Using Claude Code: Spending your effort
His working loop: interview, then build on low/medium, then verify on high.
Reading list
Hand-picked pieces on agents, evals and skills from authors whose work you can check. We link to the original; the words are theirs.
His working loop: interview, then build on low/medium, then verify on high.
Using the Jev model as an LLM judge for agent evals, with a refund-agent example that checks tool calls, not just the final reply.
Advice: put the use case first in the description.
People and bots in one thread, trust built step by step, and when to use the Bot Development Kit.
A warehouse note in the tool output caused an unwanted refund, so he splits responsibilities across agents with guardrails.
Sponsored: “thanks to Google Cloud for collaborating with me on this piece.” The demo is scripted, not a live failure.
Repo: github.com/svpino/returns-agent
The human's job now is reviewing the agent's report (decisions, assumptions, asks), not the diff.
An open-source skill that gives Claude Code, Codex, Grok Bot and others one shared Obsidian memory.
A defined job, a source-of-truth brief ('fill unknown fields with unknown') and an approval boundary.
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