Two Voice Devs

Allen has been busy! This week, he unveils the new `@langchain/google` package for LangChain JS. This major update consolidates five previous libraries into a single, standardized, and powerful tool for developers working with Gemini and Vertex AI. Allen walks Mark through the motivation behind the change, the focus on backward compatibility, and the exciting new features like simplified multimodal input/output and text-to-speech support. If you're building with Google AI and JavaScript, this is the update you've been waiting for. [00:00:57] The confusion of previous packages [00:02:52] Creating a unified package [00:03:45] Introducing @langchain/google [00:04:35] Backward compatibility [00:06:48] Multimodal inputs [00:07:54] Standardizing output and image generation [00:08:58] Text-to-Speech support [00:11:29] Simplifying parameters and reasoning [00:14:55] Future roadmap #LangChain #Gemini #NanoBanana #TextToSpeech #GoogleAI #JavaScript #TypeScript #VertexAI #OpenSource #AI #WebDevelopment #TwoVoiceDevs

Ever wonder how "Two Voice Devs" goes from a raw recording to a finished episode? In this episode, Allen Firstenberg takes Mark Tucker on a deep dive into his production workflow. They discuss how Descript’s text-based editing revolutionized their process, how Allen uses a custom Gemini CLI agent to automate show notes and descriptions, and the technical (and ethical!) journey of creating AI-generated thumbnails using Google's Nano Banana. It’s a candid look at how AI can act as a force multiplier for creators while keeping the "human in the loop." [00:00:01] Introduction and Check-in [00:01:27] Behind the Scenes: Why We Use AI [00:03:42] Descript: Text-Based Video Editing [00:05:24] Building a Knowledge Database from Transcripts [00:08:13] Editing Video Like a Document [00:12:34] Exploring Descript's AI [00:13:36] Automating Show Notes with Gemini CLI [00:14:10] The Power of System Instructions (GEMINI.md) [00:19:30] AI Thumbnail Generation with Nano Banana [00:26:10] The Ethics of Synthetic Media and Artistic Style [00:28:40] Keeping the Human in the Loop [00:33:00] Evolution of the Two Voice Devs Workflow #TwoVoiceDevs #PodcastProduction #GeminiAI #Descript #GeminiCLI #NanoBanana #Automation #ContentCreation #Ethics #GenerativeAI #AIWorkflow #PodcastEditing

Mark and Allen dive into the emerging world of Agent Skills, an open standard for extending the capabilities of AI coding assistants like GitHub Copilot, Claude Code, and Gemini CLI. They explore how these skills work, how they compare to the Model Context Protocol (MCP), and walk through creating and installing a custom skill using the `skills` CLI. They also discuss the skills.sh website by Vercel, which acts as a registry and leaderboard for the ecosystem. The conversation touches on the potential for standardization, the current fragmentation in the ecosystem, and critical security considerations for these powerful new tools. More Info: * https://agentskills.io * https://skills.sh * https://cra.mr/mcp-skills-and-agents [00:00:00] Introduction & Context: AI Agents and Tools [00:02:18] Getting Information into Context (Instructions files) [00:06:50] What are Agent Skills? (AgentSkills.io) [00:09:55] Agent Skills vs. MCP Servers [00:16:35] How Skills Work: Progressive Disclosure [00:19:50] Mark's Example: List Global NPM Skill [00:22:56] Installing Skills with skill.sh and the Skills CLI [00:26:55] Demo: Installing on GitHub Copilot [00:30:58] Demo: Installing on Gemini CLI [00:37:37] Discussion: Discovery, Standardization, and Security [00:43:05] Conclusion #AgentSkills #AI #GitHubCopilot #GeminiCLI #CodingAssistants #MCP #ModelContextProtocol #DeveloperTools #TwoVoiceDevs

