TODAY’S POD SHOT
Carl Vellotti, PM behind the largest product management Instagram account (55K followers), reveals how Claude Code transforms PM workflows through context engineering, custom commands, and AI agents.
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📆 Published: 4th October 2025 🕒
Estimated Reading Time: 6 mins. Time saved: 92 mins! 🔥
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Oh this one's a biggie. it's no secret that I am an absolute killer fanboy of speech-to-text. My latest favourite tool is definitely Wispr Flow and I'll do another update on that at some point soon. But, I'd say Claude Code is as big an enhancement to my workflow as going speech-to-text. Yep. I said it.
Learn why Claude Code reached 500M ARR in just 4 months, and discover how PMs are using it for research, writing PRDs, building prototypes, and creating AI agents - all without needing to code.
I'm going to do my own intro article to Claude Code. How to use it, how to install it, and how I've been using it over the coming weeks. But for now read on to whet your appetite on what’s possible.🤖 Claude Code for Product Managers: From Beginner to Hero | Carl Vellotti x Product Growth
This is a great guide for PMs wanting to master Claude Code. Carl walks through everything from basic setup to building multi-agent systems - all in plain English.
Claude Code has exploded onto the scene, reaching 500 million ARR in just four months. It's replacing tools like Cursor, Lovable, Replit, and Bolt for many developers and product managers. But here's the thing: most PMs don't realise they can use Claude Code for far more than just coding.
This week, we're diving into the Akash Gupta Product Growth podcast episode where Carl Vellotti - who runs the largest PM Instagram account with 60k+ followers and has been a senior PM for eight years - takes you through an incredibly detailed Claude Code tutorial.
We cover everything from why Claude Code has taken off so fast, to practical workflows for research, writing, prototyping, and building AI agents. Whether you're completely new to Claude Code or already using it, this episode is packed with actionable insights that will transform how you work.
Akash Gupta Product Growth | Carl Vellotti
🚀 Why Claude Code Has Reached 500M ARR in 4 Months
[00:00] Claude Code's meteoric rise is no accident. Carl explains three key factors:
1. Developer-First Focus Anthropic recognised early that coding was a killer use case for LLMs and made Claude fantastic at it. While ChatGPT dominated mainstream adoption, Claude won the developer community through superior coding capabilities.
2. Best Writing Model Claude isn't just good at code—it's exceptional at writing. When you compare ChatGPT to Claude for written content, ChatGPT tends towards factual information, but Claude understands intent at a deeper level. As Carl puts it: "Everyone can recognise ChatGPT's writing. Claude is the writing partner of choice."
3. Revolutionary Interface Claude Code went where no one expected: the terminal. It's purely text-based with no traditional interface at all. This makes it incredibly lightweight, fast, and accessible from any development environment.
Key Takeaway for PMs and Founders
Claude Code succeeded by dominating specific niches (developers, writers) rather than trying to compete head-on with ChatGPT's mainstream appeal. They also shipped first with the CLI approach and polished it relentlessly - being first with a clear vision matters.
🎯 What Makes Claude Code Different from Other AI Tools
[12:25] Carl breaks down how Claude Code compares to other tools in the space:
vs. GitHub Copilot/Cursor/Lovable These IDEs are built specifically for coding. They're excellent at code generation but not optimised for research, writing PRDs, or other PM work. Claude Code handles both code and non-code workflows seamlessly.
vs. ChatGPT/Claude Web Interfaces Traditional chat interfaces require constant copy-pasting. You manually add files, copy outputs, paste into new locations. Claude Code reads files directly, executes code, verifies results, and iterates—all automatically.
vs. Other CLIs (Gemini CLI, OpenAI Codex) Claude was first to market with a CLI and has the most polished implementation. Its tool use is "basically perfect," and its agent/sub-agent capabilities are highly refined.
vs. N8N/Lindy (Automation Tools) Claude Code is tactical - you give it a task right now and it executes with a plan. N8N and Lindy are for recurring automations with LLM brains guiding them. Different use cases, complementary tools.
