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Join Allen Firstenberg from Google Cloud Next 2025 as he sits down with Ankur Kotwal, Google's Global Head of Cloud Advocacy. In this episode of Two Voice Devs, Allen and Ankur dive deep into the world of Developer Relations (DevRel) at Google, discussing its crucial role as a bridge connecting Google's product teams and engineers with the global developer community.
Ankur shares his fascinating personal journey, from coding BASIC as a child alongside his developer dad to leading a key part of Google Cloud's developer outreach. They explore the ever-evolving landscape of technology, using the metaphor of "waves" – from early desktop computing and the internet to mobile apps and the current tidal wave of AI and "vibe coding."
This conversation offers valuable insights for all developers navigating the pace of technological change. Discover what Developer Relations is and how it serves as that essential bridge, functioning bidirectionally (both outbound communication and inbound feedback). Learn about the importance of community programs like Google Developer Experts (GDEs), and how developers can effectively connect with DevRel teams to share their experiences and help shape the future of products. Ankur and Allen also reflect on the need for continuous learning, understanding underlying tech layers, and the shared passion that drives innovation in our industry.
Whether you're a long-time developer or just starting out, learn how to ride the waves, connect with peers, and make your voice heard in the developer ecosystem by engaging with the DevRel bridge.
More Info:
* Google Developers Program: https://goo.gle/google-for-developers
Timestamps:
00:49 - Ankur's Role as Global Head of Cloud Advocacy
01:48 - The Bi-directional Nature of Developer Relations
02:34 - Ankur's Journey into Tech and DevRel
09:47 - What is Developer Relations? (The DevRel Bridge Explained)
12:06 - The Value of Community and Google Developer Experts (GDEs)
14:08 - Allen's Motivation for Being a GDE
18:24 - Riding the Waves of Technological Change (AI, Vibe Coding)
20:37 - The Importance of Understanding Abstraction Layers
25:41 - How Developers Can Engage with the DevRel Bridge
30:50 - Providing Feedback: Does it Make a Difference?
Hashtags:
#DeveloperRelations #DevRel #GoogleCloud #CloudAdvocacy #DeveloperCommunity #TechEvolution #AI #ArtificialIntelligence #VibeCoding #GoogleGemini #SoftwareDevelopment #Programming #Google #GoogleCloudNext #GoogleDevRel #GDG #GDE #TwoVoiceDevs #Podcast #Developers
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Join Allen Firstenberg and Alice Keeler, the Two Voice Devs, live from Day 1 of Google Cloud Next 2025 in Las Vegas! In this episode, recorded amidst the energy of the show floor, Allen and Alice dive into the major announcements and highlights impacting developers, especially those interested in AI and conversational interfaces.
Alice, known as the "Queen of Spreadsheets" and a Google Developer Expert for Workspace and App Sheet, shares her unique perspective on using accessible tools like App Script for real-world solutions, contrasting it with the high-end tech on display.
They unpack the new suite of generative AI models announced, including Veo for video, Chirp 3 for audio, Lyric for sound generation, and updates to Imagen, all available on Vertex AI. They recount the breathtaking private premiere at Sphere, discussing how Google DeepMind's cutting-edge AI enhanced the classic Wizard of Oz film, expanding and interpolating scenes that never existed – and connect this advanced technology back to tools developers can use today.
A major focus is the new Agent Builder, a tool poised to revolutionize how developers create multimodal AI agents capable of natural voice, text, and image interactions, demonstrated through exciting examples. They discuss the accessibility of this tool for developers of all levels and its potential to automate tedious tasks and create entirely new user experiences.
Plus, they touch on the new Agent to Agent Protocol for complex AI workflows, updates to AI Studio, and the production readiness of the Gemini 2.0 Live API.
Get a developer's take on the biggest news from Google Cloud Next 2025 Day 1 and a look ahead to the developer keynote.
More Info:
* Google Developers Program: https://goo.gle/google-for-developers
* Next 2025 Announcements: https://cloud.google.com/blog/topics/google-cloud-next/google-cloud-next-2025-wrap-up
00:00:31 Welcome to Google Cloud Next 2025
00:01:18 Meet Alice Keeler: Math Teacher, GDE, and App Script Developer
00:03:44 App Script: Accessible Development & Real-World Solutions
00:05:40 Cloud Next 2025 Day 1 Keynote Highlights
00:06:18 New Generative AI Models: Veo (Video), Chirp 3 (Audio), Lyric (Sound), Imagen Updates
00:09:00 The Sphere Experience & DeepMind's Wizard of Oz AI Enhancement
00:14:00 From Hollywood Magic to Public Tools: Vertex AI Capabilities
00:16:30 Agent Builder: The Future of AI Agents & Accessible Development
00:23:37 Agent to Agent Protocol: Enabling Complex AI Workflows
00:25:20 Other Developer News: AI Studio Revamp & Gemini 2.0 Live API
00:26:30 Connecting with Experts & Discovering What's Next
#GoogleCloudNext #GCNext #LasVegasSphere #SpehereLasVegas #TwoVoiceDevs #AI #GenerativeAI #VertexAI #Gemini #AgentBuilder #AppScript #Developers #LowCode #NoCode #AIInEducation #AIDevelopment #ConversationalAI #VoiceAI #MachineLearning #WizardOfOz
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Following up on our recent conversation about the Model Context Protocol (MCP), Mark and Allen take a step deeper from a developer's perspective. While still in the shallow end, they explore the TypeScript SDK, the MCP Inspector tool, and the Smithery.ai registry to understand how developers define and host MCP servers and tools.
They look at code examples for both local (Standard IO) and potentially remote (Streamable HTTP) server implementations, discussing how tools, resources, and prompts are registered and interact. They also touch on the challenges of configuration, authentication, and the practical messy realities encountered when trying to use MCP tools in clients like Claude Desktop.
