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A new SaferAI report finds Z.ai's open-weight GLM-5.2 approaches frontier AI capabilities while lacking key safety mitigations, renewing concerns that powerful open models could outpace governance and safeguards.

Anthropic has been on a cloud partnership spree in recent months, and its latest move is reportedly a $10 billion deal with AI cloud startup Volta.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Trump’s AI protectionism has come for robotics —James O’Donnell Humanoid robots usually elicit more cringe than awe: They stumble, kick children, and despite advances are still worse at using their hands than my toddler. It’s…

arXiv:2608.00001v1 Announce Type: new Abstract: This research note revisits Leibniz's mill, Turing's imitation game, and Searle's Chinese Room through the Conservation-Congruent Encoding (CCE) framework. It formalises a toy symbolic setting in which successful behaviour is measured by task performance ($W_{causal,T}$), while the efficiency with which preserved internal structure supports that behaviour is measured by operational consciousness ($\kappa_T$). Within this setup, an uncompressed lookup system and a compact generative system can in principle achieve comparable behavioural success, yet diverge sharply in $\kappa_T$: the former relies on an expanding standing store of unreused mappings, whereas the latter reuses compact internal structure. The note therefore reframes classic disputes about understanding by separating outward performance from the organisation that sustains it, and motivates why this distinction may matter for later AI-safety analysis.

arXiv:2608.00003v1 Announce Type: new Abstract: Computational Fluid Dynamics (CFD) plays an important role in modern engineering, but using open-source solvers such as OpenFOAM requires considerable knowledge and skills, as well as time-consuming configuration file setup. To reduce this burden, we propose AutoFOAM - a self-evolving large language model (LLM) agent that creates, evaluates, runs, and evolves its own OpenFOAM simulations based solely on natural-language instructions. Our model is pre-trained on the Qwen-coder 2.5-14B, which is then fine-tuned on 252 text prompts targeting 7 OpenFOAM solvers, 13 parametrized mesh templates, and a y plus-aware numerical policy. The crucial element of the algorithm is a sophisticated evolution loop composed of 7 stages. To prevent model degeneration under repeated self-training, the agent employs three complementary anti-collapse streams: RAG-augmented retry context, surgical dictionary-level patching, and prompt-diversity paraphrasing. By bridging generative artificial intelligence with rigorous fluid simulations, AutoFOAM accelerates rapid prototyping and democratizes advanced CFD workflows.

Most publishers think robots.txt keeps them out of AI Overviews. It doesn't. John Shehata's data shows what the wrong setting actually costs newsrooms. The post What Top Stories Inside AI Overviews Means For Publishers And Brands In 2026 And Beyond appeared first on Search Engine Journal .

Anecdotal report that blocking AI training bots with Cloudflare blocks Googlebot from indexing websites. The post Report That Cloudflare AI Bot Blocking Prevents Googlebot From Indexing Sites appeared first on Search Engine Journal .

Comparing which pages Google uses in AI answers against how they rank conventionally is where the new Search Console report earns its keep. The post Google Now Reports AI Search Impressions. Here’s How To Read Them appeared first on Search Engine Journal .

Oracle has the largest apparent concentration risk.

Analysing payslips and transactions to make decisions faster.