Key Takeaway for PMs and Founders
Claude Code fills a unique gap: it's an LLM interface that can actually do things (read files, run code, verify outputs) rather than just suggest them. This makes it fundamentally more powerful for PM workflows than traditional chatbots.
💻 Getting Started: Installation and Setup
[21:02] Carl walks through the incredibly simple setup process:
Installation (One Command)
# Mac users (Windows is nearly identical)
# Copy command from: https://docs.anthropic.com/claude/docs/quickstart
curl -fsSL https://install.claude.com | sh
That's it. One command installs everything you need.
Launching Claude Code
# Navigate to your project folder
cd /path/to/your/project
# Launch Claude Code
claude
You're now in Claude Code. It looks like a terminal chat interface—no fancy UI, just you and Claude.
Essential Commands
clear- Resets the conversation context (use this often)exitorESC- Exit Claude Code/init- Initialises project (creates CLAUDE file with project understanding)Shift+Tab - Toggle between auto mode and plan mode
Pricing Reality Check
Pro Plan: £17/month gets you access to Claude Code with Sonnet models
Pro Plan limitation: Sonnet is excellent for PM work but less powerful for heavy coding
Carl's Hack: Pro (£17) + Cursor (£20) = £37/month gives you Claude Code for PM work and best models for coding
Key Takeaway for PMs and Founders
Claude Code removes all barriers to entry. One command installs it, and you're talking to it in plain English immediately. For £17-37/month, you get tools that replace dozens of manual workflows.
📁 The Power of Folder Structure and Context Engineering
[30:21] This is where Claude Code becomes truly powerful for PMs. Carl demonstrates working in a demo company folder ("Streamline AI") that contains:
streamline-ai/
├── customer-interviews/
│ ├── jessica-healthcare.md
│ ├── marcus-retail.md
│ └── sarah-logistics.md
├── business-info.md
├── writing-styles/
│ ├── internal-audience.md
│ ├── technical-style.md
│ └── user-friendly.md
├── example-prds/
├── meeting-transcripts/
└── code/
Why This Structure Matters
Claude Code can instantly reference ANY file in your folder structure. This means:
Instant Context: "How many customer interviews have we done?" → Claude searches folder and answers
Cross-Reference Analysis: "Summarise differences between Jessica's and Marcus's pain points" → Claude reads both files and compares
Style Consistency: "Write a Slack message using my internal-audience style" → Claude references your style guide
Template Reuse: "Create a PRD using our standard format" → Claude uses example PRDs as templates
The Game-Changer: No Manual Copy-Paste
In ChatGPT/Claude web, you'd need to:
Open file → Copy content → Paste into chat
Repeat for every file you need
Manually combine outputs
Copy results back to files
Even with projects you have to spin up a project and when a chat runs out of context window you have to spin up another chat. Claude Code does all of this automatically. the savings just compound and compound over time.
Key Takeaway for PMs and Founders
Organise your PM knowledge base (customer research, business docs, writing styles, templates) into folders. Claude Code transforms this structure into instant, reusable context for every task. This is context engineering - and it's more valuable than prompt engineering.
💡Top Tip: You can even get Claude to organise your folder structure for you on your hard drive.
🗂️ The CLAUDE File: Your Project's Memory System
[39:05] When you run /init, Claude Code creates a file called CLAUDE (all caps) that becomes your project's permanent memory.
What It Contains
Project structure overview
Core components and how they work
Setup instructions for new team members
Rules you set for how Claude should behave
Why It's Powerful
Every Claude Code session references this file automatically. Unlike prompts that get lost in conversation history, rules in the CLAUDE file are ALWAYS remembered.
