Google Announces Voice AI Agents in AI Studio
Today's curated insights on AI product management from X/Twitter across 60+ expert sources.
Google Announces Voice AI Agents in AI Studio
From X
AI Product Launches & Updates
Voice AI agents in Google AI Studio: Logan Kilpatrick @OfficialLoganK announced you can now Vibe Code voice AI agents and experiences in Google AI Studio for free with just a prompt, leveraging the updated Gemini Live model for natural conversations. Just describe the voice experience you want using the Live API and Gemini 2.5 Pro will do the heavy lifting (more details).
AI Tools & Applications
Social Media AI Agent: LangChain AI @LangChainAI launched an open-source Social Media AI Agent that uses LangGraph to analyze your writing style and automatically generate content matching your voice (details).
LangChain + Oxylabs web scraping: LangChain AI @LangChainAI released a guide on building AI-powered web scraping solutions by combining LangChain’s intelligence with Oxylabs’ infrastructure, supporting multiple languages and integration methods (learn more).
30-minute AI agent tutorial: Aakash Gupta @aakashg0 recommended a concise 30-minute build for creating an AI agent from scratch, complete with step-by-step video guidance (watch tutorial).
Product Management Insights & Strategies
PM burnout warning: George from 🕹prodmgmt.world @nurijanian highlighted that expanding scope without added authority is the exact moment your PM career starts killing you, urging smarter boundary management.
AI roadmap realism: Aakash Gupta @aakashg0 called most AI roadmaps corporate hopium, stressing you still need a clear plan and linking to a detailed guide on crafting one.
Deep-dive AI roadmapping guides: Aakash Gupta @aakashg0 shared a suite of comprehensive resources covering AI roadmapping, discovery, strategy, PRDs, and prioritization for structured planning.
AI Industry Developments & News
Energy-Based Transformer (EBT) research: DeepLearning AI @DeepLearningAI introduced the Energy-Based Transformer, scoring candidate tokens by “energy” and using gradient steps to verify selections, outperforming vanilla models in 44 million-parameter trials.
LLM programming language debate: Sebastian Raschka @rasbt argued that LLM limitations stem from human-centric language design, challenging the notion that we should build entirely new programming languages for LLMs.