Collection catalogs
Working with AI collections
Give AI a clear task and context. Check its work and set limits on what it can do.
12 collections · 137 unique ideas. Collections may share an idea.
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Calibrate when to rely on AI instead of measuring trust as a feeling
Use recent 2025–2026 human-AI research to separate confidence, accuracy, metacognitive sensitivity and actual advice adoption so reliance can be tested rather than assumed.
Give AI the right context, memory and reusable skills
Use modern context engineering, durable memory and evaluated skills to make repeated AI work more reliable without stuffing every fact and instruction into every conversation.
Let AI do useful work without giving it accidental authority
Design AI-assisted workflows so tools, permissions, evidence, approvals, testing and monitoring match the real task and limit the blast radius of a bad output.
Let coding agents move fast inside a reviewable security boundary
Turn AI-assisted coding into an inspectable software-development workflow with bounded repository scope, untrusted-context handling, dependency checks, independent tests, protected build/CI surfaces, secret controls, egress limits and accountable human merge ownership.
Make AI work improve from failures instead of accumulating rituals
Use eval-driven development, durable guardrails, clean experiments and reusable research or automation artifacts so everyday AI workflows learn from repeated work rather than restarting from chat every time.
Make AI-assisted work earn your trust
Separate demonstrations from evaluations and make errors, evidence gaps and action authority inspectable before delegating consequential work.
Make coding agents work in inspectable increments
Turn current long-horizon coding-agent practice into concrete feature ledgers, cold-start checks, evaluator criteria and safe parallel work that a human can steer without micromanaging every line.
Make the agent loop finish, recover and stay inspectable
Turn current 2025-2026 agent engineering into practical controls for bounded loops, context, memory, checkpointing, evaluation and trajectory monitoring.
Operate AI agents as systems you can replay, verify and constrain
Add practical 2026 agent-engineering techniques for replay, evaluation, tool routing, computer use, scoped authorization and deterministic execution without turning conference claims into universal guarantees.
Protect human-origin ideas before AI enters the search
Help people and teams preserve genuinely different starting points when generative AI is useful but a shared model could narrow the initial option space.
Prove the human-AI combination, not just the AI assist
Add two missing evaluation layers to AI-assisted work: compare the combined workflow with both solo alternatives, and inspect who gains or loses instead of hiding heterogeneous effects inside one average.
Use AI where it helps, and keep the thinking that still matters
Turn recent human-AI research into practical rules for task fit, verification effort, decision ownership, learning retention and measurement of real work gains.