Example Rules You Can Set
# CLAUDE
## Project Rules
- Never commit to GitHub without asking first
- Always use UK English spelling (realise, optimise, colour)
- When doing research, return results in bullet-point format
- For PRDs, always use the template in /example-prds/
- Date format: DD-MM-YYYY
Quick Rule Addition
Mid-conversation, you can add rules instantly:
# Always ask before committing anything to GitHub
💡 Claude recognises the # and automatically adds it to project memory.
Folder-Specific Rules
Create CLAUDE files in subfolders for context-specific rules:
/prds/CLAUDE → PRD-specific guidelines
/research/CLAUDE → Research formatting rules
/prototypes/CLAUDE → Coding standards
Key Takeaway for PMs and Founders
The CLAUDE file is your "always-on" context that persists across all sessions. Set your preferences once (UK English, date formats, writing style, company context), and Claude remembers forever. This is exponentially more powerful than re-prompting every session.
✍️ Real PM Workflow: Writing a PRD with Context
[56:11] Carl demonstrates the killer PM use case: generating a PRD using multiple context sources simultaneously.
The Setup Carl has prepared:
business-info.md- Company description, product details, target marketwriting-styles/technical-style.md- Preferred writing voice for technical docsexample-prds/- Folder of well-written PRD examplesTopic: Build a feature using GPT Realtime (OpenAI's speech-to-speech model)
The Prompt
Build a feature spec for integrating GPT Realtime into our product.
Context to use:
- Business context: /business-info.md
- Writing style: /writing-styles/technical-style.md
- Reference examples: /example-prds/
- Audience: Engineering team
Research GPT Realtime first since it's a new API.
Create a plan before writing.
What Claude Does Automatically
Creates its own todo list: Research GPT Realtime → Read business info → Check PRD examples → Draft PRD
Web research: Searches for GPT Realtime documentation (didn't exist in training data)
Reads all context files: Understands your business, style preferences, PRD format
Generates comprehensive PRD: Problem statement, goals, technical constraints, implementation plan
Formats correctly: Follows example PRD structure automatically
The Result
A publication-ready PRD in 2-3 minutes that would take 2-3 hours manually. The PRD includes:
Proper formatting (Cosh recommended structure)
Technical voice (from style guide)
Business context (from business-info.md)
Real GPT Realtime details (from web research)
Links to relevant documentation
Key Takeaway for PMs and Founders
By organising your context (business docs, style guides, templates), you turn PRD drafting from hours to minutes. Claude combines disparate sources intelligently, researches what it doesn't know, and outputs in your exact format.
Clearly you can't rely on it to do everything for you. This isn’t product on autopilot ❌ .You're still going to need to read, assess, tweak, and tailor but it's so much faster. It gives you that kickstart you need.
📝 Custom Commands: Saved Prompts on Steroids
[01:11:06] Carl shows how to build reusable commands for recurring PM tasks.
The Problem with Bookmarked Prompts
We've all done this: save a great prompt from Twitter/LinkedIn, bookmark it, and... never use it again because:
Hard to find when you need it
Requires copy-paste gymnastics
Doesn't adapt to your specific context
Claude Code's Solution: Custom Commands
Create a /meeting-notes command that you can trigger instantly:
## /meeting-notes Command
Extract key information from meeting transcripts.
Required format:
- **Action Items**: Person, task, due date
- **Important Metrics**: Data points mentioned
- **Next Steps**: Concrete actions
- **Risks**: Identified blockers or concerns
Always include assignee names and specific dates.
Using the Command
/meeting-notes customer-interviews/jessica-healthcare.md
Claude immediately applies your structured format to the file.
PM-Specific Command Ideas
/customer-synthesis- Analyse interviews for themes/competitive-research- Standardised competitor analysis format/sprint-planning- Generate sprint tickets from requirements/slack-update- Format updates for team communication/feedback-analysis- Categorise and prioritise user feedback
Why This Beats Twitter Bookmarks
Context-aware: Command knows your project structure and rules
Instant access: Type the command, no searching required
Consistent output: Same format every time
Composable: Combine commands with other Claude features
Key Takeaway for PMs and Founders
Turn your best prompts into commands. Any recurring PM task (meeting notes, research synthesis, status updates) becomes a reusable tool. Build your personal PM command library over time.
