Hey folks 👋
If you’ve been building AI products for a while, you’ve probably felt this: the model works—but the UI makes it feel broken. You’re left wondering: Is the model thinking? Stuck? Silently failing ?
I’ve seen this pattern across multiple AI prototypes, dashboards, and internal tools. Not because the AI was bad, but because the interface couldn’t clearly communicate uncertainty, loading, or failure.
Most AI demos look impressive for about five minutes.
Then you try to actually use them.
Buttons feel vague. States are unclear. The AI might be powerful, but the interface makes it feel fragile in ways you don’t expect. Over time, one thing became clear: the UX problems usually aren’t about the model at all — they’re about the UI layer.
This isn’t a roundup of tren…
Hey folks 👋
If you’ve been building AI products for a while, you’ve probably felt this: the model works—but the UI makes it feel broken. You’re left wondering: Is the model thinking? Stuck? Silently failing ?
I’ve seen this pattern across multiple AI prototypes, dashboards, and internal tools. Not because the AI was bad, but because the interface couldn’t clearly communicate uncertainty, loading, or failure.
Most AI demos look impressive for about five minutes.
Then you try to actually use them.
Buttons feel vague. States are unclear. The AI might be powerful, but the interface makes it feel fragile in ways you don’t expect. Over time, one thing became clear: the UX problems usually aren’t about the model at all — they’re about the UI layer.
This isn’t a roundup of trendy UI kits or GitHub repos ranked by stars. These are frontend libraries I’ve either shipped with in real AI products, or seriously evaluated while building prototypes, dashboards, and internal tools.
In a few cases, they were the difference between a prototype that stalled and one that actually shipped.
Some saved days.
Some surfaced UX issues that would’ve otherwise been blamed on the model.
And a few popular options didn’t make the cut.
If you’re building AI products meant to be used, not just demoed — this list is for you.
TL;DR ⚡
If you’re building AI products that people actually use (not just demo):
- shadcn/ui + Tailwind → best when the UI needs to evolve alongside prompts, streaming responses, retries, and edge cases
- Radix UI → saves you from accessibility, focus, and keyboard bugs in complex AI flows
- Tremor → fast, clean analytics and monitoring visuals out of the box
- Aceternity UI → “shine moments” for landing pages and product surfaces
- The rest → accelerate setup, accessibility, dashboards, or scaffolding without reinventing the wheel
Key takeaway: AI UX breaks when the UI can’t communicate uncertainty.
Loading, partial results, retries, errors, and iteration aren’t edge cases — they’re the product.
The right UI kits don’t just make things look good.
They make async behavior, change, and ambiguity legible.
Let’s get into the ones that actually held up in real AI products. 🚀
shadcn/ui — You Own the UI (That’s the Point) 👑
shadcn/ui works because it refuses to pretend to be magic.
You copy components into your project. They live in your repo. You read them. You change them. Nothing disappears behind a black-box dependency you’ll curse six months later.
For AI products, that matters more than it sounds. Prompts evolve. Controls get weird. You add states like thinking, streaming, and partial failure. shadcn doesn’t fight you when that happens.
On one internal analytics tool, I customized the same shadcn-based message list three times in a week to handle streaming responses, retries, and per-message metadata — and it never felt like I was fighting the library.
If your AI app has an admin panel or internal tooling, this is hard to beat.
Tailwind UI — Expensive, Yes. Wasteful, No. 💸
UI Kit by the makers of Tailwind CSS
UI Blocks by the makers of Tailwind CSS
Tailwind UI looks overpriced until you’ve built the same pricing page and settings layout for the third time.
The real value isn’t the components — it’s the decisions already made: spacing, hierarchy, empty states, responsive behavior. Things you’d otherwise bike-shed while the actual AI work waits.
On one AI SaaS project, Tailwind UI cut 2–3 days from building auth, pricing, and account pages. That time went straight into designing better error handling and usage views.
Radix UI — The Stuff You Forget Until It Breaks 🧩
Radix doesn’t care how your app looks. It cares that it behaves correctly.
Focus traps. Keyboard navigation. Screen readers. Portals and layering. The details you only notice when something breaks badly.
AI apps stack interactions fast — modals on top of chats, command palettes over dashboards, toasts firing while async jobs run. Radix handles the ugly edge cases so you don’t duct-tape them later.
