<h1>AI-Powered Personal Textbook Generator: Build Your Custom Learning Path from Raw Data</h1>
<p>In the age of information overload, the bottleneck isn't finding information—it's <strong>curating and synthesizing</strong> it into a form that sticks. Standard textbooks are designed for the "average" student, often covering topics you already know or missing the specialized context you need for your job or project.</p>
<p>Imagine taking your messy research notes, a collection of PDFs, and 20 web articles, and instantly turning them into a structured, professionally formatted textbook tailored to your skill level.</p>
<p>In this guide, we'll build an <strong>AI-Powered Personal Textbook Generator</strong> workflow using Claude Code, Pandoc, and a bit of RAG logic.</p>
<h2>TL;DR</h2>
<ul>
<li><strong>The Problem</strong>: One-size-fits-all textbooks are inefficient for self-directed learners.</li>
<li><strong>The Solution</strong>: Use AI to structure raw data into a logical syllabus, generate educational content, and use Pandoc/Calibre to export to EPUB/MOBI.</li>
<li><strong>The Benefit</strong>: Optimized offline reading on e-readers (Kindle/Kobo) with content specifically relevant to your goals.</li>
</ul>
<hr>
<h2>The Architecture: From Raw Data to E-Reader</h2>
<p>A robust textbook generator follows a four-stage pipeline:</p>
<ol>
<li><strong>Ingestion &#x26; Distillation</strong>: Collect raw data (PDFs, URLs, Markdown) and use <a href="https://notebooklm.google/">NotebookLM</a> or a RAG system to find relevant connections.</li>
<li><strong>Syllabus Architecting</strong>: Use an LLM to design a logical table of contents (H1, H2, H3) based on a "User Persona" (e.g., "Software Engineer learning Quantum Physics").</li>
<li><strong>Content Expansion</strong>: Generate chapters, summaries, and quizzes.</li>
<li><strong>Format Conversion</strong>: Transform Markdown into EPUB and MOBI.</li>
</ol>
<h2>Step 1: Architecting the Syllabus</h2>
<p>Don't just ask an AI to "write a book." Start with the structure. By defining a <strong>Syllabus Template</strong>, you ensure the AI doesn't drift.</p>
<p><strong>Prompt Example for Claude Code:</strong></p>
<blockquote>
<p>"I am an English speaker moving to Tokyo for a Product Manager role. I want to learn 'Business Japanese' focused on tech meetings and networking. Create a 10-chapter textbook outline. Each chapter must include: 1. Core Vocabulary, 2. A realistic dialogue transcript, 3. Grammatical explanations for business honorifics (Keigo), 4. A self-assessment quiz."</p>
</blockquote>
<ul>
<li><strong>Tip</strong>: Ask the AI to generate "Self-Assessment Quizzes" at the end of each chapter. This turns a passive reading experience into an active learning one.</li>
</ul>
<h2>2026 Tool Spotlight: Specialized Platforms</h2>
<p>If the DIY approach with Pandoc feels too technical, several "AI-First" platforms have emerged in 2026 to streamline this:</p>
<ul>
<li><strong>Inkfluence AI</strong>: A purpose-built platform that handles the entire workflow from idea to formatted EPUB export.</li>
<li><strong>Youbooks</strong>: Excellent for long-form, coherent content with live internet research capabilities.</li>
<li><strong>Skywork AI</strong>: Features a "Canvas Mode" for visualizing complex book plots and structures before drafting.</li>
</ul>
<h2>Case Study: Personalized Japanese Course (<a href="https://github.com/chiweitw/nihongo-claude">nihongo-claude</a>)</h2>
<p>A real-world example of this workflow in action is the <strong><a href="https://github.com/chiweitw/nihongo-claude">nihongo-claude</a></strong> project. The creator used Claude Code to build a customized Japanese N3 curriculum from scratch.</p>
<h3>Key Highlights:</h3>
<ul>
<li><strong>Project Structure</strong>: Organized into 4 stages and 30 lessons, each generated as a structured Markdown file.</li>
<li><strong>Bilingual Context</strong>: Every lesson includes Japanese vocabulary, realistic dialogues, and explanations in Traditional Chinese—perfectly tailored for the learner’s background.</li>
<li><strong>The Conversion Pipeline</strong>:
<ul>
<li><strong>Markdown to EPUB</strong>: Uses a Python script (<code>convert_to_epub.py</code>) leveraging <code>ebooklib</code> and <code>markdown2</code>.</li>
<li><strong>Kindle Optimization</strong>: Integrates Calibre’s <code>ebook-convert</code> for device compatibility.</li>
<li><strong>PDF Export</strong>: Uses <code>weasyprint</code> for high-quality printable versions.</li>
</ul>
</li>
<li><strong>Automation</strong>: A <code>quick-convert.sh</code> script provides an interactive menu, allowing the user to generate any format with a single command.</li>
</ul>
<p>This project proves that with the right automation pipeline, your AI assistant becomes more than just a writer—it becomes a <strong>textbook publisher</strong>.</p>
<h2>Step 3: The Technical Bridge (Markdown to EPUB)</h2>
<p>The secret weapon for ebook formatting is <a href="https://pandoc.org/">Pandoc</a>. It handles the heavy lifting of converting structured text into the complex XML/CSS mix required by the EPUB format.</p>
<h3>The Conversion Command</h3>
<p>If you have your textbook in a file called <code>textbook.md</code>, run this via your terminal:</p>
<pre><code class="language-bash"># Convert Markdown to EPUB with a Table of Contents
pandoc textbook.md -o textbook.epub --toc --metadata title="My Personal ZK Textbook"
</code></pre>
<h3>Optimizing for Kindle (MOBI/AZW3) with Calibre</h3>
<p>While Pandoc is great for the internal structure, <strong><a href="https://calibre-ebook.com/">Calibre</a></strong> is the ultimate tool for library management and specialized Kindle formatting. If you prefer a GUI or need to convert to MOBI/AZW3 for older Kindles, Calibre's <code>ebook-convert</code> is indispensable:</p>
<pre><code class="language-bash"># Convert EPUB to MOBI using Calibre's CLI
ebook-convert textbook.epub textbook.mobi
</code></pre>
<p>Calibre also allows you to inject custom CSS for better typography and ensures your generated textbook includes a "real" cover image that shows up in your Kindle library.</p>
<h2>Why Offline Reading Matters in 2026</h2>
<p>In a world of constant notifications, moving your learning to a "distraction-free" device like a Kindle is a superpower. By generating your own textbooks, you're not just consuming content; you're building a custom internal library that grows with your career.</p>
<hr>
<h3>Internal Links</h3>
<ul>
<li>Check out our guide on <a href="file:///Users/chi-wei/dev/shareuhack/_posts/claude-code-prd-workflow/index.md">The Claude Code PRD Workflow</a> for more agentic automation tips.</li>
<li>Learn more about <a href="file:///Users/chi-wei/dev/shareuhack/_posts/how-to-become-a-frontend-engineer/index.zh-TW.md">becoming a frontend engineer</a> in the AI era.</li>
</ul>

