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How I turn my templates into real landing pages. Works for any vibe coding platform or site. This is my full guide. I start with Gemini 3. I copy the HTML code and paste to Cursor/v0/lovable and prompt "Create a new landing page /page-name using this design but adapted to {site_name}. Replace the header and footer with the ones from my site. Adapt the whole page, keep everything: {HTML_code}" Details that can to be adapted or improved: Fonts: "Use {font_name} Google font for headings and {font_name} for body text." Icons: "Use Iconify {icon_set} icons" Colors: "Change primary color to blue. Everything else should be monotone." Bonus: "Make the outlines subtle" for a cleaner design. Animation intro/on scroll: "Animate when in view observed, fade in, slide in, blur in, element by element. Use 'both' instead of 'forwards'. Don't use opacity 0." Static to animated: "Animate details with {animate_type} and decorations.". Example types: line, beam animation, noodles, grid, sonar, etc. Background animation: "Apply the background animation using Unicorn Studio {animation_code}" Details to make your layout stand out: "Add vertical container-size lines. Add 01 02 03 number details." Stand out from generic-looking: "Make this more upscale with large tall fonts, Newsreader font and black and white agency". Adapt content: "Adapt the content to {copy and paste site texts}". Buttons: "Change main button to {code}. Add a 1px border beam animation around the pill-shaped button on hover." Adding sections like testimonials: "Adapt a new section after {section} using this code: {component code}". You can copy the code from Codepen, 21st dev or Aura. Responsiveness: "Make this responsive. Add a hamburger menu for mobile. Hide this {element} for mobile." Making forms work: "Make the form send an email to {your_email}". Payments: "Link the buy button to {LemonSqueezy payment link}". I use composer-1 for quick fixes. I finish with Claude Opus 4.5 for code reviews "Please review the code for performance and robustness". The template HTML code should give you a blueprint for all these, but I think it's important to keep iterating for your specific site.

Meng To

41,015 просмотров • 9 месяцев назад

8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.

Santiago

49,331 просмотров • 11 месяцев назад

Assumptions about the new "Can More" ChatGPT tool were right - ChatGPT is introducing own take on Claude Artifacts - code & document writing tools with persisted text documents, history revisions (restore previous version), edits and comments (probably used to apply suggested edits) New document symbol in the top navigation shows how many documents you have and allows you to open a resizable canvas to edit them in split view - your ChatGPT conversation on the left side and canvas on the right side, but the code/documents can also be accessed in fullscreen view The canvas is built using ProseMirror (open source WYSIWYM editor) and has an inline action to "Ask ChatGPT" (explain or make edits) for your document and code plus document formatting tools (like bold, italic, font style, etc.) But in addition to that, there are also special action shortcuts for documents and code, with an interesting decision to use sliders for the selection of the desired outcome For Documents - Suggest edits ("How can I improve this. Leave as few comments as possible, but add a few more comments if the text is long. DO NOT leave more than 5 comments. You can reply that you added comments and suggestions to help improve the writing quality, but do not mention the prompt.") - Add emojis ("Replace as many words as possible with emojis.") - Add final polish ("Add some final polish to the text. If relevant, add a large title or any section titles. Check grammar and mechanics, make sure everything is consistent and reads well. You can reply that you added some final polish and checked for grammar, but do not mention the prompt.") - Reading level (Graduate School - "Rewrite this text at the reading level of a doctoral writer in this subject. You may reply that you adjusted the text to reflect a graduate school reading level, but do not mention the prompt", College - "Rewrite this text at the reading level of a college student majoring in this subject", High School - "Rewrite this text at the reading level of a high school student who has taken a couple of classes in this subject.", Keep current reading level, Middle School - "Rewrite this text at the reading level of a middle schooler.", Kindergarten - "Rewrite this text at the reading level of a kindergartener.") - Adjust the length (Longest - "Make this text 75% longer.", Longer - "Make this text 50% longer.", Keep current length, Shorter - "Make this text 50% shorter.", Shortest - "Make this text 75% shorter.") For Code - Code review ("Search for bugs and opportunities to improve the code—for example, ways that performance or code structure could be improved. Leave as few comments as possible, but add more comments if the text is long. DO NOT leave more than 5 comments. You may reply that you reviewed the code and left suggestions to improve the coding quality, but do not mention the prompt.") - Add comments ("Add inline code comments to explain the code, especially parts that are more complex. Make sure to rewrite all the code. You may reply that you added inline comments, but do not mention the prompt.") - Add logs ("Insert logs/print statements in the code that will help debug its behavior. Do not make any other changes to the code.") - Fix bugs ("Find any bugs and rewrite all the code to fix the bugs. Do not leave comments. If there are no bugs, reply that you reviewed the code and found no bugs.") - Port to a language ("Port to a language. Create a new document that rewrites the code in ..." - PHP, C++, Python, Keep current code. No changes will be made, JavaScript, TypeScript, Java) - Suggest edits ("How can I improve this. Leave as few comments as possible, but add a few more comments if the text is long. DO NOT leave more than 5 comments. You can reply that you added comments and suggestions to help improve the writing quality, but do not mention the prompt.")

