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Amazon Bedrock AgentCore now supports Policy Authoring, which converts natural-language policy documents into Dogwood policies. The update helps teams enforce agent controls, including new time-based constraints, with worked examples and best practices.
AWS Professional Services uses a multi-agent framework on Amazon Bedrock AgentCore to automate enterprise cloud migrations end to end. Specialized agents cover discovery, infrastructure-as-code generation, portfolio governance, and post-migration operations, reducing IaC development time from weeks to minutes.
Amazon Bedrock AgentCore Web Search now supports per-request domain and publication-date filters. Developers can control which websites agents use and require fresher sources, with filtering enforced server-side.
AWS presents three serverless patterns for invoking Amazon Bedrock AgentCore agents asynchronously from AWS Step Functions. Task-token callbacks, direct service integrations, and durable functions help eliminate idle compute costs while agents process requests.
Moore Threads’ first post-IPO half-year report shows revenue rising 147% to RMB 1.736 billion, while attributable losses narrowed sharply. However, the improvement relies heavily on concentrated cloud-computing sales, government subsidies, investment gains, and IPO-funded cash reserves rather than a clear recovery in core operations.
Alibaba’s net profit fell 75% to $1.54 billion in the June quarter as capital spending reached $9.98 billion. The spending reflects a major investment in AI infrastructure and cloud capabilities rather than a deterioration in core business performance.
An independent builder pretrained a 1.02-billion-parameter Kimi-K3 replica on 5 billion decontaminated tokens for roughly $250. The model achieved 33.4% on HellaSwag, exceeding the reported 28% score of GPT-2 124M despite receiving no instruction tuning.
Zhongbo Juli is increasing its investment in Moonshot AI once again. The move underscores the continuing competition around AGI, computing limits, and engineering execution.
The paper proposes Transition Complexity Profile (TCP), a reproducible framework for measuring how difficult game-world transition prediction is at a specified interface. It evaluates branching, interaction-driven uncertainty, opponent influence, and temporal or spatial dependencies to improve comparisons across game-modeling and reinforcement-learning benchmarks.
A Bangalore-based startup has entered the competition to create highly human-like artificial voices. Its AI model reportedly earns high scores compared with systems from OpenAI and ElevenLabs.