Field Notes

Editorial notes on AI-engineering releases and research, each tied to its primary sources.

993 notes471 topicsJSON index
1-12 of 157 matching notes
  1. linear.app

    Linear Loops Turns Recurring Work into Agent Workflows

    Linear Loops shows how product systems are starting to embed agents not as chat, but as repeatable workflows with schedules, events, run memory, and context access.

    • linear
    • agent-workflows
    • product-ops
  2. 4 sources

    Long-Running Loops Need Goals, Not Keep-Going Prompts

    Long-running agent work is useful only with a testable goal, checkpoints, a terminal condition, and recovery policy; otherwise the loop becomes expensive blind continuation.

    • claude-code
    • long-running-agents
    • agent-ops
  3. 2 sources

    Model Match: Choose the Model and Effort Together

    Model selection is becoming an engineering control: quality and cost depend not only on the model name, but on effort, task class, and a testable success criterion.

    • model-selection
    • llm-ops
    • cost-control
  4. bhavyansh001.medium.com

    Senior Engineers Do Not Read Large Codebases Like Books

    A note on reading large codebases is a useful AI coding reminder: senior practice starts with architecture, tests, types, search, and change history, not linear file reading.

    • codebase-understanding
    • senior-engineering
    • ai-coding
  5. humanlayer.dev

    The Four Stages of AI-Native Development

    An AI-native team does not start by buying agent tooling; it starts by turning one engineer's working method into reproducible rules, memory, and checks.

    • ai-native
    • engineering-teams
    • agent-ops
  6. 2 sources

    Language Models Need Sleep

    A new approach moves useful short-term context into model parameters through a consolidation phase, then has the model generate a synthetic curriculum and continue improving.

    • memory
    • loop-engineering
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