HeroUI (Previously NextUI) — When You Need Something That Just Looks “Done” ✅
HeroUI is opinionated, but in a helpful way.
Install it and your app instantly looks presentable. Not flashy. Not ultra-custom. Just finished enough to show someone.
For AI demos, prototypes, or early validation, that matters. On one multi-model routing prototype, HeroUI made it possible to ship a credible UI in under a day — most of the time went into wiring the AI, not CSS.
Chakra UI — Not Trendy, Still Useful
Chakra UI isn’t exciting anymore, and that’s kind of the point.
It’s stable, accessible by default, and predictable. For internal AI tools or dashboards where clarity matters more than aesthetics, it works — and teams ramp up fast.
On a monitoring tool for LLM workflows, Chakra handled forms, layouts, and toasts with minimal friction. Nothing fancy. Nothing broke.
Mantine — One Library, Fewer Decisions 🧱
Mantine feels like someone got tired of wiring five libraries together and just solved it.
Forms, modals, notifications, hooks — all designed to work together. For AI products with lots of user input and async states, that cohesion matters.
In one workflow editor, Mantine replaced an entire layer of glue code. Instead of juggling multiple libraries, everything just worked together.
Aceternity UI — Use It Sparingly, Win Big ✨
Aceternity UI isn’t for dashboards.
It’s for moments — landing pages, hero sections, and storytelling where you need to show what your AI does before you explain it.
Used everywhere, it’s too much. Used once or twice, it makes a product feel premium. I’ve only ever used it in small doses — and that restraint mattered more than the animations themselves.
Magic UI — Motion That Serves a Purpose 🎬
Magic UI shines in the in-between moments.
Loading. Transitions. Feedback when something heavy is happening in the background. AI interactions are sensitive here — waiting without feedback feels broken.
In one content-generation tool with frequent 3–5 second waits, adding small motion to loading states dramatically reduced “Is this stuck?” reports. The system didn’t change. The communication did.
Flowbite — Structure First, Style Second
Flowbite is practical.
Sidebars, navbars, tables — the scaffolding every SaaS app needs. If your AI tool is more about function than flair, Flowbite gets you there fast.
On an internal admin app, it provided a solid layout and table structure quickly. Styling came later. Function shipped first.
Ant Design — When “Enterprise” Is Not a Dirty Word 🏢
Ant Design is unapologetically enterprise.
If your AI product deals with real data, real users, and real consequences, AntD’s tables and forms are battle-tested. It’s not trendy, but it’s trusted — and in some contexts, that matters more.
Tremor — Analytics Without Reinventing the Wheel 📊
Almost every AI product ends up explaining numbers.
Tremor makes dashboards a non-project. Clean charts, KPI cards, sensible defaults. On a model-usage dashboard, it went from idea to shippable view in under a day — most of the work spent querying data, not configuring charts.
HyperUI — For the Pages You Don’t Want to Think About
HyperUI is great for the stuff you don’t want to design twice.
Auth screens. Pricing pages. Onboarding layouts. Drop them in, tweak a little, move on. In one AI SaaS build, it turned days of layout work into a few hours.
A Stack That Scales With Your AI Product 🧠
If I were starting a serious AI product today, I’d go with:
- shadcn/ui + Tailwind for core UI
- Radix UI for accessibility and complex interactions
- Tremor for analytics and metrics
- Aceternity UI only for landing-page highlights
A comparison table between different UI kits libraries
I’ve tried more maximal setups. This one is the least annoying long-term — and it scales with the product instead of boxing you in early.
Conclusion: Tools Help, Judgment Still Matters More 🎯
After multiple AI-driven projects, one thing is clear: the hardest part isn’t integrating the model — it’s deciding how much confidence your UI should project when the system itself is probabilistic.
No UI kit solves that for you.
What these tools give you is leverage — fewer reinventions, fewer late-night UI battles, and more time to think about what the interface should communicate.
Tools matter.
Libraries matter.
But judgment still matters more.
Thank you so much for reading till the end 🙌
I hope this sparked some useful ideas around modern UI.
If I missed anything, sorry about that—UI is evolving fast, and there’s always more to explore. Feel free to share your thoughts or suggestions in the comments; I’d love to learn from you.
If you’re curious about what’s next in UI, here’s a follow-up that builds on these ideas and connects them to real-world design direction:
And if you want to see these concepts in action, I share UI experiments and side projects on 👉 GitHub