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 products.
First up, Google AI Studio now offers free voice AI agents called Vibe Code. Announced by Logan Kilpatrick, the feature lets teams prototype conversational voice experiences with a simple prompt. Behind the scenes, the updated Gemini Live model and Gemini 2.5 Pro handle natural dialogue via the Live API. Just describe your desired voice interaction, and the system generates it seamlessly.
In related news, LangChain AI released an open-source Social Media AI Agent that leverages LangGraph to analyze your personal writing style. It then automatically generates posts and updates matching your unique voice. The toolkit is available on GitHub, allowing product teams to integrate social media automation that feels authentically human.
Additionally, LangChain AI published a guide for AI-powered web scraping by combining LangChain’s chain-of-thought intelligence with Oxylabs’ robust infrastructure. The walkthrough covers multiple programming languages and integration methods, helping teams gather data at scale and enrich LLM applications without building scraper architecture from scratch.
On a different front, AI educator Aakash Gupta shared a concise 30-minute tutorial for building an AI agent from scratch. The step-by-step video guide walks through environment setup, prompt engineering, agent loops, and API integration, enabling prototyping of functional agents in under half an hour.
Meanwhile on the product side, George from ProdMgmt World issued a burnout warning: expanding your scope without gaining corresponding authority is the moment a product manager’s career starts to suffer. He urges smarter boundary management to preserve focus and well-being.
In other news, Aakash Gupta cautioned that most corporate AI roadmaps amount to hopium without realistic plans. He emphasizes setting measurable milestones, defining success criteria, and provides a practical guide for crafting actionable AI strategies.
Separately, Gupta offered a suite of resources on AI roadmapping, covering discovery research, framework development, product requirement docs, and prioritization techniques. These materials provide a structured approach to planning AI features and aligning cross-functional teams.
In industry research, DeepLearning AI introduced the Energy-Based Transformer, a novel architecture that scores candidate tokens by “energy” instead of probability and then applies gradient steps to verify top selections. In trials with a 44 million-parameter model, the energy-driven approach outperformed standard transformers, suggesting a promising direction for more accurate text generation and efficient, robust sampling.
Lastly, Sebastian Raschka challenged creating entirely new programming languages for LLMs, arguing limitations arise from human-centric design. He also recommends actively refining existing languages to unlock the core capabilities of large models without reinventing syntax.
That’s a wrap on today’s GenAI PM Daily. Keep building the future of AI products. See you tomorrow—until then, stay curious!