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Claude's new voice model will let you reschedule your meeting or draft an email.

AegisAI co-founders developed AI agents that quickly analyze each message as a human would, paying attention to small anomalies that even the most elaborate checklist wouldn’t catch.

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. The power line that could reshape New York’s grid is hitting snags During a heat wave on July 3, New York State’s grid imported enough electricity from Canada to meet about…

Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to…

arXiv:2607.18241v1 Announce Type: new Abstract: Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over enterprise-scale datasets due to context overflow, loss of per-entity attribution, and linear latency from sequential tool calls. We present BatchDAG, a system in which an LLM generates a typed directed acyclic graph (DAG) of operations -- SQL queries, semantic searches, in-memory transforms, parallel fan-outs, and single-shot analyses -- which a deterministic engine evaluates with topological-wave parallelism and structured JSON data flow. A key optimization, entity-aware batching, groups rows by logical entity before fan-out, reducing LLM calls by up to 47x. BatchDAG is not primarily an accuracy improvement over hand-optimized pipelines; rather, it is a general-purpose orchestration layer that replaces multiple hand-engineered workflows with a single system that generates the appropriate execution strategy from natural language. In controlled experiments on 12 transcript-heavy queries, BatchDAG (3.74/5) achieves quality comparable to an expert-designed pipeline (3.25/5) and significantly outperfor

Dive into the evidence behind AI search and its effects on page performance. Learn from experts in the latest SEJ discussion. The post AI Search is Working. How to Prove It With Real Tests. appeared first on Search Engine Journal .

OpenAI killed Atlas because AI browsers were always a workaround. The real fix is a website machines can actually read. The post AI Browsers Are Backward Because Agents Never Needed The Visual Layer appeared first on Search Engine Journal .

On Alphabet's Q2 call, Pichai named coding an area to improve, saying Google needs Gemini 4 for the next frontier, as 3.5 Pro stays delayed. The post Pichai Says Google Needs Gemini 4 To Compete At The Frontier appeared first on Search Engine Journal .

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