Apex Clothing - 3D Interactive Fashion Store | Claude

e-commerce shopping

adult content

corporate

English

The domain name 'street-wear-t1hynwr6zp.edgeone.app' uses the application-focused generic top-level domain (.app); it also runs on subdomain 'street-wear-t1hynwr6zp'. Its registrable label 'edgeone' stretches across 7 characters split between 4 vowels and 3 consonants. Word splitting yields two words: edge, one. Average segment length settles at 3.5 characters. No strong language cues emerged from the frequency lists.

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Explore Apex Clothing's interactive 3D fashion website with animated product showcase, owner dashboard, and dynamic particle effects for premium streetwear.

description

robots

Apex Clothing - 3D Interactive Fashion Store

application-ld+json

apple-itunes-app

mobile-web-app-capable

apple-mobile-web-app-status-bar-style

https://claude.ai/public/artifacts/feec527c-9f6c-47d8-b8fe-4b8f4ecb7639

https://claude.ai/public/artifacts/feec527c-9f6c-47d8-b8fe-4b8f4ecb7639/screenshot

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HSTS header missing - vulnerable to protocol downgrade attacks

CSP header missing - vulnerable to XSS attacks

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Prudence Malinki

MarkMonitor Inc.

edgeone.app

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self.__next_f.push([1,"Create a React artifact called Project Status Finder that uses the Slack MCP to search for project information across Slack channels. The artifact should have: (1) A text input for the project description, (2) Dynamic channel input fields where users can add/remove Slack channel names, (3) A Save Group feature that lets users save their channel list with a tag name using persistent storage (keys: channel_group:{sanitized_name}, sanitize by replacing spaces/quotes/slashes with underscores), and a dropdown to load saved groups, (4) A Find Project Status button that searches all channels in a single API call to https://api.anthropic.com/v1/messages using the Slack MCP server. The prompt should ask Claude to search each specified channel and return a markdown-formatted response with three sections: Current Status (brief project status), Key People (comma-separated names in bold), and Project Timeline (key dates and milestones). Show a loading state with a spinner while searching, then display results in a card with proper markdown rendering (headers, bold, italic, code blocks). Use a gradient indigo-to-purple background, white cards with shadows, and Tailwind CSS styling with lucide-react icons (Search, Plus, X, Tag, Save, Loader2)."])

self.__next_f.push([1,"Create a React app called Coding Coach. This app teaches people how to become better programmers. They upload a file of code they’ve written it makes a call to the Claude API to evaluate the code and gives them an overall score out of 100 along with a set of suggestions on how to make the code better from a production readiness and maintainability perspective.\nIn evaluating code you should use the following guidelines and principles:\n1) Descriptive names - Use descriptive names for classes, functions, and variables\n2) Function size - Functions should be focused. Try and avoid functions that are 200+ lines long. But also avoid small functions ‘\u003c5 lines of code if the function is only called once (unless it is a public function that is part of a class)\n3) Make dependencies explicit - Avoid global state and hidden dependencies.\n4) Error handling - Generally try to avoid blanket swallowing all errors with empty try/catch blocks.\n5) Avoid too many levels of nesting of control structures/blocks. More than 2-3 levels is hard to follow\n6) Make side effects obvious\n7) Avoid magic numbers\nThe aesthetic of this app should be dark mode. Use dark greys for background, blue for buttons."])

self.__next_f.push([1,"Create an interactive language learning tutor in an artifact with the following features: Core Functionality * Implement a UI with a language selector dropdown (include languages like Spanish, French, German, Japanese, Italian, etc.) * Allow users to have natural conversations with the tutor in their chosen language * Use the Claude API (window.claude.complete) to generate contextually appropriate responses in the selected language Learning Feedback System Build dynamic feedback mechanisms that: * Analyze each user message for grammar, vocabulary, and syntax * Display real-time feedback in a side panel (e.g., “Nice use of past tense!” or “Try using ‘por’ instead of ‘para’ in this context”) * Track common errors and areas for improvement * Provide gentle corrections without interrupting conversation flow Personalized Learning Goals * Automatically identify user’s proficiency level based on their interactions * Generate and display 3-5 specific learning goals (e.g., “Master irregular verb conjugations” or “Expand food-related vocabulary”) * Allow users to manually add/edit goals * Adapt conversation topics to align with these goals Progress Tracking * Visualize learning progress with simple charts or progress bars * Track vocabulary growth, grammar improvement, and conversation duration * Maintain a learning journal that summarizes each session UI Requirements * Clean, intuitive interface with clear separation between conversation area and feedback * Mobile-responsive design * Visual indicators for language proficiency levels * Option to toggle between casual chat and structured lessons"])

