I’m looking to buy coding traces from designer who are very good at promoting AI to design beautiful things so I can learn from you. Please contact me. Just need 1-2 best designer who are very great prompting ai to great visual/UX design.

I am building my own coding harness. Yemrecode. Building upon the opensource codebase of OpenCode. My harness has a completely different approach than others: - I pick multiple LLMs at once and the harness will treat them as one single entity. - I set a /yunus-goal and this goal is controlled by agents where some are responsible for demanding proof and some are implementors. - I can run a swarm of various LLMs meanwhile pursuing a yunus-goal with each having their own swarm. - I can toggle YOLO mode anytime and regardless of its state, the agents will never be able to alter Yemrecode. Many more abilities. Including: Automatic anti-ai-slop controller that detects slop UI/UX design and prevents it from being added to code and demands that specific agent to remake it properly. Path detector works similarly but for detecting the intent of the agent when it tries to write to a wrong path. There is also a version control system that auto inits a git repo when code length in the folder exceeds a thousand lines in total (indicating we may want to track the changes as this may not be a one time trial and junk code). many other abilities coming soon. I am not publishing my harness yet, but after a few months of testing I may do that.

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#