This code dive generates more questions than answers about the practical hosting models, configuration complexities, and the vision for MCP in the AI ecosystem. Is it the USBC of AI tools, or more like a 9-pin serial port needing detailed manual setup? Join Mark and Allen as they navigate the current state of MCP code and ponder its future role.
If you have insights into these complexities or are building with MCP, they'd love to hear from you!
00:40 Following up on the previous MCP episode
01:20 Reconsidering MCP's purpose and metaphors
03:25 Practical challenges with clients (like Claude Desktop) and configuration
05:00 Discussing future AI interfaces and app integration
09:15 Understanding Local vs. Remote MCP servers and hosting models
12:10 Comparing MCP setup to early web development (CGI)
13:20 Diving into the MCP TypeScript SDK code (Standard IO, HTTP transports)
23:00 Running a local MCP server and using the Inspector tool
23:50 Code walkthrough: Defining tools, resources, and prompts
31:15 Exploring remote (HTTP) connection options in the Inspector
32:30 Introducing Smithery.ai as a potential MCP registry
33:45 Navigating the Smithery registry and encountering configuration confusion
36:15 Analyzing server source code vs. registry listings - Highlighting discrepancies
44:30 Reflecting on the current practical usability and complexity of MCP
46:10 Analogy: MCP as a serial port vs. USBC
#ModelContextProtocol #MCP #AIDevelopment #DeveloperTools #Programming #TypeScript #APIs #ToolsForAI #LLMTools #TechPodcast #SoftwareDevelopment #TwoVoiceDevs #AI #GenerativeAI #Anthropic #Google #LangChain #Coding #AIAPI
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Join Allen Firstenberg and Michal Stanislawek on Two Voice Devs as they dive deep into the Model Context Protocol (MCP), a proposition by Anthropic that's gaining traction in the AI landscape. What exactly is MCP, and is it the key to seamless integration of external services with large language models?
In this insightful discussion, Allen and Michal unravel the complexities of MCP, exploring its potential to solve integration pain points, its current implementation challenges with local "servers," and the crucial missing pieces like robust authentication and monetization. They also discuss the implications of MCP for AI applications, compare it to established protocols, and ponder its relationship with Google's newly announced Agent to Agent (A2A) protocol.
Is MCP a game-changer that will empower natural language interaction with all kinds of software, from Blender to Slack? Or are there fundamental hurdles to overcome before it reaches its full potential? Tune in to get a developer's perspective on this evolving technology and understand its possible future in the world of AI.
Timestamps:
00:00:55: What is MCP and what does it stand for?
00:02:35: What pain points is MCP trying to solve?
00:04:35: The local nature of current MCP "servers" and its implications.
00:07:15: MCP as a communication protocol and the concept of "tools."
00:08:35: The potential for MCP server discovery and the lack thereof currently.
00:10:25: Security and trust concerns with local MCP servers.
00:13:30: The intended architecture of MCP and the local server model.
00:16:35: The absence of built-in authentication and authorization in MCP.
00:18:35: MCP as a standardized framework and the "plugin" analogy.
00:20:35: MCP's role in defining "AI apps."
00:22:35: The need for a registry component for broader adoption.
00:23:35: What MCP clients currently exist and the breadth of adoption.
00:26:25: MCP and its application in the context of AI agents.
00:29:25: What is still needed for widespread adoption of remote MCP servers?
00:35:15: The concept of an MCP "meta server" or aggregator.
00:38:55: How does Google's Agent to Agent (A2A) protocol fit in?
00:41:45: The debate between MCP servers and specialized AI agents.
00:43:15: The right level of abstraction for tool definitions.
00:46:05: The future evolution of MCP and the importance of experimentation.
#MCP #ModelContextProtocol #AI #LargeLanguageModels #LLM #Anthropic #Claude #ClaudeDesktop #ClaudeOS #Google #Agent2Agent #A2A #GeminiOS #ServerClient #AIAgents #Developer #TechPodcast #TwoVoiceDevs #APIs #SoftwareIntegration #FutureofAI
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Following up on last week's captivating discussion, Allen Firstenberg and Noble Ackerson dive deeper into the world of Generative UI. Explore real-world examples of its potential pitfalls and discover how Noble is tackling these challenges through innovative approaches.
This episode unveils the power of dynamically adapting user interfaces based on preferences and intent, ultimately aiming for outcome-focused experiences that seamlessly guide users to their goals. Inspired by the insightful quotes from Arthur C. Clarke ("Any sufficiently advanced technology is indistinguishable from magic") and Larry Niven ("Any sufficiently advanced magic is indistinguishable from technology"), we explore how fine-tuning Large Language Models (LLMs) can bridge this gap.
Noble shares a practical demonstration of a smart home dashboard leveraging Generative UI and then delves into the crucial technique of fine-tuning LLMs. Learn why fine-tuning isn't about teaching new knowledge but rather new patterns and vocabulary to better understand domain-specific needs, like rendering accessible and effective visualizations. We demystify the process, discuss essential hyperparameters like learning rate and training epochs, and explore the practicalities of deploying fine-tuned models using tools like Google Cloud Run.
Join us for an insightful conversation that blends cutting-edge AI with practical software engineering principles, revealing how seemingly magical user experiences are built with careful technical considerations.