Allen and Mike discuss Google's new "Personal Intelligence" feature for Gemini. They explore how it connects to your personal data like Photos, Gmail, and Docs to provide context-aware answers. The conversation covers real-world use cases, privacy concerns regarding training data, and the importance of transparency and granular control in AI systems. They also touch on the "blackmail" scenario found in other AI research and what developers can learn from Google's implementation. More Info: * https://blog.google/innovation-and-ai/products/gemini-app/personal-intelligence/ [00:00:30] Google's Gemini Personal Intelligence announcement [00:01:48] Connecting personal data sources to Gemini [00:03:45] Google's unique advantage with user data [00:06:40] Real-world use case: Tracking travel history [00:07:30] Potential use case: Combining health data sources [00:09:15] Privacy: Is your data used for training? [00:12:40] The debate: Opting in vs. privacy concerns [00:16:30] AI safety and the "blackmail" scenario [00:18:50] Lessons for developers: Granular permissions and transparency [00:20:30] Verifiability and user trust [00:23:50] Conclusion #Gemini #GoogleAI #PersonalIntelligence #Privacy #MachineLearning #Developer #TechPodcast #AI #TwoVoiceDevs

Allen Firstenberg welcomes back guest host Mike Wolfson, an Android Google Developer Expert, to discuss the shifting landscape of User Experience (UX) in the age of Artificial Intelligence. As we move toward autonomous agents and multimodal interactions—incorporating voice, haptics, and environmental data—the reliance on traditional screens and touch interfaces is set to diminish. Mike shares insights on why "context" is the new "queen," illustrating the challenges of current AI with real-world examples (like his Meta glasses failing to find a decent breakfast burrito). The conversation tackles the critical question: "Who does the agent truly work for?" and explores how developers can avoid "notification fatigue" while ensuring users remain informed and in control. From the "creepy" factor of hyper-personalized data to the "Beverage Butler" concept, this episode dives deep into designing for a future where the best UI might be no UI at all. [00:00:00] Welcome and Introduction [00:01:21] The Evolution of UX: Beyond Touchscreens [00:03:52] The Importance of Context: A Meta Glasses Example [00:06:21] Privacy, Creepiness, and Agent Loyalty [00:08:43] Application Development in an AI World [00:11:34] Avoiding the "Seinfeld Assistant" of Notification Fatigue [00:17:15] Feedback Modalities: Tones vs. Speech [00:19:39] Discoverability of Features in Non-Visual Interfaces [00:23:00] The "Beverage Butler" and Future Outlook #AI #UX #UserExperience #ArtificialIntelligence #AndroidDev #ContextAwareness #VoiceUI #NoUI #TechPodcast #TwoVoiceDevs #GDE #GoogleDeveloperExpert #BeverageButler

In this episode, Mark introduces Allen to Parlant, an open-source framework for building agentic AI. They explore how Parlant differs from other frameworks like LangChain and LangGraph by using concepts like "journeys" for flexible conversation flows and "guidelines" for conditional rule application. Mark walks through the key features, including the ability to define glossaries, tools, and the engine's matching process. They also discuss the recent version 3.1 updates, such as linked journeys and behavior criticality levels. Finally, they dive into "Emcie," Parlant's managed NLP service that utilizes a teacher-student model architecture to optimize performance and cost using Small Language Models (SLMs). [00:00:00] Welcome and Introduction [00:00:43] Introduction to Parlant [00:02:03] Journeys: Flexible Conversation Flows [00:03:36] Guidelines: Conditional Rules [00:06:27] Motivations and Compliance [00:08:44] NLP Services and Providers [00:11:42] The Balance Between Rigid and Loose Conversations [00:18:43] Parlant 3.1 Updates: Linked Journeys and Behavior Levels [00:22:20] Custom Matchers [00:23:14] Emcie: Parlant's Managed NLP Service [00:25:05] Model Tiers: Jackal and Bison [00:26:14] Teacher-Student Architecture and SLMs [00:30:26] Cost and Optimization with Student Models [00:35:19] Conclusion and Wrap-up #Parlant #AI #AgenticAI #LLM #SLM #OpenSource #SoftwareDevelopment #TwoVoiceDevs #Emcie #NLP