A comprehensive guide on building a personalized textbook generator using AI, Pandoc, and Claude Code. Convert raw data into structured EPUB/MOBI formats.

AI in Education: Personalized Learning Paths

AI-Powered Personal Textbook Generator: Build Your Custom Learning Path from Raw Data

<h1>Claude Code UX Researcher: Automated Competitor Benchmarking with AI Agents</h1>
<p><strong>TL;DR</strong>: Stop wasting 10 hours a week on manual "Competitive Audits." By combining <strong>Claude Code</strong> (terminal agent) with <strong>Playwright</strong>, you can build a headless UX researcher that scrapes competitors, analyzes their UI with AI Vision, and generates a structured benchmarking matrix—using <strong>Perp DEX</strong> volume leaders as a live case study.</p>
<h2>Who This Is For</h2>
<ul>
<li><strong>UX Researchers</strong>: Tired of manual screenshots and spreadsheets, wanting to focus on strategic analysis.</li>
<li><strong>Product Managers</strong>: Needing rapid competitor feature tracking to inform PRD decisions.</li>
<li><strong>dApp Competitive Analysts</strong>: Specialized in studying interaction patterns across the Web3 ecosystem.</li>
</ul>
<h3>User Journey</h3>
<blockquote>
<p><strong>Actor</strong>: Max, Senior Designer at a DEX protocol.</p>
<ol>
<li><strong>Trigger</strong>: Runs the <code>claude-watchtower</code> script on Monday morning.</li>
<li><strong>Auto-Pilot</strong>: Agent identifies top protocols via API and captures fresh screenshots.</li>
<li><strong>AI Analysis</strong>: Claude Vision compares the "Trade Terminal" layouts against his own product.</li>
<li><strong>Outcome</strong>: Max reviews a structured Markdown report in 10 mins, saving 8 hours of manual labor.</li>
</ol>
</blockquote>
<hr>
<h2>The Problem: The "Audit Fatigue"</h2>
<p>In the fast-moving DeFi space, competitors launch new features every week. For Product Designers and PMs, keeping up means:</p>
<ol>
<li>Manually visiting 10+ dApps.</li>
<li>Taking dozens of screenshots.</li>
<li>Filling out a spreadsheet with "Yes/No" for feature parity.</li>
<li>Documenting UX copy changes.</li>
</ol>
<p>By the time you finish, the audit is already outdated. This is a classic "High Repetition, High Value" task—the perfect candidate for an <strong>AI Agent Workflow</strong>.</p>
<hr>
<h2>Level 1: The AI-Powered Handoff</h2>
<p>Instead of doing this yourself, we delegate the "Legwork" to Claude.</p>
<h3>Step 1: Automated Discovery (DeFiLlama Volume)</h3>
<p>We don't want to maintain a list of competitors manually. Use the DeFiLlama API to find the <strong>Top 10 Perp DEXs by 24h Volume</strong>. High volume usually correlates with highly optimized trading interfaces.</p>
<p><strong>Prompt to Claude Code</strong>:</p>
<blockquote>
<p>"Write a script that uses the DeFiLlama API to fetch the top 10 Perp DEX protocols by 24h Trading Volume. Save their trading interface URLs to a JSON file."</p>
</blockquote>
<h3>Step 2: The "Eyes" (Playwright)</h3>
<p>Claude Code can generate and execute a Playwright script to visit these URLs in headless mode.</p>
<p><strong>The Key Action</strong>: Capture a screenshot of the "Trading Terminal" and "Asset Selector."</p>
<pre><code class="language-javascript">// Example Playwright snippet generated by Claude
const { chromium } = require('playwright');

(async () => {
  const browser = await chromium.launch();
  const page = await browser.newPage();
  await page.goto('https://hyperliquid.xyz');
  await page.screenshot({ path: 'assets/hyperliquid-trade.png' });
  await browser.close();
})();
</code></pre>
<hr>
<h2>Level 2: AI Vision Analysis</h2>
<p>Capturing screenshots is only half the battle. Now, we use <strong>Anthropic's Vision</strong> capabilities to "Read" the UX.</p>
<p>We feed the screenshots back into Claude with a structured prompt:</p>
<blockquote>
<p>"Analyze this screenshot of 'Hyperliquid Trade Interface.' Extract:</p>
<ol>
<li>Placement of the 'Trade' panel (Left/Right).</li>
<li>Primary CTA color (Hex if possible).</li>
<li>List all visible assets (ETH, USDC, etc.).</li>
<li>Evaluate the 'Hierarchy' score from 1-5 based on visual clarity."</li>
</ol>
</blockquote>
<hr>
<h2>The Output: The Benchmarking Matrix</h2>
<p>The final result isn't a folder of images; it's a <strong>Clean Markdown Matrix</strong> that you can drop directly into your PRD or Notion workspace.</p>





































