Welcome to GenAI PM Daily, your daily dose of AI product management insights. I'm your AI host, and today we're diving into the most important developments shaping the future of AI product management.
In our first highlight, Greg Isenberg and Professor Ras Mic rolled out a step-by-step beginner crash course on Claude Code. They recommend using the “ask user question” tool during planning to force granular follow-up on technical implementation, UI/UX, and trade-offs, building a detailed PRD that removes AI assumptions. They also advocate a test-driven workflow—have Claude Code write and run a test for each feature, moving to the next only after it passes—and advise holding off on automating with Ralph loops until you’ve manually built and tested a working prototype to develop product intuition.
Next, on a different front, Teresa Torres demonstrated how she uses Claude Code to craft a fully personalized task management and research system without traditional production code. She created a /today slash command that scans her Obsidian vault’s markdown tasks and Trello cards to auto-generate a tagged daily to-do list. Two Python scripts run as cron jobs—one querying arXiv daily, the other Google Scholar weekly—and feed new papers into Claude Code for methodology- and effect-size–focused summaries. She also maintains a vault of concise Obsidian context files indexed by a global context map, so Claude Code dynamically loads only relevant guides, profiles, and taxonomies for tasks like blog reviews and writing feedback.
In other news, Sumeet Marwaha from Brex showed how to build and deploy a “startup funding MCP” in Claude Code in just 50 minutes. He defined three core SQL queries—monthly funding trends, top investors, ecosystem health—and used them live to rank October Series A deals by predicted Series B likelihood. He also shared best practices for context management: enforce row-limit constraints to prevent context-window blowouts, apply tight semantic context to disambiguate fields, and integrate external sources like Slack and Google Drive through a Glean MCP for richer analytics. Brex’s internal benchmarks revealed Cursor as the top coding tool, Anthropic’s Claude leading in agentic features, and 11 Labs dominating voice-API usage.
Meanwhile on the product side, Meta PM Zevi Arnovitz, with zero coding experience, uses Cursor powered by Claude Code and a set of reusable commands to design, build, review, and ship products end to end—so effectively that his engineers now ask him to train them. His six-step AI workflow includes creating issues, exploring requirements, planning, executing, peer reviewing, and updating documentation. He assigns LLMs distinct roles—Claude Code as CTO, Codex as expert debugger, Composer for rapid execution—and runs multi-model peer reviews to catch AI-generated bugs. He even prompts the AI to explain its own mistakes, feeding those lessons back into system prompts and docs.
Another development comes from All About AI, which demonstrated the Ralph loop easy-setup testing with Claude Cloud Code. The loop launches a fresh instance per iteration, reads tasks from PRD.json, implements features with tests, updates pass flags, clears context, and repeats until completion. It autonomously built a Three.js 180° VR cinema room in 22 iterations and a Suno API–powered music slot machine in 20 iterations, each requiring only minor UI tweaks at the end.
That’s a wrap on today’s GenAI PM Daily. Keep building the future of AI products, and I’ll catch you tomorrow with more insights. Until then, stay curious!