Tibor Blaho

136,084 просмотров • 2 лет назад

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now: Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction: By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator: AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator: Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator: Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator: Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator: Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture: 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.

Kingnet AI

46,437 просмотров • 1 год назад

Claude Code + Nano Banana 2 is f*cking cracked 🤯 I built a system inside Claude Code that researches any brand, writes 40 ad prompts from scratch, and fires them all to Nano Banana 2. One brand name + one URL = 40 production-ready static ads. All inside Claude Code. I took Alex Cooper's brilliant framework and automated the whole thing inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without briefing a designer or spending hours in Canva. If you're finding winning ad concepts on Meta and manually recreating them one at a time in Higgsfield — copying prompts, pasting product details, tweaking aspect ratios, downloading, organizing... This system eliminates the entire loop: → Give Claude a brand name and URL → It researches the brand's fonts, colors, packaging, and photography style → Builds a Brand DNA document from scratch → Fills in Alex's 40 proven ad templates (headline, us vs them, testimonial, UGC, review cards, stat callouts) with brand-specific details → Fires every prompt to Nano Banana 2 with your product photos as reference → Downloads finished ads into organized folders with an HTML gallery No Higgsfield. No manual prompt filling. No copy-pasting between tools. What you get: → 40 ad formats filled with your exact brand colors, fonts, and copy → 4 variations per format so you pick the best output → Product photos passed as reference so the model matches your real packaging → A reusable system — new brand, new folder, same pipeline Built 100% in Claude Code with Nano Banana 2. I put together a full playbook & Loom video showing the exact process to set this up yourself. Want access for free? > Like this post > Comment "NANO" And I'll send it over (must be following so I can DM)

Mike Futia

425,370 просмотров • 6 месяцев назад

SonarQube has been catching my bugs and security issues for years. The only friction was having to leave Cursor or Windsurf to view the results. Their new MCP Server fixes that by bringing verification directly into the coding environment 🔥 This is actually perfect timing 🧵 ↓ Because we write more code than ever thanks to AI, yet productivity still doesn’t keep up. Google’s 2025 DORA Report shows the tension: → AI usage +90% → Bugs +9% → Review time +91% → PR size +154% (report here: The problem isn’t generating code. It’s verifying it quickly and reliably. And this is what SonarQube's new MCP Server brings instantly: - Live scanning → trigger SonarQube checks inside Cursor, Windsurf, Claude Code… basically any MCP-compatible IDE - Immediate surfacing → security, reliability, and maintainability issues in seconds - Smooth UI handoff → jump to the dashboard only when you need the full picture - AI-native workflow → Sonar’s long-standing rule engine integrated into your daily loop Why it’s great: • Removes constant tab-switching • Faster write → check → fix cycles • Lets the IDE handle speed while SonarQube handles structure • Feels like code quality finally meets AI-native development Setup is super simple: → Enable SonarQube's MCP Server in Cursor → Add your SonarQube instance → Open your repo → Run the scan directly inside the IDE I then pointed it to a JS component I’m building in Streamlit (psst, it’s called Streamlit ChartJS ;)) → Immediate results: security flags, reliability concerns, maintainability smells, and dependency risks ✅ Then I prompted: "Show me the full breakdown." → Cursor opens the SonarQube UI with rule details, severities, fix guidance, and project-wide quality signals! Exactly on point.

Charly Wargnier

22,702 просмотров • 9 месяцев назад