<table><thead><tr><th align="left">Protocol</th><th align="left">Key Asset</th><th align="left">Primary CTA</th><th align="left">Visual Style</th><th align="left">UX Complexity</th><th align="left">24h Volume</th></tr></thead><tbody><tr><td align="left"><strong>Hyperliquid</strong></td><td align="left">USDC</td><td align="left">Deposit</td><td align="left">Trading Terminal</td><td align="left">High</td><td align="left">$1.2B+</td></tr><tr><td align="left"><strong>dYdX</strong></td><td align="left">USDC</td><td align="left">Trade Now</td><td align="left">Institutional</td><td align="left">High</td><td align="left">$800M+</td></tr><tr><td align="left"><strong>GMX</strong></td><td align="left">GLP/GM</td><td align="left">Long/Short</td><td align="left">DeFi Native</td><td align="left">Moderate</td><td align="left">$300M+</td></tr></tbody></table>
<hr>
<h3>Quantified Results: AI Agent vs. Manual Audit</h3>






























<table><thead><tr><th align="left">Feature</th><th align="left">Manual Audit (10 protocols)</th><th align="left">AI Agent (Autonomous)</th></tr></thead><tbody><tr><td align="left"><strong>Data Collection</strong></td><td align="left">~4 hours</td><td align="left">&#x3C; 5 mins</td></tr><tr><td align="left"><strong>Vision Depth</strong></td><td align="left">Subjectivity-prone</td><td align="left">Structured (Colors/Layout)</td></tr><tr><td align="left"><strong>Maintenance</strong></td><td align="left">High (Manual Reshooting)</td><td align="left">Low (Single command rerun)</td></tr><tr><td align="left"><strong>Accuracy</strong></td><td align="left">Prone to fatigue/omission</td><td align="left">Consistent rule application</td></tr></tbody></table>
<hr>
<h2>FAQ</h2>
<p><strong>Q: How do you verify AI analysis results?</strong>
A: We recommend a "Spot Check" approach. Randomly audit 10-20% of the generated snapshots to ensure the Vision model is interpreting specialized UI (like complex D3 charts or Web3 modals) correctly.</p>
<p><strong>Q: Can it analyze interfaces that require wallet connection?</strong>
A: While Playwright can inject private keys or simulate sessions, it adds complexity. Initially, it's best to focus on public post-landing page "Trade" terminals that don't require an active connection for visual benchmarking.</p>
<hr>
<h2>Risk Disclosure</h2>
<p>While AI Agents dramatically increase efficiency, be aware of these limitations:</p>
<ol>
<li><strong>Visual Dependency</strong>: This approach relies on static screenshots. Deep "Interaction Flows" (e.g., animations triggered by scroll or multi-step modals) are harder for AI to analyze without human-in-the-loop intervention.</li>
<li><strong>Token Costs</strong>: Capturing high-resolution screenshots and processing them through Vision models will incur API costs.</li>
<li><strong>Anti-Bot Measures</strong>: Some dApps might block headless browsers. You may need proxies or more "human-like" Playwright configurations.</li>
</ol>
<hr>
<h2>🚀 Why This Matters for 2026</h2>
<p>Efficiency is no longer enough. To survive in the "Human + AI" era, you must move from being a <strong>Researcher</strong> to an <strong>Architect</strong>.</p>
<p>Instead of spending 8 hours collecting data, you spend 15 minutes reviewing the <strong>AI Audit</strong> and 7 hours 45 minutes making the actual design decisions that differentiate your product. <strong>Shift from data collector to data architect.</strong></p>
<hr>
<h2>Conclusion: Build Your Watchtower</h2>
<p>Don't just keep up with the competition—observe them autonomously. By building a "Claude Watchtower," you transform a tedious chore into a strategic advantage.</p>
<p><em>Are you ready to automate your next audit? Let's build it.</em></p>

A technical guide on building an AI agent for automated UX research and competitor benchmarking using Claude Code and Playwright AI Vision.

Anthropic: Claude Code Documentation

Playwright: Automation & Screenshots

Anthropic: Vision API Reference

DeFiLlama API

Claude Code UX Researcher: Automated Competitor Benchmarking with AI Agents

<h1>Multi-AI Orchestration: Combining Specialized Tools for High-Quality Content</h1>
<p><strong>TL;DR</strong>: Stop settling for mediocre "all-in-one" outputs. By treating AI tools as a specialized team—separating logic (Text) from aesthetics (Visuals)—you can produce content that far exceeds the limits of any single generalist model.</p>
<hr>
<h2>1. The Myth of the All-in-One AI: Why Coordination Wins</h2>
<p>Is there really a single AI that can write perfectly structured copy, design layouts, and generate context-aware cinematic visuals simultaneously? While many tools claim to be "all-in-one," the results are often generic, "canned" outputs that lack depth and precision.</p>
<h3>1.1 The Theoretical Background: Multi-Agent Systems (MAS)</h3>
<p>In AI research, the concept of <strong>Multi-Agent Systems (MAS)</strong> and <strong>Distributed Intelligence</strong> suggests that complex problems are best solved not by a monolithic program, but by multiple specialized agents working together. This collaborative approach enhances accuracy, scalability, and the specialized "expertise" of the final output.</p>
<h3>1.2 Why This Matters for Content Creators</h3>
<p>The limitation isn't just the AI's capability—it's the trade-off between versatility and specialization. Dedicated tools like Midjourney (for aesthetics) or Claude (for logical structure) have weights and optimizations that generalist models simply haven't matched yet.</p>
<hr>
<h2>2. 🏗️ Core Logic: Connecting the "Brain" and the "Eyes"</h2>
<p>The "Orchestration Workflow" is built on the simple principle of <strong>Division of Labor and Handoff Points</strong>.</p>
<h3>2.1 Decision Guide: When to Switch Tools?</h3>





























