AI Product Engineering
Designing, building, and shipping successful AI-native products.
Agentic UX Design Patterns
Ten concrete UX patterns for building AI agent products where users can trust autonomous systems they can't fully observe.
2026-06-10
AI Product Case Studies
Six deep dives into AI products that work — Cursor, Perplexity, Fin, Harvey, Replit, and Linear — with the specific decisions that made them succeed.
2026-04-07
AI Product Mistakes to Avoid
A sharp checklist of 13 traps that kill AI products — from shipping without evals to building chat when you needed a workflow.
2026-04-04
AI Product Patterns That Work
Ten concrete AI product patterns — with real examples from Copilot, Cursor, Notion, and Linear — that consistently ship well in production.
2026-05-24
AI Workflows vs AI Chat
A decision framework for developers and PMs choosing between chat and workflow patterns when building AI-powered products.
2026-04-17
Building AI Features Users Actually Want
How to identify AI features worth building using jobs-to-be-done, shadow workflows, and cheap validation — before you waste a sprint on something users ignore.
2026-05-30
Building AI-Native Applications
What separates AI-native from AI-powered, and how to architect applications where the LLM is a first-class primitive, not a bolted-on feature.
2026-05-27
Chat Interfaces Are Not Enough
Chat became the default AI interface because of ChatGPT, not because it's the right choice — here's when to ditch it and what to build instead.
2026-04-16
Designing for AI Uncertainty
Pretending AI is certain when it isn't is the root cause of most AI UX failures — here's how to communicate uncertainty without breaking user trust or making your product useless.
2026-05-01
Designing Great AI UX
The UX principles and concrete patterns that separate AI products users trust from ones they abandon — latency, uncertainty, progressive disclosure, and more.
2026-04-01
Designing Trust into AI Products
Trust in AI products isn't a UX polish problem — it's an architecture decision that determines whether users actually rely on your product when it matters.
2026-04-25
From Prototype to Production AI Product
The gap between a working AI demo and a production-grade product is mostly not about the AI — here's the engineering work that actually bridges it.
2026-05-26
How Successful AI Startups Design Experiences
The model is rarely the differentiator — here's what separates great AI products from forgettable ones, drawn from seven companies that got it right.
2026-06-12
Human-in-the-Loop Design Patterns
A practitioner's guide to designing AI systems with the right level of human oversight — covering the full autonomy spectrum and seven concrete HITL patterns.
2026-05-23
Lessons from 100 AI Products
Hard-won patterns from watching AI products succeed and fail — distilled into 18 lessons that show up again and again.
2026-06-16
Product-Market Fit for AI Startups
AI PMF is harder to measure than traditional PMF because the technology creates false positives — everyone tries it once, few come back. Here's how to tell the difference.
2026-04-17
The AI Product Manager's Playbook
A practitioner's guide to the PM practices that are actually different when you're building AI-powered products — from roadmapping to evals to model upgrades.
2026-06-06
The Best AI Features We've Seen This Month
A curated breakdown of the most interesting AI product launches from mid-2026 — what shipped, why it matters, and what it tells us about where AI UX is heading.
2026-05-21
The Future of AI User Interfaces
Chat is a transitional interface — the next wave of AI UX is ambient, contextual, and invisible by design.
2026-06-07
Why Most AI Products Fail
Most AI products fail not because the AI is bad, but because of predictable product mistakes developers and founders keep making.
2026-06-21