🤖 Sub-Agents: Multiple AI Perspectives on Demand
[01:24:41] One of Claude Code's most powerful features: creating specialised agents that review work from different perspectives.
The Multi-Agent Concept
Instead of asking Claude for general feedback, spin up specific agents:
Designer Agent (pink colour) - Reviews from UX/UI perspective
Engineer Agent (blue colour) - Checks technical feasibility
Executive Agent (gold colour) - Assesses business impact
How to Define Agents
Create agent files in your project:
## designer-agent.md
You are a senior product designer with 10 years of experience.
When reviewing PRDs:
- Focus on user experience and interface clarity
- Flag missing user flows or edge cases
- Suggest improvements to usability
- Use constructive, design-focused language
Colour: pink
Using Agents
Please review /prds/gpt-realtime-feature.md from the perspective of:
- Designer
- Engineer
- Executive
Put all feedback in a new file: /reviews/gpt-realtime-feedback.md
What Happens
Claude spins up three separate agent instances simultaneously:
Each agent has its own context (no cross-contamination)
They run in parallel (not sequentially)
Each provides feedback in their specialised voice
Results compile into one comprehensive review
The Agent Library: subagents.cc
Carl recommends subagents.cc, a database of pre-built agents:
Legal advisor
Compliance checker
Security reviewer
Accessibility specialist
Marketing copywriter
One-Command Installation
# Pulls the legal-advisor agent into your project
claude agent add legal-advisor
Key Takeaway for PMs and Founders
Sub-agents give you instant expert perspectives without hiring experts. Build a panel of specialised agents (legal, design, engineering, exec) that review your work before it goes to real stakeholders. Find gaps early, iterate faster.
🔬 Advanced PM Use Case: Testing Prompts Across LLMs
[01:39:05] Carl demonstrates a sophisticated workflow: testing multiple prompt variations across different AI models to find the best combination.
The Scenario
You're building a YouTube transcript summariser. You need to:
Create three different summary prompts (short, medium, detailed)
Test each prompt against multiple models (ChatGPT, Gemini, Grok)
Compare results to pick the best prompt/model combination
Manual Approach (Painful)
Write prompt → Copy → Paste into ChatGPT → Copy output → Paste into comparison doc
Repeat for Gemini, Grok
Repeat for 3 prompts × 3 models = 9 manual copy-paste cycles
Claude Code Approach (Automated)
I'm building a YouTube transcript summariser.
Create three unique prompts:
1. Insights-focused (short)
2. Educational breakdown (medium)
3. Critical analysis (detailed)
Test each prompt against: ChatGPT, Gemini, Grok
Use /transcripts/example.md as test input
Output results to /prompt-testing/results-[prompt-name].md
Format: Show prompt, then show all three model responses side-by-side
What Claude Does
Generates three well-designed prompts
Runs each prompt through all three models (using your API keys)
Creates formatted comparison files
Runs everything in parallel
Result: 9 test runs completed automatically in minutes instead of hours.
Why This Matters for PMs
You can now scientifically test:
Which prompt format works best for your use case
Which model performs best for your specific task
Optimum prompt length vs. output quality
Cost vs. quality tradeoffs across models
Key Takeaway for PMs and Founders
Claude Code automates experimental workflows that would be prohibitively manual otherwise. When building AI features, you can now systematically test prompt variations and model performance - giving you data-driven confidence in your product decisions.
🔁 Plan Mode: Think Before You Act
[01:56:11] Carl introduces one of the most important Claude Code features: Plan Mode.