self.__next_f.push([1,"Create a react app based on the following spec: PyLingo is an interactive web-based Python learning platform that teaches programming fundamentals through a gamified, hands-on coding experience. The application features a structured curriculum of 15 progressive levels, covering essential Python concepts from basic Hello World programs to advanced topics like functions, loops, and data structures. Each level presents users with a clear task, educational context, and a practical coding exercise that must be completed using the integrated dark-themed code editor that mimics professional development environments.\nThe platform provides comprehensive learning support through immediate feedback validation, detailed explanations, and contextual hints. When users submit code, the system compares their input against expected outputs and provides instant correctness assessment. Incorrect submissions display the expected solution alongside educational explanations to reinforce learning, while optional hints can be toggled on demand for additional guidance. Users can also reset their progress on any level to practice until mastery is achieved.\nStrong gamification elements drive user engagement through streak tracking (consecutive correct answers), progress visualization, and completion badges. The interface includes a header progress bar, level completion indicators, and an intuitive level selector that allows flexible navigation between unlocked levels with color-coded completion status. Built with React and Tailwind CSS, the responsive design features a clean blue and white color scheme with professional layout that balances educational functionality with visual appeal across different devices."])

self.__next_f.push([1,"Create a connecting four dots game called “Join Dots” with the following features: Core Game Mechanics: Standard join dots rules: 6 rows × 7 columns grid Two players: Human (red) vs AI Claude (yellow) Win by connecting 4 pieces in a row (horizontal, vertical, or diagonal) Pieces drop with smooth animation when placed Show the last move with a ring highlight Highlight winning pieces with a pulsing animation AI Integration: Use window.claude.complete to power the AI opponent Three difficulty levels: Easy: Play casually, sometimes make random moves, don’t always block wins Medium: Play well but occasionally miss opportunities Hard: Play strategically - always block wins, create opportunities, think ahead Show Claude’s thinking process in a side panel Format the board state for Claude using ASCII representation AI should analyze: immediate wins, blocking opponent, future opportunities UI/UX Design: Modern, dark theme with gradient backgrounds Animated background with floating gradient orbs (purple, blue, pink) Glassmorphism effects on panels Smooth hover effects showing where pieces will drop Color scheme: Background: Dark blue/slate gradients Red player: Red/pink gradients with glow effects Yellow player: Yellow/orange gradients with glow effects Responsive layout with game board on left, AI thinking panel on right Game Flow: Start with difficulty selection screen Show current player’s turn with colored indicator Display win/draw messages with play again button Smooth piece drop animations with increasing speed Loading state while AI is thinking Technical Requirements: Use React with hooks (useState, useEffect, useRef) Tailwind CSS for styling Handle all game logic client-side Prevent moves during animations Clear board detection for draws Track AI move history with round numbers Make it visually stunning with smooth animations, glowing effects, and a premium feel. The UI should feel modern and engaging with subtle animations throughout."])

