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.comChapters00:00 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
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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.Chapters00:00 Introduction to ChatGPT Launch and Demos01:40 Exploring Open Code and LangChain04:37 Local Inference and Olamma Integration07:25 Cloud Acceleration with Turbo Service10:11 Open Source Model Benchmarks and Feedback
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EP - 10 - Claude Code Sub Agents Demo and Deep Dive
Notes to come.
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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.
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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.Chapters00:04:42 - 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 & DemosInsightsThe 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.KeywordsQwen3 CodaOpenAI AgentJSON Veo3AI PolicyLangGraphNeo4jGraphRAGMCP📢 Show Links📰 News & UpdatesAdvanced version of Gemini with Deep Think (IMO Gold Medal – DeepMind)QWEN3 CoderModelStudio ConsoleWebDev ArenaIntroducing ChatGPT Agent (OpenAI)Model training in simple terms🏛️ Executive OrdersPromoting The Export of the American AI Technology StackAccelerating Federal Permitting of Data Center InfrastructurePreventing Woke AI in the Federal Government🧪 Demos and DiscussionVeo3 JSON mode – Examples, Templates and DocsJSON Veo AppJSON Veo App – GitHubThe Build VaultThe Build Podcast – GitHubJupyter Notebook Embeddings DemoAssemblyAI
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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