Start with a situation
What would you like to work on?
Collections bring useful ideas together. A learning path or playbook also gives you an order to follow.
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Check a decision before you commit
Choose a review aid for an uncertain explanation, a numerical estimate or a persuasive description. Keep a decision note with evidence, limits and a reason to revise. English pilot for editorial and usability review, not an approved intervention.
Make the task usable across different ways of accessing it
Ten bounded interface checks for keyboard, enlargement, forms, status, speech input, motion, targets and tables.
Learn and explain without hiding the difficult part
Ten ways to expose relations, adapt support and test understanding while preserving evidence limits.
Language that survives a new situation
Ten bounded practices for moving from familiar examples to usable listening, vocabulary and interaction.
Make follow-through easier without a discipline contest
Choose practical supports for starting, repeating and returning to useful actions while protecting essential responsibilities.
Connect, cooperate and disagree without guessing minds
Use specific acknowledgment, information-seeking and explicit responsibility to make ordinary relationships easier to navigate.
Protect health through ordinary care and clear information
Improve specific prevention and medicine-literacy tasks without diagnosing yourself, prescribing treatment or copying another country's care schedule.
Remove household hazards before they become an emergency
Check installed protections, safe supervision and emergency boundaries without practising hazards or improvising technical safety systems.
Find the user problem before building more machinery
Ten distinct research and pilot methods for observing tasks, testing structure and delivering an initial real benefit.
Write asynchronous messages that can be acted on without a meeting
Structure email, chat, tickets and handoffs around action, ownership, evidence, uncertainty and final state so readers can route, decide and execute without reconstructing the author's mental model.
Choose a thinking tool from the shape of the problem
Use a small set of sense-making tools to distinguish ordered, complex and fast-changing situations, expose system structure and give difficult decisions the right amount of thought.
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.
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.
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.
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.
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.
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.
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.
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.
Keep assessment preparation executable under pressure
Translate a preparation goal into monitored behavior, resilient fallback sessions, missed-session recovery, bounded scope and a safer response to assessment anxiety without confusing distress with readiness.
Prepare for the assessment you will actually face
Turn an assessment, exam or professional review into observable performance demands, a baseline, a gap-weighted plan and practice that matches the real response format.
Protect attention while using external memory on purpose
Reduce interruption costs and memory failures by batching low-urgency signals, designing explicit reminders and using cognitive offloading without accidentally outsourcing knowledge that still needs to be learned.
Keep useful work flowing when demand exceeds capacity
Apply queue, capacity, fairness and overload-control ideas to software, agent fleets and operational workflows so backlog, retries and one noisy workload do not turn pressure into collapse.