✨ Product & UX
Designing AI features users actually want. Latency, trust, streaming, citations, undo, the "AI moment" in a product.
The workflow
flowchart LR
A[User insight] --> B[Define AI moment<br/>where LLM helps]
B --> C[Design loop<br/>input → AI → review]
C --> D[Latency & UX<br/>streaming, skeletons]
D --> E[Trust signals<br/>citations, undo, edit]
E --> F[Measure adoption<br/>+ task success]
Trust > capability. Citations, undo, and visible thinking matter more than model size.
Key takeaways
Videos (30)
tldraw.computer - Steve Ruiz, tldraw
Spatial canvases like tldraw turn AI generation into composable graphs where drawing, annotation, and node-wiring become the programming interface.
Climbing the Ladder of Abstraction: Amelia Wattenberger
The future of AI interfaces isn't chatbots — it's structured UIs that automate small steps and let users zoom up and down a ladder of abstraction.
Survive the AI Knife Fight: Building Products That Win — Brian Balfour, Reforge
Differentiation in AI products comes not from the model but from how you combine your proprietary data and functionality around commodity AI Lego blocks.
Taste & Craft: A Conversation with Tuomas Artman, CTO Linear & Gergely Orosz, @pragmaticengineer
When AI makes shipping cheap, taste and disciplined product judgment — not speed — become the differentiating moat.
The End of Apps — Kitze, Sizzy.co
Personal agentic assistants built on Claude Code-style harnesses are replacing traditional productivity apps and SaaS.
ChatGPT is poorly designed. So I fixed it
You can patch ChatGPT's split-personality UX today with Realtime API + tool calls that route between voice, text and reasoning models on the fly.
Building the platform for agent coordination — Tom Moor, Linear
Linear's edge in the agent era is being the pragmatic, high-bar coordination layer where humans and AI agents share work, not a rushed copilot.
Why your product needs an AI product manager, and why it should be you — James Lowe, i.AI
For AI products, resolve the AI capability uncertainty with evals and real-user tests before product building — then go wide on features and ruthlessly strip back.
Design like Karpathy is watching — Zeke Sikelianos, Replicate
Your docs' primary audience is now an LLM — ship llms.txt, curl snippets, and an MCP server, not pretty branded pages.
The Intelligent Interface: Sam Whitmore & Jason Yuan of New Computer
The next computer interface uses pose, audio, gesture, and tone as implicit signals so the device adapts to humans, not the reverse.
From Arc to Dia: Lessons learned building AI Browsers – Samir Mody, The Browser Company of New York
Build prompt/eval tooling directly into your product so the whole team can iterate AI features with real user context — and use techniques like GEPA over RL.
The era of unbounded products: Designing for Multimodal IO: Ben Hylak
Win at AI product design by imposing app-specific structure — hierarchy, familiarity, surfacing — on top of the unbounded chat/multimodal canvas.
Everything is ugly, so go build something that isn't — Raiza Martin, Huxe (ex NotebookLM)
In the chaos of AI-augmented teams, individual taste and personal clarity become the durable engine for shipping non-generic, beautiful products.
Form factors for your new AI coworkers — Craig Wattrus, Flatfile
AI form factors range from invisible to conversational, and the designer's new job is character-coaching the model and crafting the right box around an inherently parallel collaborator.
Feedback Loops are All You Need — Mehedi Hassan, Granola
Generic chat features fail in production — wrap your LLM in tight, user-driven feedback loops that constrain it to the workflows you ship.
Excalidraw: AI and Human Whiteboarding Partnership - Christopher Chedeau
Treat AI as a chance to rethink the product, not as a sprinkle on the old UI — and only ship AI features that match how users actually use your tool.
Good design hasn’t changed with AI — John Pham, SF Compute
Feature parity is no longer a differentiator in the AI era — speed, trust, accessibility, and delight are the durable design moats.
Second Order Effects of AI: Cheng Lou
Predict AI's second-order effects by asking who is doing the learning and where information bandwidth can be widened beyond current low-bandwidth interfaces.
Building AI Products That Actually Work — Ben Hylak (Raindrop), Sid Bendre (Oleve)
Build AI products by shipping, observing real production behavior and iterating — evals are necessary but they cannot tell you how good your product actually is.
Books reimagined: AI to create new experiences for things you know — Lukasz Gandecki, TheBrain.pro
Pair multiple AI primitives behind a polished UX and hide the AI itself—books with scene-matched music, avatars, and voice Q&A become a new medium.
On Curiosity — Sharif Shameem, Lexica
Building demos driven by curiosity is the primary way to discover what frontier models can actually do.
The Bitter Layout or: How I Learned to Love the Model Picker — Maximillian Piras, Yutori
Chat plus model picker is the AI-era equivalent of mode-laden UIs; design for the moving boundary between integrated and modular AI stacks rather than fixed model capabilities.
Shipping Products When You Don't Know What they Can Do — Ben Stein, Teammates
PM for autonomous-agent products requires shipping despite genuine uncertainty about what your own product can do — a new discipline is emerging.
Don't just slap on a chatbot: building AI that works before you ask
Stop bolting chat onto products — proactive, context-aware AI that acts inside the existing workflow beats reactive chatbots.
Personality Driven Development: Exploring the Frontier of Agents with Attitude
Giving agents explicit forms and personalities is great branding and onboarding shorthand but inherits decades of human anthropomorphic expectations you must manage.
Shipping something to someone always wins — Kenneth Auchenberg (ex. Stripe, VSCode)
Build for sub-day OODA feedback loops with named real users — continuously viable products beat big-bang launches, especially in AI where iteration speed is the moat.
Designing AI To Scale Human Thought — Jun Yu Tan, Tusk
AI products that augment human thinking (blind-spot detection, cognitive partnership, proactive guidance) beat those that automate the human out of the loop.
Your AI Agent Isn't an Engineer: The Art of Thoughtful Anthropomorphism
Stop marketing AI agents as engineers — frame them as augmentation tools with transparent capabilities and limitations to build durable developer trust.
Invisible Users, Invisible Interfaces: Accelerating Design Iteration with AI Simulation - Alex Liss
Simulated user personas can give designers an inner feedback loop that surfaces friction across a category at scale before any human user study runs.
Build Dynamic Products, and Stop the AI Sideshow — Eliza Cabrera (Workday) + Jeremy Silva (Freeplay)
Differentiated AI products integrate AI directly into core product strategy via a crawl-walk-run progression, not via quarantined 'AI features' that fail to solve real customer pain.