<table><thead><tr><th align="left">Task Component</th><th align="left">Recommended Field</th><th align="left">Handoff Trigger</th><th align="left">Why skip the All-in-One?</th></tr></thead><tbody><tr><td align="left"><strong>Logic &#x26; Structure</strong></td><td align="left">Logic Models (Claude 3.5)</td><td align="left">Once the framework is solid.</td><td align="left">Generalists often include filler or lack depth.</td></tr><tr><td align="left"><strong>High-End Visuals</strong></td><td align="left">Pro Imagery (Midjourney)</td><td align="left">Convert text to MJ Prompts.</td><td align="left">Integrated generators lack cinematic control.</td></tr><tr><td align="left"><strong>Layout &#x26; Final Delivery</strong></td><td align="left">Specialized UX (Gamma / Canva)</td><td align="left">Once all assets are ready.</td><td align="left">Purpose-built tools support better hierarchy and 4K output.</td></tr></tbody></table>
<h3>2.2 Phase 1: The Brain (Logic, Strategy, and Hierarchy)</h3>
<p>Start with a logic-heavy model to define the soul of your content.</p>
<ul>
<li><strong>Task</strong>: Creating outlines, hierarchy, core messaging, and visual prompts for the next agent.</li>
<li><strong>Key Decision</strong>: Do not attempt to generate visual assets here. Keep it structured text only.</li>
</ul>
<h3>2.3 Phase 2: The Lens (Visual Presence and Aesthetics)</h3>
<p>Handoff the text requirements to specialized engines.</p>
<ul>
<li><strong>Task</strong>: Convert concepts into high-production value visual assets.</li>
<li><strong>Decision Point</strong>: Use Midjourney for cinematic or artistic flair; use DALL-E 3 for rapid, logical icons.</li>
</ul>
<hr>
<h2>3. 🛠️ Practical Case: Generating a Pro-Level PPT</h2>
<p>Imagine you need to create a keynote presentation on "The Future of DeFi":</p>
<ol>
<li><strong>Step A (Claude)</strong>: "Generate a 10-slide outline for a DeFi presentation. For each slide, provide a specific visual prompt for a cyberpunk, abstract financial image."</li>
<li><strong>Step B (Midjourney)</strong>: Batch run the prompts generated in Step A, ensuring style consistency using <code>--sref</code>.</li>
<li><strong>Step C (Gamma)</strong>: Import the text structure and manually replace the generic AI-generated images with your high-quality Midjourney assets.</li>
</ol>
<p><strong>The Result</strong>: A presentation that looks custom-designed, not auto-generated.</p>
<hr>
<h2>4. ⚠️ Risks &#x26; Constraints (The Trade-offs)</h2>
<p>Before adopting a multi-AI workflow, you must account for these practical challenges:</p>
<ul>
<li><strong>Coordination Overhead</strong>: Moving data between tools requires a manual "handoff." For low-priority tasks, this is often overkill.</li>
<li><strong>Tool Costs &#x26; Latency</strong>: Subscribing to multiple Pro tiers (Claude + Midjourney + Gamma) is expensive, and the total generation time is 3-5x longer than a single-prompt approach.</li>
<li><strong>Style Consistency Challenges</strong>: While tools like <code>--sref</code> help, manually fine-tuning styles across different platforms still requires a "Human-in-the-loop" to ensure visual harmony.</li>
</ul>
<hr>
<h2>5. ⚖️ Decision Rule: When NOT to Use This Workflow</h2>
<ul>
<li><strong>Best For</strong>: External presentations, high-traffic blog posts, hero images, and in-depth reports.</li>
<li><strong>Skip It When</strong>: Writing internal memos, personal notes, or low-priority status updates.</li>
</ul>
<p><strong>Rule of Thumb</strong>: If the priority of the content is lower than the 30-minute coordination cost, use an "All-in-one" single prompt.</p>
<hr>
<h2>6. FAQ</h2>
<p><strong>Q: How do I maintain visual consistency across all images?</strong>
A: Use consistent Style Reference parameters (like <code>--sref</code> or <code>--cref</code> in Midjourney) and lock in core aesthetic keywords across your prompts.</p>
<p><strong>Q: Isn't this more work than using an AI PPT generator?</strong>
A: Yes, it takes about 20% more time, but the quality increase is roughly 500%.</p>
<hr>
<h2>7. 🚀 Evolution: Moving to Automated Pipelines</h2>
<p>Once you master the logic of manual "handoffs," the next step is automation via Multi-Agent platforms.</p>
<h3>7.1 Using Agent Platforms (Dify, Coze)</h3>
<p>Platforms like <strong>Dify</strong> or <strong>Coze</strong> allow you to build workflows that handle the coordination automatically:</p>
<ul>
<li><strong>Node 1 (Reasoning)</strong>: Input title -> Output structure + Prompts.</li>
<li><strong>Node 2 (Vision)</strong>: Prompts -> Pull from Image APIs.</li>
<li><strong>Node 3 (Output)</strong>: Merge -> Deliver to Workspace.</li>
</ul>
<h3>7.2 When to Automate?</h3>
<ul>
<li><strong>High Repetition</strong>: Daily news graphics or product descriptions.</li>
<li><strong>Scaling Up</strong>: When content volume exceeds manual management.</li>
</ul>
<hr>
<h2>8. Conclusion: From Operator to Orchestrator</h2>
<p>The future of productivity isn't about finding a smarter AI; it's about becoming a better <strong>Orchestrator</strong>. By coordinating specialized tools, you unlock a level of quality that generalists simply can't touch. <strong>Don't let one model do your thinking—let a team of models build your vision.</strong></p>

A comprehensive guide to Multi-AI Orchestration. Learn how to coordinate specialized tools for text and image generation to optimize your content pipeline, improve quality, and understand the trade-offs of Multi-Agent systems.

