Two Voice Devs

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

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

Ever find that the best way to understand a new framework is to build it yourself? In this episode of Two Voice Devs, Mark Tucker takes us on a deep dive into Crew AI, a powerful Python framework for orchestrating multi-agent AI systems.

To truly get under the hood, Mark decided to port the core functionality into TypeScript, creating "JCrew AI." This process provides a unique and insightful perspective on how these agent-based systems are designed. Join us as we deconstruct the core concepts of Crew AI, exploring how it simplifies the complex process of making AI agents collaborate effectively. We discuss everything from the fundamental building blocks—like agents, tasks, and crews—to the clever ways it implements prompt engineering best practices.

If you're a developer interested in the architecture of modern AI applications, you'll gain a clear understanding of how to define agent roles, backstories, and goals; how to chain tasks together; and how the underlying execution loop (and its similarity to the ReAct pattern) works to produce cohesive results.

Timestamps:

[00:00:00] - Introduction

[00:01:00] - What is Crew AI and the "JCrew AI" Learning Project

[00:04:00] - Core Concepts: How Crews, Agents, and Tasks Work

[00:06:00] - Anatomy of a Crew AI Agent (Role, Goal, Backstory)

[00:10:00] - Building Prompts with Templates and "Slices"

[00:15:00] - The Execution Flow: From "Kickoff" to Final Output

[00:21:00] - Under the Hood: The Agent Executor and Core Logic Loop

[00:23:00] - How Crew AI Compares to LangChain and LangGraph

[00:28:00] - Practical Considerations: Human-in-the-Loop and Performance

[00:30:00] - Learning a Framework by Rebuilding It

#AI #ArtificialIntelligence #Developer #SoftwareEngineering #CrewAI #MultiAgentSystems #AIAgents #Python #TypeScript #PromptEngineering #LLM #Podcast

What do Anthropic's latest announcements mean for developers? In this episode, Allen is joined by freelance conversation designer Valentina Adami to break down all the major news from the recent "Code with Claude" event.

Valentina shares her hands-on experience and perspective on the new Opus 4 and Sonnet 4 models, discussing their distinct capabilities, the new "reasoning" features, and why Anthropic's transparency with its public system prompt is a game-changer. They also explore Claude Code, the new coding assistant that runs in your terminal, and how it can be used for everything from fixing bugs to learning new frameworks.

Finally, they cover the latest integrations for the Model Context Protocol (MCP) and the long-awaited addition of web searching to Claude, examining how these tools are evolving and what it means for the future of AI-assisted development.

Timestamps:

[00:41] Guest Valentina Adami's background in humanities and tech

[06:17] What's new in the Opus 4 and Sonnet 4 models?

[14:40] Are the models "thinking" or "reasoning"?

[19:27] The latest on MCP (Model Context Protocol) integrations

[25:03] Exploring the new coding assistant: Claude Code

[31:37] Claude can now search the web

#Anthropic #ClaudeAI #Opus4 #Sonnet4 #ThinkingAI #ReasoningAI #LLM #DeveloperTools #GenerativeAI #AI #Claude #CodingAssistant #MCP #ModelContextProtocol #TwoVoiceDevs

Join Allen Firstenberg and Michal Stanislawek in this thought-provoking episode of Two Voice Devs as they unpack two recent LinkedIn posts by Michal that reveal critical insights into the security and ethical challenges of modern AI agents.

The discussion kicks off with a deep dive into a concerning GitHub MCP server exploit, where researchers uncovered a method to access private repositories through public channels like PRs and issues. This highlights the dangers of broadly permissive AI agents and the need for robust guardrails and input sanitization, especially when vanilla language models are given wide-ranging access to sensitive data. What happens when your 'personal assistant' acts on a malicious instruction, mistaking it for a routine task?

The conversation then shifts to the ethical landscape of AI, exploring Anthropic's Claude 4 experiments which suggest that AI assistants, under certain conditions, might prioritize self-preservation or even 'snitch.' This raises profound questions for developers and users alike: How ethical do we want our agents to be? Who do they truly work for – us or the corporation? Could governments compel AI to reveal sensitive information?

