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Aakash GuptaI should be charging $999 for this Claude Code Tutorial
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At a glance
WHAT IT’S REALLY ABOUT
Claude Code tutorial for PMs: workflows, agents, PRDs, automation wins
- Claude Code’s key advantage is moving beyond chatbot prompting into a file-system-first workflow where it can read, write, and refactor real project artifacts (docs, transcripts, code) with minimal friction.
- The episode demonstrates practical PM workflows—summarizing customer interviews, generating PRDs with business context and style guides, and converting meeting transcripts into structured action items—using folders as “context engineering.”
- Claude Code differentiates from IDE-first tools (Cursor/Copilot/Lovable/Replit) by being more flexible for non-coding work, stronger at writing, and highly capable in the terminal with reliable tool use and task planning.
- Advanced capabilities include Plan Mode (planning without editing), custom slash commands (saved prompts), sub-agents for parallel work and role-based reviews, and MCPs to add new tools like Reddit access.
- The conversation closes with why Claude Code grew quickly (strong coding focus, writing quality, intent understanding) and a detour into Carl’s Instagram growth system, including an LLM-assisted meme creation tool (“Meme Mage”).
IDEAS WORTH REMEMBERING
5 ideasClaude Code’s real unlock is file-system-native context, not “better prompts.”
By working inside a project folder, Claude can discover, read, and modify relevant artifacts (interviews, meeting notes, specs, code) without constant copy/paste, making “context engineering” fast and repeatable.
Run `init` early to create a durable project memory (CLAUDE file).
The CLAUDE file becomes always-on guidance that Claude references every session (repo structure, setup instructions, rules like “never commit without asking”), reducing repeated setup and preventing common mistakes.
Use Plan Mode to avoid “manifestation hell” on complex tasks.
Plan Mode (Shift+Tab) prevents file edits while Claude proposes steps and outputs, letting you correct structure/format before execution—especially important when generating many files or running multi-step coding tasks.
Turn recurring PM workflows into slash commands for consistent structure.
Custom commands act like stored prompts (e.g., meeting notes format with action items/metrics/risks) so outputs match your team’s expectations and voice, reducing the “did you use ChatGPT?” vibe.
Sub-agents enable two different accelerators: parallelization and perspective diversity.
Claude can spawn multiple agents to analyze multiple inputs at once (three interviews in parallel) or to critique the same artifact from distinct roles (designer/engineer/executive), improving speed and coverage.
WORDS WORTH SAVING
5 quotesIt takes you out of the interface of just a chatbot, and it lets you build new workflows.
— Carl Vellotti
As we’ve moved from prompt engineering into… context engineering, what you can give the LLM to work with is so key.
— Carl Vellotti
The last thing that makes this really cool… is it can actually run code.
— Carl Vellotti
There’s a little dopamine burst as you see it work through [the checklist].
— Carl Vellotti
Have LLMs do the work that you hate, not the work that you love.
— Carl Vellotti
SPEAKERS
Aakash Gupta
hostPodcast host and product/AI creator running an interview-style tutorial on Claude Code.
Carl Vellotti
guestProduct manager and creator who runs a large PM-focused Instagram account and demonstrates Claude Code workflows.
Aakash Gupta
hostPodcast host and product/AI creator (does sponsor reads and episode transitions).
Aakash Gupta
hostPodcast host and product/AI creator (short prompts and clarifying questions during the demo).
Aakash Gupta
hostPodcast host and product/AI creator (brief reactions and clarifying questions during the walkthrough).
Aakash Gupta
hostPodcast host and product/AI creator (asks facilitation questions and reads sponsor ad).
Aakash Gupta
hostPodcast host and product/AI creator (brief interviewer follow-ups on the guest’s Instagram/AI workflow).
High quality AI-generated summary created from speaker-labeled transcript.