Two Voice Devs

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

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

Join Allen Firstenberg and Mark Tucker in this heartwarming Thanksgiving episode of Two Voice Devs! In a year of rapid advancements and shifts in the tech landscape, they take a moment to express sincere gratitude for the people, opportunities, and community that make their developer journey so rewarding. They reflect on the challenges and triumphs of the past year, the importance of support networks, and the exciting future of AI and development. Whether you're a seasoned coder or just starting out, this episode is a reminder of the shared passion and connection that fuels our ever-evolving world of technology.

Timestamps:

0:00:00: Introduction and Thanksgiving greetings

0:02:00: Reflection on the Two Voice Devs journey and community support

0:03:42: Gratitude for family and personal connections

0:05:14: Appreciation for supportive work environments and colleagues

0:06:26: Acknowledging the wider developer community and industry advancements

0:07:19: Gratitude for fulfilling careers and the constant learning process

0:10:53: A look forward to future discussions on emerging technologies

0:11:05: Expressing appreciation for each other's partnership on the podcast

0:12:04: Thanks to guest hosts and the importance of diverse voices

0:13:07: An invitation to connect and share your thoughts

#podcast #developers #softwaredevelopment #GenAI #AI #artificialintelligence #community #gratitude #thanksgiving #tech #technology #careers #learning #innovation #TwoVoiceDevs #coding #programming

Join Allen Firstenberg and Noble Ackerson, hosts of the Two Voice Devs podcast, for a lively and insightful Q&A session recorded live at Voice & AI 2024! We dive into the burning questions surrounding AI's impact on software development, exploring the potential threats and exciting opportunities presented by tools like GitHub Copilot and Cursor. From the future of junior developers to the ethics of non-deterministic systems, we tackle it all with our signature blend of technical expertise and opinionated discussion. Don't miss this engaging conversation with a passionate audience, packed with thought-provoking insights and a few laughs along the way.

0:00:00 - Introduction to Two Voice Devs and our live show format

0:01:26 - Noble Ackerson introduces himself and his AI journey.

0:02:00 - Allen and Noble's first meeting at a Google Glass hackathon.

0:06:47 - Q&A begins! Is DevAI a threat to developers?

0:07:08 - Defining DevAI and its role in software development.

0:07:54 - Noble's perspective: AI as an enabler, not a threat.

0:11:55 - The importance of context and prompting in AI systems.

0:14:48 - Addressing the challenge of misalignment between AI goals and user needs.

0:17:02 - Using experience and context to enhance AI's reliability.

0:20:05 - The importance of developer experience in evaluating AI-generated code.

0:20:27 - Ethical considerations of non-deterministic AI systems.

0:24:36 - Exploring the ethics of AI models and data usage.

0:27:29 - The role of designers and developers in responsible AI implementation.

0:27:43 - Agentic systems and the potential for explaining AI's reasoning.

0:29:34 - The need for human oversight in AI systems.

0:30:04 - Ethical concerns regarding AI and potential self-interest of companies.

0:30:55 - The future of AI models: LLMs, neuro-symbolic approaches, and hybrids.

0:31:59 - Reinforcement Learning with Human Feedback (RLHF) and instilling values in AI.

0:33:30 - Where to find Allen and Noble online.

#AI #ArtificialIntelligence #SoftwareDevelopment #DevAI #Podcast #LiveQA #VoiceAndAI #Ethics #LLM #MachineLearning #Developers #Coding #Programming #Technology #FutureofWork #Innovation #GitHubCopilot #Cursor #ResponsibleAI #ExplainableAI #NeuroSymbolicAI #RLHF #TwoVoiceDevs

Tired of wrestling with platform-specific machine learning model formats? Join Allen Firstenberg and Mark Tucker on Two Voice Devs as they explore ONNX (Open Neural Network Exchange), a game-changing open format built to streamline your ML model deployment workflow. Discover how ONNX empowers you to train models in your preferred framework (PyTorch, TensorFlow, scikit-learn, etc.) and seamlessly execute them across diverse platforms (Windows, Mac, Linux, iOS, Android, Web) using the efficient ONNX Runtime.

In this episode, we delve into:

[00:00:00] Introduction: A warm welcome and a quick overview of the show's agenda.

