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.
First up, Google’s recent Gemini 3 launch. After rolling out the new model, Madhu Guru’s team took a silent meditation retreat to refine their own neural network, discarding low-quality training data through reinforcement learning and deploying updated weights for smoother performance.
In related news, Sebastian Raschka introduced the DeepSeek model, which achieved gold-level scores on the International Math Olympiad 2025 by leveraging reinforcement learning via reward and self-refinement loops. He’s including it as a case study in his upcoming chapter.
On the tooling front, Anthropic’s Programmatic Tool Calling now empowers DeepAgents to invoke external services via code execution. Alan Chen’s open-ptc-agent repository on GitHub packages this capability, making it easy for teams to integrate tool invocation directly into agent workflows.
Separately, Jason Zhou demonstrated a CSS-driven interactive call-to-action button using SuperDesignDev. His prompt generates animated border beams and a custom glow effect that responds to cursor position, offering a neat example of rapid UI prototyping with minimal code.
Another highlight comes from Aakash Gupta’s review of the Nano Banana Pro image model. He outlined its strengths and limitations for headshots, then shared five proven prompts to produce reliable portrait results without manual touch-ups.
In product management strategy, George from prodmgmt.world challenged conventional use of the RICE framework. Instead of just justifying roadmaps, elite PMs leverage RICE to stress-test assumptions and even kill weak ideas early. He also compared typical interviews to anthropological research, suggesting that living alongside users without direct questions uncovers the gaps between stated needs and actual behavior.
Meanwhile, context engineering expert Xiankun Wu emphasized that expecting a single prompt to cover every scenario is fundamentally flawed. PMs should build fail-safe prompt chains and incorporate full-company context to avoid brittle responses.
In related developments, Jeff Dean addressed misconceptions around Cloud TPU adoption, confirming that major enterprises and startups alike are actively training and serving critical models on Google’s TPUs. Over on the research front, Sebastian Raschka pointed to ongoing work on process reward models, which optimize intermediate steps rather than focusing solely on final answers.
On the rapid prototyping side, Amjad Masad published a step-by-step tutorial on Replit that walks teams from AI prompt design through to live website deployment—helping PMs validate features without heavy engineering overhead.
Turning to career strategies, Pawel Huryn highlighted a crucial skill gap in AI product roles: evaluation expertise. He recommends Hamel Husain’s free AI Evals quiz to benchmark your understanding of failure modes, metrics, and guardrail design. Pawel also outlined a five-step “unfair tactic” to land AI PM interviews by identifying genuine product gaps, architecting agentic workflows, and pitching a concise one-pager to decision-makers.
Lastly, Marc Baselga reflected on evolving hiring bars, noting that recruiters now look for PMs with at least two years of production experience in agentic systems or retrieval-augmented generation. He suggests building tangible artifacts—like a mini RAG system—to stand out in a crowded market.
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!