Timestamps:
0:00:00 Introduction and Recap of Generative UI
0:03:20 Demonstrating Generative UI Pitfalls with a Smart Home Dashboard
0:05:15 Dynamic Adaptation and User Intent
0:11:30 Accessibility and Customization in Generative UI
0:13:30 Encountering Limitations and the Need for Fine-Tuning
0:17:50 Introducing Fine-Tuning for LLMs: Adapting Pre-trained Models
0:19:30 Fine-Tuning for New Patterns and Domain-Specific Understanding
0:20:50 The Role of Training Data in Supervised Fine-Tuning
0:23:30 Generalization of Patterns by LLMs
0:24:20 Exploring Key Fine-Tuning Hyperparameters: Learning Rate and Training Epochs
0:30:30 Demystifying Supervised Fine-Tuning and its Benefits
0:33:30 Saving and Hosting Fine-Tuned Models: Hugging Face and Google Cloud Run
0:36:50 Integrating Fine-Tuned Models into Applications
0:38:50 The Model is Not the Product: Focus on User Value
0:39:40 Closing Remarks and Teasing Future Discussions on Monitoring
Hashtags:
#GenerativeUI #AI #LLM #LargeLanguageModels #FineTuning #MachineLearning #UserInterface #UX #Developers #Programming #SoftwareEngineering #CloudComputing #GoogleCloudRun #GoogleGemini #GoogleGemma #HuggingFace #AIforDevelopers #TechPodcast #TwoVoiceDevs #ArtificialIntelligence #TechMagic
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Allen and Noble dive deep into the fascinating world of Generative UI, a concept that goes beyond simply using AI to design interfaces and explores the possibility of UIs dynamically generated in real-time by AI LLMs, tailored to individual user needs and context. Noble, a returning Google Developers Expert in AI, clarifies the crucial distinction between generative UI and AI-aided UI generation. They discuss potential applications like dynamic menus and personalized settings, while also tackling the challenges around predictability, usability, and the role of established design patterns. Discover how agents, constrained within defined boundaries, can power this technology and the current limitations when it comes to generating complex UI components. Join the conversation as they explore the cutting edge of how AI could revolutionize the way we interact with software.
Timestamps:
00:00:00 - Introduction and Noble's return as a Google Developers Expert in AI
00:02:00 - Defining Generative UI and distinguishing it from AI-aided design
00:03:30 - Exploring potential examples of Generative UI based on user needs and context
00:04:45 - The difference between traditional static UIs and dynamic generative UIs
00:06:45 - How LLMs can be leveraged for real-time UI generation
00:07:15 - The overlap and distinction between Generative UI and Conversational
UI
00:08:30 - Challenges of Generative UI: Predictability and guiding users
00:09:30 - The importance of maintaining established UX patterns in Generative UI
00:12:30 - Traditional UI limitations and the promise of personalized generative UIs
00:14:00 - Context-specific information access and adapting to user roles
00:15:30 - An example of Generative UI in a business intelligence dashboard
00:17:00 - A six-stage pipeline for how Generative UI systems might work
00:19:00 - The concept of "agents on rails" in the context of UI generation
00:20:30 - The reasoning and tool-calling aspects of generative UI agents
00:22:30 - Tools as the core of UI generation and component recognition challenges
00:24:30 - Demonstrating the dynamic generation of UI components (charts)
00:27:30 - Exploring interactions and limitations of the generative UI demo
00:29:15 - The "hallucination" of UI components and the need for fine-tuning
00:31:30 - Conclusion and future discussion on component fine-tuning
#GenerativeUI #AI #LLM #UserInterface #UX #AIDesign #DynamicUI #TwoVoiceDevs #GoogleDevelopersExperts #TechPodcast #SoftwareDevelopment #WebDevelopment #AIAgents
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DeepSeek AI is turning heads, achieving incredible results with older hardware and clever techniques! Join Allen and Roya as they unravel the secrets behind DeepSeek's success, from their unique attention mechanisms to their cost-effective AI training strategies. But is all as it seems? They also tackle the controversies surrounding DeepSeek, including accusations of data plagiarism and concerns about censorship. This episode is a must-listen for anyone interested in the future of AI!
Timestamps:
0:00 Why DeepSeek is creating buzz
1:06 Unveiling DeepSeek's Two Key Models
2:59 Understanding the Power of Attention
4:12 What is the latent space?
5:55 The nail salon example: Multi-Head Attention Explained
10:02 The doctor/cook/police analogy: Mixture of Experts Explained
13:51 AI vs. AI: DeepSeek's Cost-Saving Training Method
16:01 Hallucinations: Is AI Training Too Risky?
20:59 What are Reasoning Models and Why Do They Matter?
26:53 LLMs are pattern systems explained
28:22 How DeepSeek is using old GPUs
32:53 OpenAI vs. DeepSeek: The Data Plagiarism Debate
39:32 Political Correctness: The Challenge of Guardrails in AI
42:16 Why Open Source is Crucial for the Future of AI
43:20 Run DeepSeek locally on OLAMA
43:56 Final Thoughts
Hashtags: #DeepSeek #AI #LLM #Innovation #TechNews #Podcast #ArtificialIntelligence #MachineLearning #Ethics #OpenAI #DataPrivacy #Censorship #TwoVoiceDevs #DeepLearning #ReasoningModel #AIRevolution #ChinaTech
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Amazon has announced Alexa Plus, powered by large language models (LLMs), and developers are buzzing with anticipation (and a healthy dose of skepticism!). Join Mark Tucker and Allen Firstenberg, your Two Voice Devs, as they dissect the news, explore the potential of the AI-native SDKs, and debate whether this overhaul will reignite the spark for Alexa development.
In this deep dive, we cover:
* The basics of Alexa Plus: What it is, who gets it for free, and how it differs from classic Alexa skills.
* The fate of classic Alexa skills: Are they migrating, evolving, or being left behind? We explore how current skills might benefit from AI enhancements.
