The Build - Ai dev and product show.

Cameron Rohn and Tom Spencer
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Weekly deep dives on the most interesting dev, ai and product releases, research updates and emerging trends in the AI engineering, agent development and software industry.

  1. 13 小時前

    EP - 13 - We're back! ai futures, Claude fails and going All In.

    Chapters 00:00 Welcome Back and Project Updates 03:30 Insights from the All-In Summit 06:21 Claude's Performance Issues and Alternatives 09:38 Exploring New Tools and Interfaces 12:38 Innovations in AI and Image Generation 15:34 The Future of Content Creation and AI 18:32 AI's Impact on Music Production 21:22 Reflections on AI in Creative Industries 24:35 Concluding Thoughts from the All-In Summit 34:03 Exploring Individuality and Ambition 35:32 Technological Innovations in Warfare 37:33 Cultural Nuances in AI Development 38:46 Emerging AI Technologies and Their Applications 40:39 AI Models and Future Predictions 45:45 OpenAI's Strategic Moves and Market Positioning 53:29 OpenAI's Acquisitions and Future Directions 56:38 Oracle's Role in AI Development 01:01:22 The Future of AI in Creative Production

    1 小時 2 分鐘

  2. 8月16日

    Ep 12 - GPT 5 Post hype, Groq Desktop, LangChain DeepUi and Social Agent

    Lots of demos, deep discussions and ai injections. Check the vault for the show notes, links and more at buildaipod.com Chapters00:00 Introduction and Personal Updates00:46 Diving Deeper into GPT-5 Features and User Reactions01:48 Comparing GPT-5 with Previous Models04:08 User Feedback and Market Reactions06:37 Final Thoughts on GPT-5 and Future Directions10:57 Navigating Coding Agents and Abstraction Layers13:40 The Evolution of GPT Models and Their Applications18:07 Inference Compute and the Future of AI Investment19:57 Exploring Grok Desktop and Its Features27:25 Interoperability and Customization in AI Tools35:22 Introducing the Vault: A Centralized Resource for AI Applications42:02 Exploring Deep Agents and UI45:28 Running Deep Agents Locally47:19 Enhancing User Experience with UI Components50:13 Integrating Claude Code with Deep Agents56:37 Transitioning to Social Media Agents57:43 Complexity of Social Media Agent Architecture01:04:10 Monitoring and Managing Social Media Agents01:09:25 Streamlining Content Generation Processes01:12:54 Optimizing Code Repositories for AI Agents01:13:58 Navigating Corporate Culture Shifts01:16:49 Exploring Options Trading with AI01:22:47 Integrating AI with Financial Data01:28:42 Full Loop Development in AI Projectssign up to the vault to get all the details about the show - buildaipod.com

    1 小時 36 分鐘

  3. 8月8日

    EP 11 - Open ai OSS via open coder, Langchain open SWE, local inference with ollama turbo, virtual audiences content testing

    We recorded August 7th, right before ChatGPT launched. We dove into GPT open source, OpenCode, Ollama Turbo, and deep agent setups. I wanted to see LangChain’s open suite and test agent environments. OpenCode stood out for its flexibility — multiple model providers, easy local setup, works with Ollama Turbo for $20/month. LM Studio runs similarly. I’m considering a high-spec NVIDIA rig and DGX Spark for local inference. GPT-OSS is cheap, fast, and excellent for coding and tool-calling, but weaker on general knowledge. Running it locally means more setup work but more control. Hybrid local-plus-cloud routing feels inevitable. We demoed OpenAgent Platform — fast, multi-provider agents without writing code. Then explored LangChain SWE — an open-source, multi-threaded coding agent with planner/programmer loops, GitHub integration, Daytona sandboxes, and detailed token-cost tracking. We looked at Vercel’s v0 API for quick generative UI, and the potential to run it privately for internal teams. I closed with Google’s upcoming AI-mode ads and Societies.io — a virtual audience simulation tool for testing and optimizing content before publishing. Chapters 00:00 Introduction to ChatGPT Launch and Demos 01:40 Exploring Open Code and LangChain 04:37 Local Inference and Olamma Integration 07:25 Cloud Acceleration with Turbo Service 10:11 Open Source Model Benchmarks and Feedback

    1 小時 39 分鐘

  4. 8月3日

    EP - 10 - Claude Code Sub Agents Demo and Deep Dive

    Notes to come.

    1 小時 42 分鐘

  5. 8月1日

    EP - 10 - Part 1 - Ai Labs Open Source Crushing It, Alpha Earth, Claude Code Sub Agents and GTP5 coming soon

    Part one of two, for this week's show. Updates , links and notes coming soon. Check out buildaipod.com and the Vault for all the details.

    1 小時 4 分鐘

  6. 7月25日

    EP - 9 - AI Exec Orders, Qwen 3 Coder, JSON Veo3 Demo and Graph RAG Deep Dive with Neo4J

