Field Notes

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

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1-12 of 157 matching notes
  1. x.com

    Working backwards before coding

    Good pre-coding eval asks whether the finished function matches a customer contract written before implementation: future announcement first, implementation plan second.

  2. freestyle.sh

    Agent APIs Need Fewer Magic Tricks and More Facts

    Agent-first APIs should return explicit fields, precise errors, raw facts, and traceable metadata so models can repair tool calls deterministically instead of guessing world state.

    • api-design
    • agents
    • tool-use
  3. launchdarkly.com

    Agent Steps as Graphs: Workflows Become Observable

    Agent graphs turn loosely narrated steps into a controllable structure: transitions, conditions, tool calls, state, and clear places for human intervention.

    • agent-graphs
    • workflow-observability
    • agent-control
  4. arxiv.org

    Agentic Self-Improvement Is Becoming Its Own Field

    A survey of self-improvement in modern agentic systems maps how agents improve prompts, memory, tools, plans, and workflows, while showing why guardrails matter.

    • agentic-systems
    • self-improvement
    • research
  5. 2 sources

    AI Coding Workflow: From Idea to Verifiable Work

    AI coding works better when checkable artifacts stand between the idea and the code: specs, tickets, TDD, fresh-context review, and manual QA.

    • coding-agents
    • workflow
    • verification
  6. howdy.com

    AI-Native Engineering Teams Are Smaller and Stricter

    AI-native engineering changes team size, roles, onboarding, metrics, and review: code is no longer written only by humans, but context and quality ownership get stricter.

    • ai-native-teams
    • engineering-management
    • context-engineering
  7. reuters.com

    Anthropic Cuts Limits and Rents Compute from Meta

    Compute is becoming part of the AI product contract: Claude limits, plans, and availability depend not only on prompts but on where the lab can find capacity.

    • compute
    • ai-infrastructure
    • anthropic
  8. medium.com

    Capital One's VulnHunter Points AI at Real Attack Paths

    Capital One's VulnHunter shows a useful shift in AI security tooling: an agent should connect a fix to a reproducible attack path, not only generate a diff.

    • security
    • code-agents
    • vulnerability-management
  9. github.com

    Claude HUD Turns Agent Observability into a Status Bar

    Claude HUD shows that agent observability can begin as a small status line for context health, tools, running agents, and progress instead of a large dashboard.

    • agent-observability
    • claude-code
    • operator-ux
  10. pillar.security

    Coding Agent Sandboxes Break in Places Teams Do Not Expect

    Pillar shows that agent sandboxes must be assessed not only around the agent process, but around files, configs, allowlisted commands, and local daemons the host later trusts.

    • agent-security
    • coding-agents
    • sandbox
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