Evaluating AI-Assisted Work as a Team
Use team questions to examine task scope, review effort and delivery outcomes. Evaluate AI-assisted work with shared evidence rather than reactions, labels or promises of superiority.
Use team questions to examine task scope, review effort and delivery outcomes. Evaluate AI-assisted work with shared evidence rather than reactions, labels or promises of superiority.
Define acceptance criteria, use deterministic checks for verifiable properties, and review the checks themselves. Passing tests increases confidence within their coverage; it does not prove every behavior correct.
What Anthropic’s Mythos Preview security research demonstrated—and how engineering teams can turn AI findings into validated fixes.
AI can help an individual complete broader implementation tasks. Evaluate that benefit alongside integration, review, ownership and the collaboration a project still requires.
Use AI to prepare concise updates and decision briefs. Choose between asynchronous work and a meeting based on uncertainty, participation and the decision required.
How Claude Code computer use works, how to enable it, and how to turn desktop interaction into evidence for a development task.
Keep the AI workflow as simple as its requirements allow. Add orchestration, retrieval and automation for demonstrated needs while preserving useful checks and review.
A source-linked update on Claude Mythos: distinguish naming speculation, vendor evaluations and actual model availability.
A Claude Code audit for Things 3: read SQLite safely, score task structure, propose fixes, and apply approved changes through AppleScript.
Configure Claude Code model, effort and permission modes explicitly, with documented limits and realistic recovery expectations.
An experimental Claude Code rendering option that improved flicker in one reported test; support and behavior can change.
Compare Claude Code installation methods, identify package ownership, and migrate to the native installer with verification.