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.
On the product front, Claude has launched its new web fetch tool, allowing agents to retrieve and analyze content from any URL without extra infrastructure. Additionally, Claude rolled out the Claude Code Analytics API, enabling teams to pull daily metrics—sessions, code lines, and pull requests—via a simple API call.
In related news, Hugging Face introduced a free experiment tracking library that supports logging images, videos, tables, and metrics in one unified workspace, helping teams streamline model evaluation and reproducibility.
Meanwhile, Anthropic’s Gemma 3n is now available on the Play Store for on-device, offline use, offering speech-to-text, translation, and batch audio inference up to 30 seconds. Microsoft’s Copilot Labs added three MAI-Voice-1 powered modes—Scripted, Emotive, and Story—for richer audio generation.
On the product insights front, Lenny Rachitsky published a video on building evaluation systems that move beyond vanity dashboards using top AI lab frameworks. Shreyas Doshi introduced a radical leadership framework, reframing common challenges as product leadership problems and providing an accompanying guide. Meanwhile, George Nurijanian shared an AI prompt collection for generating, prioritizing, and validating assumptions via high signal-to-noise experiments.
In broader industry developments, Guillermo Rauch observed two AI workloads on Vercel—Agent-as-a-Service and DIY agents—noting enterprise adoption of AaaS via ChatGPT Enterprise and Vercel tooling. At NVIDIA AI, Chris Dallago and Martin Steinegger explained how AI is accelerating protein folding breakthroughs to speed drug discovery. Prem Natarajan from Capital One joined NVIDIA AI to show how generative and agentic AI can reduce cognitive burden for banking customers.
Turning to learning, Deeplearning.ai and SAP launched a course on knowledge graphs for API discovery, taught by Perva GK. It teaches representing facts as nodes and edges, building procurement graphs linking purchase requests to approvals and orders, and replacing flat API lists with structured graphs so agents can filter irrelevant APIs and call only what they need.
In a recent tutorial, Peter Yang built a YouTube research agent with Claude Code in 15 minutes. He compared three approaches—Data API, HTML scraping, and yt-dlp—choosing yt-dlp to avoid keys and quotas. He scripted a slash command to fetch 20 videos, rank the top ten by views with key insights and next-video suggestions, then extended it to batch-process channels from a markdown file.
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!