures. So for most of you, the whole manual fits in the box every time. If the manual is bigger than the box, someone has to pick. Then a librarian step runs first: it reads the question, pulls the few pages that m

Service interruptions hit ChatGPT, Claude, Grok, and Gemini practically simultaneously.

Christopher was sick of being ghosted by AI recruiters. So he unleashed ChatGPT on his robot interviewer.

OpenAI recently published an engineering account of GPT-Live. It described how they designed the system to maintain continuous voice interaction while separating latency-sensitive media processing from broader application work. The live path contains the media pipeline and inference loop, while delegation, tool use, persistence, and other application logic run behind an asynchronous RPC boundary. By Eran Stiller

OpenClaw has released OpenClaw 2.0, a major update to the open-source personal AI agent that changes its installation process, browser interface, memory, skills, automations, plugins, security, and collaboration features. By Daniel Dominguez

OpenAI said that the integration provides read-only access to health records for clinicians.

Versions of OpenAI's ChatGPT and SpaceXAI's Grok will join Google's Gemini on the Pentagon's central portal for AI tools.

Explosive growth comes with a new regulatory burden in the European Union.

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

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.

The Relay Q, due next year, is the latest attempt to reposition voice as the most seamless method for human-computer interaction.

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