* Alexa's New AI SDKs (Alexa+):
** Action SDK: Turn your existing APIs into voice experiences. Is it all about selling stuff?
** WebAction SDK: Integrate your website with Alexa using low-code workflows. But how does it really work?
** Multi-Agent SDK: Surface your existing bots and agents through Alexa. What's the difference between these and existing Alexa skills?
* The Big Questions: Personalization, monetization, notifications, handling hallucinations, response times, identity, and more!
* And finally, our predictions! Will Alexa Plus make developing for Alexa fun again? Mark and Allen give their takes!
Whether you're a seasoned Alexa developer or just curious about the future of voice interfaces, this episode is packed with insights, questions, and a healthy dose of developer humor. Subscribe to Two Voice Devs for more cutting-edge discussions on voice technology!
More Info:
* https://developer.amazon.com/en-US/blogs/alexa/alexa-skills-kit/2025/02/new-alexa-announce-blog
Timestamps:
0:00:00 Introduction
0:01:00 Alexa Plus Overview
0:02:00 Pricing & Classic Skills
0:05:00 Access & Availability
0:06:00 Alexa AI SDKs
0:12:00 Action SDK
0:21:00 WebAction SDK
0:27:00 Multi-Agent SDK
0:31:00 Big Questions for Developers
0:36:00 Will Alexa Be Fun Again?
0:41:00 Response Times & Notifications
0:45:00 Multimodal Experiences
0:46:00 Conclusion
#Alexa #AlexaPlus #VoiceDevelopment #AI #LLM #Amazon #Skills #VoiceFirst #Podcast #Developer #Tech #ArtificialIntelligence #TTS #ASR # ConversationalAI
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Allen Firstenberg and Linda Lawton dive deep into the power of Google's Imagen 3 Editing API. Discover how to effortlessly edit and enhance images, opening up a world of creative possibilities for developers!
* Learn how the In-Painting/In-filling feature can quickly remove wires from an image, add highlights, and correct shading on images that the AI generated, and more.
* Explore how to create your own 3D-printed objects from scratch using AI.
* Discover how you can reference images to put models or products into a specific scene.
* Learn how to use the Out-Painting feature to extend images beyond their original boundaries, transforming portraits into landscapes and beyond.
Also, be prepared for some unexpected and hilarious AI hallucinations along the way as Allen tries to zoom out from an image multiple times! Plus, the duo discusses the ethical implications of AI-generated content and how creatives can leverage these tools to enhance their own artwork.
Don't miss this exciting exploration of Imagen 3 and its potential to revolutionize image manipulation for developers and creators alike!
Timestamps:
00:00:00 Introduction
00:00:55 Imagen 3 Editing API
00:04:36 In-Painting/In-Filling
00:04:52 Generating 3D Models
00:09:00 Vertex AI Studio
00:10:15 Imagen and Gemini Together
00:13:14 Generating Images with Reference Images
00:20:11 Out-Painting
00:31:00 Ethical Implications
#Imagen3 #AI #ImageEditing #GoogleAI #VertexAI #VertexAISprint #MachineLearning #DeveloperTools #GenerativeAI #GenAI #3DPrinting #AIArt
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Are you building AI models and systems? Then you need to understand AI ethics! In this episode of Two Voice Devs, Allen Firstenberg welcomes Parul, a Senior Production Engineer at Meta, to dive deep into the world of AI ethics. Learn why fairness and bias are critical considerations for developers, and discover practical techniques to mitigate bias in your AI systems.
Parul shares her experiences and passion for AI ethics, detailing how biases in training data and system design can lead to unfair or even harmful outcomes. This episode provides concrete examples, actionable advice, and valuable resources for developers who want to build more ethical and equitable AI.
More Info:
* Fairlearn: https://fairlearn.org/
* AIF360: https://aif360.readthedocs.io/en/stable/
* what-if tool: https://pair-code.github.io/what-if-tool/
Timestamps:
00:00:00 Introduction
00:00:20 Guest Introduction: Parul, Meta
00:02:22 What is AI Ethics?
00:06:13 Why is AI Ethics Important?
00:08:15 AI Systems vs. AI Models
00:09:52 Examples of Bias in AI Systems
00:12:23 Minimizing Biases: Developer Responsibility
00:14:53 Tips for Minimizing Unfairness and Biases
00:19:40 Fairness Constraints: Demographic Parity
00:23:17 The Bigger Picture: Roles & Responsibilities
00:29:23 Monitoring: Bias Benchmarks
00:32:00 Open Source Frameworks for AI Ethics
00:34:02 Call to Action & Closing
#AIethics #Fairness #Bias #MachineLearning #ArtificialIntelligence #Developers #OpenSource #EthicalAI #TwoVoiceDevs #TechPodcast #DataScience #AIdevelopment
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Are you overwhelmed by the sheer number of Large Language Models (LLMs) available? Choosing the right LLM for your project isn't about picking the most popular one – it's about understanding your specific needs and rigorously evaluating your options.
In this episode of Two Voice Devs, Allen Firstenberg and guest host Brad Nemer, a seasoned product manager, dive deep into the world of LLM evaluation. They go beyond the marketing buzz and explore practical tools and strategies for making informed decisions.
Whether you're a developer, a product manager, or just curious about the practical applications of LLMs, this episode provides invaluable insights into making the right choices for your projects. Don't get caught up in the hype – learn how to evaluate LLMs effectively!
More Info:
https://www.udacity.com/blog/2025/01/how-to-choose-the-right-ai-model-for-your-product.html
[00:00:00] Introduction: Meet Brad Niemer
[00:00:38] Brad's Journey to Product Management & AI
[00:03:12] Collaboration with Noble Ackerson and the LLM Evaluation Challenge
[00:05:23] The Role of a Product Manager.
[00:07:43] Product manager relation to engineering.
