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Ant Ling released Ling-3.0-flash on August 4 as a 124B-total / 5.1B-active MoE with native hybrid-linear attention (KDA + Gated MLA, 5:1 alternating) trained from day zero, MIT-licensed weights, and an SGLang HiCache + Mooncake serving stack that cuts TTFT 60–80% on long inputs. Active parameters drop ~8x vs the 1T-class predecessor while matching or beating it on SWE-Bench Pro, MCP-Atlas, and SkillsBench. The real story is not the benchmark chart. It is that hybrid-linear is now the default architecture for open-weight frontier models, and Ant Ling shipped the first one trained that way from scratch.
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A guide to dependency review, supply-chain risk triage, and remediation workflows.
A practical walkthrough of Model Context Protocol server structure, authentication, and tool registration.
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