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ExternalSoftware Engineering
DEV Community

MCP Python SDK Extension Method Collisions: Fail Before the Server Starts

MCP Python SDK extension method collisions are configuration defects, not runtime edge cases. If two extensions claim the same method—or one claims a core MCP method—the server should reject that setup before it accepts a request. I prefer making method ownership an executable contract so registration order can never decide which handler wins. The official MCP Python SDK extension documentation defines three useful safeguards: core methods cannot be registered as extension methods, duplicate ext

mcppythontesting
DEV CommunityRead original
ExternalSoftware Engineering
GitHub Changelog

GitHub Copilot weekly releases — September 7

This week, GitHub Copilot introduces Jira integration in Copilot app and adaptive model orchestration with Project HydraFusion in Copilot CLI. We also introduced new agent automation in Visual Studio Code… The post GitHub Copilot weekly releases — September 7 appeared first on The GitHub Blog.

GitHub ChangelogRead original
ExternalTechnology Trends
HN RSS Best

Nine coding harnesses vs. your laptop

Article URL: https://nasutton.notion.site/Nine-coding-harnesses-vs-your-laptop-3d139990182b80d59fa3cf500f0450ba?pvs=74 Comments URL: https://news.ycombinator.com/item?id=49651221 Points: 183 # Comments: 70

HN RSS BestRead original
ExternalAI
AWS Machine Learning Blog

Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference

Amazon SageMaker Inference now offers prefix-aware routing, a routing strategy that sends requests sharing the same prompt prefix to the same instance so the KV cache stays warm. In benchmarks on Llama 3.1 70B, it reduced P50 time-to-first-token by up to 77% and raised KV cache hit rates from about 25% to over 80%.

Advanced (300)Amazon SageMaker AIAnnouncements
AWS Machine Learning BlogRead original
ExternalAI
AWS Machine Learning Blog

Reduce inference cold starts on Amazon SageMaker HyperPod with model caching

Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to seconds, how it works, and how to enable it.

Amazon SageMaker HyperPodAnnouncementsExpert (400)
AWS Machine Learning BlogRead original

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