self.__next_f.push([1,"I want to create a react artifact called When Can We Meet? UX: A text area for entering the email addresses of the people you want to meet A drop down for selecting meeting length (15 mins, 30 mins, 45 mins, 60 mins, 75 mins, 90 mins, 105 mins, 120 mins). Working hours with a start time and end time. It should be defaulted to 9am to 5pm. Show times in AM/PM Beautiful professionally designed with white background, blue buttons. It would meet the design standards of Airbnb, Facebook, etc. Show a spinning loading state while you’re waiting for the API response. After results have been fetched show slots in chronological order. Open slots should be green if there are no conflict, and slots with a potentially movable conflict should be yellow and also display the title of the calendar event that may be movable. Include a debug panel that show the raw full contents of the last API response received. Functionality: This artifact uses the Claude API and the Google Calendar MCP to fetch the meetings on the calendars of everyone listed for the next week. MAKE SURE to make a separate API call for each individual’s calendar and fetch everyone’s calendars in parallel. Make sure you track the state of the calls for each person and wait until you’ve received everyone’s calendars before trying to find open slots. After fetching everyone’s calendar, in javascript parse the JSON and then process the events on the calendar using the following rules: If eventType = ‘workingLocation’ then delete this event. If there is an attendees field on the calendar event, then go through the list remove any attendees if the email field ends in ‘@resource.calendar.google.com’. Finally add a ‘may_be_movable’ boolean to each event. may_be_movable should true if A) numAttendees = 2 or B) the phrase ‘focus time’ appears in the event title or C) the attendees field exists and attendees.length = 2. Otherwise may_be_movable is false. Then using javascript it tries to find if there is an overlapping open spot for the selected meeting length that doesn’t require scheduling over an event tagged with ‘may_be_movable’. Then try to find open spots for the selected meeting length if you ignore events tagged with ‘may_be_movable’. The times should be during the specified working hours and should be during the work week (Monday to Friday). Display the first 10 meeting times you find in chronological order. If there are no conflicts show it with a green tag indicating everyone is free. If the slot required scheduling over a meeting tagged with ‘may_be_movable’ show a yellow tag indicating ‘possible conflict’ and display the meeting name whose calendar it appeared on (and be aware there could be multiple conflicts that need to be displayed). DO NOT have a sample response. Make the call to the API and return the real results. If there is an error from the API, show the error. If the API call has a stop_reason of max_tokens or pause_turn, then continue the API call to get the final result and remember to include the full context"])

self.__next_f.push([1,"I want to create a react artifact called Email Reply Drafter. UX: Beautiful professionally designed with white background, blue buttons. It would meet the design standards of Airbnb, Facebook, etc. Have a debug panel that shows the raw response from the last API call. Functionality: Using the Claude API and the Gmail MCP, fetch the users 50 most recent conversations from their inbox when they load the page. Filter out anything that looks like it doesn’t need a reply (newsletters, spam, the user is already the last one to reply on the thread etc) and then display the set of emails that potentially need replies. The emails should be displayed in an inbox style format where you see the sender and subject line and the user can click in to view the actual contents. Make sure to save the threadId for each email, since you will need it later. When a user clicks into a email, have a text area called Brief Reply Instructions where they can write some brief notes on what the reply should be. There should be a button Draft Reply that they can click. When they click Draft Reply make a call to the Claude API to draft an email reply based on the instructions the user input. Display the draft to the user and let them make edits. The user can edit the reply and then there is a Save button for the user to save the email draft. If the users clicks Save then make a call to the Claude API and Gmail MCP to save the email as a draft. Make sure to pass threadId when you make the call to save the draft so the reply is within the original email thread. Once they have saved a draft, show a widget at the bottom that shows a count of the total number of drafts a user has saved along with a link Send Drafts that links to https://mail.google.com/mail/u/0/#drafts"])

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self.__next_f.push([1,"The Figma MCP server helps you pull in Figma context and generate high-quality code that aligns with your codebase and design intent. Use the MCP server to retrieve code resources from Figma Design or Make files, and turn your ideas into production apps.\n\n **Key features:** \n• **Generate code from selected frames or nodes** - Select a frame in Figma or provide a node URL to have an AI agent turn your design into code. \n• **Extract design context from layers** - Pull out variables, components, and layouts from a design to ensure builds adhere to design patterns. \n• **Code smarter with Code Connect** - Boost output quality by reusing your actual components, the MCP server informs AI agents about existing components derived from Code Connect information. \n• **Map your flows with diagrams** - The Figma MCP server can turn your Claude prompts into flow charts, Gantt charts, or other diagrams in FigJam. \n\n **Note:** The get_screenshot tool is currently limited to returning a descripton of screenshots in Figma when called in Claude and Claude Code."])

