How I AI“I’m incapable of doing my job without AI”: How this PM uses Claude + ChatGPT as his second brain
CHAPTERS
Why this PM is “incapable” without AI: outsourcing context switching
Claire and Amir set the premise: PM work is constant context switching across meetings, initiatives, and stakeholders. Amir explains how he offloads that cognitive burden into siloed AI “brains” in Claude and ChatGPT to keep momentum and decision quality high.
Inside a GPT/Claude “project brain”: files, instructions, and persistent threads
Amir demos what a typical project looks like: a growing set of uploaded files plus explicit instructions, alongside long-running conversation threads. The structure is designed to accumulate context over time rather than restarting from scratch.
What to upload first: kickoffs, PRDs, and any “ping-pong” reference material
Amir explains his starting kit for a PM knowledge base: anything that defines the situation and goals (kickoff decks, PRDs, docs). He calls the early phase “ping pong”—feeding references so the AI understands what success looks like and can collaborate effectively.
Finding unbiased customer truth: why Amir went to Reddit for AI agents signals
Leading an AI agents initiative at monday.com, Amir needed external, non-company narratives about what people expect from “agents.” He sought broad, unbiased conversations to ground internal hype and align priorities with real user expectations.
Claude as a technical copilot: step-by-step building a Reddit scraper
Amir walks through using Claude to go from an ambitious prompt (“scrape everything said about monday.com online”) to a practical plan. Claude narrows constraints (APIs, paywalls), then provides a beginner-friendly setup guide, environment steps, and a working script.
From scrape to ‘monstrous’ dataset: producing 34,000 rows (plus competitor angles)
The scraper outputs a large CSV of Reddit conversations (~34k rows) tied to Amir’s themes (agents/AI/monday.com). He extends the approach to competitor comparisons and additional files to capture a wider market narrative.
Claude as analyst: frequency tables, prioritization weights, and quote-based validation
Amir returns to Claude with the dataset and asks for an analyst-style breakdown: topics, frequencies, and weighted prioritization. To reduce hallucination risk, he spot-checks via keyword searches and requests direct quotes as evidence to verify themes.
Turning raw research into a persistent brain: uploading Reddit CSVs + internal context
Amir shows how the scraped Reddit files become part of the project’s knowledge base alongside internal kickoff materials and company/product references. This creates a single AI workspace that understands both monday.com context and external market expectations.
Knowledge base mechanics: ‘everything is text’ + strong instructions and pushback behavior
They discuss practical KB-building tricks: printing webpages or slides as PDFs and uploading them as scoped source-of-truth artifacts. Amir also emphasizes instruction design—he wants the AI to be candid, challenge ideas, and avoid overly supportive agreement.
Day-to-day PM acceleration: building narratives, PRDs, and fast stakeholder answers
Amir explains his workflow loop: use the AI to ‘ping-pong’ toward an outline or narrative (PRD/product review), then re-upload the refined doc to strengthen the brain. The result is rapid, context-aware responses—useful for async stakeholder requests like marketing copy.
Custom writing coach: importing Lenny/Wes Kao guidance to shorten Slack messages
After receiving feedback that his writing was too long, Amir builds a custom GPT to rewrite messages concisely while preserving a natural voice. He loads newsletter content and referenced books as style guidelines, then uses it daily by pasting drafts for rewrites.
AI for professional development: turning feedback into reusable coaching systems
Claire highlights a broader pattern: using AI to operationalize performance feedback and self-improvement. Amir notes that pre-AI, manager editing cycles were slow and momentum-killing; now, coaching is instant and repeatable, and shareable with colleagues.
Product interview prep with GPT voice mode: natural mock interviews + targeted improvement
Amir describes why voice mode is transformative for interview prep: it simulates real-time pressure and verbal delivery better than text. He uses either a custom interview-prep GPT or freestyle voice sessions, optionally adding his CV and the job description for context, and tracks skill improvements over repeated runs.
Recap, ‘power user’ mindset, and lightning round: fixing AI when it’s wrong
Claire summarizes Amir’s workflows: data scraping + analysis, second-brain projects, writing coaching, and voice-based interview practice. In the lightning round, Amir argues the biggest miss for non-power-users is being unable to ‘be everywhere at once,’ and he shares his tactic for bad outputs: provide an example and a benchmark (sometimes with a raised voice).
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