Filtering a dog bark from street noise or isolating a sound source with a single click on a video: Meta’s SAM Audio brings the company’s visual segmentation approach to the audio world. The model lets users edit audio using text commands, clicks, or time markers. Code and weights are open source.
The Wall Street Journal ran its own test of Anthropic's AI kiosk, and the results were far messier. Within three weeks, the AI vendor "Claudius" racked up losses exceeding $1,000. The AI gave away nearly its entire inventory, bought a PlayStation 5 for "marketing purposes," and even ordered a live fish.
Journalists found they could manipulate Claudius into setting all prices to zero through clever prompting. Even adding an AI supervisor named "Seymour Cash" couldn't prevent the chaos. Staffers staged a fake board resolution, and both AI agents accepted it without question. One possible explanation for why the kiosk agent couldn't follow its own rules: a context window overloaded by excessively long chat histories.
Things went better at Anthropic's own location. After software updates and tighter controls, the kiosk started turning a profit. But the AI agents still found ways to go off-script—drifting into late-night conversations about "eternal transcendence" and falling for an illegal onion futures trade. Anthropic's takeaway: AI models are trained to be too helpful and need strict guardrails to stay on task.
ChatGPT's grip on the generative AI market continues to slip, according to new data from Similarweb. The chatbot's share of website traffic dropped from 87.2 percent to 68 percent over the past year. Google Gemini, meanwhile, is surging, jumping from just 5.4 percent a year ago to 18.2 percent today.
Grok from X.AI is showing modest growth, now sitting at 2.9 percent. DeepSeek holds steady at around 4 percent, while Claude and Perplexity each hover near 2 percent. Microsoft Copilot remains flat at 1.2 percent. Similarweb also notes that daily visits across all AI tools have dipped slightly overall. The data comes from December 25, 2025, with additional details available in the full report.
Gemini's recent surge likely stems from the new Gemini 3 model and especially the Nano Banana Pro image generator. Even after ChatGPT rolled out its own image update, Gemini still leads the pack on quality. No other image model follows prompts as precisely or handles text as reliably, making it particularly useful for slides and infographics.
Prompt engineers, take note: Jane Manchun Wong has uncovered the system prompt for Waymo's unreleased Gemini AI assistant, a specification over 1,200 lines long buried in the Waymo app's code.
The assistant (still) runs on Gemini 2.5 Flash and helps passengers during their ride. It can answer questions, adjust the air conditioning, and change the music, but it can't steer the vehicle or alter the route. The instructions draw a clear line between the AI assistant (Gemini) and the autonomous driving system (Waymo Driver).
The prompt uses a trigger-instruction-response pattern throughout: each rule defines a trigger, an action instruction, and often example responses. Wrong and correct answers appear side by side to clarify the desired behavior. For ambiguous questions: first clarify, then draw conclusions, finally deflect. Hard limits are enforced through prohibition lists with alternative answers. Wong's full analysis has many more details.
Australia's financial regulator, Austrac, is pushing back against banks that rely too heavily on AI to generate suspicious activity reports (SARs). According to industry sources, Austrac officials have met with several banks recently to urge more careful use of AI. One major bank was reportedly reprimanded in a private meeting.
Banks have used machine learning to flag suspicious transactions for years. But the shift toward modern large language models only picked up over the past two years, as banks saw the technology as a way to cut costs.
Austrac deputy chief executive Katie Miller said the agency doesn't want a flood of "low-quality" computer-generated reports packed with data but lacking real intelligence value. She warned that banks might be submitting large volumes of reports simply to avoid penalties.
The banks are leaning towards the ends of higher quality but smaller amounts. The more data you’ve got, there's a problem of noise. If banks were looking to use artificial intelligence just to increase the volume (of reports), that’s something we need to assess.
According to Salesforce leadership, confidence in large language models (LLMs) has slipped over the past year. The Information reports the company is now pivoting toward simple, rule-based automation for its Agentforce product while limiting generative AI in certain use cases.
"We all had more confidence in LLMs a year ago," said Sanjna Parulekar, SVP of product marketing at Salesforce. She points to the models' inherent randomness and their tendency to ignore specific instructions as primary reasons for the shift.
The company also struggles with "drift" - where AI agents lose focus when users ask distracting questions. Salesforce's own studies confirm this behavior remains a persistent challenge.
A spokesperson denied the company is backtracking on LLMs, stating they are simply being more intentional about their use. The Agentforce platform, currently on track for over $500 million in annual sales, allows users to set deterministic rules that strictly constrain the AI's capabilities.
Nvidia is spending $20 billion on a holiday gift to itself: a struggling chip startup and its founders. The deal offers both tax advantages and a defense against Google’s TPUs in one move.
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