惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Martin Fowler
Martin Fowler
D
DataBreaches.Net
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
The GitHub Blog
The GitHub Blog
Blog — PlanetScale
Blog — PlanetScale
Microsoft Security Blog
Microsoft Security Blog
A
About on SuperTechFans
Vercel News
Vercel News
L
LangChain Blog
B
Blog RSS Feed
Y
Y Combinator Blog
IT之家
IT之家
H
Hackread – Cybersecurity News, Data Breaches, AI and More
GbyAI
GbyAI
V
V2EX
博客园 - 三生石上(FineUI控件)
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
阮一峰的网络日志
阮一峰的网络日志
有赞技术团队
有赞技术团队
D
Docker
V
Visual Studio Blog
aimingoo的专栏
aimingoo的专栏
Last Week in AI
Last Week in AI
月光博客
月光博客

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Agents are building their own UIs now. Here's when that's...
Sireesha Pul · 2026-04-30 · via DEV Community

This is a submission for the Google Cloud NEXT Writing Challenge

During the developer keynote at Google Cloud NEXT '26, a GDE demoed FinnishIt: an AI-powered Finnish language tutor built on the GenUI SDK for Flutter. You give it a topic, it asks refining questions, then generates a custom deck of interactive flashcards specific to that context. Role-play scenarios shift from text to tap-and-drag word puzzles to fill-in-the-blank modules depending on what the AI determines you need right now.

Every session produces a different interface.

That's the point where I stopped treating A2UI as conference noise and started paying attention.

A2UI is an open standard Google donated to the community at NEXT '26. It lets agents generate UI dynamically at runtime. The GenUI SDK for Flutter is the developer-facing layer that makes it practical to build with. Most coverage either skipped it or described it without asking the more useful question: when does this actually make sense to use?

Where GenUI earns it

FinnishIt works because the interface IS the learning experience.
There is no predetermined layout that would serve a user practicing spoken conversational Finnish the same as one drilling grammar for the YKI citizenship test. The right exercise type, difficulty, and interaction pattern all depend on what the AI assesses the user needs right now. Hardcoding any of that would produce a worse product.

The dynamic generation isn't a feature on top of the app. It is the app.

The same logic applies to onboarding flows, and this is where I think GenUI has untapped potential.

Most onboarding flows are static decision trees in disguise. You collect preferences on screen one, goals on screen two, then route users down one of two or three predetermined paths. The result feels personalized but is just filtered content behind a fixed interface.

Consider a personal finance app. Someone who opens it saying "I want to stop overspending" has a completely different mental model than someone who says "I want to start investing" or "I have irregular income and need to plan around it." Those aren't just different content buckets. They're different journeys, with different concepts to introduce, different decisions to make up front, and a different definition of what "getting to value" even means.

A GenUI-powered onboarding flow could read what a user brings to that first session and generate the next step as a direct response: not a static screen two, but a computed one.

A personal style app makes the case even more clearly, because here the interaction type itself changes, not just the content.

Someone who opens a style app saying "I have a job interview next week" needs an occasion-specific outfit construction flow: clear goal, tight timeline, specific constraints. Someone who says "I'm trying to figure out my personal style" needs a discovery experience: visual-first, exploratory, maybe swipe-on-images or mood board style. Someone who says "I want to build a capsule wardrobe on a budget" might need a wardrobe audit flow that starts with photographing what they already own.

These are not variations on the same form. They require different interface primitives: camera, swipe cards, visual grids, checklists. GenUI earns it here because you genuinely cannot know which one to show until the user tells you what they're trying to do.

The right interaction depends on the context. The context arrives at runtime.

A decision filter

Before reaching for GenUI, three questions:

Is the interface the experience, or is it a container for a fixed one?
In FinnishIt, the dynamically generated exercise is the product. That's different from a news reader or a task manager, where content arrives through a stable interface. Not every app benefits from a layout that changes each session.

Does the user need to find the same thing in the same place next time?
Adaptive learning, personalized onboarding, style discovery: each session is meant to feel different. An e-commerce checkout, a settings screen, a navigation menu: users build trust and speed through repetition. Those interfaces earn nothing from variation.

Is this an exploratory action, or one that requires confident understanding of what's about to happen?
Payment confirmation, account deletion, anything irreversible: users need to know exactly what they're looking at. Dynamic layout introduces uncertainty at exactly the wrong moment.

Where it doesn't fit

The failure cases aren't about regulation or compliance. They're about what users need from an interface to trust it.

A checkout flow that looks different each time isn't personalization. It's friction.

High-frequency task interfaces derive part of their value from the fact that users can operate them without thinking. Email, task management, booking flows: variability works against that entirely.
There's also a quieter design system concern. Most product teams ship against a component library: specific tokens, spacing rules, interaction patterns. An agent that approximately matches those patterns is not the same as one that respects the contract. That gap shows up in production in ways that are hard to articulate and easy to notice.

The open bet

A2UI and GenUI aren't solutions looking for a problem. There's a real category of app where static UI has always been the wrong answer: the kind where the right interaction depends on context that only arrives at runtime.

FinnishIt is an early, polished example of what that looks like when it's done well. Personalized onboarding, adaptive learning, style discovery: same category.

What I'm watching is whether developers build intuition for where this pattern belongs, or whether the next few years surface a wave of apps that introduced variability in exactly the places their users needed stability.

If you've seen agent-generated UI get it right, or quietly get in the way, I'd like to hear about it.