Latest
AWS 出的手把手教程，教你用 OpenClaw 加 AgentCore 的 memory 功能做一个能记住你、越用越懂你的个人助手，适合动手党。
AWS 出了套完整教程，用 AgentCore、Nova Sonic 和知识库搭语音客服，能改座位查延误，还会写数据前跟你确认，适合做智能体开发的人照着搭。
AWS 手把手讲 ISO/IEC 42005:2025 怎么做 AI 影响评估，还要上合规的企业可以直接照着梳理流程。
一个把小说做成漫画再动画的省钱工作流，Codex skill 管流程，Seedance 和 MiniMax H3 的费用坑都替你踩过了。
The pattern replaces fragile copy-paste promotion of agents and knowledge bases with a versioned, auditable pipeline, which matters once Quick deployments span multiple teams or accounts. Platform engineers and AI platform builders on AWS can adapt the MCP server on Bedrock AgentCore to enforce governance gates in their own multi-account setups.
Evaluation has shifted from output quality to verifying tool selection, constraint compliance and decision transparency in agent pipelines. Teams building production multi-agent systems on AWS can reuse this pattern to add explainability checks before deployment. The supply chain scenario makes it concrete for operations and logistics teams already running AWS infrastructure.
Agentic retrieval turns one-shot RAG into an iterative loop where the model decides when and what to retrieve, which changes both answer quality on compound questions and per-query cost. Builders choosing between plain Bedrock Knowledge Bases and an agent layer can use the trace-event comparison to quantify the trade-off. Developers in ASEAN can deploy the stack in the Singapore or Jakarta AWS regions.
想给业务加自动判断能力的话看这篇，Jev 每百万 token 才 0.042 美元，还跟 GLiDE 等 5 家开源方案对比了。
Willison 建议云服务默认设硬性预算上限，AWS 九月已上线 spend limit，Google Cloud 七月也有 Spend Caps，用智能体跑项目的人值得了解一下。
The pipeline shows that consumer devices can be daisy-chained to work around unified-memory ceilings on laptops, with gains concentrated in prefill rather than generation. Developers running Qwen3.8-27B-class models on 24GB machines can try layer-splitting across USB-C devices to cut long-context ingest times. Compared with cloud inference, this keeps everything local but requires custom software and offers no speedup below 64K for output tokens.
Microsoft 写的一篇实操教程：Agent 跟你说写入成功了，数据库里其实没有。教你拿真实数据状态当验证依据，做 Agent 工具调用的应该看看。
This tool represents a practical solution to a persistent challenge in enterprise AI development - the lack of specialized training data. For developers and data scientists in organizations implementing AI agents, AutoSynthData offers a way to create tailored datasets without relying on scarce or expensive external sources. The benchmark results suggest it could significantly reduce the time and resources needed to deploy effective enterprise AI solutions.
This case demonstrates how contextual bandits can optimize content personalization beyond simple A/B testing, addressing a common challenge in marketing optimization. For businesses in ASEAN looking to improve conversion rates, AWS offers a practical implementation that balances exploration and exploitation in personalization strategies.
This integration provides developers with a scalable, persistent memory solution for multi-agent systems, addressing a key challenge in AI agent development. For teams building complex AI applications, this implementation offers a practical approach to memory management that can be adapted for various use cases beyond investment research.
This reveals a fundamental security flaw in multi-agent systems that goes beyond individual sandboxing. Developers building agent-based applications must implement strict isolation between agent communications and shared resources. The vulnerability affects any system using independently-deployed personal agents like Muse that interact through shared channels.
This implementation shows how to build a production-ready claims QA system with proper citation tracing, addressing a critical need in insurance and legal sectors. Developers can adapt this workflow for any document-heavy industry requiring auditable answers, with AWS handling the infrastructure complexity.
This implementation shows how to orchestrate multiple AI agents working together on a creative task, which could be applied to other collaborative workflows beyond music production. Developers can use this approach to build more complex AI systems that require persistent state and handoffs between specialized agents.
This case study reveals the practical challenges of applying AI models to specialized domains like agriculture, where domain knowledge is crucial for accurate data interpretation. The detailed workflow and concrete examples of how AI tools were used (and where they fell short) provide valuable insights for developers working on agricultural visualization projects in Southeast Asia, where smart farming adoption is growing.
This implementation detail matters for developers building complex AI workflows that require branching logic. The distinction between static and dynamic branching helps optimize task distribution, especially for batch processing with variable quantities. For developers working with LangChain or similar frameworks, this pattern can streamline multi-step AI interactions.
This article addresses the critical gap between purchasing AI tools and achieving actual business value, which many organizations overlook. Business leaders should read this to understand the necessary steps beyond model acquisition, including building custom knowledge bases and integration systems, to make AI truly functional in their operations.
This method addresses a critical gap in AI agent reliability by focusing on source verification rather than just fact-checking. Developers and organizations implementing MCP agents can now build more trustworthy systems that reduce the risk of propagating false information, particularly important in applications where accuracy is critical.
This demonstrates how AI can automate security testing at scale, potentially reducing manual code review efforts. Developers can implement this open-source tool to proactively identify vulnerabilities in their Android applications before they can be exploited.
This retrospective offers a concrete timeline of how text classification has evolved, showing practitioners which architectures perform best on specific tasks. For developers in Southeast Asia working with multilingual content, the comparison of model efficiency could inform resource allocation decisions when implementing classification systems in resource-constrained environments.
AWS 教你在一个容器里同时跑 FLUX.2-klein 和 Wan2.1-VACE，文生图再图生视频一条流水线，教程里有完整部署步骤。
AWS 手把手教程：在 SageMaker 上用 vLLM-Omni 跑 Qwen3-TTS，流式输出语音，想做实时语音应用的可以跟着搭。