[00:13:46] Exploring Evaluation Tools: Hugging Face
[00:16:58] Exploring Evaluation Tools: Chatbot Arena (Human Evaluation)
[00:20:30] Chatbot Arena: Code Generation Evaluation
[00:24:43] Evaluating LLMs: Beyond Chatbots and Truth
[00:26:11] Exploring Evaluation Tools: Artificial Analysis (Quality, Speed, Price)
[00:28:47] Exploring Evaluation Tools: Galileo (Hallucination Report)
[00:31:16] Case Study: DeepSeek and the Importance of Contextual Evaluation
[00:34:53] The Future of LLM Testing and Quality Assurance
[00:37:49] Wrap Up contact information.
#LLM #LargeLanguageModels #AIEvaluation #ProductManagement #TechTalk #TwoVoiceDevs #HuggingFace #GenAI #GenerativeAI #ChatbotArena #ArtificialAnalysis #Galileo #DeepSeek #ChatGPT #Gemini #Mistral #Claude #ModelSelection #AIdevelopment #SoftwareDevelopment #Testing #QA #RAG #MachineLearning #NLP #Coding #TechPodcast #YouTubeTech #Developers
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Google's white paper on AI Agents has sparked debate – are they truly the next leap in AI, or just large language models dressed up with new terminology? Join Allen and Mark of Two Voice Devs as they dive into the details, exploring the potential of Google's framework while also critically examining its shortcomings. They analyze the core components of agents – models, tools, and orchestration – highlighting the value of defining tools as capable of taking actions. But they also raise key questions about the blurry line between models and agents, the confusing definitions of extensions and functions, and the critical omission of authentication and identity considerations. This episode is a balanced take on a fascinating and complex topic, offering developers valuable insights into the evolution of AI systems.
Key Moments:
[00:00:20] The core definition of agents: A promising start, or too broad?
[00:05:08] Model vs. Orchestration: Understanding the decision-making layers.
[00:17:33] "Tools" Unpacked: Exploring actions, extensions, and functions
[00:25:14] The crucial gap: Authentication, Identity, and User context.
[00:29:36] Reasoning techniques: React, Chain, and Tree of Thought explained.
[00:35:41] The model-agent debate: Where is the boundary line?
[00:37:45] Setting the stage for Gemini 2.0?
[00:39:06] A valuable discussion starter, but with room to grow.
Hashtags:
#AIAgents #GoogleAI #LLM #GenerativeAI #AIInnovation #TechAnalysis #TwoVoiceDevs #AIDevelopment #AIArchitecture #SoftwareEngineering #DeveloperPodcast #GeminiAI #MachineLearning #DeepLearning #AITools #Authentication #TechDiscussion #BalancedTech
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Join Allen Firstenberg as he welcomes Lee Mallon, a first-time guest host, for an in-depth discussion about the future of development, user experiences, and the exciting potential of AI-driven personalization! Lee shares his journey from coding on a Toshiba MX 128k to becoming CTO of Yalpop, a company reinventing learning through personalized experiences. This isn't just another AI hype-cast – it's a deep dive into how we can shift our mindset to truly put users at the center of our development process, leveraging new tech to create delightful and efficient experiences.
Lee and Allen discuss everything from the limitations of current recommendation engines to the emerging potential of AI agents and just-in-time interfaces. This is a must-watch for any developer looking to stay ahead of the curve and build truly impactful applications.
#AI #ArtificialIntelligence #GenAI #GenerativeAI #Personalization #UserExperience #UX #Development #WebDev #FutureOfTech #LLMs #LargeLanguageModels #AIagents #MachineLearning #SoftwareDevelopment #Programming #WebDevelopment #TwoVoiceDevs #Podcast #TechPodcast #Innovation #Code #Coding #Developer #TechTrends #UserCentricDesign #Web4 #NoCode #LowCode #DigitalTransformation
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Hold onto your keyboards, folks! AI is shaking up the software engineering world, and in this electrifying episode of Two Voice Devs, Allen and Mark are diving headfirst into the chaos. We're not just talking about the theory – we're getting real about how AI coding tools are actually impacting developers right now. Is this the end of coding as we know it, or the dawn of a new era of software creation?
More Info:
* https://newsletter.pragmaticengineer.com/p/how-ai-will-change-software-engineering
* https://addyo.substack.com/p/the-70-problem-hard-truths-about
[00:00:00] Introduction: Meet Allen and Mark and hear about their busy start to the year.
[00:00:39] The Trigger: Discover the article from The Pragmatic Engineer that sparked this conversation about the role of AI in software engineering.
[00:02:16] Addressing the Panic: We discuss the common fear: is AI going to steal developer jobs?
[00:03:34] Key Article Points: Allen breaks down the seven key areas of the article: how developers are using AI, the "70% Problem," and more.
[00:04:43] Design Patterns & Craftsmanship: Mark discusses how AI-driven development relates to established software patterns and developer craftsmanship.
[00:07:44] The Knowledge Paradox: Unpack the key difference in how senior and junior developers use AI and the potential issues it raises.
[00:10:06] AI vs. Stack Overflow: We explore the differences between getting code from AI and from community platforms like Stack Overflow.
[00:12:49] Personal Experiences: Allen and Mark share how they're actually using AI tools in their coding workflows.
[00:17:09] AI Usage Patterns: Discussing the "constant conversation", "trust but verify", and "AI first draft" patterns.
[00:20:55] The 70% Problem Revisited: Is AI just getting us part way there?
[00:23:24] AI as a Team Member: Exploring the idea of AI as a pair programming partner and whether it's actually helping.
[00:24:41] Trusting your Experience: the importance of listening to the gut feeling of an experienced developer when AI-generated code "feels" wrong.