Happy New Year! Allen and Mark kick off 2026 by looking back at the whirlwind of AI developments in 2025. From the explosion of agentic frameworks like LangGraph, Google's Agent Development Kit, and the Microsoft Agent Framework to the emergence of protocols like MCP and A2A, it was a year of rapid evolution. They discuss the rise of "vibe coding," the state of voice assistants like Alexa Plus and Gemini, and the challenges of monetization and discovery in a model-centric world. What lies ahead in 2026? The duo shares their big predictions! What do we need on the hardware side? Perhaps something new from Google, Microsoft, OpenAI, Amazon, or someone else? What companies are we rooting for? What's next for agents? Tune in and find out! Timestamps: [00:00:00] Introduction and Happy New Year [00:01:13] Reflecting on a busy 2025: Google's weekly announcements [00:02:05] New terms: MCP (Model Context Protocol) and A2A (Agent-to-Agent) [00:03:52] The shift to Agents and Agentic solutions [00:05:40] Framework evolution: Microsoft Agent Framework and LangGraph [00:07:54] The rise of Coding Assistants and "Vibe Coding" [00:09:25] State of Voice: Alexa Plus and Gemini on smart devices [00:11:15] Smart Glasses and the future of ambient AI [00:12:40] MCP: Challenges with discovery, monetization, and security [00:15:10] Microsoft Foundry and low-code agent building [00:20:25] Live Streaming Models: Video, Audio, and Text [00:22:00] 2026 Predictions: Voice Flow acquisition [00:23:05] Prediction: Moving from Chatbots to Autonomous Agents [00:25:20] Prediction: The role of Small Language Models (SLMs) [00:27:10] Closing thoughts and 2026 outlook Hashtags: #AI #GenerativeAI #Agents #AutonomousAgents #MCP #A2A #LangGraph #Gemini #VoiceAssistant #SmartGlasses #VoiceFirst #SLM #VoiceFlow #TwoVoiceDevs #YearInReview #2026Predictions #VibeCoding

Allen and Mark discuss the architecture of Model Context Protocol (MCP) servers, using Allen's experience with "Vodo Drive" as a case study. They dive into critical considerations for building effective agents, focusing on security, managing API complexity, and enforcing business logic. The conversation explores how to move beyond simple REST API wrappers to create high-level, context-aware tools that ensure safety and efficiency. [00:00:00] Welcome and Introduction [00:00:54] Lessons from Vodo Drive for MCP [00:02:54] The Importance of Security in MCP Servers [00:03:36] Managing API Complexity and Business Logic [00:05:58] Authentication and Authorization Challenges [00:07:37] OAuth Scopes and User-Controlled Access [00:10:48] Handling Complex APIs like Google Workspace [00:13:58] Designing High-Level Tools vs. Low-Level wrappers [00:19:35] Dynamic Tool Lists and Context Awareness [00:24:14] Agents Acting On Behalf of Users, Not As Users #MCP #ModelContextProtocol #AI #Agents #VodoDrive #Security #API #GoogleWorkspace #SoftwareArchitecture #TwoVoiceDevs

In this episode of Two Voice Devs, Mark Tucker and Allen Firstenberg dive into the world of GitHub Copilot. Mark shares his recent experience preparing for the GitHub Copilot (GH-300) certification and walks us through the various features and modes of the tool within Visual Studio Code. They discuss the differences between "Ask," "Edit," and "Agent" modes, how Copilot integrates with your workspace and terminal, and the power of using different AI models like Sonnet and Gemini. Whether you're new to AI coding assistants or looking to get more out of your current setup, this episode provides a practical overview of what GitHub Copilot can do today. [00:00:00] Introduction and updates [00:01:26] The GitHub Copilot (GH-300) Certification [00:02:30] GitHub Copilot in Visual Studio Code [00:03:44] Clarifying the different "Copilots" [00:04:29] Inline Chat and using "Explain" [00:05:46] Selecting different AI models [00:07:39] The Chat Window: Ask, Edit, and Agent modes [00:08:06] Using context variables (@workspace, @terminal, #files) [00:11:36] Demonstrating "Ask" mode [00:14:33] Demonstrating "Edit" mode [00:16:24] Demonstrating "Agent" mode [00:22:36] Custom instructions and specifications [00:25:42] How Copilot works behind the scenes (Proxy & Safety) [00:27:00] Conclusion #GitHubCopilot #VSCode #AIcoding #SoftwareDevelopment #TwoVoiceDevs #GH300 #Certification #DeveloperTools #Programming #TechPodcast