IBM: What is a Multi-Agent System?

NVIDIA: Multi-Agent AI Collaboration Concepts

Anthropic: Claude 3.5 Sonnet Capabilities

Midjourney Documentation

Multi-AI Orchestration: Combining Specialized Tools for High-Quality Content

<h1>Zero-Maintenance Feedback: Building a Telegram + AI Vision Triage Bot</h1>
<h2>TL;DR</h2>
<ul>
<li><strong>Frictionless Intake</strong>: Use a Telegram group as your "Inbox". Forward any feedback or screenshot there, and let the bot handle the rest.</li>
<li><strong>AI-Powered Triage</strong>: Use LLMs (like GPT-4o or Gemini) to analyze images, extract error text, and categorize the intent (Bug, Feature, etc.).</li>
<li><strong>Jira Automation</strong>: Automatically create tickets <em>with</em> attachments or link to existing ones, keeping your requirement management loop clean.</li>
</ul>
<hr>
<p>The problem? <strong>Triage is a chore.</strong> Moving that image from your phone to Jira, writing a description, and tagging it properly takes enough effort that 50% of feedback just dies in your "Saved Messages".</p>
<p>As an <strong>AI-augmented PM</strong>, your goal is to automate the mundane so you can focus on the product strategy. Here is how to build a <strong>Zero-Maintenance Feedback Loop</strong>.</p>
<h2>The Workflow: From Screenshot to Structured Ticket</h2>
<p>Instead of a complex app or a heavy SDK, we stay where the users (and your teammates) already are: <strong>Telegram</strong>.</p>
<h3>1. The Inbox: A Telegram "Triage" Group</h3>
<p>Create a private Telegram group and add your bot as an <strong>Administrator</strong>.</p>
<blockquote>
<p><strong>IMPORTANT: The Privacy Gotcha</strong>
By default, Telegram bots cannot see group messages unless they are commands or mentions. To fix this, you must <strong>Turn Off Privacy Mode</strong> via <a href="https://t.me/botfather">@BotFather</a> (under Bot Settings > Group Privacy) or ensure the bot is an Admin with "Manage Messages" permissions.</p>
</blockquote>
<ul>
<li><strong>Step</strong>: Every time you see a bug or a user sends you feedback, simply forward it to this group.</li>
<li><strong>Why</strong>: It centralizes chaos. You don't need to open your laptop; you just forward and forget.</li>
</ul>
<h3>2. The Trigger: Manual Agent vs. Automated Task</h3>
<p>Depending on your workflow, you can handle the message ingestion in two ways:</p>
<ol>
<li>
<p><strong>Option A: Manual / Agentic (Claude Skill)</strong>:</p>
<ul>
<li><strong>How</strong>: You manually trigger a task in your AI environment (like Antigravity or a CLI tool).</li>
<li><strong>Trigger</strong>: "Claude, fetch the last 10 messages from the Triage Group and process them."</li>
<li><strong>Benefit</strong>: High control, saves on automated API calls, and allows you to add immediate context (e.g., "This relates to the current sprint").</li>
</ul>
</li>
<li>
<p><strong>Option B: Automated / Scheduled (n8n &#x26; Cron)</strong>:</p>
<ul>
<li><strong>How</strong>: Set up a recurring task using <strong>n8n</strong> or a local <strong>Cronjob</strong>.</li>
<li><strong>Trigger</strong>: Every hour (or every message via Webhook), the system automatically sweeps the group.</li>
<li><strong>Benefit</strong>: True "zero-maintenance". Feedback flows into Jira even while you sleep, using dedicated LLM API keys for background processing.</li>
</ul>
</li>
</ol>
<h3>3. The Brain: AI Vision Analysis (GPT-5.2 / GPT-4.5 Turbo)</h3>
<p>The bot sends the photo to a multimodal LLM. In 2026, you have two prime choices:</p>
<ul>
<li><strong>GPT-5.2</strong>: The gold standard for complex reasoning. Best if you need the AI to cross-reference your specific technical specs or PRDs.</li>
<li><strong>GPT-4.5 Turbo</strong>: 70% cheaper and hyper-optimized. Perfect for simple "Is this a bug?" classifications.</li>
</ul>
<p><strong>The Prompt</strong>:</p>
<blockquote>
<p>"Analyze this screenshot. Extract any visible error messages, identify the UI components involved, and determine if this is a Bug, Feature Request, or UX Improvement."</p>
</blockquote>
<p><strong>Structured Output Example</strong>:</p>
<pre><code class="language-json">{
  "triage_type": "bug",
  "priority": "P2",
  "summary": "Login page failure on mobile",
  "description": "User reported 404 error during SSO login flow. Error text: 'Service Unavailable'.",
  "components": ["Auth", "Mobile Web"],
  "suggested_action": "check_existing_ticket: AUTH-123"
}
</code></pre>
<h3>4. The Execution: Jira Integration</h3>
<p>Finally, the system calls the Jira API.</p>
<ul>
<li><strong>New Issues</strong>: For unique bugs, it creates a ticket, uploads the original screenshot as an attachment, and sets the priority.</li>
<li><strong>Existing Tickets</strong>: If the AI detects it's a known issue, it simply adds the new screenshot as a comment to the existing tracker.</li>
</ul>
<h2>Setup Guide (Preview)</h2>
<p>[Detailed technical steps for n8n configuration and API scripts will be expanded in the next iteration.]</p>
<h3>Technical Stack</h3>
<ul>
<li><strong>Entry</strong>: Telegram Bot API (via BotFather).</li>
<li><strong>Orchestration</strong>: n8n (for low-code) or a Python script using <code>telethon</code> and <code>jira-python</code>.</li>
<li><strong>Vision</strong>: OpenAI GPT-4o or Google Gemini Pro Vision.</li>
</ul>
<h2>Conclusion</h2>
<p>The best tools are the ones you actually use. By bridging the gap between a casual message and a formal requirement tracker, you ensure that no valuable feedback "slips through the cracks".</p>