[00:26:06] Programming Languages are Easy for AI: The simplicity and consistency of programing grammars.
[00:27:47] Is English the New Programming Language?: We debate the idea that natural language is becoming as important as coding and discuss what "programming" really means.
[00:30:36] The Problem with Trying to Make Programming Easy: Historical attempts to make programming easier are revisited.
[00:32:37] Programming vs the Rest of the Job: The core job of a software developer is more than just programming and writing code.
[00:37:21] Quality & Craftsmanship in the Age of AI: We explore what will make software stand out in the future and how crafting great software still matters.
[00:40:27] AI for Personal Software: Could AI drive a renaissance in personal software, similar to the spreadsheet?
[00:42:53] The Importance of AI Literacy: Mastering AI development is the new skill to make developers even more valuable.
[00:43:47] Closing Thoughts: The essential skills of developers remain crucial as we move into the future of AI driven coding.
[00:44:59] Call to Action: We encourage you to join the conversation and share your thoughts on AI and software development.
This isn't just another tech discussion – it's a high-stakes debate about the so
ul of software engineering. Will AI become our greatest ally, or our ultimate replacement? Tune in to find out!
#AIApocalypse #CodeRevolution #SoftwareEngineering #ArtificialIntelligence #Coding #Programming #Developers #TechPodcast #TwoVoiceDevs #MachineLearning #AICoding #FutureofCode #TechDebate #DeveloperSkills #CodeCraft #AIvsHuman #CodeNewbie #SeniorDev #JuniorDev #TechTrends
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Join Mark and Allen, your favorite Two Voice Devs, as they explore the exciting (and sometimes frustrating!) world of Gemini 2.0's search grounding capabilities and how to use it with LangChainJS! Allen shares his recent holiday project: a deep dive into Google's latest AI tools, including the revamped search grounding feature, and how he made it work seamlessly across Gemini 1.5 and 2.0. We'll show you the code and demonstrate the differences between using search grounding and not, using real-world examples. Learn how to build your own powerful, grounded AI applications and stay ahead of the curve in the rapidly changing AI landscape!
In this episode, you'll discover:
[00:00:00] Introduction to Two Voice Devs and what we've been up to
[00:00:24] Allen discusses tackling bug fixes and updates with Gemini 2.0 and LangChain
[00:00:51] The new Gemini 2.0 Search Grounding Tool: what's new? What does it mean to be "agentic"?
[00:02:13] Allen dives into the Google Search Tool, understanding the differences between 1.5 and 2.0, and building a layer for easy use in LangChain
[00:03:06] Allen walks us through the code! The magic of setting up a model with or without search capabilities in LangChainJS
[00:04:48] Using output parsers and annotating your results in LangChainJS
[00:05:53] Similarities between Perplexity's results, and how LangChainJS handles output
[00:06:46] Running the same query with and without grounding, and the dramatic difference in the response (Who won the Nobel Prize for Physics in 2024?)
[00:08:26] A closer look at how LangChainJS presents its source references and how to use them in your projects.
[00:12:55] Taking advantage of tools that Google is providing
[00:13:20] The goal of keeping backward compatibility for developers
[00:15:39] Exploring how this is a version of RAG and how that compares to using external data sources
[00:16:50] What are data sources in VertexAI and how they relate?
[00:19:14] What is the cost? How is Google pricing the search capability?
[00:20:59] More to come soon from Allen with LangChainJS!
Don't miss this deep dive into cutting-edge AI development! Like, subscribe, and share if you find this information helpful!
#Gemini #LangChain #LangChainJS #AI #ArtificialIntelligence #GoogleAI #VertexAI #SearchGrounding #RAG #RetrievalAugmentedGeneration #LLM #LargeLanguageModels #OpenSource #TwoVoiceDevs #Programming #Coding #GoogleSearch #DataScience #MachineLearning #Innovation #TechPodcast #TechVideo
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Happy New Year from Two Voice Devs! Join Allen Firstenberg and Mark Tucker as they recap the whirlwind that was 2024 in AI and tech, and make some bold predictions for 2025. We're diving deep into the biggest players, the most exciting innovations, and the developer challenges that lie ahead. From OpenAI's O3 and Google's Gemini 2.0 to the rise of Anthropic and the resurgence of head-mounted wearables, we preview the stories we'll be talking about this year. Plus, we discuss the important questions around the cost, both financial and environmental, of this new AI landscape.
This episode is packed with insights for any developer looking to navigate the rapidly evolving world of artificial intelligence. Don't miss our discussion on what "agents" actually mean, and what the future holds for voice assistants.
Timestamps:
[00:00:00] Intro & Happy New Year! - Welcome to Two Voice Devs and a fun fact to kick things off!
[00:01:14] Looking Back at 2024: A recap of the biggest AI movers of the past year.
[00:01:32] OpenAI & Google Dominate: Analyzing the impact of OpenAI and Google's announcements, including O3, Gemini 2.0 Thinking, Sora, Veo, and Imagen.
[00:04:02] OpenAI's Internal Turmoil and Google's Notebook LM: A look into the organizational chaos at OpenAI and the impressive upgrade of Notebook LM using Gemini 2.0.
[00:05:24] Apple Intelligence, Amazon & the Catch-Up Game: Discussions around the progress of Apple and the challenges Amazon is facing, along with Anthropic.
[00:08:15] Meta's LLAMA Models and Ray Bans: Exploring the surprising impact of
Meta's AI models and the resurgence of head-mounted wearables.
[00:10:04] Developer Realities: Fine-Tuned Models & DevOps: Mark discusses the importance of smaller, fine-tuned models and DevOps practices for language models.
[00:11:54] The Environmental & Ethical Concerns of AI: A critical discussion about the environmental impact, ethical concerns, and privacy considerations of large language models.
