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. 2 sources

    Codex-Maxxing Separates Code From Work State

    OpenAI's Codex-maxxing guide frames durable threads, reviewable memory files, connectors, skills, heartbeat automation, and human approval as the operating loop for long-running Codex work.

    • codex
    • long-running-agents
    • operational-memory
  2. 2 sources

    Cursor Treats the Cloud Agent Environment as the Product

    Cursor's cloud-agent environment write-up shows why agent performance depends on dependencies, commands, security boundaries, end-to-end tests, and self-healing diagnostics.

    • coding-agents
    • developer-tools
    • agent-environments
  3. akamai.com

    LLM Inference Fails Quietly Before It Fails Loudly

    Akamai's vLLM load tests show why 200 OK is a weak health signal and why bounded admission, context limits, and an external queue should precede autoscaling.

    • infrastructure
    • ai-models
    • api-design
  4. 2 sources

    Mem0 Shows Agent Memory Should Not All Live in Context

    Mem0's Claude Code experiment separates durable memory from the conversation window: retrieve the relevant slice, survive /clear, and avoid loading every memory file up front.

    • agent-memory
    • context-engineering
    • mcp
  5. 2 sources

    miniSQLite Makes the Coding-Swarm Claim Inspectable

    The released Rust database turns Cursor's swarm experiment into an artifact that can be read, built, tested, and challenged instead of accepted as a benchmark chart.

    • coding-agents
    • developer-tools
    • agents
  6. 3 sources

    OpenAI Shows Efficiency Is a Full-Stack Agent Problem

    OpenAI's GPT-5.6 efficiency write-up connects model training, inference optimization, and the Codex/ChatGPT Work harness into one compounding cost-performance loop.

    • agent-harnesses
    • inference
    • models
  7. 2 sources

    OpenAI's ARC-AGI-3 Jump Was a Harness Result

    OpenAI's ARC-AGI-3 write-up shows why agent benchmarks measure the model plus the runtime harness: retained reasoning and compaction changed both score and token use.

    • evals
    • agent-harnesses
    • context-engineering
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