How We Do AI - Two Professionals. One Mindset.

The episode opens with Paco sharing his experience using an AI-powered Personal Knowledge Assistance (PKA) system to prepare a 60-slide presentation for his marketing agency's annual convention of 70+ people. He contrasts this year's output — generated using his PKA combined with Claude Design for polish — against last year's effort using Gamma, calling the difference 'literally insane.' The PKA system, built on Claude, pulls from months of structured journal entries, project notes, and captured ideas to generate a coherent first draft complete with brand-consistent design, structure, and key messaging. The process took roughly six hours, which Paco acknowledges is still too long but attributes largely to the limitations of Claude Design's token consumption and speed rather than the PKA itself.

The co-host shares his own parallel experience, noting that his IPA (Intelligent Process Automation) system — which manages his iCore website — produced even better design output than Claude Design because it already contained full business context, eliminating the need to manually re-feed design systems. He references a complete website and membership platform overhaul accomplished in under 24 hours using his multi-agent setup running on Claude Opus with a 1-million token context window, at a cost exceeding $1,000 in API credits, which he argues was clearly worth the return on investment.

Paco then demonstrates his Slack-based interface to the PKA, showing how he can query his local database through a Slack channel called 'Mindset,' receiving instant threaded responses from his AI system. He explains the full capture workflow: information sent via Slack on mobile is automatically triaged, enriched, tagged, and stored as a structured journal entry — including images — without any manual effort. The system then connects disparate pieces of knowledge, such as linking Henry Ford concepts with IKEA examples, and surfaces these connections when building presentations or articles.

The hosts distinguish between their approach and simpler AI tool usage, emphasizing that the PKA evolves from Personal Knowledge Management (manual, friction-heavy) to Personal Knowledge Assistance (automated, AI-driven connections). They argue this is the critical shift that compounds over time, with the AI learning the user's thinking style, communication preferences, and business context from ongoing journaling.

The co-host then showcases his AI agent library — a set of named, role-specific agents (Larry the orchestrator, Iris the design guardian, Felix the front-end developer, Pixel for thumbnail creation, a researcher using Perplexity API, etc.) — each specialized and model-assigned based on task requirements. He explains that Larry ran a 15-hour uninterrupted codebase overhaul without ever needing to compact the conversation context, thanks to the 1-million token window and a working folder system that preserved context between agent handoffs. He also discusses using model-agnostic, tool-agnostic local folder structures as the foundation, pointing any AI model (Claude, Gemini, Codex) at the same system.

In closing, both hosts address audience questions about model selection per agent, noting they settled on Opus for the orchestrator for reliability despite cost, while acknowledging Sonnet could save costs. They also confirm using external APIs — Perplexity for web research, Gemini Nano for image generation, AssemblyAI, and MX — integrated through individual agents to keep the system efficient and avoid bloating the main context window.