TheAnswer EngineAI News · Sydney

Friend, the AI wearable, can now talk to its users — for an enhanced price.

As experts have warned for the last two years, some companies — like Microsoft and now Google — are finding and patching an exponential number of bugs in their products, thanks to the use of LLMs and AI tools.

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. A fundamental flaw leaves LLMs strikingly vulnerable to attack It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a…

It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conference on Machine Learning, a top AI conference, this month. The claim has huge implications for the safety of this technology, which…

arXiv:2607.26119v1 Announce Type: new Abstract: Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterparts on mathematical reasoning tasks; Yet the mechanistic basis for this advantage remains unclear. We therefore ask, what internal representational differences enable RL models' superior performance? Our work presents two converging lines of evidence: First, linear probes trained on layer-wise hidden states reveal that RL models tend to achieve higher accuracy in predicting answer correctness compared to SFT models, indicating more linearly separable and structured representations. Second, mean ablation studies show that RL models develop a hierarchical architecture where deeper layers become progressively more critical, whereas SFT models distribute importance uniformly across layers. Together, these findings demonstrate that RL training fundamentally restructures how models represent and process reasoning problems. Finally, we analyze token-count variability under repeated sampling across problems to assess adaptive compute allocation. While we observe higher variability in some RL-tune

New Similarweb figures reveal how narrowly ChatGPT distributes its outbound clicks, and what that concentration means for publishers chasing AI referral traffic. The post AI Search Isn’t Replacing Google, It’s Layering On Top – Similarweb Data appeared first on Search Engine Journal .

Eight weeks of AI Overviews data in Search Console reveals impressions without clicks, inflated position-one rankings, and misleading averages that can mask real traffic losses. The post Google’s ‘Generative AI’ Search Console Data Is A Trap For Marketers appeared first on Search Engine Journal .

Anthropic's Boris Cherny, head of Claude Code, explains how to become one of the top 1% of users who get things done. The post Head Of Anthropic’s Claude Code Says Prompt Engineering Not That Important appeared first on Search Engine Journal .

Adds Now Assist to its AI toolset.