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, Alibaba’s Qwen team updated Qwen3-TTS with streaming inference powered by vLLM, Voice Design plus model cloning for a consistent tone, and plans to release a 25Hz open-source version with Instruct-style controls. In related news, Vercel introduced its AI Dashboard built on Opus 4.5, Sandbox and Gateway. Dubbed the “Jarvis of data,” it lets teams query business and product metrics on demand.
Shifting to tools and applications, a podcast from Harrison Chase explored context engineering for long-horizon agents—covering agent harnesses, coding agents and using execution traces as truth—while Google AI Studio’s PM lead Logan Kilpatrick showed how to embed a toggle for five design variations in one UI. Render now supports end-to-end AI coding agents, letting teams define stack and infrastructure in one config file, link GitHub and have the agent manage outages, testing and communications. Vercel also trimmed 80 percent of its agent tools to reduce complexity. George contrasted Claude Code for multi-file debugging and refactoring with Claude Cowork for PDF form filling, DOCX edits and file management; Greg Isenberg recommended starting in plan mode, switching to Opus 4.5 for one-shot code and keeping a shared Cloud.md in Git to update guidance. AI Jason showed how lightweight skill+CRI integrations slash token use by over 70 percent, letting teams scale thousands of skills, while All About AI showcased a six-step pipeline using Gemini 3 Flash, Qwen 3 TTS and Omnihuman to generate 20-second avatar clips in under seven minutes.
Turning to product management strategies, George recommends using ChatGPT as a mock interviewer to practice design questions, set timers and get feedback on user segmentation, success metrics, technical constraints and prioritization. He also advises encapsulating key skills in portable scripts and workflows—using markdown and custom tools—so you can contribute immediately in new roles instead of starting from scratch. In addition, align your prioritization framework—RICE, ICE, Opportunity Solution Trees or WSJF—with your data maturity, project stage and risk profile, noting most challenges stem from unclear objectives rather than the framework itself. Karthick Nethaji shared an Intent Engineering framework that treats AI agent capabilities like feature specs by defining clear constraints, decision autonomy and success criteria to avoid unpredictable behavior. Meanwhile, Henry Finkelstein highlighted resources like Dan Shipper and livecasts on recursive Git workflows and cloud-based agent flows, while warning of governance and alignment gaps when scaling from solo agents to team-based implementations.
Finally, industrywide, the skills.sh marketplace has surpassed 20,000 agent skills, underscoring rapid ecosystem growth, and Alan Cowen recently moved from Hume AI to Google DeepMind, reinforcing continued voice AI advancements and shared infrastructure.
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!