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Moonshot AI Releases 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔 to Replace Fixed Residual Mixing with Depth-Wise Attention for Better Scaling in Transformers

Residual connections are one of the least questioned parts of modern Transformer design. In PreNorm architectures, each layer adds its output back into a running hidden state, which keeps optimization stable and allows deep models to train. Moonshot AI researchers argue that this standard mechanism ...

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A Coding Implementation to Design an Enterprise AI Governance System Using OpenClaw Gateway Policy Engines, Approval Workflows and Auditable Agent Execution

In this tutorial, we build an enterprise-grade AI governance system using OpenClaw and Python. We start by setting up the OpenClaw runtime and launching the OpenClaw Gateway so that our Python environment can interact with a real agent through the OpenClaw API. We then design a governance layer ...

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Meet OpenViking: An Open-Source Context Database that Brings Filesystem-Based Memory and Retrieval to AI Agent Systems like OpenClaw

OpenViking is an open-source Context Database for AI Agents from Volcengine. The project is built around a simple architectural concept: agent systems should not treat context as a flat collection of text chunks. Instead, OpenViking organizes context through a file system paradigm, with the goal of ...

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LangChain Releases Deep Agents: A Structured Runtime for Planning, Memory, and Context Isolation in Multi-Step AI Agents

Most LLM agents work well for short tool-calling loops but start to break down when the task becomes multi-step, stateful, and artifact-heavy. LangChain’s Deep Agents is designed for that gap. The project is described by LangChain as an ‘agent harness‘: a standalone library built on top of ...

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