#DesignX

If you design with AI, this is all you need 1. — cheaper than lovable 2. — premium website prompts: 800+ 3d websites, motion sections. Paste into Claude, ship. 3. — landing page inspiration done right. 4. cool gallery for web design inspo 5. — top notch landings 6. — fonts behind brands 7. — award websites 8. — mobile UI animations save these for your next project

If you're a UI/UX designer, bookmark this. 1.⁠ ⁠ - 600+ design Prompts and 200+ components with full source code for 3D and scroll-driven sites. 2.⁠ ⁠ - navbar inspiration 3.⁠ ⁠ - better footers 4.⁠ ⁠ - CTA inspiration 5.⁠ ⁠ - Real product design styles What did I miss?

It's over. AI is generating insane 3D websites, animations & UI from just a prompt. Web design will never be the same 🤯 10 wild places to see it: 1. - 600+ award-winning prompts with full source code for 3D and scroll-driven sites. Copy, paste into Claude, Cursor or Lovable, launch live. Edit it until it's yours. 2. - animated shader backgrounds, as code, video or stills 3. - hand-picked sites with serious craft 4. - UI motion done right 5. - copy-paste components from the community 6. - animated components, big visual impact 7. - animated components for landing pages 8. - the engine behind most 3D on the web 9. - shader experiments that look unreal 10. - idea to working app Save this 🔖 you'll need it before your next project.

your AI designs need better references? save these 👇 1. — 192+ site references 2. — 51 sections 3. — AI video kits 4. — CTA inspiration 5. — Web design inspiration 6. — 600+ animation prompts 7. — Website inspiration 8. — Design resources 9. — Design community 10. — Creative inspiration 11. — Motion design inspiration 12. — Footer inspiration 13. — Landing page inspiration 14. — AI design prompts 15. — Landing page inspiration

Introducing design-to-code Pixel-exact websites from any design, in one shot → A skill for every agent. → From Figma frames, Screenshots or mockups to real, responsive code → Checks itself against your design before calling it done → Six sites in the video, each built from one prompt npx skills add Atharvsinh-codez/skills

If you design with AI, save this 1. — best vibecoding tool 2. — top notch landings 3. — free templates 4. — 1 prompt 1 website 5. — world best icons 6. — fonts behind brands 7. — design community 8. — award websites 9. — mobile UI animations 10. — footer inspiration 11. — next level design 12. — backgrounds 13. — big brands posts 14. — animated components 15. — figma designs 16. — real app screens 17. — almost free sms 18. — premium logos 20. — cool anim bookmark this

holy f*ck, 2027 is going to be a nightmare for web designers who still ignore AI some are already building in hours what used to take days. others haven’t even tried the tools. 7 GitHub repos you should learn before your competitors do: Graphify -> drop your whole brand folder (PDFs, screenshots, moodboards, video), Claude maps it into one graph and never forgets your style UI UX Pro Max -> 192 palettes, 74 font pairings, 192 UX rules baked into Claude awesome-design-md -> 84 ready DESIGN.md files inspired by top brand sites, Claude picks up the whole design system in one go designlang -> "npx designlang pulls any site's colors, fonts and spacing into files your agent can use Hallmark -> 50 themes, opens your page in a real browser and checks it before you ship Impeccable -> 62 rules that catch bad design, then critique, polish and audit huashu-design -> one sentence -> animation, prototype, slides or infographic, 6 directions to pick from Figma Context MCP -> Claude reads your Figma file and builds it pixel by pixel OpenPencil -> open source Figma alternative that opens .fig files React Bits -> 215+ animated components, copy and paste -> 43+ smooth transitions ready to drop in designer-skills -> 273 skills that cover the full design process, from research to handoff the virus cell you're watching? built solo with Claude Code plus a few of these tools. want to make something like this? like + save this post, comment "COV" or DM me and I'll send you the exact prompt.