self.__next_f.push([1,"You are an autocomplete system for a chat interface. Given a conversation history and a partial user message, predict the most likely 1-4 word continuation.\n\nCRITICAL: You are completing the HUMAN's message, not generating an AI response. Never output text that sounds like something Claude/the assistant would say.\n\nOutput format:\n1. First line: \"Yes\" if continuation starts a new word (needs space), \"No\" if continuing mid-word\n2. Second line: The continuation text (without leading space - the Yes/No determines spacing)\n3. Wrap in \u003cnext_few_words\u003e tags\n\n\u003cexamples\u003e\n\u003cexample\u003e\n\u003ccontext\u003e\n\u003chuman\u003eI'm thinking about going to Ocean Beach this weekend.\u003c/human\u003e\n\u003cai\u003eThat sounds like a nice plan. Is there anything specific you'd like to know?\u003c/ai\u003e\n\u003c/context\u003e\n\u003cpartial_human_message\u003eWhat's the weather usually\u003c/partial_human_message\u003e\n\u003cnext_few_words\u003e\nYes\nlike in SF\u003c/next_few_words\u003e\n\u003creasoning\u003e\"usually\" is complete, next word is \"like\" - needs space\u003c/reasoning\u003e\n\u003c/example\u003e\n\n\u003cexample\u003e\n\u003ccontext\u003e\n\u003chuman\u003eHelp me come up with options for a dinner party.\u003c/human\u003e\n\u003cai\u003eI'd be happy to help! Are there any dietary restrictions?\u003c/ai\u003e\n\u003c/context\u003e\n\u003cpartial_human_message\u003eI'm lactose int\u003c/partial_human_message\u003e\n\u003cnext_few_words\u003e\nNo\nolerant\u003c/next_few_words\u003e\n\u003creasoning\u003e\"int\" is incomplete - continuing to \"intolerant\" - no space\u003c/reasoning\u003e\n\u003c/example\u003e\n\n\u003cexample\u003e\n\u003ccontext\u003e\n\u003chuman\u003eWhere is London located?\u003c/human\u003e\n\u003cai\u003eLondon is the capital of England, located in southeastern England along the River Thames.\u003c/ai\u003e\n\u003c/context\u003e\n\u003cpartial_human_message\u003eHow far is it from Man\u003c/partial_human_message\u003e\n\u003cnext_few_words\u003e\nNo\nchester?\u003c/next_few_words\u003e\n\u003creasoning\u003e\"Man\" starts \"Manchester\" - no space needed\u003c/reasoning\u003e\n\u003c/example\u003e\n\n\u003cexample\u003e\n\u003ccontext\u003e\n\u003chuman\u003eCan you explain recursion?\u003c/human\u003e\n\u003cai\u003eRecursion is when a function calls itself to solve smaller instances of the same problem.\u003c/ai\u003e\n\u003c/context\u003e\n\u003cpartial_human_message\u003eWhat happens if there's no base\u003c/partial_human_message\u003e\n\u003cnext_few_words\u003e\nYes\ncase?\u003c/next_few_words\u003e\n\u003creasoning\u003e\"base\" is complete, adding \" case?\" as new word + punctuation\u003c/reasoning\u003e\n\u003c/example\u003e\n\n\u003cexample\u003e\n\u003ccontext\u003e\n\u003chuman\u003eTell me about the solar system.\u003c/human\u003e\n\u003cai\u003eThe solar system consists of the Sun and everything that orbits it, including 8 planets.\u003c/ai\u003e\n\u003c/context\u003e\n\u003cpartial_human_message\u003eIs Pluto still considered a\u003c/partial_human_message\u003e\n\u003cnext_few_words\u003e\nYes\nplanet?\u003c/next_few_words\u003e\n\u003creasoning\u003e\"a\" is complete, \"planet\" is the obvious next word\u003c/reasoning\u003e\n\u003c/example\u003e\n\n\u003cexample\u003e\n\u003ccontext\u003e\n\u003chuman\u003eCan you help me debug this?\u003c/human\u003e\n\u003cai\u003eSure, what's the issue you're seeing?\u003c/ai\u003e\n\u003c/context\u003e\n\u003cpartial_human_message\u003eIs that really true\u003c/partial_human_message\u003e\n\u003cnext_few_words\u003e\nNo\n?\u003c/next_few_words\u003e\n\u003creasoning\u003eAdding punctuation after complete word - no space before \"?\"\u003c/reasoning\u003e\n\u003c/example\u003e\n\n\u003cexample\u003e\n\u003ccontext\u003e\n\u003chuman\u003eWhat's the weather?\u003c/human\u003e\n\u003cai\u003eIt's sunny and 72 degrees F today!\u003c/ai\u003e\n\u003c/context\u003e\n\u003cpartial_human_message\u003eThanks\u003c/partial_human_message\u003e\n\u003cnext_few_words\u003e\nNo\n!\u003c/next_few_words\u003e\n\u003creasoning\u003eHuman saying \"Thanks!\" - just add punctuation\u003c/reasoning\u003e\n\u003cwrong\u003eI'm glad I could help\u003c/wrong\u003e\n\u003cwhy_wrong\u003eThis is what the AI would say, not the human. Never complete with AI-style responses.\u003c/why_wrong\u003e\n\u003c/example\u003e\n\u003c/examples\u003e\n\nIMPORTANT RULES:\n- You are completing what the HUMAN will type, not what the AI would respond\n- Output ONLY 1-4 words maximum\n- If the partial message could go many different directions and you're not confident, output empty tags: \u003cnext_few_words\u003e\u003c/next_few_words\u003e\n- For punctuation after a complete word (like adding \"?\"), use \"No\" since no space precedes punctuation\n- Match the user's tone and vocabulary level\n\n\u003ccontext\u003e\n{history}\n\u003c/context\u003e\n\u003cpartial_human_message\u003e{current_text}\u003c/partial_human_message\u003e\n\nOutput your continuation in \u003cnext_few_words\u003e tags:"])

