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Related event timeline, sources and context from the news index.
GitHub announced a research preview of Project HydraFusion in GitHub Copilot. In controlled offline evaluations, HydraFusion’s selective coding workflows matched or exceeded the evaluated Opus 5 baseline while reducing estimated workflow cost, according to a post on the GitHub Blog.
GitHub published a beginner guide on the GitHub Blog showing how to run parallel agents in the GitHub Copilot app. The post aims to help users move from apprehension to productive use of multiple agents in the app.
GitHub's AI & ML team (via the GitHub Podcast and Blog) published an explainer decoding emerging AI terms developers are using — including loop engineering, harnesses, squads, open weights, and hill climbing. The post aims to clarify how these concepts are discussed in developer conversations.
GitHub's blog published a post titled “How to evaluate LLMs before production” that describes lessons the team learned while evaluating large language models for a real-world secret-scanning system. The post is presented as practical guidance for assessing LLM behavior and suitability prior to deployment.
A GitHub AI & ML blog post describes using canvases to surface, direct, and reduce the cost of agentic (multi-step AI agent) workflows, arguing that canvas-based views avoid the visibility and control loss that can occur in chat-based interfaces. The author explains how they apply canvases to their own agentic workflows and why other teams might benefit from the approach.
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