Designing with AI doesn't make bad designers good. It's actually making the already good designers really, really good, and leaving everyone else exactly where they were. Still not good at design and it’s even more noticeable now.

i'm not expert in design but i made these redesign bcs i thought they was a little too much over detailed (you guys can judge) #callingallvillains

slopping a clean design

the Head of Design at X just open-sourced his taste (a skill that makes your agent's UI look production-grade) I broke down how it works ↓ Benji wrote down the hundred small decisions he makes when he designs product UI by hand, so an agent can make them too the skill is built around one sentence: "it reads as a real product, simplified to what the viewer needs, framed with intent, and every motion has a cause" it names the 5 ways agent-made UI goes wrong: > too much, real words in every cell, packed tight > too little, a wireframe that doesn't read as a product > boxed, the whole app shrunk into a card like a screenshot > flat, everything has the same weight > bland, your brand's style never shows up then the agent works in 7 steps, and most of them happen before it writes any code: 1. medium: still or animation, where it sits, what size 2. look: light, dark, blueprint or glass, one accent 3. story: 3 to 5 beats 4. examples: start from the closest of dozens of gold examples 5. screen: a few real words, placeholder lines for the rest 6. choreograph: overlapping beats and a loop that ends where it began 7. rehearse: film it, read every frame, fix what it finds the agent films its own work, a script creates every frame on a sheet and monitors one-frame jumps and text that doesn't fit its box, which a screenshot can't show it reads your design tokens too, so the UI uses your real colours, fonts and corner radiuses he built it by giving the same short prompt to 2 new agents with no other context, one with the skill and one without, then comparing the results side by side when something looked off, he wrote down what was wrong, updated the skill and ran the whole test again, instead of fixing it by hand works in Grok Build, Claude Code, Codex and Gemini: npx skills add benjitaylor/skills --skill stageshow Benji says most results still take him a few rounds of feedback, and the first pass is already a big step up from what an agent makes on its own

Stageshow: a skill that helps your agent make better faux UI stills, animations, bento grids, and more. npx skills add benjitaylor/skills --skill stageshow

／ 🏆業界シェアNo.1 #マイバチ工房極 から 新色グリップ採用の #DesignX が新登場❕✨ ＼ 皆様からの意見を参考に「デザイン性」と「性能」を兼ね備えた高品質のマイバチを製作いたしました。 汚れの目立たない黒を基調としながらも、遊び心あふれるデザインを目指し開発した ”オリジナルグリップ” を採用した商品となります！ しかも本日、バチの日です 本日限定で公式サイトすべての商品が８％オフでお買い求めいただけます‼️ 割引コード：MU6BUBET 🔔 こちらは新商品となりますので、注文が殺到してしまった場合、発送が遅れる可能性がございます。 ご検討中の方はお早めにお買い求めくださいませ。 ↓ご購入はこちら↓ 公式サイト: Amazon: 楽天:

holy f*ck, NO HUMAN WEB DESIGNER WANTS TO COMPETE WITH THIS IN 2027. you’re watching AI agents build websites at a speed most humans can’t match. and the scary part? these tools are available to almost anyone. 7 GitHub repos you NEED to learn before your clients find someone who already has: 1. - BUILD ENTIRE WEBSITES WITH AI generate, edit and run full-stack web apps with AI. 2. shadcn/ui - PROFESSIONAL UI IN MINUTES beautiful, customizable components for modern websites. 3. Magic MCP - YOUR AI FRONTEND DESIGNER generate polished React components directly from your AI coding workflow. 4. Playwright MCP - AI THAT TESTS ITS OWN WORK let AI agents open websites, click buttons and catch UI bugs. 5. Chrome DevTools MCP - FIX BROKEN WEBSITES FASTER give AI access to browser debugging, performance and network diagnostics. 6. Vercel Agent Skills - MAKE AI CODE PRODUCTION-READY React optimization, frontend best practices and web design reviews. 7. Webstudio - BUILD VISUALLY, KEEP CONTROL an open-source visual website builder with reusable components and styling. the real threat isn’t AI replacing every designer. it’s ONE person using AI to deliver what used to take an entire team. 2027 is getting closer. learn these tools before your competitors do. LIKE + BOOKMARK + REPOST this so your designer friends see it. and be honest in the comments: do you know anyone who’s already LOST THEIR JOB because of AI?