The Problem
LLMs are built to be helpful. If you say "can you add authentication to this app?", they'll immediately start coding. But often they'll:
Miss requirements you didn't explicitly state
Take an approach you didn't want
Code themselves into a corner
By the time you realise, they're 10 files deep in the wrong direction.
The Solution: Plan Mode
Press Shift+Tab to toggle into Plan Mode.
In Plan Mode, Claude:
Cannot edit files (safe environment)
Can still search and read (understands your codebase)
Creates detailed plans (step-by-step approach)
Presents todo checklist (you can review before execution)
Example Workflow
[In Plan Mode]
I want to build a prototype that:
1. Takes YouTube transcripts
2. Generates three types of summaries
3. Tests each summary against multiple LLMs
4. Outputs comparison files in markdown format
Create a plan for this, starting with the data model.
Claude's Plan Output
Plan Created:
✓ Current State Analysis
✓ Data Model Design
✓ Summary Prompt Creation (3 variations)
✓ LLM Integration Setup
✓ Output Format Structure
Steps:
1. Create transcript data model (supports multiple formats)
2. Build summary prompt templates
3. Set up API connections (ChatGPT, Gemini, Grok)
4. Create comparison output structure
5. Build runner script for parallel execution
Reviewing the Plan
You can now:
Spot missing requirements: "Actually, output as CSV not markdown"
Correct assumptions: "Use JSON format for the data model"
Add constraints: "Keep all temp files in /testing/ folder"
Then Execute
Looks good, but change output format to CSV with columns for each model.
Claude updates the plan, you confirm, then it executes flawlessly.
Key Takeaway for PMs and Founders
Always use Plan Mode for complex tasks. It's the difference between "fixing mistakes for 2 hours" and "executing perfectly in 30 minutes." The upfront thinking time saves multiples on the backend.
⚡ Parallel Execution: Multiple Tasks at Once
[02:06:11] One of Claude Code's superpowers: running multiple instances simultaneously.
The Concept
You can have multiple Claude Code sessions running in parallel terminals, each working on different tasks simultaneously.
Example: UX Research at Scale
You have three customer interview transcripts. Instead of:
Analyse interview 1 → Wait → Analyse interview 2 → Wait → Analyse interview 3
You can:
Analyse all three customer interviews in parallel using a UXR agent.
What Happens
Claude Code:
Spins up three separate UXR agent instances
Gives each agent one transcript
Runs all three analyses simultaneously
Appends insights to each file when complete
Practical PM Applications
Competitor Research: Research 10 competitors in parallel instead of sequentially
Feature Analysis: Analyse multiple feature requests simultaneously
Content Creation: Generate multiple content variations in parallel
Data Processing: Process large datasets by splitting and parallelising
The Time Savings
Sequential: 10 tasks × 5 minutes each = 50 minutes Parallel: 10 tasks at once = 5 minutes total
Carl's Real Usage
"Engineers who are really good at this will have 6 instances of Claude Code all running at the same time."
Key Takeaway for PMs and Founders
Any task you'd normally do one-by-one can likely be parallelised with Claude Code. Research that would take a full day becomes a single hour. This is how PMs are achieving 10X productivity gains.
🔌 MCPs: Giving Claude Superpowers
[02:24:41] Model Context Protocol (MCP) servers let you give Claude Code access to external tools and data sources.
What Are MCPs?
MCPs are APIs specifically built for LLMs to use. They extend what Claude Code can do beyond its base capabilities.
Example: Reddit MCP
Reddit blocks standard web scraping, but the Reddit MCP gives Claude direct access:
Find the automation thread on r/ProductManagement and extract all pain points mentioned.
