Why do AI-generated UIs all look so generic? It's the workflow, not the prompt 🎨 A practical playbook for producing genuinely beautiful UIs. Title: Generating Beautiful UIs URL: 🎨 Overview This post lays out a practical methodology for generating beautiful UIs with AI. The thesis is that there's no single magic technique — what works is a disciplined workflow built on pre-defined design systems and fast iteration loops. ❓ Challenges Solved AI-generated UIs tend to come out generic and predictable. The post names the common failure modes. ・Dashboard-ification: turning everything into a dashboard ・Nested cards: redundant cards inside cards ・Instruction leakage: prompt instructions bleeding into the on-screen copy ・Weak compositional logic: layouts that break down and lack beauty or resonance 💡 Methodology & Proposed Approach The post recommends a methodical workflow built from these steps. ・Use component libraries: shadcn/ui via MCP integration ・Pre-define the design system: keep design tokens as readable files to prevent hallucination ・Enforce constraints: use Tailwind config to block drift ・Iterate with vision models: feed screenshots to run a visual improvement loop ・Generate multiple options before committing ・Test with hostile, realistic data during development 🌍 Use Cases / Experimental Results Combining fast inference with a disciplined workflow turns AI from a gimmick into a real prototyping accelerator. ・Codex-Spark runs at ~1,200 tokens/sec on Cerebras, generating several design options in minutes ・With proper tooling, components compile on the first attempt ・Tighter feedback loops reduce wasted tokens The conclusion: AI is a fast, overconfident junior designer that still needs human art direction, not an autonomous replacement. #UIDesign# #GenerativeAI#

Some say that Codex is not as good as Claude regarding UI design. However, in my experience, it excels at "debugging" UI issues. You can often resolve UI problems effectively if you provide a clear description of the issue. Unlike Claude, it won't create problems in later edits.

Fun interactive science app ideas | Part 3 Played around with generating 3D biological structures and made an app to explore them interactively UI Design GPT Images 2 Code Gemini 3.1 Pro More demos ↓

🤖 What's actually working in AI-assisted development? ⚡️ @1337bytes and @gustavovalverde will run an interactive workshop on practical AI workflows - research, benchmarking, UI design, end-to-end builds. Live build if time allows. 🧠 📍Oct 27–29, 2026, Cancún Bring your knowledge to Cancún - apply here →

Ant Group has open-sourced the Ming-Image-0.1-Design family. Two 6B models: • Ming-Image-0.1-Design — text-to-image for UIs, infographics, posters, and other text-rich designs. It can also generate transparent RGBA assets. • Ming-Image-0.1-Design-Layer — decomposes a flattened design into independently editable RGBA layers. Ant also open-sourced two Agent Skills: • Ling UI Design Skill • Image-to-Editable-PPT Skill Ming-Image-0.1-Design ranks #1# among open-weight models on Artificial Analysis’s UI/UX Design leaderboard.

The model so cheap, it may as well be free: DeepSeek V4 Flash. It scores the same as Gemini 3.6 Flash but costs 18× less. I compared 8 models in this video. DeepSeek's a competent coder, but it falls short on UI design. Check out the results.