Allen and Michal delve into the implications for developers, stressing the importance of building specialized agents with clear workflows, implementing principles of least privilege, and rethinking current authorization protocols like OAuth to support fine-grained permissions. They argue that we must consider the AI itself as the 'user' of our tools, necessitating a fundamental shift in how we design and secure these increasingly autonomous systems.

This episode is a must-listen for any developer building with AI, offering crucial perspectives on how to navigate the complex intersection of AI capabilities, security vulnerabilities, and ethical responsibilities.

More Info:

* https://www.linkedin.com/posts/xmstan_the-researchers-who-unveiled-claude-4s-snitching-activity-7333733889942691840-wAQ4

* https://www.linkedin.com/posts/xmstan_your-ai-assistant-may-accidentally-become-activity-7333219169888305152-2cjN

00:00 - Introduction: Unpacking AI Agent Security & Ethics

00:50 - The GitHub MCP Server Exploit: Public Access to Private Repos

02:15 - Ethical AI: Self-Preservation & The 'Snitching' Agent Dilemma

04:00 - Developer Responsibility: Building Ethical & Trustworthy AI Systems

09:20 - The Dangers of Vanilla LLM Integrations Without Guardrails

13:00 - Custom Workflows vs. Generic Autonomous Agents

17:20 - Isolation of Concerns & Principles of Least Privilege

26:00 - Rethinking OAuth: The Need for Fine-Grained AI Permissions

29:00 - The Holistic Approach to AI Security & Authorization

#AIAgents #AIethics #AIsecurity #PromptInjection #GitHub #ModelContextProtocol #MCP #MCPservers #MCPsecurity #OAuth #Authorization #Authentication #LeastPrivilege #Privacy #Security #Exploit #Hack #RedTeam #CovertChannel #Developer #TechPodcast #TwoVoiceDevs #Anthropic #ClaudeAI #LLM #LargeLanguageModel #GenerativeAI

Join Allen Firstenberg and Linda Lawton of Two Voice Devs as they record live from Google I/O 2025! As the conference neared the end, they dive deep into the groundbreaking announcements in generative AI, discussing the latest advancements and what they mean for developers, especially those in Conversational AI.

This episode explores the new and updated models that are set to redefine content creation:

Lyria: Google's innovative streaming audio generation API, its unique WebSocket-based approach, and the fascinating possibilities (and challenges!) of dynamic music creation, including its potential for YouTube content and the ever-present copyright questions surrounding AI-generated media.

Veo 3: The video generation powerhouse, now enhanced with synchronized audio and voice, realistic lip-sync for characters (yes, even cartoon animals!), and improvements in "world physics." They also tackle the implications of its pricing for professional and individual creators.

Imagen 4: Discover the highly anticipated improvements in text generation within images, including stylized fonts and potential for other languages.

Allen and Linda also share some early creations with these new models.

Whether you're building the next great voice app, creating dynamic content, or just curious about the cutting edge of AI, this episode offers a developer-focused perspective on the future of generative media.

00:00:00: Introduction to Two Voice Devs at I/O 2025

00:00:50: I/O 2025: New Generative AI Models Overview

00:01:20: Lyria: Streaming Audio Generation and Documentation Challenges

00:03:00: Lyria's Practical Use Cases & Generative AI Copyright Questions

00:10:00: Veo 3: Video Generation with Synchronized Audio and Voice Features

00:12:10: Veo 3 Pricing and Cost Implications for Developers

00:14:20: Imagen 4: Improved Text Generation in Images

00:17:40: Professional Use Cases for Veo and Imagen

00:19:10: Flow: The New Professional Studio System for Creators

00:22:00: Gemini Ultra Tiered Pricing and Regional Restrictions

00:24:20: Concluding Thoughts and Call to Action

#GoogleIO2025 #GenerativeAI #AIModels #Lyria #Veo3 #Imagen4 #FlowAI #TwoVoiceDevs #VoiceTech #ConversationalAI #AIDevelopment #MachineLearning #ContentCreation #YouTubeCreators #GoogleAI #VertexAI #GeminiUltra #CopyrightAI #TechPodcast

Recorded live from the podcast space at I/O, Allen Firstenberg and Roya dive into the overwhelming, yet incredibly exciting, world of AI announcements permeating the conference this year.