Claude uses the Reddit MCP to:
Access the thread
Read all comments
Extract and categorise pain points
Return structured summary
Popular MCPs for PMs
Google Drive: Read/write files from Google Drive
Notion: Sync databases, read pages, create content
Slack: Monitor channels, send messages, retrieve history
Linear: Create issues, update status, track progress
GitHub: Repository access, PR reviews, issue management
Pulse MCP Server Directory: over 6,500 (and counting) MCP servers to do pretty much anything
Installation (Simple)
# Example: Add Notion MCP
claude mcp add notion
# Authenticate once
claude mcp auth notion
PM Workflow Example
Pull this week's customer interviews from Google Drive →
Synthesise themes →
Create Notion database entries →
Post summary to #product Slack channel
All automated through MCPs.
Building Your Own MCPs
If you have internal tools, you can build custom MCPs to integrate them with Claude Code. This opens up company-specific workflows.
Key Takeaway for PMs and Founders
MCPs connect Claude Code to your entire tech stack. Your knowledge base in Notion, your files in Google Drive, your team communication in Slack - all become accessible to Claude. This transforms Claude from a local tool to your central workflow orchestrator.
🎨 Real Demo: Building a Workflow Builder Prototype
[02:39:05] Carl live-codes a visual workflow builder (think N8N-style) using Claude Code + Cursor.
The Specification
Build an app where users can:
Add workflow nodes to a canvas
Connect nodes together
Drag nodes around
See connections update in real-time
The Prompt
Build a workflow builder interface with:
- Canvas with drag-and-drop nodes
- Ability to connect nodes with lines
- Node types: Trigger, Action, Condition
- Connections should visually update as nodes move
Start with the data model. Create a plan first.
What Claude Does
Creates Plan (todo checklist):
Design data model for nodes and connections
Build canvas component
Implement drag-and-drop
Create connection rendering
Add node type variations
Executes Plan:
Writes React components
Implements state management
Adds SVG connection rendering
Styles the interface
Runs Local Server:
Starts development server
Opens in browser
The Result
In ~5 minutes: A working prototype with draggable nodes, visual connections, and real-time updates.
Why This Matters for PMs
You can now:
Prototype complex UIs before involving designers
Test interaction models with real working interfaces
Demo concepts to stakeholders with actual functionality
Explore ideas without developer resources
The "Vibe Coding" Reality Check
Carl emphasises: This is prototyping, not production code. It's perfect for:
Exploring ideas
Testing concepts
Stakeholder demos
User testing
It's NOT for:
Production deployments
Mission-critical features
Complex backend systems
Always involve engineers for production work.
Key Takeaway for PMs and Founders
Claude Code lets PMs build sophisticated interactive prototypes in minutes. This accelerates the feedback loop from "idea" to "testable prototype" from weeks to hours. Use it for exploration, not production.
📊 Carl's Instagram Success: PM Memes at Scale
[02:56:11] Carl shares his secret weapon for running the largest PM Instagram account.
The Challenge
Post 2 memes per day, 5 days per week, for 2.5 years = 1,300+ original memes. How?
The Solution: Meme Mage
Carl built a custom tool (his first vibe coding project) that:
Template Library: Database of meme templates with joke structure analysis
Persona System: PM persona with examples of jokes PMs find funny
Video Analysis: Gemini analyses videos and matches them to templates
Caption Generation: Creates 10-20 caption variations for each video
Human Curation: Carl picks the best and refines wording
Why This Works
LLMs struggle to create truly funny jokes from scratch, but they excel at:
Understanding joke structures from examples
Matching new situations to existing patterns
Generating variations on a theme
The Instagram Formula
Carl's most successful meme categories:
PM vs Engineer relationships - Always performs well, highly shareable
PM not doing work - Relatable across roles
Consistency - 2x daily posting compounds over time
Engagement Strategy
Post to stories (drives DMs and responses)
Respond to every DM personally
Optimise for shareability, not just likes
Key Takeaway for PMs and Founders
AI tools work best when you understand their strengths. LLMs aren't replacing human creativity - they're augmenting it. Carl's system generates 90% complete work that he refines into 100% complete content. That's the sweet spot.
