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Deep, recent reads ranked by depth and freshness.

  1. Application Metrics caught my broken size estimator

    An engineer integrated application metrics into a client-side video conversion tool to monitor file size estimation accuracy without using invasive telemetry. By tracking key performance indicators and analyzing the metric distributions, they discovered that an AI-generated estimation formula was flawed and producing results off by up to 83 percent. Replacing the broken logic with a correct bitrate-based formula brought the size estimation accuracy to within five percent of real-world results.

    DEPTH ■■■■■15 min read7 weeks ago

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  2. GrabMart data for faster disease outbreak detection

    The organization proposes a conceptual data pipeline to analyze aggregated GrabMart transaction data for symptom-related product demand, aiming to supplement clinical surveillance for disease outbreaks. The design relies on data aggregation, statistical baseline comparisons, and privacy safeguards like differential privacy without involving individual health inference. The system remains in an early planning phase requiring rigorous validation, privacy review, and public health oversight.

    DEPTH ■■■■■5 min read2 weeks ago

    Data EngineeringMachine Learning

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  3. Seer, the Sentry MCP and CLI, or your own coding agent: where each one fits

    Engineers often struggle to choose between native debugging agents, Model Context Protocols, and command-line tools when integrating error tracking into their workflows. Sentry demonstrates how its debugging assistant Seer, the Sentry MCP, the Sentry CLI, and various coding agent integrations complement each other rather than compete. The resulting setup allows teams to diagnose complex application errors and automatically generate pull requests through whichever surface they prefer.

    DEPTH ■■■■■11 min read3 weeks ago

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  4. Measuring the back/forward cache with Application Metrics

    Engineering teams often lack visibility into back/forward cache performance because successful cache hits leave no telemetry traces and browser heuristics frequently change. To solve this blind spot, the team introduced an opt-in application metrics integration in their JavaScript SDK that models browser bfcache signals into hits, specific miss reasons, and reload durations. As a result, developers can track, query, and fix silent navigation regressions using customizable dashboards.

    DEPTH ■■■■■6 min read4 weeks ago

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  5. Building Sentry's Laravel AI Integration

    The Sentry team built a Laravel AI integration to support Agent Tracing during an internal hackweek. Because Laravel AI lacked events for individual LLM invocations, the integration correlates outgoing HTTP requests with active invocations using provider URL prefixes. The resulting zero-config feature is available in sentry-laravel 4.27 and automatically traces agents, LLM requests, and tool calls.

    DEPTH ■■■■■4 min read5 weeks ago

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  6. Debugging our AI search assistant with agent tracing

    The Sentry team developed a Search Query Assistant to convert natural language prompts into search syntax, which initially failed on custom numerical attributes due to incorrect field formatting in the system prompt. By utilizing their own AI Conversation view and agent tracing, they identified the root cause in the tool call results and system prompt generation. This observability workflow enabled them to reproduce, fix, and verify the issue both locally and in production.

    DEPTH ■■■■■4 min read6 weeks ago

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