Mark Tucker and Allen Firstenberg dive into Microsoft Copilot Studio, a low-code tool for creating conversational agents with a focus on enterprise integrations. Mark demonstrates how to build a file upload agent that summarizes invoices using a Large Language Model (LLM). They explore the studio's interface, including topics, triggers, and the designer canvas, while comparing it to familiar tools like Dialogflow. The discussion also touches on the concept of autonomous agents, flows that can be triggered by events like emails, and Microsoft's strong push for enterprise adoption. [00:00:00] Welcome and Introduction [00:00:49] Introducing Microsoft Copilot Studio [00:02:09] Creating a New Agent [00:05:08] Understanding Topics and Triggers [00:06:57] Testing the Agent: Greeting Topic [00:10:44] Building a File Upload Agent [00:12:37] Implementing the File Upload Logic [00:15:29] Summarizing Invoices with LLMs [00:17:08] Enterprise Tools and Connectors [00:19:00] Flows and Server-Side Triggers [00:21:33] Deployment and Channels [00:23:10] Agents vs. Bots: Autonomous Capabilities [00:24:44] Comparisons with Dialogflow and Google's Ecosystem #MicrosoftCopilotStudio #CopilotStudio #LowCode #AI #ArtificialIntelligence #Chatbots #ConversationalAI #EnterpriseAI #Dialogflow #CCAI #LLM #GenerativeAI #TwoVoiceDevs #Developer #TechPodcast

Allen and Mark return after a hiatus for their annual Thanksgiving episode. They reflect on a busy year, expressing deep gratitude for the community's concern and support. The conversation explores the vital importance of human connection in a tech-centric world, the impact of mentorship, and finding balance between passion projects and life outside of work. [00:00:00] Welcome back & addressing the hiatus [00:01:38] Professional gratitude & new projects [00:02:22] The kindness of the community [00:03:58] The importance of human connection in tech [00:05:40] Reflecting on family, friends, and blessings [00:07:48] The lasting impact of mentorship [00:09:12] Balancing technology and life #Thanksgiving #Gratitude #TechCommunity #Mentorship #WorkLifeBalance #HumanConnection #TwoVoiceDevs #VoiceTech #DeveloperLife

Join Allen Firstenberg and Noble Ackerson in a deep dive into the evolving world of AI agent protocols. In this episode of Two Voice Devs, they unpack the Agent-to-Agent (A2A) protocol, comparing it with the Model Context Protocol (MCP). They explore the fundamental differences, from A2A's conversational, stateful nature to MCP's function-call-like structure. The discussion also touches on the new Agent Payment Protocol (AP2) and its potential to revolutionize how AI agents interact and transact. Is A2A the key to unlocking a future of autonomous, ambient AI? Tune in to find out! [00:01:00] What is the A2A protocol? [00:04:00] A2A vs. Model Context Protocol (MCP) [00:10:00] What does A2A bring that MCP doesn't? [00:15:00] Ambient and Autonomous Agents [00:19:00] A2A solves the "Tower of Babel" problem [00:24:00] The difference between A2A and MCP: stateful vs. stateless [00:27:00] Agent Payment Protocol (AP2) [00:33:00] What does A2A promise for autonomous agents? [00:38:00] Downsides and challenges of A2A [00:44:00] Google, Gemini, and the future of A2A #A2A #MCP #AI #ArtificialIntelligence #AgentToAgent #ModelContextProtocol #TwoVoiceDevs #TechPodcast #FutureOfAI #AutonomousAgents #AIAgents #AP2 #AgentPaymentProtocol #GoogleGemini #Anthropic

Allen and Mark are back to discuss AI agent frameworks again. This time, Allen compares Google's Agent Development Kit (ADK) with LangChainJS and LangGraphJS. He walks through building a simple agent in both frameworks, highlighting the differences in their approaches, from configuration by convention in ADK to the explicit configuration in LangGraph. They also explore the web-based testing environments for both, showing how each allows for debugging and inspecting the agent's behavior. The discussion also touches on the upcoming LangChain version 1.0 and its focus on backward compatibility. [00:00:00] - Introduction [00:01:09] - Comparing agent frameworks: Google's ADK and LangChainJS [00:02:20] - A look at the ADK code [00:06:55] - A look at the LangChainJS code [00:13:20] - The web interface for testing [00:19:10] - ADK's web interface [00:22:30] - LangGraph's web interface [00:27:20] - LangGraph's state management [00:32:15] - Final thoughts #AI #AgenticAI #GoogleADK #LangChain #LangGraph #JavaScript #Python #TwoVoiceDevs