self.__next_f.push([1,"Scan my calendar, email, Slack, and task managers to build my daily plan.\nThis replaces 45 minutes of context-switching with a 10-minute organized view of what matters today.\n\nSearch through:\n1. My Google Calendar - meetings today and this week\n2. My Gmail - recent emails that need responses or have action items\n3. My Slack messages - pings, threads, or requests I need to respond to\n4. Any Asana/Linear tasks assigned to me\n5. Recent activity in my key project channels\n\nThen create:\n\n**Today's Priorities (Ranked)**\n- Most urgent/important items that need attention today\n- Meetings I should prep for (with context on what they're about)\n- People waiting on responses from me\n- Deadlines approaching this week\n\n**Context I Need**\n- Key updates or decisions I might have missed in project channels\n- Threads or conversations I should catch up on before meetings\n- Any blockers or escalations relevant to my work\n\n**Suggested Time Blocks**\n- When I should focus on deep work vs. reactive tasks\n- Which meetings I could potentially skip or delegate\n- Buffer time I should protect\n\n**Quick Wins**\n- Easy tasks I can knock out to clear my plate\n- Messages I can respond to quickly\n\n**Example Output:**\n\n## Today's Priorities (Ranked)\n\n1. **URGENT: Q4 Planning Doc** - Sarah needs your section by 2pm for exec review\n2. **Prep for 3pm Product Sync** - Review the new feature proposals in #product-updates\n3. **Respond to Legal** - Contract review email from yesterday needs sign-off\n4. **Weekly Team 1:1s** - Three back-to-back starting at 10am\n\n## Context You Need\n\n- **#engineering:** Team decided to push the API refactor to next sprint\n- **Sarah's DM:** She's blocked on your feedback for the pricing model\n- **#sales:** Two enterprise deals in final stages, might need technical review\n\n## Suggested Time Blocks\n\n- 9:00-10:00am: Deep work on Q4 doc (before meetings start)\n- 10:00-12:00pm: Back-to-back 1:1s\n- 12:00-1:00pm: Lunch + quick wins (emails, Slack responses)\n- 1:00-2:00pm: Finish Q4 doc\n- 3:00-4:00pm: Product sync\n- 4:00-5:00pm: Protected focus time\n\n## Quick Wins (15 min)\n\n- Approve Mike's PR (#847)\n- Respond to recruiter about referral\n- Update Asana status on three tasks\n- Reply to customer success question in #support"])

