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Google unveiled Custom Feeds at its Made on YouTube event, a feature letting users type natural-language requests to generate a personalized homepage feed. The tool uses large language models to interpret prompts like genre or mood combinations and build a matching set of video suggestions. YouTube tested the feature earlier this year and plans a full rollout to all US users across mobile, desktop and TV.
A technical blog post demonstrates how the buzzy AI concept known as 'Jev' can be replicated using an open-source Qwen3-0.6B model and roughly 25 lines of Python code. The example loads the model, prompts it to classify an email as legitimate, spam, or phishing, and extracts probability scores directly from the model's output logits rather than relying on an external API or specialized training pipeline.
An investigation identified more than 80,000 AI relay servers that allow users in China to obscure their location and identity while querying frontier large language models hosted in the US. The relays effectively let these users bypass geographic and access restrictions placed on advanced AI systems.
TypeSafe's Jev, a rapidly adopted AI classification model, has become the fastest-growing model in Vercel's AI Gateway history. A commentator argues OpenAI could quickly replicate Jev's core approach and bundle it directly into its own models and agents, since OpenAI's LLMs already function as implicit classifiers that could be retrained for this purpose.
In a new essay, writer Cory Doctorow argues that people project intention and meaning onto AI systems like Anthropic's Claude much as religious believers see purpose in a sunset. He suggests this instinct to read agency into statistical text generators is a kind of collective delusion rather than evidence of genuine understanding inside the machine.
The United Nations unveiled the UN System Data Commons, a new platform built on Google's open-source Data Commons technology that lets people query UN statistics using plain-language questions and supports the Model Context Protocol so AI systems can pull data directly. It replaces the older UNData portal, which relied on manual browsing rather than conversational search. The announcement came alongside a UNICEF study showing leading chatbots answered development-data questions correctly only about 21% of the time.
IEEE Computer Society highlights that most AI products rely on large language models for reasoning and generation, but these models depend heavily on retrieving relevant background information before producing answers. When that retrieved data is outdated, weak, or irrelevant, the resulting output suffers regardless of the model's sophistication.
A study published in Cell introduces a specialized AI toolkit for longevity research, including large language models trained specifically on ageing-biology data, 17 benchmark tasks to evaluate performance on ageing-related questions, and an interface linking these models with AI research assistants. When tested against major commercial models from companies like OpenAI and DeepSeek, the purpose-built ageing models outperformed the larger general-purpose systems on most benchmark tasks.
Climate scientist Zeke Hausfather analyzed his own usage of Anthropic's Claude Code assistant and calculated that a typical day of work consumed between 1.2 and 5.9 kilowatt-hours of electricity, comparable to running two refrigerators nonstop for a day. He found that 96 percent of the roughly 3.2 billion tokens processed over eight weeks came from the agent repeatedly re-reading its own accumulated memory at each step, rather than generating visible output.
At the 18th BRICS Summit in New Delhi, President Xi Jinping said China would spearhead artificial-intelligence cooperation among the bloc's developing economies, including creating an open-source AI community, sharing large language model development, and running training seminars. He also proposed a digital ecosystem cloud platform, an engineer cultivation alliance, and a youth exchange program focused on scientific innovation.
AI companies are pouring billions into massive data centers and power plants to support 'agents'—AI systems that autonomously execute multi-step tasks rather than simply answering questions. Unlike a single chatbot query, an agent can generate hundreds of self-prompts to complete complex jobs, such as building an entire website over several hours. OpenAI recently touted a swarm of over 10,000 agents exchanging 2.7 million messages to tackle a longstanding math problem, though mathematicians have disputed the significance of that result.
Amid a public spat over an OpenAI math-proof controversy and an Anthropic researcher's resignation over safety concerns, AI ethicist Timnit Gebru weighed in publicly, arguing that apocalyptic warnings about AI 'killing humanity' are overstated and self-serving. Gebru, known for her contentious 2020 exit from Google over a suppressed bias research paper, is releasing a book next year detailing her experiences and views on the industry's ideological drift.