Join Allen Firstenberg and Mark Tucker as they dive into Google's latest Gemini 2.5 models and their much-touted "thinking" capabilities. In this episode, they explore whether these models are genuinely reasoning or just executing sophisticated pattern matching. Through live tests in Google's AI Studio, they pit the Pro, Flash, and Flash-Lite models against tricky riddles, analyzing the "thought process" behind the answers. The discussion also covers the practical implications for developers, the challenges of implementing these features in frameworks like LangChainJS, and the broader question of what this means for the future of AI.[00:00:00] - Introduction to Gemini 2.5 "thinking" models[00:01:00] - How "thinking" models relate to Chain of Thought prompting[00:03:00] - Advantages of separating reasoning from the answer[00:05:00] - Exploring the models (Pro, Flash, Flash-Lite) in AI Studio[00:06:00] - Thinking mode and thinking budget explained[00:09:00] - Test 1: Strawberry vs. Triangle[00:15:00] - Test 2: The "bricks vs. feathers" riddle with a twist[00:17:00] - Prompting the model to ask clarifying questions[00:25:00] - Is it reasoning or just pattern matching?[00:28:00] - Practical applications and the future of these models[00:35:00] - Implementing reasoning models in LangChainJS[00:40:00] - Conclusion#AI #GoogleGemini #ReasoningModels #ThinkingModels #LLM #ArtificialIntelligence #MachineLearning #LangChain #Developer #Podcast #TechTalk #TwoVoiceDevs

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