[00:13:15] Allen's 2024 Contributions: Langchain.js and GDE Presentations: Allen shares his work with open-source projects, LangChain, and traveling as a GDE
[00:16:56] 2025: Predictions and "Agents": The focus is on the emergence of "agents" and the uncertainty surrounding their definition.
[00:19:18] Defining "Agents": Allen lays out his predictions of what makes an agent.
[00:20:58] The Resurgence of Voice Assistants: Discussing the future of voice assistants and the potential revival with the emergence of new technologies.
[00:23:59] Google's Project Astra and Android XR: Exploring the new integrations in the voice and AI spaces.
[00:24:46] Home Assistant: An Open Source Alternative: A deep dive into this lesser discussed project and it's voice hardware offering.
[00:27:09] Amazon's Catch-Up: Is Amazon ready to get back into the AI and voice assistant game?
[00:28:01] Looking forward into the future of LLMs: Predictions on LLMs and where they're going.
[00:29:20] Outro: Thank you for joining Two Voice Devs.
#AI #GenAI #ArtificialIntelligence #MachineLearning #LargeLanguageModels #LLM #OpenAI #Google #Gemini #Sora #Anthropic #Meta #LLAMA #AppleIntelligence #Amazon #Alexa #VoiceAssistant #Wearables #MetaRayBans #AndroidXR #GoogleGlass #Agents #Developer #Tech #Innovation #LangChain #GDE #TwoVoiceDevs #Podcast #YouTube #2024Recap #2025Predictions #TechTrends #Programming #Coding #OpenSource
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It's the holiday season, and the AI world has been showering us with gifts! Join Mark and Allen on Two Voice Devs as they unwrap a mountain of new announcements and releases from Amazon, Meta, Google, and OpenAI. From groundbreaking new models to developer-friendly tools, this episode is packed with insights on the latest advancements in AI. We'll explore the features and potential of each new "present" and discuss what it means for you, the developer.
[00:00:00] Intro and Holiday Greetings: Mark and Allen kick off the show, reflecting on the recent flurry of AI releases.
[00:00:15] The AI Gift Giving Season: A lighthearted introduction on the sheer volume of new AI tools being released.
[00:01:41] Amazon Nova Models: Amazon's surprising release of multiple new models, including Micro, Lite, and Pro, with a peek at Canvas (image generation) and Reel (video generation).
[00:04:42] Meta's Llama 3.3: The focus on multilingual capabilities and open-source nature of Llama 3.3.
[00:05:38] OpenAI vs. Google Announcement Showdown: The back-and-forth between Google and OpenAI with a focus on developer-related announcements.
[00:06:40] Google's Imagen 3 & Veo: Google's new advancements in image and video generation available on Vertex AI, including image editing via prompting.
[00:07:28] OpenAI's Sora Release: OpenAI makes their impressive video generation model available, but notably, not yet via API.
[00:08:34] OpenAI's Canvas for Code: Explore how you can interact with code as a chatbot on a virtual canvas.
[00:09:21] Microsoft's Expanded Copilot Free Tier: A note about Microsoft expanding access to their code tool.
[00:09:38] Google's Jules: The AI Bug Detective: An introduction to Google's automated bug-fixing system which proposes fixes in a version control branch.
[00:11:09] OpenAI's O1 Model: The official release of the O1 model with function calling, structured output, and image input capabilities.
[00:11:42] Gemini 2.0 API: Google's improved Gemini API, now in public preview, offering better performance with optimized tools.
[00:14:01] OpenAI's Real-Time API & WebRTC: Details about real time APIs, including WebRTC support for simplified browser-to-server connections.
[00:16:15] Google's Gemini 2.0 Live API: Real-time streaming API using WebSockets for multimodal input and output, with demos available on AI Studio.
[00:17:01] Google's New SDKs: A deep dive into the unified libraries for AI Studio and Vertex AI, simplifying things for developers.
[00:18:10] OpenAI's new Java and Go Libraries: OpenAI ups their game by adding libraries to match Google's supported development platforms.
[00:19:49] Google's PaliGemma 2 and Android XR: Vision-enabled open model, and a new Android platform for headsets and smart glasses.
[00:22:04] Wrapping Up: Mark and Allen discuss which tools they're most excited about for the break and what's in store for the future.
Let us know in the comments what you're most excited about, or if you noticed anything we missed. We’ll discuss it on future episodes.
#AI #ArtificialIntelligence #MachineLearning #GenerativeAI #LLM #LargeLanguageModels #AmazonNova #Llama3 #Gemini2 #OpenAI #GoogleAI #VertexAI #AIStudio #ChatGPT #GPT #O1 #Reasoning #ImageGeneration #VideoGeneration #DeveloperTools #Coding #Programming #WebRTC #AndroidXR #TechNews #TwoVoiceDevs
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Ever felt like your tech presentations, tutorials, or even code explanations are falling flat? You're not alone! In this episode of Two Voice Devs, Allen and Mark dive deep into the art of effective communication in tech, exploring how to move beyond just listing facts to building a compelling narrative that actually helps people understand.
Inspired by a recent presentation that Allen felt was "just okay," they tackle the challenge of how to present information in a way that resonates, whether you're on stage, creating content, or mentoring new developers.
[00:00:00] Introduction to Two Voice Devs
[00:00:16] End-of-year craziness and the inspiration for the episode
[00:00:40] Allen's experience with a presentation that felt flat despite positive feedback.
[00:01:31] The realization of a missing narrative in the presentation.
[00:02:27] Discussion of building narrative into different types of content.
[00:02:33] Deep dive into the structure and content of Allen's Gemini presentation.