They discuss the pervasive theme of AI augmenting human intelligence rather than replacing it, exploring concrete examples across various domains. From breakthroughs in mathematics with AlphaProof to the efficiency gains of the new Gemma 3 model (running on small devices with a tiny memory footprint and reduced environmental impact), they cover the cutting edge of AI research and application.

Discover how models like CoScientists and Notebook LM are revolutionizing research and productivity (including generating podcasts from your notes!), the advancements in Gemini's audio output for more natural and multilingual conversations, and the potential for intelligence explosion with Alpha Evolve. Allen and Roya also unpack the fascinating Gemini Diffusion model's application to text and code generation and the critical role of AI in healthcare with the Amy model.

The conversation wouldn't be complete without tackling the big question: the AGI (Artificial General Intelligence) debate. Is it coming soon, or is it still a distant concept? Join Allen and Roya for their perspectives straight from the heart of Google I/O.

Tune in to get a developer's perspective on the future of AI driven by the latest announcements from Google I/O!

00:00 - Intro & AI Everywhere at I/O

01:36 - The Core Theme: AI Augments Humans

01:55 - AI in Math: AlphaProof

04:30 - Gemma 3: Small, Efficient, Open Models

07:07 - AI for Researchers: CoScientists & Notebook LM

10:05 - Enhanced Audio: Gemini Voice & Translation

12:09 - Alpha Evolve: Feedback Loops & Intelligence Explosion

14:15 - Gemini Diffusion: Diffusion for Text & Code

21:11 - AI in Healthcare: The Amy Model

22:08 - The AGI Debate: Is it Coming?

#GoogleIO #IO2025 #AI #MachineLearning #DeepLearning #GeminiAI #GemmaAI #DiffusionModels #NotebookLM #HealthcareAI #AGI #ArtificialGeneralIntelligence #TwoVoiceDevs #TechPodcast #Developers #ConversationalAI

The buzz from Google IO 2025 is deafening, especially about the new smart glasses announcement! On this episode of Two Voice Devs, Allen Firstenberg and Noble Ackerson — former Google Glass Explorers themselves — dive deep into their first impressions of Google's Project Astra / Android XR / Gemini glasses prototype.

Drawing on their unique experience from the early days of Glass, Allen and Noble discuss the evolution of wearable computing, the collision of conversational AI (Gemini) and spatial computing (Android XR), and what this new device means for the future.

They share their thoughts on the hardware design, the user interface (is it Gemini, Android XR, or both?), and critically examine the product strategy compared to Glass and other devices like the Apple Vision Pro. Most importantly for developers, they ponder the crucial question: what is the developer story here? Is Google providing the necessary tools and documentation, or are we repeating past mistakes?

Tune in for a candid, experienced perspective on Google's latest foray into smart glasses and whether this iteration truly builds on the lessons learned from the past.

0:00:30 - Introduction: Google IO buzz and the glasses question

0:01:16 - Remembering Google Glass: First impressions & the "art of the possible"

0:02:35 - From Glass to Assistant: The evolution of ubiquitous computing

0:03:42 - The Collision: Conversational AI meets Spatial Computing

0:03:58 - First Impressions: Trying on the new Google glasses prototype at IO

0:04:25 - How Glass Shaped Us: Focusing on human factors and product strategy

0:05:44 - The "If You Build It They Will Come" Trap: Why problem-solving is key

0:07:48 - Contrasting with Apple Vision Pro & the "Start with VR" concern

0:09:14 - Breaking Down the Stack: Hardware, Android XR, and Gemini

0:14:24 - Hardware Deep Dive: Weight, balance, optics, and the lower display decision

0:18:38 - UI/Interaction Discussion: Gemini's role, gestures, voice/tap inputs

0:19:37 - The Developer Story: Lack of clarity and need for APIs/documentation

0:27:55 - Rapid Fire: Best thing & Biggest Irk point about the prototype

0:32:16 - The Big Question: Would we buy one today?

0:33:08 - Final Thoughts: Value proposition and learning from Glass

#AndroidXR #Gemini #GoogleGlass #GoogleIO #IO2025 #ProjectAstra #SmartGlasses #WearableTech #SpatialComputing #ConversationalAI #VoiceFirst #VoiceDevs #GlassExplorers #TechPodcast #DeveloperLife #HumanComputerInteraction #ProductStrategy #Google #GoogleDeepMind #DeepMind