🎯 When to Use Claude Code vs Other Tools
Carl provides clear guidance on tool selection:
Use Claude Code For:
✅ Research and web searches
✅ Writing docs, PRDs, meeting notes
✅ File analysis and synthesis
✅ Simple prototyping
✅ Context-heavy workflows
✅ Multi-step automation
Use Cursor/IDE For:
✅ Heavy coding work
✅ Production-quality features
✅ Complex refactoring
✅ Backend development
Use N8N/Lindy For:
✅ Recurring automations
✅ Scheduled tasks
✅ Webhook-triggered workflows
✅ Integration automation
Carl's Personal Stack:
Claude Code (£17): PM work, research, writing, simple prototypes
Cursor (£20): When coding gets complex, access to best models
N8N: (free when run locally) Recurring automations (like automated meme posting)
Total cost: £37/month for a complete AI-powered PM toolkit.
Key Takeaway for PMs and Founders
Don't expect one tool to do everything. Build a lightweight stack that covers your common workflows. £37/month eliminates dozens of hours of manual work—that's 100X ROI.
💡 The Future: What AI Means for PMs
[03:24:41] Carl shares his perspective on how AI is transforming the PM role.
The Shift
2024: "I'm a non-technical PM, I focus on strategy" 2025: "I use AI to execute tactical work, I focus on strategy"
The difference: PMs can now actually execute technical work without being technical.
What This Doesn't Mean
❌ AI is replacing PMs ❌ PMs should become full-stack developers ❌ Strategic thinking matters less
What This Does Mean
✅ Tactical work (research, analysis, prototyping) becomes faster
✅ More time for strategic work (customer conversations, alignment, vision)
✅ Higher leverage per PM (can explore more ideas independently)
✅ Faster feedback loops (prototype → test → iterate in hours not weeks)
The "Glue Work" Problem
PMs spend huge amounts of time on:
Data cleanup
Meeting notes synthesis
Status updates
Research compilation
Documentation
AI doesn't replace the judgment calls, but it eliminates the glue work surrounding them.
Carl's Prediction
"The new PM skillset isn't learning to code - it's learning to work with AI effectively. Understanding when to use it, how to give it context, and where human judgment is still critical."
Key Takeaway for PMs and Founders
The question isn't "Should I learn Claude Code?" The question is "What busywork am I still doing manually?" Every hour spent on manual work is an hour not spent on customers, product, or strategy.
🎬 Final Thoughts
This is a great Claude Code tutorial you'll find, and Carl makes it accessible whether you're technical or not. The key insights:
Context engineering > Prompt engineering - Organise your knowledge base and Claude becomes exponentially more useful
The CLAUDE file is your superpower - Set your rules once, they persist forever
Plan mode prevents mistakes - Always plan before executing complex tasks
Sub-agents multiply perspectives - Get designer/engineer/exec feedback instantly
MCPs connect everything - Your entire stack becomes accessible to Claude
£37/month eliminates dozens of hours - Claude Code + Cursor = complete PM toolkit
If you're a PM and you're not using Claude Code yet, you're working 10X harder than you need to. Start small - install it, try the /init command, build one custom command- and build from there.
The PMs who master AI-assisted workflows will ship faster, explore more ideas, and focus their energy where it actually matters: talking to customers and making strategic decisions.
🔗 Links & Resources
🎥 Full Episode: https://www.youtube.com/watch?v=4nthc76rSl8
📚 Claude Code Docs: https://docs.anthropic.com/claude/docs/quickstart
🤖 Sub-Agents Library: https://subagents.cc
📱 Carl's Instagram: @PM.nish (55K followers)
📰 Carl's Newsletter: The Full Stack PM
Want more AI-powered PM workflows? Check out our previous Pod Shots on:
That’s a wrap.
As always, the journey doesn't end here!
Please share and let us know what you liked or want changing! 🚀👋
Alastair 🍽️.