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- AI Strategy (315)
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- Product Management Leadership (306)
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How to Build Product Verification Loops for AI Software Factories
A practical operating model for linking factory tests and AI evals to post-ship evidence, product decisions, and compounding learning.
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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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A Practical Framework for Measuring New Feature Success
A practical framework for deciding whether a new feature earns adoption, improves the user experience, and contributes to a meaningful product or business outcome.
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Feature Management as a Product Development Discipline
A practical guide to controlled feature releases, evidence-based rollout decisions, cross-functional ownership, and the governance needed to manage complexity.
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Migrate Analytics Platforms Without Chaos: 7 Proven Lessons to Plan, Move, and Land Cleanly
Migrating analytics platforms doesn’t have to derail roadmaps or erode trust. I share seven battle-tested lessons—shaped by work with Human37 and Amplitude—that help teams align on…
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How Agentic Analytics Reshapes Product Development Roadmaps
A practical synthesis of how product agents connect behavioral analytics, controlled experiments, roadmap decisions, and accountable automation.
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How to Build a Resilient Experimentation Program at Scale
A practical operating model for producing trustworthy decisions through layered evaluation, governed measurement, and reversible delivery at scale.
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An AI Operating Model That Measures Outcomes, Not Activity
A practical way to connect AI quality, customer behavior, revenue, risk, and delivery metrics so teams know what to scale, fix, or stop.
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Analytics-Led Growth Engineering: A Practical Operating Model
Build a growth engineering system that connects trusted behavioral data, focused experiments, safe releases, and durable activation and retention gains.
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Supercharge Core Web Vitals with Amplitude’s Global Agent: Faster Rankings, Happier Users
Core Web Vitals are a direct lever on user experience and SEO, so I use Amplitude’s Global Agent and Amplitude AI Agents to measure and improve…
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Built for Your Biggest Days: How We Engineer Fair, Reliable Scale Without Downtime
Enterprise teams ask sharper questions about scale—and they should. I share how we handle 150k+ requests/sec, shard our source-of-truth data with Vitess and PlanetScale, and reshape…
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I Pointed a “Ralph Wiggum” AI Loop at My Product for a Week—The Data That Stopped Chaos
I pointed a “Ralph Wiggum loop” at my product for a week to see how an agentic AI would perform under tight guardrails. The loop moved…
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From Vision to Execution: Building Agentic, Data‑Driven Products with Real‑World Rigor
Agentic AI demands more than dashboards—it requires a system that observes, reasons, and acts with measurable impact. I connect behavioral analytics to action by pairing Amplitude-style…
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How to Run AI-Assisted Feature Launches That Drive Growth
A practical operating model for using agents, feature flags, behavioral data, and decision rules to turn each feature launch into a measurable growth loop.
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AI Product Validation: From Promising Demo to Proven Value
A practical evidence ladder for validating AI demand, model quality, user value, safety, and unit economics before expanding production exposure.
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Stop Misleading A/B Tests: Master Sample Size Assumptions for Reliable Results
Sample size calculators are only as reliable as the assumptions behind them. I show how I validate baseline rates, set a meaningful minimum detectable effect (MDE),…
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How to Build Cost-Effective PR Review Agents In-House
A practical blueprint for building an in-house PR review agent that controls model spend, limits false positives, and improves review flow.
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Product Experimentation for AI Systems: A Practical Playbook
A practical playbook for testing prompts, retrieval, and policy changes with decision-ready metrics, safe rollouts, and reliable instrumentation.
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How to Run AI-Accelerated Product Discovery and Delivery
A practical model for using agents, analytics, prototypes, experiments, and guardrails to shorten product learning without weakening judgment.
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An End-to-End AI Product Workflow From Discovery to Deployment
A practical workflow for turning customer evidence into a bounded AI use case, measurable evaluations, a guarded rollout, and a reliable production loop.
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Browse topics
- AI Strategy (315)
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