[00:04:04] The real message Allen was hoping to convey, and where the presentation fell short.
[00:05:34] The importance of the "why" behind the "what" when presenting new features and concepts.
[00:05:50] Exploring the concept of "telling a story" to make technical concepts easier to understand.
[00:06:29] How individual learning experiences influence the way that you present material.
[00:07:51] Balancing the desire to include all the information, while also keeping a succinct message.
[00:08:50] Pivoting to talking about other ways of imparting information.
[00:09:07] Mark's method of learning and creating diagrams, which then turn into a video.
[00:11:08] The challenge of jumping into code without sufficient background.
[00:12:10] Presenting information in the order that makes sense to you and why.
[00:12:59] Learning by creating and being willing to share even when you are still learning.
[00:13:39] Why committing to a presentation helps you learn a subject.
[00:14:44] Using social media to get information out there quickly, and also, sample projects.
[00:15:27] How starting with small chunks of code can help with understanding
[00:16:31] Using AI tools to explain code.
[00:17:13] How developers need to understand why code works, and not just that it works.
[00:18:58] Why it's important to make learning a conversation and asking questions.
[00:19:29] Mentoring and understanding where students are starting from.
[00:20:54] How in-person feedback is both a benefit and a challenge.
[00:22:12] Creating a safe space for collaborating and learning together.
[00:23:38] Working together to get a level of understanding.
[00:24:13] Call to action for audience to share their techniques.
#TechContent #TechTutorials #DeveloperPresentations #Mentoring #SoftwareDevelopment #CodeTutorial #DevTips #TechNarrative #CommunicationSkills #TwoVoiceDevs #Coding #SoftwareEngineering #Teaching #Learning #AI #Gemini #Storytelling #MadeToStick #TechnicalCommunication #DevFest #Programming
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Join Mark and Allen on Two Voice Devs this week as they delve into a critical discussion about data scraping, large language models (LLMs), and the ethical responsibilities of developers. From the recent controversy surrounding BlueSky data scraping and Hugging Face datasets to the complexities of copyright law and personal privacy in the age of AI, this episode explores the gray areas and tough questions facing developers today. Hear their perspectives on the potential misuse of publicly available data, the challenges of anonymization, and the importance of upholding ethical standards in a rapidly evolving technological landscape. They also share personal anecdotes about navigating privacy policies and the dilemmas of data collection for business versus personal use. Tune in to gain valuable insights and contribute to the conversation about responsible development practices.
[00:00:00] Introduction
[00:01:04] Mark's deep dive into BlueSky's architecture and the data scraping controversy.
[00:02:27] Discussion on BlueSky's data policy and user ownership.
[00:05:32] Copyright implications of using scraped data in LLMs.
[00:06:22] Exploring ethical data sources for LLM training (Wikipedia, Reddit, etc.).
[00:08:31] Real-world examples of potential copyright infringement in image and video generation.
[00:09:34] Hugging Face's guidelines and the removal of the BlueSky dataset.
[00:12:19] The curious case of the "David Meyer" bug in ChatGPT and its implications for data privacy.
[00:14:24] Allen's personal dilemma with Vodo Drive's privacy policy and data collection for model training.
[00:16:50] Balancing business needs with ethical data practices.
[00:17:00] Allen's challenge gathering Gemini release notes and his ethical solution.
[00:19:20] The ethical responsibilities of software engineers, drawing parallels to the Challenger disaster.
[00:21:19] The developer's role in advocating for ethical data usage.
[00:22:21] Call to action: Share your thoughts and perspectives!
#DataScraping #LLMs #AIethics #DeveloperEthics #Privacy #Copyright #BlueSky #HuggingFace #SoftwareEngineering #DataPrivacy #AI #TwoVoiceDevs #Podcast #TechPodcast #WebSockets #DataScience #EthicalAI #ResponsibleAI #TechEthics #Gemini #GoogleAI
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The Jovo open-source framework, a beloved tool for building cross-platform voice applications, is being archived. Join Mark and Allen as they discuss Jovo's history, its impact on the voice development landscape, and what its sunset means for developers. While the news might be bittersweet, we take this opportunity to celebrate Jovo's contributions and explore the valuable lessons learned from its innovative approach to voice app development. We delve into the framework's key features, including its plugin and pipeline architecture, and discuss how these concepts can still inspire future voice projects. Plus, Mark shares his personal experiences using Jovo and hints at exciting potential future directions for forked versions of the framework. Whether you're a seasoned Jovo user, a curious voice developer, or interested in open-source contributions, this episode offers insights and inspiration for your next voice project.
More info:
- https://github.com/jovotech/jovo-framework
- https://www.youtube.com/watch?v=5rce0KGFyz8
[00:00:00] Introduction and Disappointing News
[00:01:41] What was Jovo?
[00:06:18] Early Jovo Encounters
[00:07:38] The Vision of Jovo
[00:09:24] Jovo's 4 P's: Purpose, Platforms, Pipelines, and Plugins
[00:14:47] Abstraction Layers and Modern Analogies (LangChain, GenKit)
[00:17:20] The Official Announcement: Jovo's Archiving
[00:18:44] What Archiving Means for Developers
[00:22:25] Reflections on Jovo's Impact and Future Directions
[00:25:44] The Importance of Contributing to Open Source
[00:26:19] Lessons Learned from Jovo and Open Source Contributions
#Jovo #VoiceDevelopment #OpenSource #VoiceApps #AlexaSkills #GoogleAssistant #Chatbots #Frameworks #SoftwareDevelopment #TypeScript #JavaScript #Innovation #Community #Collaboration #NextJS #React #LangChain #GenKit #VoiceFlow #Podcast #TwoVoiceDevs #Webhooks #APIs #NLU
- Visa fler