[00:01:18] What is ONNX?: Unraveling the mysteries of ONNX and its purpose in the ML ecosystem.

[00:02:38] Model Preparation: Understanding how to prepare models for ONNX conversion and the concept of inference.

[00:04:05] Hugging Face Example: A practical demonstration of a BERT model in ONNX format on Hugging Face.

[00:06:00] The Developer's Perspective: Why ONNX matters for developers building applications that leverage ML models.

[00:07:24] ONNX Optimization: How ONNX optimizes models for inference and the trade-offs involved.

[00:08:56] The Cross-Platform Advantage: Breaking free from framework limitations and enabling deployment flexibility.

[00:11:19] ONNX Runtime Introduction: Exploring the ONNX Runtime and its support for various languages and platforms.

[00:14:04] ONNX Runtime Deep Dive: A closer look at the ONNX Runtime website and its features.

[00:15:45] ONNX for Mobile and Web: Extending ONNX's reach to mobile devices and web browsers.

[00:16:56] Conversion Process: Learn how to convert models from different formats to ONNX.

[00:18:08] Performance Considerations: Addressing concerns about performance and speed in ONNX.

[00:19:58] Code Examples: Practical code snippets demonstrating ONNX Runtime usage in JavaScript, Python, and C#.

[00:23:23] ONNX and MLOps: Integrating ONNX into your MLOps pipeline for seamless deployment.

[00:23:42] Netron Tool Introduction: Visualizing ONNX models using the Netron tool.

Whether you're a seasoned data scientist or a developer just beginning your ML journey, this episode provides valuable insights into leveraging ONNX for efficient and cross-platform model deployment. Share your experiences and questions in the comments below!

Thumbnail by Imagen 3 with prompt:

Cartoon ink and paint, with a touch of tech.

Scene: Two podcast hosts, sitting in front of microphones,

smiling and engaging in conversation.

Both hosts are male, caucasian, software developers in their early 50s,

wearing glasses, and are clean shaven.

The host on the left is wearing an olive t-shirt and a brown flat cap.

The host on the right is wearing a light blue polo shirt.

Warm, inviting lighting.

Background:

A polished, dark, onyx gemstone, reflecting light and giving it depth.

The gemstone facets should subtly reflect stylized icons of different

operating systems (Windows logo, Apple logo, Android logo, a cloud icon),

hinting at cross-platform compatibility.

Dark, sleek, and mysterious, with the onyx stone as the centerpiece.

The reflected platform icons should be subtle and not overly distracting.

The overall impression should be one of sophisticated power and hidden

potential, alluding to the capabilities of ONNX.

Negative prompt:

beards

#ONNX #MachineLearning #ML #AI #ArtificialIntelligence #DeepLearning #ModelDeployment #CrossPlatform #PyTorch #TensorFlow #ScikitLearn #MLOps #SoftwareDevelopment #WebDevelopment #MobileDevelopment #JavaScript #Python #CSharp #HuggingFace #Netron

Dive into the world of AI-powered learning with Allen and Mark as they explore Google's innovative NotebookLM. This cutting-edge tool offers a fascinating glimpse into the potential of Google's Gemini AI model. NotebookLM allows you to centralize your notes, documents, and even audio/video transcripts, transforming them into an interactive knowledge base. Discover how its conversational interface lets you ask questions, generate summaries with citations, and even create podcasts from your source material!

Allen and Mark discuss how NotebookLM serves as a compelling example for developers looking to build their own Gemini-based applications. They break down how its features, like intelligent summarization, citation generation, and conversational Q&A, can be replicated and customized using the Gemini API. This episode provides valuable insights and inspiration for developers eager to harness the power of Gemini for their own projects.

They also cover practical use cases for students, developers, and anyone looking to personalize their learning experience, while addressing NotebookLM's current limitations. Join the conversation and share your thoughts on how you might use this exciting new technology!

Timestamps:

[00:00:00] Introduction

[00:00:50] What is NotebookLM? Core Functionality and Features

[00:02:47] Asking Questions and Getting AI-Generated Summaries with Citations

[00:04:23] Following Citations and References within the Notebook

[00:06:24] Potential Use Cases for Students (PDF Import and Analysis)

[00:07:17] Creating Outlines and Other AI-Assisted Note Enhancements

[00:07:51] Supported File Types and Data Handling

[00:09:10] Generating Podcasts from Source Material (with audio example!)