In this episode of Two Voice Devs, Mark and Allen dive into the new experimental Text-to-Speech (TTS) model in Google's Gemini 2.5. They explore its capabilities, from single-speaker to multi-speaker audio generation, and discuss how it's a significant leap from the old days of SSML. They also touch on how this new technology can be integrated with LangChainJS to create more dynamic and natural-sounding voice applications. Is this the return of voice as the primary interface for AI? [00:00:00] Introduction [00:00:45] Google's new experimental TTS model for Gemini [00:01:55] Demo of single-speaker TTS in Google's AI Studio [00:03:05] Code walkthrough for single-speaker TTS [00:04:30] Lack of fine-grained control compared to SSML [00:05:15] Using text cues to shape the TTS output [00:06:20] Demo of multi-speaker TTS with a script [00:09:50] Code walkthrough for multi-speaker TTS [00:11:30] The model is tuned for TTS, not general conversation [00:12:10] Using a separate LLM to generate a script for the TTS model [00:13:30] Code walkthrough of the two-function approach with LangChainJS [00:16:15] LangChainJS integration details [00:19:00] Is Speech Markdown still relevant? [00:21:20] Latency issues with the current TTS model [00:22:00] Caching strategies for TTS [00:23:30] Voice as the natural UI for AI [00:25:30] Outro #Gemini #TTS #VoiceAI #VoiceFirst #AI #Google #LangChainJS #LLM #Developer #Podcast

Join Allen and Mark as they take a first look at the newly released GPT-5 from OpenAI. They dive into the details of what's new, what's changed, and what's missing, frequently comparing it to other models like Google's Gemini. From the new mini and nano models to the pricing wars with competitors, they cover the landscape of the latest LLM offerings. They also discuss the new features for developers, including verbosity settings and constrained outputs with context-free grammars, and what this means for the future of AI development. Is GPT-5 the leap forward everyone was expecting, or a sign that the rapid pace of AI evolution is starting to plateau? Tune in to find out! [00:00:00] Introduction and the hype around GPT-5 [00:01:00] Overview of GPT-5, mini, and nano models [00:02:00] The new "thinking" model and smart routing [00:03:00] Simplifying models for developers [00:04:00] Reasoning levels vs. Gemini's "thinking budget" [00:06:00] Pricing wars and new models [00:07:00] OpenAI's new open source models [00:08:00] New verbosity setting for developers [00:09:00] Constrained outputs and context-free grammars [00:12:00] Using LLMs to translate to well-defined data structures [00:14:00] Reducing hallucinations and medical applications [00:16:00] Knowledge cutoff dates for the new models [00:18:00] Coding with GPT-5 and IDE integration [00:19:00] More natural conversations with ChatGPT [00:21:00] Missing audio and image modalities vs. Gemini [00:22:00] Community reaction to the GPT-5 release [00:24:00] The future of LLMs: Maturing and plateauing [00:26:00] The need for better developer tools and agentic computing #GPT5 #OpenAI #LLM #AI #ArtificialIntelligence #Developer #TechTalk #Podcast #AIDEvelopment #MachineLearning #FutureOfAI #AGI #GoogleGemini #TwoVoiceDevs