A professional guide for AI-augmented PMs and Indie Hackers on automating user feedback collection and triage using GPT-5.2 Vision and Telegram bots.

Telegram Bot API

Jira REST API

Zero-Maintenance Feedback: Building a Telegram + AI Vision Triage Bot

<h1>The "Claude Code" PRD Workflow: A Secret Weapon for Technical PMs</h1>
<h2>TL;DR</h2>
<ul>
<li><strong>Hybrid Efficiency</strong>: Use targeted API scripts for "bulk" data transport (precision + token savings) and Claude Code/MCP for reasoning and refinement.</li>
<li><strong>Offline-First</strong>: Break free from slow, distracting web editors. This workflow allows you to stay focused in your preferred IDE (Cursor, VS Code) or AI environment (Claude Code, Antigravity) while drafting in Markdown.</li>
<li><strong>Safety First</strong>: Always use sandbox environments and "Review Diffs" mindsets to avoid catastrophic bulk overwrites.</li>
</ul>
<hr>
<p>As a Technical Product Manager, your job sits at the intersection of high-level strategy and deep technical implementation. The tools of the trade—often a mix of Jira, Confluence, and Slack—can feel disconnected from the actual engineering reality. It's time to borrow a page from the developer's handbook: <strong>Treat your requirements like code.</strong></p>
<p>Enter the <strong>Claude Code PRD Workflow</strong>.</p>
<h2>The Problem: The Browser &#x26; Context Bottleneck</h2>
<p>Drafting a PRD usually involves:</p>
<ol>
<li>Opening Confluence and wrestling with its WYSIWYG editor.</li>
<li>Juggling 15+ tabs of technical documentation and Jira tickets.</li>
<li>Manually summarizing technical constraints from Slack or GitHub.</li>
</ol>
<p>This process is not only slow but also prevents you from getting the most value out of an LLM. Simply dumping raw, unstructured data into an AI leads to token waste and lower-quality outputs.</p>
<h2>The Solution: The "Hybrid" Pull/Push Workflow</h2>
<p>The most efficient version of this workflow doesn't rely <em>only</em> on AI agents for data transport. Instead, it uses a <strong>Hybrid approach</strong>:</p>
<ol>
<li><strong>API Scripts</strong>: For precise "Pulling" and "Pushing" of raw data (saves tokens, ensures formatting).</li>
<li><strong>Claude Code + MCP</strong>: For intelligent reasoning, cross-referencing, and drafting.</li>
</ol>
<h3>Step 1: "Pull" Your Context (Precision over Agents)</h3>
<p>While you <em>can</em> ask an AI agent to "Find and fetch the docs," it's often more efficient to use a simple script targeting the <a href="https://developer.atlassian.com/cloud/jira/platform/rest/v3/intro/">Jira REST API</a>.</p>
<p><strong>The Hybrid Move</strong>: Use a script to handle <strong>complex filtering conditions</strong> and precisely control the fetch scope. This is where API scripts shine—mapping specific Jira issue types, recursive child page fetching, or filtering by custom fields that might confuse an agentic search.</p>
<pre><code class="language-bash"># Example: Precision pull with complex flags
python scripts/fetch_jira.py --jql "project = 'AUTH' AND status = 'In Progress' AND labels in ('v2-refactor')" --depth 2 --output target_scope.md
</code></pre>
<p>By handling the "transport" via API, you provide Claude with a clean, pre-filtered context. This ensures accuracy when the scope is non-trivial and saves thousands of tokens that an agent might waste trying to "navigate" or "guess" the correct set of files.</p>
<h3>Step 2: Intelligent Cross-Referencing &#x26; Drafting</h3>
<p>This is where the magic happens. You're not just editing text; you're developing requirements with an intelligent partner that has "eyes" across your entire stack.</p>
<p><strong>The Multi-Source Context</strong>: Use <strong>Claude Code + MCP</strong> to cross-reference data pulled from Jira and Confluence.</p>
<ul>
<li><strong>Prompt</strong>: "Claude, analyze the <code>user-stories</code> I just pulled from Jira and cross-reference them with the <code>architecture-spec</code> on Confluence. Identify any technical gaps in our proposed API auth flow."</li>
</ul>
<p><strong>Advanced Technique: "Portable RAG" with NotebookLM</strong>
When your technical documentation grows into the hundreds of pages, even the largest context window can struggle.</p>
<ul>
<li><strong>The Strategy</strong>: Use <a href="https://notebooklm.google.com/">NotebookLM</a> as your external Brain. Index massive PDF specs, legacy confluence exports, and Slack transcripts.</li>
<li><strong>The Bridge</strong>: Use tools like the <a href="https://github.com/PleasePrompto/notebooklm-skill">NotebookLM Claude Skill</a> to query your indexed knowledge base directly from your CLI. This allows you to "Chat with your Documentation" and pull precise snippets into your PRD draft without manual searching.</li>
</ul>
<h3>Step 3: "Push" Updates (The Safety Protocol)</h3>
<blockquote>
<p><strong>⚠️ Safety First</strong>: Updating production Confluence or Jira via automated tools carries the risk of bulk overwrites. If your script or agent fails to parse a block correctly, you could accidentally wipe out months of collaborative history.
<strong>⚠️ 安全至上</strong>：使用自動化工具更新生產環境的 Confluence 或 Jira 具有極高的風險。如果解析出現故障，可能會意外抹除數月來的協作歷史。</p>
</blockquote>
<p><strong>Standard Safety Operating Procedure (SOP)</strong>:</p>
<ol>
<li><strong>Sandbox First</strong>: Always run your "Push" scripts against a trial Jira project or an orphaned Confluence space first.</li>
<li><strong>Review Diffs</strong>: Treat documentation updates like Code Reviews. Use a tool to see exactly what changed between your local <code>requirements_v2.md</code> and the cloud version before committing the push.</li>
<li><strong>Draft Mode</strong>: When possible, push as a "Draft" or "Comment" rather than overwriting the main page content.</li>
</ol>
<h2>Common Pitfalls &#x26; How to Avoid Them</h2>
<ul>
<li><strong>ADF Formatting</strong>: Confluence uses Atlassian Document Format (ADF). Converting Markdown -> ADF can be messy. Use robust libraries like <code>atlassian-python-api</code> to handle the heavy lifting.</li>
<li><strong>Context Window</strong>: For massive PRDs (50+ pages), don't dump it all into one prompt. Use RAG or index specific sections using <a href="https://modelcontextprotocol.io/">Anthropic's MCP</a>.</li>
<li><strong>Permissions</strong>: Ensure your API tokens have "Least Privilege"—only grant write access to the specific projects or spaces you are managing.</li>
</ul>
<h2>Authoritative Resources</h2>
<ul>
<li><strong><a href="https://academy.anthropic.com/">Anthropic Academy</a></strong>: Best practices for engineering with Claude.</li>
<li><strong><a href="https://support.atlassian.com/rovo/docs/set-up-the-rovo-mcp-server/">Atlassian Rovo MCP Server</a></strong>: The official bridge between Claude and your Atlassian data.</li>
<li><strong><a href="https://developer.atlassian.com/cloud/jira/platform/rest/v3/intro/">Jira Cloud API Reference</a></strong>: Documentation for building custom precision scripts.</li>
<li><strong><a href="https://modelcontextprotocol.io">Model Context Protocol</a></strong>: The open standard for AI data integration.</li>
</ul>
<p>By bridging the gap between product requirements and the tools developers actually use, you don't just write better PRDs—you build better products.</p>

