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- AI Strategy (315)
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Turn Claude Code Into a Trusted Teammate: My 3-Layer Memory System You Can Copy
I used to waste hours repeating the same context to AI and getting generic, off-the-mark feedback. By giving Claude Code a persistent, file-based memory, I now…
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AI at Home, Impact at Work: Experiments That Supercharged My Product Leadership
I reflect on a standout All Things Product conversation that reinforced how low-stakes AI experiments at home can dramatically improve how we build AI-powered products at…
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From Chaos to Consistency: How I Built a Scalable AI Content Design Agent with RAG
I built an AI content design agent that scales quality without sacrificing judgment by grounding it in a rigorous design system and a curated RAG knowledge…
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What I Learned from Trainline’s Agentic AI: Building a Trusted Travel Assistant at Scale
Agentic AI only works at scale when orchestration, tools, and guardrails are designed together. Trainline’s approach demonstrates how to pair scalable reasoning with deep domain context,…
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Why We’re Building Our Next AI R&D Hub in Berlin—and Hiring 100 to Power Fin’s Growth
We’re opening our next AI R&D hub in Berlin to accelerate Intercom and our AI Agent, Fin, and we plan to hire 100 people across engineering,…
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Context Is King: My Playbook to Prep Product Teams for High-Impact AI Collaboration
AI becomes a powerful teammate when we give it the right context. In this commentary, I share how I operationalize context—decision logs, success metrics, and machine-readable…
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Inside Japan’s AI Marketing Shift: How 500 Teams Boost Efficiency, Results, and Careers
New research from Japan underscores how AI, anchored in disciplined analytics, is accelerating marketing efficiency, measurable results, and career growth. I break down why a unified…
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How Luminance Builds Legal-Grade™ AI at Scale: My Product Lens on Trust and GTM
I break down how Luminance operationalizes Legal-Grade™ AI for enterprise by combining domain-specialized, agentic AI with rigorous governance and human-in-the-loop controls. I highlight the product metrics…
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Governed GenAI Delivery: A Practical Operating Model
A practical operating model for shipping GenAI with explicit risk ownership, evaluation gates, human review, controlled rollout, and rollback.
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From $2M to $100M ARR: Inside fal’s Explosive Pivot and the Future of Generative Media
Generative media is shifting from novelty to necessity, and fal’s journey offers a blueprint for navigating that shift. By pivoting from data infrastructure to generative inference,…
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Supercharge Your Engineering Org: Alignment, AI, and Productivity from Adobe to Etsy
Engineering speed is a function of alignment, not heroics. In this first-person breakdown, I unpack how modern engineering teams can scale productivity with outcomes vs output…
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Building Products in a Post-LLM World: Hard-Won Lessons, Skeptic Busters, and Team Playbooks
I break down how to build products in a post-LLM world with a pragmatic playbook you can put to work immediately. You’ll learn how Sprig’s journey…
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Inside Rewind AI’s Playbook: PMF Breakthroughs, Bold Twitter Fundraise, and the Future of AI
I explore Rewind AI’s path to product-market fit, the cultural habits that enable fast validation, and the bold Twitter-first fundraising strategy that rallied users and capital.…
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Goal-Setting for AI Products: How I Plan, Prioritize, and Confidently Ship in a Nonlinear GenAI World
Planning AI roadmaps is fundamentally different from traditional software. I share how I set outcomes-first OKRs, balance research and product development, and scale safely without over-planning.…
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Inside Bard’s Playbook: How to Ship AI Fast, Build Ethically, and Outlearn Competitors
Building AI products demands speed and responsibility. I unpack the Bard blueprint—how principled constraints, early public learning, and tight product theses enable rapid iteration without sacrificing…
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Deliberate Practice for Product Teams: How AI and On‑Demand Learning Unlock Mastery
Deliberate practice is the missing link between product learning and lasting behavior change. In this episode recap, I share why a blended model—cohort-based structure plus on-demand…
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Inside Alyx: Dogfooding, Evals, and Observability That Power an Agentic AI Future
This is a pragmatic, first-hand look at how Alyx was built by dogfooding Arize’s own platform—moving from messy notebooks to a disciplined eval and observability stack.…
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AI Coworkers That Actually Work: Inside Neople’s Guardrails, Evals, and Customer-Ready Agents
I explore how Neople builds “digital coworkers” that blend automation reliability with AI flexibility to handle real customer work. You’ll see how the team balances code,…
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How Braintrust Nailed Product-Market Fit: Paranoia, Patience, and High-Bar Quality
Braintrust’s playbook for product-market fit in GenAI pairs a relentless quality bar with deliberate go-to-market restraint. By transforming evaluation pain into an end-to-end platform for building…
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Inside the AI‑First Web: Designing Agent‑Friendly APIs, Prioritizing Accuracy, and Scaling Trust
AI is becoming the web’s second user, and that reality changes how we design, measure, and monetize our products. I break down why accuracy must beat…
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