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Deep, recent reads ranked by depth and freshness.

  1. We gave AI agents a mobile dev environment

    Engineering teams outgrew their local machines while running parallel AI mobile development agents, constrained by heavy builds and lack of iOS tools on Linux. To solve this, they built Remote Dev using warm pools of remote Macs connected via SSH alongside separate Linux VMs for Android virtualization, paired with agent skills and live browser streaming. This setup shifted workflow bottlenecks, increasing AI-merged pull requests from 6 percent to 61 percent.

    DEPTH ■■■■■9 min read3 days ago

    ArchitectureMobile

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  2. Taking a look under your agent’s hood

    This post discusses the challenges and methods of applying observability to AI agents, which combine traditional distributed systems telemetry with new requirements for monitoring LLMs and multi-step tool calls. The discussion highlights how non-deterministic agent behavior and increased speed of execution make continuous production feedback loops essential for upstream development. Ultimately, integrating observability data into automated testing and development loops helps teams iterate on agent performance.

    DEPTH ■■■■■25 min read2 weeks ago

    Machine LearningObservability

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