Product Management
- AI Strategy (429)
- Generative AI (113)
- IT Leadership (38)
- Leadership (81)
- Product Management (394)
- Product Management Leadership (373)
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How to Build AI Agent Infrastructure That Proves Value
A practical operating model for tracing agent runs, diagnosing failures, controlling routing costs, and connecting performance to business outcomes.
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Cheaper AI Models: Measure the Cost of Accepted Results
A practical framework for routing AI work, controlling agent loops, and measuring whether lower token prices reduce cost per accepted business result.
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How to Roadmap AI Agents, Robotics and Dedicated Hardware
A practical framework for sequencing agent autonomy, robotics, and dedicated hardware without letting compelling demos outrun product evidence.
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How to Find Commercial Opportunities in Emerging AI Markets
A practical framework for turning AI market shifts into paid wedges, validating demand, and evolving repeatable services into defensible products.
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Designing AI Products That Learn Without Losing User Trust
A practical framework for placing AI in the workflow, capturing meaningful feedback, governing adaptation, and turning usage into safer product learning.
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Persistent Memory for AI Assistants: A Product Playbook
A practical guide to deciding what an AI assistant should remember, operating its memory lifecycle, and measuring whether users can trust it.
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How to Evaluate and Optimize Open Models for Production
A practical framework for deciding whether an open model is production-ready, then improving quality, latency, cost, and reliability.
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Production AI Agent Operations: A Practical Operating Model
A practical operating model for reliable AI agents, covering orchestration, safe retries, evaluations, controlled releases, monitoring, and incidents.
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How to Give Autonomous Agents Context and Permission to Act
A practical operating model for agents that detect work, use current context, take bounded action, and escalate before consequences outrun control.
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How to Benchmark AI Models for Cost, Quality, and Risk
A practical framework for choosing AI models by cost per accepted result, workflow reliability, failure severity, and production economics.
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What Modern Product Managers Are Actually Responsible For
A practical framework for defining a product manager’s accountabilities, decision rights, operating cadence, boundaries, and performance.
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Chinese Frontier AI Models: A Product Leader’s Decision Guide
A practical framework for choosing Kimi K3, Qwen 3.8 Max, or DeepSeek V4 Flash by completion reliability, accepted-result cost, and product fit.
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Context Engineering: How to Build Reliable AI Applications
A practical framework for designing context, diagnosing agent failures, budgeting each turn, and evaluating AI applications before production.
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SpaceX Orbital AI: How to Evaluate the Revenue Case
A practical framework for separating SpaceX’s reported AI revenue from the technical and economic proof required to scale orbital compute.
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How to Design Persistent Memory for Reliable AI Agents
A practical framework for deciding what an AI agent should remember, retrieving it safely, correcting stale facts, and proving that memory helps.
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Hidden Assumptions in Product Discovery: A Practical Test
A practical method to expose, rank, and test the desirability, viability, feasibility, usability, and ethical assumptions behind product ideas.
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Visual Search for Messy B2B Catalogs: An Intent-First Playbook
A practical framework for turning ambiguous product images into intent-aware retrieval, trustworthy ranking, and measurable B2B sourcing outcomes.
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Persistent AI Agent Memory: A Knowledge Graph Blueprint
A practical blueprint for modeling, governing, retrieving, and evaluating knowledge graph memory that an AI agent can safely use over time.
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AI Video Generation Advances: A Product Leader’s Playbook
A practical framework for turning gains in video coherence, control, and open-source access into workflows, evaluations, and sound product bets.
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Cost-Aware AI Model Selection: Pay for Accepted Work
A practical framework to compare AI models by accepted-result cost, test quality, choose hosting, and route production work with safe fallbacks.
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