The Good Stuff, with Pete and Andy - Episode 9: Tools, Tools, Tools Hosts: Pete and Andy (with bonus ambient drum and bass from a nearby camper van) We dive deep into the practical tools we're using for AI development, exploring the difference between AI as tools versus human-at-the-edge workflows, and discussing the technical complexity of building local AI systems. Key Discussion Points: The Cold Open: Screen Time and Digital Minimalism (00:00-05:52)** Pattern Matching vs. Reasoning in AI (07:00-17:20) Apple's recent paper questioning whether LLMs truly "reason" or just do sophisticated pattern matching How thinking models work The relationship between human thought and AI pattern matching How AI systems handle novel problems and the role of entropy AI Tools in Practice (18:50-32:00) Why Cursor has gained such traction compared to alternatives The importance of context management and local file access Pete's experience with OpenAI's Codex vs. local tools like Cline and Cursor The dopamine feedback loops that make certain tools more engaging Local vs. Cloud AI Systems (30:00-40:00) The benefits of running AI systems locally rather than in web apps Avoiding the complexity of SaaS How local processing leverages your computer's existing power and storage The privacy advantages of keeping personal data on your own machine Memory and Knowledge Graphs (38:52-50:00) The limitations of basic RAG (Retrieval Augmented Generation) systems Introduction to graph RAG systems that provide richer context How Stakwork uses self-improving graph databases for better AI performance The importance of solving the "memory problem" for effective AI systems LLMs as translation layers between human language and structured data Personal Knowledge Graphs (50:00-58:00) Pete building a personal knowledge graph system Using Docker containers and API interfaces for local AI development The challenge of managing context across multiple AI tools and workflows Bethan's book "The Human Edge" Building AI Systems: Technical Complexity (58:00-01:10:00) How accessible it is for non-developers to build AI systems with current tools The "slow code" approach: treating development as a learning experience Apply Git liberally! Andy's experiments with N8N for workflow automation and content creation pipelines Workflow Automation vs. Autonomous Agents (01:16:00-01:22:00) Comparing deterministic workflows to autonomous agents Why most business tasks are better suited to static workflows The role of humans in AI systems: providing intent and experience Enumeration vs. abstraction: building specific workflows rather than trying to create universal solutions Development Stack and Tools (01:13:00-01:16:00) Pete's current toolkit: Cline, Visual Studio, O3, Claude, Codex, Code, Personal Graph.... Plans for a local Nostr-based ebook reader with cross-device syncing Paying for all the tools! Conspiracy Corner: Moon Mysteries and Dead Internet Theory (01:25:00-01:35:00) Discussion of moon landing anomalies Dead Internet Theory and how algorithms shape both content creation and consumption The decline of film quality AI-generated content and the future of creativity Key Quotes: "The job of the LLM isn't to be everything... what LLMs are specifically good at is translating stuff into and out of human language" "The price of bullshit is also dropping to zero" "We're not here to raise low agency children!" "Everyone loves a sausage!"