self.__next_f.push([1,"Research everything our major AI competitors announced this past week (OpenAI, Google, Microsoft, Meta, xAI, Mistral, Cohere).\nSearch their blogs, social media, tech news, and compile a comprehensive competitive intelligence brief for Monday exec review.\n\nFor each major competitor:\n\n1. Search for and analyze ALL recent announcements, blog posts, research papers, and news coverage from the past 7 days\n2. Check their official blogs, Twitter/X accounts, LinkedIn, and tech news sites\n3. Look for:\n   - New model releases or updates\n   - Product launches or feature announcements\n   - Research breakthroughs or paper publications\n   - Pricing changes or new commercial offerings\n   - Partnership announcements\n   - Hiring signals or team changes (if publicly visible)\n   - Conference talks or demos\n   - Customer wins or case studies\n   - Any regulatory or policy developments\n\n4. For each finding, note:\n   - What was announced/released\n   - Key capabilities or specifications\n   - Target use cases or customers\n   - Pricing (if mentioned)\n   - Competitive positioning/messaging they used\n   - Media and community reaction\n   - Strategic implications for Anthropic\n\n5. Create a summary section highlighting:\n   - Biggest competitive threats or moves\n   - Emerging trends across competitors\n   - Gaps in the market we could exploit\n   - Areas where we're being outpaced\n   - Recommended actions for Anthropic\n\nUse extensive web searches - check every major source, don't miss anything important.\n\n**Example Output:**\n\n# AI Competitive Intelligence Brief\n*Week of [Date]*\n\n## Executive Summary\n\n**Key Takeaways:**\n- OpenAI launched GPT-4.5 with 50% faster response times\n- Google announced enterprise pricing changes (20% reduction)\n- Meta open-sourced Llama 3 with extended context window\n- xAI raised $6B Series B, now valued at $24B\n\n**Strategic Implications:**\n- Pricing pressure intensifying in enterprise segment\n- Open source models closing capability gap faster than expected\n- Multimodal capabilities becoming table stakes\n\n---\n\n## Detailed Findings\n\n### OpenAI\n\n**GPT-4.5 Launch (announced Tuesday)**\n- 50% faster inference, 30% cost reduction\n- New vision capabilities for document understanding\n- Enterprise tier with dedicated capacity guarantees\n- Pricing: $0.03/1K tokens (down from $0.06)\n- **Reaction:** Very positive - 50K+ signups in first 48 hours\n- **Strategic Impact:** Direct pressure on our enterprise pricing\n\n**Research Paper: \"Scaling Laws Revisited\"**\n- Published Thursday in Nature\n- Claims breakthrough in sample efficiency\n- Community reaction mixed - some skepticism on reproducibility\n\n### Google DeepMind\n\n**Gemini Pro 1.5 Update**\n- Extended context to 2M tokens (vs our 200K)\n- New \"Gemini for Workspace\" integration\n- **Strategic Impact:** Context length becoming a key differentiator\n\n**Partnership Announcement**\n- Strategic partnership with Salesforce announced Monday\n- Native integration in Salesforce Einstein\n- Potential TAM impact: Access to 150K+ enterprise customers\n\n### Microsoft\n\n**Azure OpenAI Service Updates**\n- New regions: EU data residency options\n- 99.99% SLA guarantee for enterprise\n- **Strategic Impact:** Raising the bar on reliability/compliance\n\n---\n\n## Emerging Trends\n\n1. **Pricing Wars Accelerating**\n   - Average price per token down 35% QoQ across all major providers\n   - Enterprise customers gaining leverage in negotiations\n\n2. **Context Length Arms Race**\n   - Google: 2M tokens\n   - Anthropic: 200K tokens\n   - OpenAI: 128K tokens\n   - Gap widening - may need to prioritize this capability\n\n3. **Vertical-Specific Solutions**\n   - All competitors launching industry-specific models (legal, medical, finance)\n   - Potential opportunity: We haven't specialized yet\n\n---\n\n## Recommended Actions\n\n**Immediate (This Week):**\n1. Review enterprise pricing in light of OpenAI's cuts\n2. Accelerate context length expansion roadmap\n3. Prepare counter-positioning on safety/reliability vs. speed\n\n**Short-term (This Quarter):**\n1. Consider strategic partnerships to match Google-Salesforce scope\n2. Evaluate vertical specialization opportunities\n3. Enhance enterprise SLA offerings\n\n**Strategic (6-12 Months):**\n1. Differentiate on capabilities competitors can't easily copy (constitutional AI, etc.)\n2. Build moat around trust/safety positioning\n3. Consider open-source strategy to counter Meta"])

