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  1. 36Kr · AI60

    OpenAI acquires startup Pierre Computer, targeting Microsoft GitHub's code hosting business

    OpenAI acquires startup Pierre Computer, further expanding the Codex programming platform's capabilities and challenging Microsoft GitHub. Some technology from Pierre Computer has already been applied to Codex, and OpenAI plans to integrate its code storage functionality. Financial terms of the deal were not disclosed, and Pierre's open-source projects will remain open-source.

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  1. Microsoft Official Blog72

    Microsoft opens the Copilot harness through Foundry, supporting the development of multi-model intelligent agents

    Microsoft opens the GitHub team's Copilot harness built by GitHub through Microsoft Foundry, providing a fully managed way to build intelligent agents that can reason, call tools, and complete long tasks.

    Why it matters: Microsoft opens the multi-model harness supporting multiple Copilot products internally to Foundry, allowing readers to judge the trade-offs of building their own orchestration layer based on this.

  2. Artificial Analysis Articles72

    Artificial Analysis Reviews GitHub Copilot Fusion: Performance slightly lower than Opus 5.5, single-task cost halved

    Artificial Analysis tested GitHub Copilot CLI's Copilot Fusion highest settings in Coding Agent Index v2.0, with a single task cost of 5.59 USD, while Claude Code with Opus 5.5 (max) costs 11.42 USD, only 2 points lower in overall score.

    Why it matters: Artificial Analysis used Coding Agent Index to test Copilot Fusion, providing a cost and token comparison with Claude Code at the same performance level.

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  1. GitHub Blog · AI & ML

    GitHub's secret detection model: Discover more secrets with less noise

    GitHub released a dedicated secret detection model that combines a fine-tuned ModernBERT classifier with regular expressions, reducing false positives by 11.5% while achieving a higher acceptance rate and shortening post-push scan execution time by 86%, with a cost reduction of 98%. The model distinguishes real credentials from similar items by analyzing file paths, variable names, and contextual code, making general secret detection extendable to push protection possible.

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