Join Mark and Allen in this episode of Two Voice Devs as they explore the fascinating world of AI agents. They break down what agents are, how they work, and what sets them apart from earlier AI technologies. The discussion covers key concepts like "context engineering," and the essential components of an agentic system, including prompts, RAG, memory, tools, and structured outputs. Using a practical example of a prescription management chatbot for veterans, they demonstrate how agents can handle complex tasks. They compare various frameworks for building agents, specifically focusing on OpenAI's Agent SDK (for TypeScript) and Microsoft's Semantic Kernel (for C#). They also touch on other popular frameworks like LangGraph and Google's Agent Developer Kit. Tune in for a detailed comparison of how OpenAI's Agent SDK and Microsoft's Semantic Kernel handle state, tools, and the overall agent lifecycle, and learn what the future holds for these intelligent systems. [00:00:00] - Introduction [00:01:02] - What is an AI Agent? [00:03:12] - Context Engineering and its components [00:06:02] - The role of the Agent Controller [00:08:01] - Agent Mode vs. Agent AI [00:09:36] - Use Case: Prescription Management Chatbot [00:13:42] - Handling Large Lists of Data [00:16:15] - Tools and State Management [00:21:05] - Filtering and Searching with Tools [00:27:08] - Displaying Information and Iterating through lists [00:30:10] - The power of LLMs in Agentic Systems [00:35:18] - Sub-agents and the future of agentic systems [00:38:25] - Comparing different Agent Frameworks [00:39:00] - Wrap up #AIAgents #TwoVoiceDevs #ContextEngineering #OpenAIAgentSDK #SemanticKernel #LangGraph #GoogleADK #LLMs #GenAI #AI #Developer #Podcast #TypeScript #CSharp

Join Mark and Allen for a very special 250th episode as they celebrate five years of Two Voice Devs! You won't want to miss the unique, AI-animated opening that takes them to new heights, or the special closing that brings it all home, both created with the help of Veo 3. In between, they take a look back at the evolution of voice and AI technology. From the early days of Alexa and Google Assistant to the rise of LLMs and generative AI, they discuss the shifts in the industry, the enduring importance of context, and what the future might hold for agentic AI, security, and the developer experience. [00:02:45] - Where did we think the industry would be in 5 years? [00:05:30] - How LLMs and Generative AI changed the landscape [00:11:05] - Context Engineering is the new Prompt Engineering [00:14:30] - The explosion of frameworks, libraries, and models [00:18:00] - The importance of guardrails and security [00:22:30] - Where are things going in the near term? [00:27:30] - The future of devices and developer platforms [00:30:00] - Right-sizing models and the cost of AI [00:33:30] - The importance of community and having fun #TwoVoiceDevs #VoiceAI #ArtificialIntelligence #LLMs #GenerativeAI #AIAgents #VoiceFirst #TechPodcast #ConversationalAI #AICommunity #FutureOfTech #AIEthics #AISecurity #DeveloperExperience #HotAirBalloon #Veo3

In this episode, guest host Andrew Connell, a Microsoft MVP of 21 years, joins Allen to unravel the complexities of Microsoft's AI strategy, particularly within the enterprise. They explore the world of Microsoft 365 Copilot, distinguishing it from the broader AI landscape and consumer tools like ChatGPT. Andrew provides an insider's look at how Copilot functions within a secure, private "enclave," leveraging a "Semantic Index" of your organization's data to provide relevant, contextual answers. The conversation then shifts to the developer experience. Discover the different ways developers can extend and customize Copilot, from low-code solutions in Copilot Studio to creating powerful "declarative agents" with JSON and even building "custom engine agents" where you can bring your own models and infrastructure. If you've ever wondered what Microsoft's AI story is for businesses and internal developers, this episode provides a comprehensive and honest overview. Timestamps: [00:00:01] - Introducing guest host Andrew Connell [00:00:54] - What is a Microsoft 365 developer? [00:01:40] - Andrew's journey into the Microsoft ecosystem [00:05:00] - 21 years as a Microsoft MVP [00:06:15] - Enterprise Cloud vs. Developer Cloud [00:08:06] - Microsoft's AI focus for the enterprise [00:10:57] - What is Microsoft 365 Copilot? [00:13:07] - How Copilot ensures data privacy with a "secure enclave" [00:14:58] - Understanding the Semantic Index [00:16:31] - Is Copilot a Retrieval Augmented Generation (RAG) system? [00:17:23] - Responsible AI in the Copilot stack [00:19:19] - The developer story for extending Copilot [00:22:43] - Building declarative agents with JSON and YAML [00:25:05] - Using actions and tools with agents [00:27:00] - How agents are deployed via Microsoft Teams [00:32:48] - Where does Copilot actually run? [00:36:20] - Key takeaways from Microsoft Build [00:41:20] - The spectrum of development: low-code to full-code [00:43:00] - Full control with Custom Engine Agents [00:49:30] - Where to find Andrew Connell online Hashtags: #Microsoft #AI #Copilot #Microsoft365 #Azure #SharePoint #MicrosoftTeams #MVP #Developer #Podcast #Tech #EnterpriseSoftware #CloudComputing #ArtificialIntelligence #Agents #LowCode #NoCode #RAG