[00:11:47] Using NotebookLM with Technical Documentation (Gemini API Example)

[00:14:17] Managing and Selecting Sources within a Notebook

[00:14:53] Downsides and Limitations (No API, Manual Processes)

[00:17:43] Comparison to ChatGPT and GPTs

[00:18:41] Sharing and Collaboration Features

[00:19:26] Potential Applications for Developers (Project Management, Code Analysis)

[00:25:11] Integrating with Automated Testing and CI/CD

[00:25:34] Potential Integration with Git Repositories and Version Control

[00:27:12] The Future of AI-Powered Knowledge Systems

[00:28:04] More Potential Use Cases (Research Paper Analysis)

[00:28:36] Customized Learning and Problem Solving with AI

[00:30:06] Conclusion and Call to Action

#NotebookLM #AI #ArtificialIntelligence #MachineLearning #Developers #Programming #SoftwareDevelopment #Productivity #Learning #Education #Podcast #GoogleAI #Gemini #Chatbots #KnowledgeManagement #NoteTaking #AItools #TechPodcast #TwoVoiceDevs

Boo! Join Two Voice Devs for a special Halloween episode filled with chilling tales from the software development crypt. Mark and Allen recount true stories of coding nightmares, from dropped databases to runaway pings, and offer words of wisdom for surviving your own development horrors. Listen with the lights on (if you dare) as they explore the spooky side of coding, complete with a chilling Halloween soundtrack. Don't forget to share your own scary developer stories in the comments!

Timestamps:

[0:00:00]: Intro and Halloween greetings

[0:00:14]: Drop Dead Data: Mark's database disaster

[0:00:04]: The Growing DNS ID: Allen's DNS scare

[0:00:06]: Death by a Thousand Pings: A coworker's network nightmare

[0:00:08]: The Big O Monster: Allen's tales of inefficient algorithms

[0:00:15]: Imminent Demise: Mark's story of a company's downfall

[0:00:18]: The AI Ghost in the Machine: Allen's cautionary tale about AI-generated code

[0:00:20]: Campfire reflections and call for listener stories

#Halloween #DeveloperDisasters #AI #AICoding #LLMCoding #AIDeveloperTools

Join Mark and Allen, your Two Voice Devs, as they delve into the crucial world of data labeling for machine learning model training. Whether you're a seasoned data scientist or a developer just starting to explore AI, understanding data labeling is essential for building effective models. In this episode, they explore various data labeling techniques, from manual labeling for simple voice apps to automated approaches using open-source libraries like Snorkel. Discover how labeled data powers everything from chatbots and voice assistants to spam filters and advanced language models like BERT. They discuss practical examples and highlight the role developers play in preparing and refining data for optimal model performance.

Timestamps:

[00:00:00] Introduction

[00:00:18] What is data labeling?

[00:03:41] Jovo example: Manual data labeling for voice apps and chatbots

[00:08:01] Labeling with slots and entities

[00:13:54] BERT example: Automated labeling during model training

[00:18:52] BERT inference and fine-tuning

[00:25:36] Snorkel: Programmatic data labeling with Python

[00:29:43] Snorkel example and labeling functions

[00:31:23] Leveraging LLMs for data labeling and augmentation

[00:33:24] The role of developers in data labeling

[00:35:02] Call to action: Share your data labeling experiences!

Thumbnail by Imagen 3 with prompt:

Cartoon ink and paint, with a touch of tech.

Scene: Two podcast hosts, sitting in front of microphones, smiling and engaging in conversation.

Both hosts are male, caucasian, software developers in their early 50s, wearing glasses, and are clean shaven.

The host on the left is wearing a blue t-shirt and a brown flat cap.

The host on the right is wearing a light blue polo shirt.

Warm, inviting lighting.

Background:

Individual data items, represented by squares or circles. Some are marked with a red A while others are marked with a green B. There are dotted lines grouping some of them together.

