eval-driven development
- AI Strategy (429)
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- Leadership (81)
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- Product Management Leadership (373)
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Prioritizing AI Across Product Value and System Design
A practical framework for ranking AI features and platform work through user outcomes, evaluation readiness, information budgets, and measured bottlenecks.
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How AI Is Redesigning Product Management and Knowledge Work
A practical operating model for delegating product work to AI while protecting judgment, evidence quality, accountability, and customer outcomes.
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Google AI Engineering Interview Prep: A Practical Plan
A practical plan to prioritize coding, ML depth, system design, RAG, agents, and resume stories without memorizing a speculative question bank.
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How to Make AI Agents Finish and Hand Off Work Cleanly
A practical completion contract, handoff format, stop rule, and evaluation system for agents that deliver results without creating more work.
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How to Measure AI Product Quality and Prove Real ROI
A practical framework for linking AI evaluations to successful tasks, retained usage, business outcomes, and the full cost of delivering value.
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From AI Capability to Measurable Workplace Productivity
A practical operating model for choosing AI work, defining acceptable output, controlling review costs, and proving productivity in the workflow.
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How to Build Proactive AI Assistants Without Losing Control
A practical framework for turning new AI models into proactive assistants with clear scope, permissions, evaluations, and human control.
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Anthropic’s Agentic AI Operating Model for Product Leaders
A practical framework for building agentic products with durable context, bounded autonomy, task-level evals, and pods that own outcomes.
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Why People Dismiss AI Achievements and How Leaders Respond
Learn to spot moving goalposts, frame AI capability claims precisely, and build evidence skeptical stakeholders can judge without hype or hand-waving.
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How Neural Networks Represent Information—and Why It Matters
A practical model of weights, activations, neurons, features, and circuits—and how to judge claims about what an AI system has learned before trusting them.
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How to Govern AI Product Experiments Without Slowing Teams Down
A practical operating model for testing AI in stages, setting evidence gates, and turning legal, privacy, and safety concerns into product controls.
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How to Evaluate an Agentic Customer Support Platform
A practical framework for choosing agentic support workflows, testing platform controls, designing handoffs, and measuring verified resolutions.
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A Practical Path Into AI Engineering Without a CS Degree
A practical roadmap for turning domain expertise, software fundamentals, and a production-minded portfolio into a credible AI engineering candidacy.
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How Product Leaders Should Respond to Frontier AI Launches
A practical framework for deciding which frontier AI launches to ignore, watch, test, or adopt based on product evidence rather than hype.
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Reliable AI Agents: Architecture, Evals, and Release Gates
A practical architecture and evaluation playbook for turning fragile AI agent demos into bounded, observable systems you can release with confidence.
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Why Customer-Facing AI Still Needs Human Expertise
A practical framework for turning expert judgment into product rules, human checkpoints, evaluations, and safer customer-facing AI decisions.
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How to Evaluate AI Models Beyond Headline Benchmarks
A practical framework for testing AI models on real workflows, failure modes, consistency, human effort, and cost before making a production choice.
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How to Build a Durable Career in AI Product Management
A practical guide to choosing an AI product lane, building credible evidence in your current role, and judging job offers beyond titles and hype.
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AI Memory Freshness: How to Handle Contradictory Facts
Design AI memory as a governed decision ledger so your product retrieves the latest approved truth, exposes conflicts, and preserves history.
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Where AI-Driven AI Research Breaks Down—and What to Do
A practical operating model for using AI in AI R&D without mistaking persuasive output, faster iteration, or self-evaluation for reliable discovery.
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