A professional guide for Technical PMs on using Claude Code, Custom API Scripts, and MCP to streamline the PRD process. Learn to sync with Jira/Confluence efficiently and safely.

Anthropic Academy - Claude Engineering

Model Context Protocol (MCP) Official Site

Jira Cloud Platform API Reference

The PRD Revolution: A High-Efficiency Offline-First Git-like Workflow

<p>Recently, I introduced <strong>Claude Code</strong> into my Product Management (PM) workflow, and it has been the biggest change in my way of working lately. Specs and prototypes that used to take days can now be done in a few hours, and the AI considers edge cases a hundred times more thoroughly than I do.</p>
<p>This isn't just about asking ChatGPT questions; it's about deeply integrating AI into the <strong>Workflow</strong>. As stated in Anthropic's <a href="https://www.anthropic.com/research/building-effective-agents">Building Effective Agents</a> guide, true productivity gains come from shifting AI from a simple "chatbot" to an "Agent" capable of executing complex tasks.</p>
<h2>Core Process: One-Person Product Team</h2>
<p>My new workflow looks roughly like this, a convergence process from vague to specific:</p>
<blockquote>
<p><strong>Requirement Intake</strong> → <strong>Claude Skills</strong> (SOP) Initial Analysis → <strong>Sub-agents</strong> (Multi-perspective) Review &#x26; Debate → Generate High-Quality <strong>PRD + Prototype</strong> → Cross-Department Communication → Spec Confirmation</p>
</blockquote>
<p>It's like having a virtual team on call, ready to work for you at any moment. Rather than just a PM, I act more like the <strong>Commander of an AI Team</strong>.</p>
<hr>
<h2>1. Claude Skills: Writing Your SOP into the Brain</h2>
<p>The concept of <strong>Skills</strong> is simple: write down the Standard Operating Procedures (SOP) in your head for the AI to strictly follow. This maps to the "Workflows" concept mentioned by Anthropic—guiding LLMs through predefined paths to ensure consistency.</p>
<p>Previously, when writing a PRD, we had to pay attention to naming consistency, formatting norms, team idioms, and remember to reference old documents. The human brain easily misses details; changing A but forgetting creating a bug in B.</p>
<p>Now, I encapsulate these rules into Skills. Here is a real-world <code>requirement-analyzer</code> Skill example:</p>
<pre><code class="language-markdown"># Skill: Requirement Analyzer

## Role
You are a Senior Technical Product Manager (TPM), skilled at converting vague business requirements into structured User Stories.

## Input
- Raw Input
- Target Audience

## Output Format (Markdown)
1. **Executive Summary**: One sentence explaining what this feature does.
2. **User Stories**: Format as "As a &#x3C;role>, I want to &#x3C;action>, so that &#x3C;benefit>".
3. **Acceptance Criteria (Gherkin Syntax)**:
   - Given [Context]
   - When [Action]
   - Then [Expected Result]
4. **Edge Cases**: List at least 3 extreme conditions (e.g., Network failure, Empty data, Insufficient permissions).