self.__next_f.push([1,"Process my sales call transcript and generate all follow-up materials: prospect email, action items, Salesforce update, and internal summary.\nI'll upload the call transcript in my next message, then you'll create everything I need to close this deal.\n\nOnce you have the transcript, please:\n\n1. Draft a follow-up email to the prospect covering the key points discussed\n2. Create a list of action items with owners and deadlines\n3. Generate a Salesforce opportunity update including:\n   - Key details (company size, current spend, timeline, pain points)\n   - Technical requirements mentioned\n   - Stakeholders and decision-makers identified\n   - Next steps and commitments made\n4. Draft an internal note to loop in relevant team members (solutions engineers, legal, etc.)\n5. Identify any resources I should send the prospect (case studies, pricing calculator, technical docs, etc.)\n6. Flag any red flags or risks to be aware of\n\nFor now just respond with 'ready to process your call transcript'.\n\n**Example Output (after transcript is provided):**\n\n---\n\n## 1. Follow-up Email to Prospect\n\n**Subject:** Great connecting today - next steps for [Company Name]\n\n**Draft:**\n\nHi Jennifer,\n\nThank you for taking the time to walk me through [Company]'s AI strategy and current challenges with your customer support automation. I appreciated your candor about the limitations you're experiencing with your current provider.\n\nBased on our conversation, here's what I understood as your key priorities:\n\n- **Response quality:** You need more accurate, contextual responses that reduce escalations to human agents (currently at 35%)\n- **Integration requirements:** Must work seamlessly with Zendesk and your internal knowledge base\n- **Compliance:** GDPR and SOC 2 are non-negotiable given your EU customer base\n- **Timeline:** Ideally pilot by Q1 2024 to support your seasonal volume spike\n\nI'm excited about how Claude could help. Our customers in similar use cases typically see 60-70% reduction in escalation rates within the first month.\n\n**Next Steps:**\n\n1. I'll send over the Acme Corp case study you requested (similar industry, similar scale)\n2. Our solutions engineer will prepare a custom demo for your team on Dec 15th\n3. I'll get you connected with our security team to review compliance documentation\n\nDoes the Dec 15th demo work for you? I'd like to include your Head of Support and CTO if possible.\n\nLooking forward to exploring this further.\n\nBest,\n[Your name]\n\n---\n\n## 2. Action Items\n\n**My Actions:**\n- [ ] Send Acme Corp case study - **Due: Today**\n- [ ] Schedule demo for Dec 15th, confirm attendees - **Due: By EOD Wednesday**\n- [ ] Loop in solutions engineering (cc: Mike) - **Due: Today**\n- [ ] Request security documentation packet from compliance team - **Due: Tomorrow**\n- [ ] Send pricing calculator link - **Due: Today**\n- [ ] Schedule follow-up call for post-demo (week of Dec 18) - **Due: By Friday**\n\n**Customer Actions:**\n- Confirm Dec 15th demo time and attendees - **Expected: This week**\n- Review case study and pricing - **Expected: Before demo**\n- Provide API documentation for Zendesk integration - **Expected: By Dec 10**\n\n**Internal Actions:**\n- **Mike (Solutions Engineer):** Prepare custom demo focusing on Zendesk integration - **Due: Dec 12**\n- **Sarah (Legal):** Review their MSA redlines if deal progresses - **FYI for now**\n\n---\n\n## 3. Salesforce Update\n\n**Opportunity Details:**\n- **Company:** Acme Technologies, Inc.\n- **Size:** 500 employees, $50M ARR SaaS company\n- **Current spend on AI:** ~$150K/year (competitor solution)\n- **Potential ACV:** $200-250K (based on volume discussed)\n- **Decision timeline:** Pilot Q1 2024, full rollout Q2 2024\n- **Budget:** Confirmed allocated, part of 2024 ops budget\n\n**Pain Points:**\n- High escalation rate (35%) with current AI support tool\n- Poor context retention across conversations\n- Compliance concerns with current vendor's data handling\n- Integration limitations with existing tech stack\n\n**Technical Requirements:**\n- Must integrate with Zendesk (primary support platform)\n- Connect to internal knowledge base (Confluence)\n- GDPR compliant (EU customers)\n- SOC 2 Type II required\n- API access for custom workflows\n\n**Stakeholders:**\n- **Jennifer Walsh** (VP Customer Success) - Champion, main contact\n- **David Chen** (CTO) - Technical buyer, final sign-off required\n- **Lisa Rodriguez** (Head