ICOR with Tom | AI Productivity

The speaker demonstrates how modern AI models (Claude Sonnet 3.5) enable anyone to build sophisticated productivity applications without extensive coding knowledge, using the newly rebuilt myICOR membership platform as proof. He argues that pre-built platforms like Notion, Circle, and Mighty Networks are becoming obsolete as AI democratizes custom application development, and announces a live workshop teaching the conceptual and technical skills needed to build such apps.

A developer used Claude Sonnet 5.5 to build a fully functional ICOR-based productivity application in just one hour by providing the AI with four years of accumulated productivity knowledge and content. The resulting app successfully implements complex ICOR methodology concepts including inbox management, task prioritization, interruption handling, and life dimension tracking, though it needs UI/design refinement.

A content creator demonstrates how Claude's Sonnet 3.5 model with Code Interpreter can replace multiple productivity apps by building three fully-functional applications in under an hour with minimal prompting. The creator showcases a note-taking app with database features, a Mac application integrating their custom Obsidian plugins and ICOR life management methodology, and highlights the unprecedented speed and polish achievable without traditional development skills.

A creator comparing an 8-hour AI team project with a 15-minute Claude Sonnet 5.5 solution reveals that excessive guardrails and restrictions paradoxically hindered the multi-agent system's performance. The speaker demonstrates how reducing constraints and context allows AI to produce more creative and functional results, though both approaches have significant limitations for production use.

The speaker clarifies the distinction between the ICOR methodology (a tool-agnostic productivity framework) and its implementations like the ICOR for Life scaffold and myPKA AI team, emphasizing that users can adopt the core principles in any tool they prefer. They announce plans to restructure their membership platform by separating these components to reduce confusion and help users understand they're not locked into specific tools or systems.

A demonstration comparing AI slide deck creation using Claude alone versus Claude integrated with Jev, showing that the hybrid approach completed the task in 21 seconds for $8 with proper formatting, while Claude-only took 3+ minutes and cost $49 with poor formatting.

The creator demonstrates why Claude is their preferred AI for work by comparing it with ChatGPT across multiple tests, showing that Claude better adheres to custom instructions and agentic workflows defined in local folder structures. The key advantage lies in using organized, LLM-agnostic folder systems with agents.md files rather than relying on auto-memory features.

A video demonstrating that Claude Code is significantly more powerful than Claude Cowork for professional knowledge workers, showing how Claude Code better understands folder context, enforces safety rules, orchestrates sub-agents with different models, and creates persistent file outputs rather than temporary artifacts.

The creator addresses criticism about switching to Obsidian after previously promoting other tools, clarifying that his tool-agnostic ICOR methodology remains unchanged and that Obsidian is simply an interface layer for his folder-based system. He emphasizes that the underlying folder structure, AI automation, and productivity principles are identical regardless of which tool visualizes the content.

This video explores why Claude and other AI models produce inconsistent results, examining how model selection (Haiku vs Sonnet vs Opus vs Claude 3.5 Fable), effort levels, prompt clarity, and system context dramatically affect output quality. The speaker demonstrates these differences through a Venn diagram creation task and advocates for using organized folder systems with documented SOPs and code-based solutions to ensure consistent AI performance.

Claude now has the ability to interact with multiple sessions, allowing a streamlined orchestration of tasks through an agent called Larry. This integration can simplify task management and enhance productivity by enabling a single main session to oversee various sub-sessions or tasks.

Tom explains why he disabled Claude's native memory feature and instead built a local folder-based system (myPKM) to maintain persistent context with AI agents. He shares an audit revealing systemic issues like contradictory documentation, forgotten rules, and parallel session conflicts, then outlines how to systematically fix these problems using the ICOR productivity methodology.

The speaker demonstrates how he uses AI (Claude) integrated with his local folder system to automate invoice collection for his accountant, downloading 36 invoices in 5 minutes with minimal manual intervention. He explains his AI team methodology, work streams, and how persistent local knowledge bases enable AI to handle recurring tasks independently without constant re-explanation.

A creator demonstrates a comprehensive local-folder-based system for managing AI-generated deliverables asynchronously, featuring an interactive browser interface that tracks decisions, changes, and conversations without relying on external databases or web applications. The system enables collaborative editing, visual annotations, and automatic context preservation across multiple parallel AI projects.

The speaker demonstrates how AI-powered weekly reports can automatically synthesize journal entries, notes, and captured content into personalized insights without manual review. Two different implementations are shown—one dashboard-style and one essay-style—proving the same underlying system can be customized to individual preferences and consumption habits.

The speaker has replaced traditional weekly reviews and manual note-linking with an AI-powered system built on a local folder structure called a 'scaffold.' This approach eliminates the time-consuming review ritual while enabling the AI to automatically connect notes and surface meaningful patterns that humans would miss.

The creator demonstrates real-world AI applications in their productivity business using Claude Fable and Opus, showing how Fable excels at comprehensive analysis of complex systems while comparing its performance to Opus on specific tasks. The key finding is that Fable's holistic understanding justifies its higher cost for business-critical audits and optimizations.

The hosts discuss the importance of double-checking AI outputs and validating information, emphasizing that this is not a new problem but rather a longstanding practice required with any information source. They explore how AI tools can be customized to individual workflows and contexts, arguing that understanding foundational methodology matters more than specific tools.

The speaker shares how an AI confidently generated a completely fabricated $67.4 billion statistic while researching AI hallucinations, then explains his systematic approach to combating AI misinformation: using dual independent search engines to cross-check answers and identify where hallucinations occur.

AI companies are racing to own users' context and memory through integrated tools like Claude in Slack and ChatGPT's auto-memory features, creating a lock-in trap. The speaker argues users should own their context (expensive, irreplaceable) in local plain text files while renting the AI model (cheap, replaceable), and demonstrates his personal knowledge assistant system built on markdown files that work with any AI model.