This week's designs #DesignX #designer

We built what you wished Figma could be. There's not a single design tool that does all of this. Launching the new @Dessn_ai 🌸👇

Before → After Turn your AI slop into the best-looking design of all time. Generated in Claude. Designed in @figma

Your Harness. You Define It. Meet Creator Mode in DeepSeek Harness. Turn ideas into plugins that you can use, from a design workspace to a sandbox game or a 3D LLM visualizer. What will you create?

MACHINE LEARNING ENGINEER ROADMAP │ ├── 1. Programming Fundamentals │ ├── Python │ │ ├── Variables & Data Types │ │ ├── Loops & Conditionals │ │ ├── Functions & Modules │ │ ├── OOP & Exception Handling │ │ ├── List Comprehensions │ │ ├── File Handling │ │ └── Virtual Environments │ ├── Data Structures & Algorithms │ ├── Git & GitHub │ └── Linux & Command Line │ ├── 2. Mathematics for Machine Learning │ ├── Linear Algebra │ │ ├── Vectors & Matrices │ │ ├── Matrix Operations │ │ ├── Eigenvalues & Eigenvectors │ │ └── Matrix Decomposition │ ├── Calculus │ │ ├── Derivatives │ │ ├── Partial Derivatives │ │ ├── Gradients │ │ └── Chain Rule │ ├── Probability │ │ ├── Random Variables │ │ ├── Probability Distributions │ │ └── Bayes' Theorem │ └── Statistics │ ├── Mean, Median & Mode │ ├── Variance & Standard Deviation │ ├── Hypothesis Testing │ └── Correlation & Regression │ ├── 3. Data Analysis & Manipulation │ ├── NumPy │ ├── Pandas │ ├── Matplotlib │ ├── Seaborn │ ├── Data Cleaning │ ├── Data Transformation │ ├── Exploratory Data Analysis (EDA) │ ├── Missing Values & Outliers │ └── Feature Engineering │ ├── 4. SQL & Database Fundamentals │ ├── SQL Queries │ ├── Joins & Subqueries │ ├── Aggregations │ ├── Window Functions │ ├── PostgreSQL │ ├── Database Design │ └── Data Warehousing Basics │ ├── 5. Machine Learning Fundamentals │ ├── What Is Machine Learning? │ ├── Supervised Learning │ │ ├── Linear Regression │ │ ├── Logistic Regression │ │ ├── Decision Trees │ │ ├── Random Forest │ │ ├── Support Vector Machines │ │ └── K-Nearest Neighbors │ ├── Unsupervised Learning │ │ ├── K-Means Clustering │ │ ├── Hierarchical Clustering │ │ └── Principal Component Analysis │ ├── Reinforcement Learning Basics │ ├── Training & Testing Data │ ├── Cross-Validation │ ├── Overfitting & Underfitting │ ├── Regularization │ └── Hyperparameter Tuning │ ├── 6. Machine Learning Algorithms │ ├── Linear & Polynomial Regression │ ├── Naive Bayes │ ├── Gradient Boosting │ ├── XGBoost │ ├── LightGBM │ ├── CatBoost │ ├── Ensemble Learning │ └── Model Evaluation & Optimization │ ├── 7. Machine Learning Frameworks │ ├── Scikit-learn │ ├── XGBoost │ ├── LightGBM │ ├── CatBoost │ ├── Feature Pipelines │ ├── Model Serialization │ └── Custom Transformers │ ├── 8. Deep Learning │ ├── Neural Network Fundamentals │ ├── Perceptrons │ ├── Activation Functions │ ├── Forward & Backpropagation │ ├── Gradient Descent │ ├── Loss Functions │ ├── Optimizers │ ├── Convolutional Neural Networks (CNNs) │ ├── Recurrent Neural Networks (RNNs) │ ├── LSTMs & GRUs │ ├── Transformers │ └── Transfer Learning │ ├── 9. Deep Learning Frameworks │ ├── PyTorch │ │ ├── Tensors │ │ ├── Autograd │ │ ├── nn.Module │ │ ├── DataLoaders │ │ └── Training Loops │ ├── TensorFlow │ ├── Keras │ ├── GPU Acceleration │ └── Distributed Training Basics │ ├── 10. Natural Language Processing (NLP) │ ├── Text Preprocessing │ ├── Tokenization │ ├── Stemming & Lemmatization │ ├── Word Embeddings │ ├── Word2Vec & GloVe │ ├── Attention Mechanisms │ ├── Transformers │ ├── BERT & GPT Architectures │ ├── Hugging Face │ └── Fine-Tuning Language Models │ ├── 11. Computer Vision │ ├── Image Processing │ ├── Image Classification │ ├── Object Detection │ ├── Image Segmentation │ ├── OpenCV │ ├── YOLO │ ├── Vision Transformers │ └── Image Augmentation │ ├── 12. Generative AI & LLM Engineering │ ├── Large Language Models (LLMs) │ ├── Prompt Engineering │ ├── Embeddings │ ├── Vector Databases │ ├── Retrieval-Augmented Generation (RAG) │ ├── Semantic Search │ ├── LLM Fine-Tuning │ ├── LoRA & QLoRA │ ├── AI Agents │ ├── Model Evaluation │ └── LLM Inference Optimization │ ├── 13. MLOps & Model Deployment │ ├── ML Model Lifecycle │ ├── Experiment Tracking │ ├── MLflow │ ├── Weights & Biases │ ├── Model Versioning │ ├── Data Versioning (DVC) │ ├── Model Serving │ ├── FastAPI & Flask │ ├── REST APIs │ ├── Batch & Real-Time Inference │ ├── Model Monitoring │ └── Model Retraining │ ├── 14. Cloud & Infrastructure │ ├── AWS │ │ ├── Amazon S3 │ │ ├── EC2 │ │ ├── SageMaker │ │ └── Lambda │ ├── Google Cloud │ │ ├── Vertex AI │ │ └── BigQuery │ ├── Microsoft Azure ML │ ├── Docker │ ├── Kubernetes │ ├── CI/CD Pipelines │ ├── Terraform │ └── GPU & Cloud Computing │ ├── 15. Data Engineering for ML │ ├── ETL & ELT Pipelines │ ├── Apache Airflow │ ├── Apache Spark │ ├── Kafka │ ├── Data Lakes │ ├── Data Warehouses │ ├── Feature Stores │ └── Data Quality & Validation │ ├── 16. Model Optimization & Production │ ├── Model Quantization │ ├── Model Pruning │ ├── Knowledge Distillation │ ├── ONNX │ ├── TensorRT │ ├── Model Compression │ ├── Latency Optimization │ ├── Scalability │ └── Production Reliability │ ├── 17. Responsible AI & Security │ ├── AI Ethics │ ├── Bias & Fairness │ ├── Explainable AI (XAI) │ ├── SHAP & LIME │ ├── Data Privacy │ ├── Model Security │ ├── Adversarial Attacks │ └── AI Governance │ ├── 18. Build Real-World Projects │ ├── House Price Prediction System │ ├── Customer Churn Prediction │ ├── Fraud Detection System │ ├── Recommendation Engine │ ├── Spam Detection System │ ├── Stock Price Forecasting │ ├── Image Classification App │ ├── Object Detection System │ ├── Sentiment Analysis API │ ├── RAG-Based AI Assistant │ ├── AI Document Processing System │ └── End-to-End ML Production Pipeline │ └── 19. Career Preparation ├── Build a Strong GitHub Portfolio ├── Create an ML Engineering Resume ├── Master ML System Design ├── Practice Python & SQL Interviews ├── Solve ML Case Studies ├── Learn Model Deployment at Scale ├── Contribute to Open Source ├── Write Technical Articles └── Apply for Machine Learning Engineer Roles FINAL GOAL: Become a Production-Ready Machine Learning Engineer

I’ve been noticing the same thing over and over: we’re working in Codex and Claude, but still need a canvas to explore ideas and collaborate together quickly and on the fly. We made Twombly. A little design tool we built for ourselves that we’re excited to share with everyone. Give it a try!

Designing with agents shouldn't mean working alone. Twombly is the collaborative tool @MSchwaibold and I built for ourselves. It's a shared canvas that lives beside your agent. Share a link with your team to huddle, get feedback, and iterate together, right where you already work. Available for free today.

From the present: New York Botanical Garden by Wolff Olins, 2024 Now on Brand Archive: #logos #designx #branding #logodesign #brandarchive #design #graphicdesign #rebrand