Negative prompt:

beards

#DataScience #MachineLearning #ML #AI #DataLabeling #DataTraining #ModelTraining #Jovo #BERT #Snorkel #Developers #SoftwareDevelopment #VoiceFirst

Join us for a fascinating conversation with John G, a seasoned voice developer, as he shares his insights into Apple's approach to AI and the future of Siri. John discusses his journey from helping content creators to the Alexa ecosystem and then into the Apple world, driven by the potential of App Intents and the evolving landscape of Apple Intelligence. We delve into the technical details, exploring how App Intents, the semantic index, and on-device LLMs are shaping the future of app development on Apple platforms. Get ready to unravel the complexities and discover the exciting possibilities of building voice-enabled experiences in the Apple ecosystem.

Learn more:

* https://justbyspeaking.com

* https://justbyspeaking.com/2024/06/14/meet-art-museum.html

* https://machinelearning.apple.com/research/introducing-apple-foundation-models

Timestamps:

[00:00:00] Introduction

[00:01:23] John's Journey into Programming and Apple Development

[00:04:12] Entering the Voice Era with Alexa

[00:10:03] Transitioning Away from Alexa

[00:11:58] Discovering App Intents and the Apple Ecosystem

[00:15:54] Introducing Art Museum: John's First iOS App

[00:18:05] History of Siri and SiriKit

[00:22:25] The Rise of App Intents

[00:27:20] App Intents Implementation Details

[00:30:01] The Pervasiveness of App Intents in iOS

[00:32:40] Unveiling Apple Intelligence

[00:36:10] App Intents and the Semantic Index

[00:40:03] App Intents, Domains, and Parameters

[00:43:42] Adapters and the Foundation Model

[00:46:43] Developer Access to the On-Device Model

[00:49:44] Predefined App Intents (Schemas)

[00:51:34] App Shortcuts and Apple Intelligence

[00:52:56] The Future Potential of Apple Intelligence

[00:59:28] Conclusion

#Siri #GenAI #GenerativeAI #ConversationalAI #VoiceFirst #AppleIntelligence #Bixby #AppIntents #LargeLanguageModel #Apple #iOS #MacOS #AppleDeveloper #SoftwareDevelopment #AppDevelopment

Thumbnail by Imagen3 with prompt:

Cartoon ink and paint, with a touch of tech.

Scene: Two podcast hosts, sitting in front of microphones, smiling and engaging in conversation.

The host on the left is a clean-shaven male caucasian in his early 50s, wearing a light blue polo shirt and glasses.

The host on the right is a young man wearing glasses, an olive-green knit cap,

a short beard and mustache that are light brown, and a dark blue t-shirt.

Warm, inviting lighting.

Background:

A stylized representation of an iPhone displaying a glowing screen border surrounding a symbolic representation of "apple intelligence" the apple logo with a human brain.

Join Mark and Xavier on Two Voice Devs as they dive into the world of generative AI development with Firebase GenKit and GitHub Models. Xavier, a Google Developer Expert in AI, Microsoft MVP in AI, and GitHub Star, shares his insights on these emerging technologies and his open-source project that bridges the gap between them. Discover how Firebase GenKit offers a simpler, more modular approach to building GenAI applications compared to frameworks like LangChain. Learn about GitHub Models and how they provide easy access to various LLMs for experimentation and prototyping. Xavier also discusses the challenges and rewards of open-source contribution and how community feedback fuels innovation. This episode is a must-listen for developers looking to explore the exciting landscape of generative AI.

More Info:

* https://firebase.google.com/docs/genkit

* https://github.com/xavidop/genkitx-github

* https://docs.github.com/en/github-models

Timestamps:

[00:00:00] Introduction

[00:00:50] What is Firebase GenKit?

[00:06:08] What are GitHub Models?

[00:09:24] Moving GitHub Models to Production

[00:10:30] The GenKitX GitHub Plugin: Origin, Approach, and Challenges

[00:14:14] Coding with Firebase GenKit

[00:16:07] The Importance of Open Source

[00:19:46] Using Firebase GenKit with Voiceflow

[00:20:35] Conclusion and Call to Action

#GenerativeAI #GenAI #OpenSource #Firebase #FirebaseGenKit #GitHub #LangChain #TypeScript #Golang