Join Allen Firstenberg and guest host Isaac Johnson, a Google Developer Expert with a deep background in DevOps and SRE, as they dive into the world of command-line AI assistants. In this episode, they compare and contrast two powerful tools: Anthropic's Claude Code CLI and Google's Gemini CLI. Isaac shares his journey from coding with Fortran in the 90s to becoming a GDE, and explains why he often prefers the focused, context-aware power of a CLI tool over crowded IDE integrations. They discuss the pros and cons of each approach, from ease of use and learning curves to the critical importance of using version control as a safety net. The conversation then gets practical with a live demo where both Claude and Gemini are tasked with generating system architecture diagrams for a real-world project. Discover the differences in speed, cost, output, and user experience. Plus, learn how to customize Gemini's behavior with `GEMINI.md` files and explore fascinating use cases beyond just writing code, including podcast production, image generation, and more. [00:00:30] - Introducing the topic: AI assistants in the command line. [00:01:00] - Guest Isaac Johnson's extensive background in tech. [00:03:00] - Why use a CLI tool instead of an IDE plugin? [00:07:30] - Pro Tip: Always use Git with AI coding tools! [00:09:30] - The cost of AI: Comparing Claude's and Gemini's pricing. [00:12:15] - The benefits of Gemini CLI being open source. [00:17:30] - Live Demo: Claude Code CLI generates a system diagram. [00:21:30] - Live Demo: Gemini CLI tackles the same task. [00:27:30] - Customizing your AI with system prompts (`GEMINI.md`). [00:31:30] - Beyond Code: Using CLI tools for podcasting and media generation. [00:40:30] - Where to find and connect with Isaac Johnson. #AI #DeveloperTools #CLI #Gemini #Claude #GoogleCloud #Anthropic #TwoVoiceDevs #TechPodcast #SoftwareDevelopment #DevOps #SRE #AIassistant #Coding #Programming #FirebaseStudio #Imagen #Veo

John Gillilan, our official Apple correspondent, returns to Two Voice Devs to unpack the major announcements from Apple's latest Worldwide Developer Conference (WWDC). After failing to ship the ambitious "Apple Intelligence" features promised last year, how did Apple address the elephant in the room? We dive deep into the new "Foundation Models Framework," which gives developers unprecedented access to on-device LLMs. We explore how features like structured data output with the "Generable" macro, "Tools" for app integration, and trainable "Adapters" are changing the game for developers. We also touch on the revamped speech-to-text, "Visual Intelligence," "Swift Assist" in Xcode, and the mysterious "Private Cloud Compute." Join us as we analyze Apple's AI strategy, the internal reorgs shaping their product future, and the competitive landscape with Google and OpenAI. [00:00:00] Welcome back, John Gillilan! [00:01:00] What was WWDC like from an insider's perspective? [00:06:00] Apple's big miss: What happened to last year's AI promises? [00:12:00] The new Foundation Models Framework [00:16:00] Structured data output with the "Generable" macro [00:19:00] Extending the LLM with "Tools" [00:22:00] Fine-tuning with trainable "Adapters" [00:28:00] Modernized on-device Speech-to-Text [00:29:00] "Visual Intelligence" and app integration [00:32:00] The powerful "call model" block in Shortcuts [00:36:00] Swift Assist and BYO-Model in Xcode [00:39:00] Inside Apple's big AI reorg [00:42:00] The Jony Ive / OpenAI hardware mystery [00:45:00] How Apple, Google, and OpenAI will compete and collaborate #Apple #WWDC #AI #AppleIntelligence #FoundationModels #LLM #OnDeviceAI #Swift #iOSDev #Developer #TechPodcast #TwoVoiceDevs #Siri #SwiftAssist #OpenAI #GoogleGemini #GoogleAndroid