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Writing at Scale: AI-Powered Content Creation
300+ Deep Dives: Human-Authored, AI-Produced
AI & Technology
Continuity-first engineering to prevent AI agent context loss
Continuity-first agentic systems anchor today’s changes to yesterday’s decisions with memory and rationale. Learn how to prevent AI agent context loss using constraints, ledgers, and fail-closed workflows.
When to Use Multi-Agent LLMs: Choose Structure Over Orchestration
Most demos confuse delegation with intelligence. This piece explains when to use multi-agent LLMs—only when distinct value functions conflict—and how to design arbitration to resolve real tradeoffs.
Design for Outcomes: Make Content Machine-Actionable
Design for outcomes, not clicks. Learn how to make content machine-actionable by encoding intent and exposing callable actions so agents can complete tasks end-to-end.
Career Growth
Content & Personal Brand
Education & Learning
Engineering Strategy
How to Design AI-Human Workflows: Guardrails for Reliable, Auditable Collaboration
Design judgment-centered AI-human workflows that add constraints and friction, delegate by intelligence type, and review reasoning so outputs are reliable and auditable.
How to Prioritize Engineering Work with a Calm, Multi-Horizon Cadence
Stop chasing an endless backlog by choosing what matters across daily, weekly, and quarterly horizons. Learn how to prioritize engineering work, triage clearly, and communicate calm, transparent rationale.
Manage AI Adoption in Teams by Coaching Habits, Not Adding Tools
AI delivers consistent gains when managers coach disciplined team habits—prompting, context, validation, guardrails, and reflection. Learn how to manage AI adoption in teams with a simple, repeatable cadence.
Leadership
Mindset & Well-Being
Productivity
Professional Development
Work-Life Balance