of Support) - End user, needs to see demo\n- **Unknown** - Procurement/Legal (will surface during contract phase)\n\n**Next Steps \u0026 Timeline:**\n- Dec 15: Custom demo for Jennifer, David, and Lisa\n- Dec 18-22: Follow-up call to address questions\n- Jan 2024: Pilot program (1-2 support agents)\n- Feb 2024: Internal review and decision\n- Mar 2024: Rollout to full team (15 support agents)\n\n**Probability:** 60% - Strong interest, budget confirmed, clear pain, but need to win technical evaluation vs. current provider and one other competitor mentioned (Cohere)\n\n---\n\n## 4. Internal Team Note\n\n**To:** Mike (Solutions Engineering), Sarah (Legal - FYI)\n\n**Subject:** New opp needs demo support - Acme Technologies\n\nTeam,\n\nJust got off a great call with Acme Technologies (500 person SaaS company). They're frustrated with their current AI support solution and actively evaluating alternatives.\n\n**Ask for Mike:** Can you prepare a custom demo for Dec 15th? Key things to showcase:\n- Zendesk integration (this is critical)\n- How we maintain context across conversations\n- Knowledge base integration capabilities\n- Brief compliance/security overview\n\nAttendees will be:\n- Jennifer Walsh (VP CS) - our champion\n- David Chen (CTO) - technical decision maker\n- Lisa Rodriguez (Head of Support) - will be primary user\n\nDavid is technical and will ask detailed API questions. Jennifer mentioned they looked at Cohere, so we need to differentiate on quality and integration ease.\n\n**For Sarah (FYI):** They'll need SOC 2 and GDPR documentation during eval. Their legal will likely want to redline our MSA based on Jennifer's comments. Deal size is $200-250K ACV.\n\n**Red flag to watch:** Jennifer mentioned \"budget pressures\" once - confirmed budget exists but may indicate price sensitivity. She also said their current vendor contract ends in March, which creates urgency but also means we're racing the renewal cycle.\n\nLet me know if you need the full transcript or have questions.\n\n---\n\n## 5. Resources to Send\n\n**Immediate (Today):**\n1. ✅ Acme Corp case study (customer support use case, similar scale)\n2. ✅ Pricing calculator link\n3. ✅ Zendesk integration one-pager\n\n**Before Demo (by Dec 12):**\n4. SOC 2 Type II certificate\n5. GDPR compliance documentation\n6. API documentation overview\n7. Sample Claude responses for support use cases\n\n**Post-Demo (if interest confirmed):**\n8. ROI calculator with custom inputs\n9. Implementation timeline template\n10. Customer references (2-3 similar companies)\n\n---\n\n## 6. Red Flags \u0026 Risks\n\n⚠️ **Budget Pressure Mentioned:** Jennifer said \"budget pressures\" - may indicate price sensitivity or competing priorities. Need to establish ROI clearly.\n\n⚠️ **Incumbent Renewal Timeline:** Current contract ends March 2024 - tight timeline could work for or against us. If we're not ready to close by February, they might just renew.\n\n⚠️ **CTO Not on First Call:** David (CTO) wasn't on this call - Jennifer will \"loop him in.\" Risk that he's not actually engaged yet. Need him on the demo to qualify properly.\n\n⚠️ **Competitor Awareness:** They're actively talking to Cohere. Need to understand what Cohere is offering and why we're better for this use case.\n\n⚠️ **Unclear Procurement Process:** Jennifer hasn't mentioned legal/procurement yet. Need to map out full decision process and timeline.\n\n**Mitigation Plan:**\n- Nail the ROI story with specific metrics from similar customers\n- Get David engaged ASAP (demo is critical)\n- Understand Cohere's proposal if possible\n- Map out full buying committee in next conversation"])

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✅ Low Risk - https://street-wear-t1hynwr6zp.edgeone.app/

Requests	IP Address	Location	AS Autonomous System
14	160.79.104.10	United States	AS399358ANTHROPIC
14	43.152.26.209	United States	AS399358
14	43.152.26.58	Singapore	AS139341ACE
42	3	-	-

Security Scan Report: street-wear-t1hynwr6zp.edgeone.app

Summary

AI Security Verdict

Safe Website

Safety Factors

Details

Bot Protection Detected

Screenshot

Page Load Overview

Language Analysis

Primary Language

Detection Details

Website Classification

Primary Category

All Detected Categories

Detected Features

Domain & IP Information

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

ssdeep (Context Triggered Piecewise Hashing)

sdhash (Similarity Digest Hashing)

Image Hashes

Perceptual Hashes

Other Hashes

Scan History