## Rules
- Must use this format: `FE-[Feature Name]-001` as ID.
- Prioritize Mobile First experience.
</code></pre>
<p>When these rules are written as Code (or System Prompts), AI produces output according to preset standards, stable and consistent. Those time-consuming and error-prone cross-referencing tasks are all handed over to AI.</p>
<h2>2. Sub-agents: Virtual Avatars for Perspective Taking</h2>
<p>If Skills are the "Hands", then <strong>Sub-agents</strong> are the "Brain".</p>
<p>When writing specs alone, it's easy to fall into blind spots. According to Basecamp's <a href="https://basecamp.com/shapeup">Shape Up Methodology</a>, the biggest risk in product development is rushing into development before the work is properly "Shaped".</p>
<p>To avoid this, I created different Agents to conduct "Self-Dialectics":</p>
<ul>
<li><strong>User Researcher Agent</strong>: Questions if this flow is intuitive? Is there a better experience?</li>
<li><strong>Dev Agent</strong>: Evaluates technical feasibility, clarifies how to open the database schema, and how to connect APIs.</li>
<li><strong>QA Agent</strong>: Specifically looks for loopholes, listing all possible edge cases.</li>
<li><strong>Stakeholder Agent</strong>: Challenges the necessity of this requirement from a business value perspective.</li>
</ul>
<h3>Simulation Dialogue</h3>
<blockquote>
<p><strong>Me (PM)</strong>: "I want to build a 'One-Click Weekly Report' feature."</p>
<p><strong>Dev Agent</strong>: "Technically feasible, but consider Context Window limits. If a user has over 500 tickets per week, the API might timeout. I suggest adding Pagination or handling it as an Async Job."</p>
<p><strong>QA Agent</strong>: "Agreed. Also, what if there are absolutely no tickets this week? Should the report be blank or show default text? And if the API call fails, should there be a Retry mechanism in place?"</p>
<p><strong>User Researcher Agent</strong>: "Wait, do users really want 'One-Click'? They essentially want to edit the AI output most of the time. I suggest the flow be: Generate Draft -> User Preview/Edit -> Save &#x26; Send."</p>
</blockquote>
<p>In operation, it's like holding an efficient meeting inside the computer. Previously, these confirmations took several meetings back and forth; now, they can be completed within a few hours. This is the ultimate form of <strong>"Shift Left"</strong>—problems are discovered and solved before the first line of code is written.</p>
<h2>3. Instant Prototype: No More Imagining in Communication</h2>
<p>This is the most powerful part. Even precise text is not as good as a working visual.</p>
<p>Previously, specs were often written but the design wasn't ready. When discussing with engineers or bosses, everyone could only "imagine individually," leading to big misunderstandings discovered only after launch.</p>
<p>Now focusing on Claude Code combined with <code>v0.dev</code> or Tailwind CSS, I can directly generate frontend code, giving me a <strong>working Interaction Prototype</strong>.</p>
<ul>
<li><strong>Before</strong>: "Clicking this button pops up a Modal with three options..." (Engineer: ??)</li>
<li><strong>After</strong>: "Just look at this HTML file, click it and you'll see." (Engineer: Got it.)</li>
</ul>
<p>Communication efficiency is much faster, and technical details can even be verified before development. No picture, no truth; now I almost always bring a Prototype to meetings.</p>
<h2>4. Git Flow: Docs as Code</h2>
<p>The last key change is <strong>Git Flow</strong>. This aligns perfectly with Atlassian's <a href="https://www.atlassian.com/agile/knowledge-management/docs-as-code">Docs as Code</a> philosophy.</p>
<p>Previously, documents were scattered across Google Docs, Confluence, or Slack conversations, eventually becoming unmaintained trash (Stale documentation). Now I try to put all outputs into Git version control.</p>
<h3>Recommended File Structure</h3>
<pre><code class="language-bash">project-root/
├── src/                # Source Code
├── docs/
│   ├── adr/            # Architecture Decision Records
│   ├── prd/            # Product Requirement Documents
│   │   ├── 2026-02-feature-A.md
│   │   └── 2026-03-feature-B.md
│   └── specifications/ # Technical Specs
├── .claude/            # AI configurations
│   ├── skills/         # Defined Skills
│   └── prompts/        # Common Prompt Templates
└── README.md
</code></pre>
<p>The benefits are:</p>
<ol>
<li><strong>Single Source of Truth</strong>: Code changes, docs change right beside it.</li>
<li><strong>Code Review</strong>: Documentation changes go through the Pull Request process, ensuring they are reviewed.</li>
<li><strong>Traceability</strong>: Who changed what spec and when is clearly recorded in the Git Log.</li>
</ol>
<h2>Conclusion</h2>
<p>Current applications are still very rudimentary, but they have significantly reduced the chance of "producing low-quality documents" and saved a lot of time reinventing the wheel.</p>
<p>Many PMs worry AI will replace them, but I believe AI replaces the "Doer", not the "Thinker". By handing over tedious work (writing docs, drawing charts, searching info) to automation, PMs can spend their time where it truly matters: <strong>Decision Making and Communication</strong>.</p>
<p>Future PMs might be more like commanders of an AI team, and your value will depend on how many AI Agents you can orchestrate to solve complex problems for you.</p>

How to evolve from 'AI Chat' to 'AI Workflow' using Claude Code, Skills, and Sub-Agents for automated PRD & Prototype generation. Includes Prompt examples and Git Flow structure.

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