Welcome back! We have visual models that can handle 4K scenes with factual grounding. Agents that build and test software. Tools that read entire research papers with actual intent. Even 3D reconstruction that turns a single photo into a usable object. The stack is starting to behave like an ecosystem rather than a collection of demos, and the direction is becoming hard to miss.
In today’s Generative AI Newsletter:
• Google launches Nano Banana Pro with Gemini 3 level reasoning
• Fox News and Palantir build an AI assisted newsroom
• Anthropic uncovers the first AI orchestrated cyberattack
• Google rolls out Scholar Labs for deep academic search
• Meta debuts SAM 3D for instant photo to mesh conversion
Latest Developments
Google has launched Nano Banana Pro, its new Gemini 3 image model that upgrades text accuracy, supports 2K and 4K generation, blends up to fourteen images, preserves the identities of five people, and pulls factual context from Search into visuals. Adobe made it available in Firefly and Photoshop on day one, so creators can use it immediately without touching an API.
What is new:
Sharper text: The model generates clean lettering across fonts and languages without warped or broken characters.
Multi image control: It blends fourteen inputs and keeps up to five people consistent for product shots and brand work.
Search grounded visuals: It uses real world information from Google Search to build accurate infographics and reference based scenes.
Pro grade output: It supports 2K and 4K images with controllable lighting, angles, and color, plus watermark free export for Ultra users.
The model is slower and pricier than the original Nano Banana, but the quality jump is already pushing creators to switch. Nano Banana Pro inherits Gemini 3’s reasoning, which lets it handle complex layouts and multi image compositions with unusual stability. The larger shift lands inside the creative stack itself. Adobe is becoming a hub where the best models sit under familiar tools, and Google is moving toward a system that blends language, vision, and code into one working brain.
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Fox News has spent the past year letting Palantir’s engineers sit inside its newsroom to build a full suite of AI tools that map how stories get made. It is the most significant editorial tech shift the company has made in years, even as its CEO insists that AI is an assistant rather than a threat. The partnership creates a digital duplicate of Fox’s workflows and editorial habits, giving Palantir unusual access to one of the most influential newsrooms. Fox insists this is a business deal with full IP ownership and no training access. The company also keeps repeating one line for comfort. AI will not replace editorial judgment.
Fox’s new AI tools:
Topic Radar gives reporters fast background briefings so they can jump into a story without hours of prep.
Text Editor checks writing for Fox style, broken links, and technical slipups before an editor even touches it.
Article Insights looks at how stories perform and shows writers what could have driven more engagement.
On the record executives say the tools handle chores, not content creation, and Fox controls all of its data.
Fox’s CEO Suzanne Scott is telling students to embrace AI because she believes the panic around job loss is misplaced. The claim sounds comforting, but every newsroom in the world is wrestling with the same tension. AI that tags stories is harmless. AI that shapes stories is not. The partnership raises a familiar question for the industry. If software becomes the second editor in the room, who gets the final word when the machine disagrees with the humans?
Anthropic says it uncovered a China-linked threat group that manipulated Claude Code into running what may be the first large-scale AI-orchestrated espionage operation. The attackers broke the job into small, harmless-looking tasks, then chained them together until the model was doing real offensive work across roughly 30 companies and government agencies.
What the investigation found:
Network intelligence: Claude scanned systems, mapped infrastructure, and highlighted high-value databases at machine speed.
Exploit generation: It researched vulnerabilities, wrote attack code, and tested entry points across multiple targets.
Access harvesting: The model pulled credentials, flagged privileged accounts, created backdoors, and sorted stolen data by sensitivity.
Machine-run cadence: 80 to 90 % of the campaign ran through Claude in looping tasks, with humans nudging only the big decisions.
Anthropic caught the activity in September, shut it down, and alerted affected organizations.The case forces a new question for security teams. The threat is no longer a lone intruder typing in the dark. It is an AI system that can work through thousands of actions in minutes with no fatigue, no hesitation, and no sense of boundaries. One investigator described it as “watching a cyberattack unfold at a tempo no human crew could touch.” The irony is that the same agents that enable these attacks are about to become standard in defensive stacks.
Google has released Scholar Labs, a new AI tool designed to surface research papers by analyzing the full text of scientific studies and the structure of a user’s question. The tool identifies topics, relationships, and methods inside a query and matches them with papers across disciplines. Scholar Labs is already rolling out to a limited set of logged-in users and is meant to operate as a more concept-driven version of Google Scholar.
What Scholar Labs does:
Semantic matching The system reads entire papers to find concepts and methods that align with the user’s research question.
Full-text relevance ranking It weighs the content of a paper, its publication source, its authors, and its citation activity.
Reasoning notes Each result includes an explanation of why the paper was selected, showing which parts of the query and study align.
No citation filters Users cannot sort by impact factor or citation count, since Google believes most people cannot guess meaningful thresholds.
Instead of relying on the usual signals of credibility like citation counts or journal prestige, it elevates the patterns inside the text itself. This puts more weight on what the research says and less on how the field rewarded it. The challenge for Google will be simple to describe and difficult to solve. Scholar Labs must prove that its deeper reading of text can surface trustworthy science without the guardrails that guide traditional academic search, especially in fields where quality varies widely.
SAM 3D is Meta’s new model that converts a single photo into a full 3D object or human mesh. You take a picture, select what you want, and the system rebuilds the entire shape, including the parts you cannot see. It works on everyday objects like shoes, furniture, gadgets, or bags, and it also recreates full 3D humans for pose work, avatars, and creative projects.
Core functions:
• Instant 3D assets: Creates textured 3D models from one photo for shops, creators, AR filters, or quick previews.
• Human reconstruction: Generates full body meshes using Meta’s new human rig, which lets you adjust pose, proportions, and details more easily.
• Real world accuracy: Trained on nearly one million images and over three million mesh ratings to handle messy, real-life photos.
• Easy to try: Available in the Segment Anything Playground where anyone can upload an image and turn it into a 3D asset.
• Practical uses: Helps sellers show items in a room, gives creators fast assets for games or animation, and lets robotics teams test perception in 3D.
Try this yourself:
Pick an object in your room. Take a quick photo and upload it to the Segment Anything Playground. Generate the 3D model, rotate it, and check how well SAM 3D predicts the hidden sides. Test small objects, reflective surfaces, or furniture to see where the model performs best.
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