Why are trying to accomplish with developing this PKM OR how are we trying to improve our personal knowledge management toolchain?

We want or need better information ... to make better investments, not just money but of our time ... to find better employment or business opportunities ... to be better stewards of our time, our lives, our energy/ambitions ... to better align our time, resources, energies with our Creator's purpose or will for our lives.

The whole PKM thing is geared toward getting better information, or more precisely, getting better information results from gaining better control over information provenance in one's Personal Knowledge Management (PKM) system ... which involves transcending just accepting what information one comes across and being more systematic in tracking the origin, history, and context of one's information sources and one's notes on one's sources. Better information is about transforming a chaotic or adhoc PKM, moving from a simple collection of information and gathering of intelligence into a more systematic, reliable, verifiable [or auditable] base knowledge ... not just know what one thinks one knows, but knowing precisely where the ideas came from and how likely to be true, realistic and actionable those ideas are.

Thus far, the actions that we have take toward the bigger objective might be summarized by the following:

  1. Establishment of PKM System: The daily journals document the thinking behind the setup of a comprehensive Personal Knowledge Management (PKM) system using mdBook for publishing, Foam for notetaking with P.A.R.A. architecture, and GitHub Projects for managing a 100-day project across five phases, incorporating Rust development and considering future Python/Mojo integrations.

  2. AI Coding Assistants and Tools: Extensive exploration of AI coding agents like Cline, Devin, and Codex, including their integration with OpenRouter, browser extensions, and productivity tools such as Zen and Dia browsers, emphasizing fundamental dev tasks and competitive analysis in the evolving toolscape.

  3. Automation and Protocols: Focus on building automation infrastructure with MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols for secure, interoperable agentic workflows, including GitHub Actions for CI/CD, and understanding mechanics behind task automation and data flows.

  4. Monetization and Economics: Deep dive into data as currency, micropayments, and economic models for AI services, including kernel-level tolling in CloudKernelOS, verifiable computation (zkML/opML), and secure payment protocols like x402, with emphasis on avoiding abuse of information technologies.

  5. Productivity and Human-AI Collaboration: Emphasis on effective use of browsers and tools for productivity, exploring human-in-the-loop AI collaboration, browser-based development environments, and the need for integrated knowledge engineering environments to foster relationships and knowledge sharing.