    In this episode, we dive into the latest AI news, covering new open-source models like Qwen3 Coder and OpenAI's new agent. We discuss a major AI breakthrough at the Math Olympiad, where models achieved gold medal scores, and unpack recent AI executive orders from the Trump administration. In our demo segment, we show how to use JSON for ad vanced AI video generation. We also introduce "The Build Vault," our new project for creating a searchable knowledge base from our podcast content using tools like LangGraph and Neo4j. Chapters 00:04:42 - New Open-Source AI Models00:13:45 - AI Wins Gold at the Math Olympiad00:22:42 - Trump Administration's New AI Policies00:40:48 - Demo: Advanced AI Video Generation with JSON01:00:25 - Introducing "The Build Vault"01:09:25 - How We Built the Vault: GraphRAG & Demos Insights The pace of AI development is accelerating faster than predicted, especially in complex reasoning.Training AI agents in virtual, sandboxed environments is becoming a new industry standard.Structured data like JSON provides granular control for getting better results from creative AI models.New government policies are set to significantly impact AI development, data center construction, and federal use.Knowledge graphs and retrieval-augmented generation (RAG) are powerful techniques for building intelligent apps on top of unstructured data. Keywords Qwen3 CodaOpenAI AgentJSON Veo3AI PolicyLangGraphNeo4jGraphRAGMCP 📢 Show Links📰 News & Updates Advanced version of Gemini with Deep Think (IMO Gold Medal – DeepMind) QWEN3 Coder ModelStudio Console WebDev Arena Introducing ChatGPT Agent (OpenAI) Model training in simple terms 🏛️ Executive Orders Promoting The Export of the American AI Technology Stack Accelerating Federal Permitting of Data Center Infrastructure Preventing Woke AI in the Federal Government 🧪 Demos and Discussion Veo3 JSON mode – Examples, Templates and Docs JSON Veo App JSON Veo App – GitHub The Build Vault The Build Podcast – GitHub Jupyter Notebook Embeddings Demo AssemblyAI

    2 小時 3 分鐘

  7. 7月19日

    Ep 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.

    Tom and Cam explore recent AI advancements, with particular focus on the Kimi model, its capabilities, and developer implications. They address SaaS industry challenges, including rising customer acquisition costs and the trend toward consumption-based pricing models. The conversation highlights developers' growing influence in AI technology development and the critical role of customer retention in SaaS business success. They also discuss enterprise AI adoption, RAG (Retrieval-Augmented Generation) applications, and effective data vectorization techniques. Additional topics include Cognition's acquisition of Windsurf, the continuing importance of ETL processes, and how local models can improve data processing efficiency. Throughout their discussion, they emphasize the value of layered data management approaches and how traditional methods remain relevant alongside emerging technologies. Chapters 00:00 Introduction and Technical Setup03:56 Exploring the Kimmy Model15:46 Developer-Centric AI Models23:25 Rapid Development in AI Tools25:00 Exploring Kimmy's Capabilities29:21 SaaS Industry Challenges and Changes33:23 Customer Acquisition Cost Insights38:13 The Future of SaaS in an AI-Driven World42:47 RAG and Vectorization in AI Development59:18 Understanding UMAP and Clustering in Data Representation01:02:14 Building a Mobile Inspection Tool for Real Estate01:05:23 Transforming Natural Language into Structured Data01:09:46 The Importance of ETL Processes in AI01:14:50 Defining Effective ETL Pipelines01:20:23 Exploring RAG and Its Applications01:28:39 The Role of Vector Stores in Data Management Links https://github.com/lmcinnes/umap https://www.nomic.ai https://pair-code.github.io/understanding-umap/ https://www.pinecone.io/ https://superlinked.com/ https://www.meilisearch.com/ https://www.pinecone.io/learn/vector-database/ LLM vectorization - https://bbycroft.net/llm UMAP - Vizualisation of embeddings, Nomic Atlas Vizualisation - https://atlas.nomic.ai/data/andrewgao22/hacker-news/map https://projector.tensorflow.org/ Example Superlinked Demo -https://hotel-search-recipe.superlinked.io/ https://docs.unsloth.ai/basics/kimi-k2-how-to-run-locally https://developers.googleblog.com/en/gemini-embedding-available-gemini-api/ https://moonshotai.github.io/Kimi-K2/ https://platform.moonshot.ai/docs/introduction#text-generation-model https://docs.superlinked.com/getting-started/why-superlinked Keywords AI, Kimi2 Model, SaaS, Technology, Coding, Developer Tools, Machine Learning, Open Source, API, Performance, SaaS, AI adoption, cloud computing, RAG, vectorization, ETL, Cognition, Windsurf, local models, data processing

    1 小時 25 分鐘

  8. 7月13日

    EP 7 - A2A, Agent Dev Kit Deep Dive and Demos, Grok 4, Gemini Chrome Nano

    We chat through Grok 4 and Vending Bench, Chrome with Nano, Comet from Perplexity, A2A and the ADK kit and more from the AI, Agent engineering world. Chapters 00:00 Introduction 00:34 Grok 4, Vending Bench 06:25 Vending Bench Benchmarking 09:43 Replit brings the Vibes to Microsoft 18:05 Browser wars, Nano in Chrome and Comet launch 29:00 Understanding A2A Protocol and Agent Development Kit 31:34 Agents as Problem Solvers vs Tools 37:29 Agent Card Specifications and Metadata 42:27 Robustness and Reliability of A2A Protocol 51:06 Practical Applications and Code Demonstration 57:47 Understanding Agent Communication and Debugging 01:06:42 Agent Cards and Composability in Development 01:13:20 Demonstrating the A2A Web Application 01:15:17 Exploring JavaScript Agents and Orchestrator Functionality 01:20:41 Connecting Agents for Travel Planning 01:23:12 Clarifying User Interactions in Travel Booking 01:26:25 Introducing the Build Vault and RAG Agents 01:27:12 Video Agent Demonstration and Challenges 01:36:59 Future Integrations and Closing Thoughts

    1 小時 28 分鐘
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簡介

Weekly deep dives on the most interesting dev, ai and product releases, research updates and emerging trends in the AI engineering, agent development and software industry.

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