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Continues after the advertisementGemini 4 Carbon was designed to handle more complex problems than those addressed by Gemini 4 Argon, the model recommended by Google for everyday tasks, software engineering, and professional activities. To do so, the new AI uses a larger number of tokens, with longer reasoning cycles and response verification.The model also ranked first in three independent benchmarks, each focusing on a different skill: CursorBench 4.0 te

Continues after the advertisementThe goal of the new feature is to consolidate the work within Claude so that users do not need to transfer content to other programs. The tools share project context, so an idea organized in Whiteboards can become a spreadsheet in Sheets and then a dashboard in Data.Claude Whiteboards groups notes by topic and creates schedules with dates and assignees, and Claude Sheets generates spreadsheets with tabs, formulas, formattin

Continue after the adsThis year’s event features more than 70,000 participants from 164 countries, over 10,000 startups, and nearly 2,000 investors. Companies such as Intel, Google, Meta, Apple, and Atlassian are also present, while smaller companies showcase solutions in areas such as artificial intelligence, healthcare, education, and financial services.📱 See today’s best deals on CT Ofertas’ WhatsAppAmong the main topics on the agenda is the advancement

Surprisingly, Apple has announced a radical expansion of Siri AI’s capabilities. In the future, every iPhone app will be able to connect to the AI system: Siri AI will then be able to operate apps, perform functions in multiple steps, and work across app boundaries. Developers can already test this in the iOS 27.3 beta; the first apps with Siri integration are expected to follow for iPhone users in spring 2027. Apple CEO John Ternus recently emphasized tha

What comes next is mostly a guessing game, even for the people who study how boards are supposed to handle crises. Two corporate governance professors, Ann Lipton of the University of Colorado and Todd Henderson of the University of Chicago, say there is no real precedent for a CEO caught up in a suspected honeypot. And while they have views on what the investigation should ask, like everyone else, they’re watching to see how it plays out. “There have actu

Efforts to benchmark agents tasked with complex work show that they aren’t typically reliable enough to function without close supervision. LiveBench 2.0 shows Claude Fable 5.2 and ChatGPT-6 Astra succeeding on 82.9 and 75.6 out of 100 tasks, respectively, and an effort by the AI infrastructure company EON to test agents in a simulated corporate environment found the most successful model tested, Claude Opus 4.8, performed tasks at 76.8% accuracy. Now, a s

Corporate leaders can now assign the agent a wide range of tasks, and it will complete them unsupervised, the company says. To prove the point, Databricks CEO Ali Ghodsi showed me how he uses it internally. Instead of a product demo, he fired up his actual agent and walked me through his most recent chats and charts. He showed me how it analyzed the parts of an employee satisfaction survey where the company didn’t score well, and how it helped him rate (an

If true, that would represent an almost 50% jump from the $6.7 billion in sales the company reportedly recorded in the second quarter. That would also mark a sizable increase in its revenue growth rate, which was previously said to be about 18% between the first and second quarters of the year. OpenAI’s profitability picture also appears to have improved, with its adjusted operating margin narrowing from -150% in the second quarter to -109% in the third qu

The Chinese government has been active in economic espionage for decades, and with semiconductors at the center of the AI boom, there’s intense pressure to infiltrate any company involved. At the same time, many of the executives getting rich off that boom have made themselves easy targets. As CEO of investment intel firm Heron Intelligence, Nate Singer has a unique view into the problem. His firm works on corporate due diligence: Essentially, making sure

@ikuo0An AI That Controls a Game Using Document VectorsMachine LearningAIJavaScriptGame DevelopmentTAG24@@TAG26@@0

@whitemarshallHow Accurately Can GPT Image 2 and Nano Banana Pro Render Product Image Text? — An Empirical Test with Identical PromptsAILLMImage GenerationAPITAG23TAG250TAG30TAG32@@Posted at 2026-11-11TAG35TAG37TAG39TAG41 Introduction The most alarming aspect of automatically generating e-commerce images is not the image “quality.” It is the text for prices and brand names. If an image is delivered with $49.99 rendered as $4.99, that is not a matter of cor

@tauchiiiAgent Change Reviews, Brought Together in One Place with osFoundry's Merge StationosFoundryAIAI agentsCode reviewProductivity improvement0Posted at 2026-11-11Once you start giving AI agents serious work to do, changes begin arriving from all directions. One agent rewrites the copy on a notebook's pricing page, another cleans up duplicate rows in a data channel, team members revise the onboarding checklist, and work still remains in your local sand

@tauchiiiWhen you assigned AI agents to departments and gave them roles, osFoundry became something you only “check”osFoundryAIAI agentsautomationbusiness efficiencyTAG28@@0Posted at 2026-11-11

@whitemarshallDeepSeekIf your weekly report with DeepSeek is still inconsistent after two weeks, start by clarifying the request for this rounddeepseekAIpromptengineeringautomationTAG23@@TAG250TAG30@@TAG32@@Posted at 2026-11-11

@geolin878Which three categories of questions are more worth prioritizing for GEO?geoSEOAI0Posted on 2026-11-11Many companies treat GEO as “SEO with a different name.” The first thing they do is pull out their existing keyword lists, make a few edits, and start writing content. This easily leads them astray because Generative Engine Optimization is about how AI selects and cites content, which works differently from the traditional search-engine logic of c

@softbrixai1(SoftBrixAI)AI Data Engineering: Pipelines, Quality Checks and Embeddings That Make AI Systems WorkAILLMragdataengineeringMachineLearning0Posted at 2026-11-11 AI Data Engineering: Pipelines, Quality Checks and Embeddings That Make AI Systems Work Most AI systems do not fail because the model is too weak. They fail because the data reaching the model is incomplete, inconsistent, out of date, or impossible to trace back to its source. A language