Why are trying to accomplish with developing this PKM OR how are we trying to improve our personal knowledge management toolchain?
Continuous Self-Improvement
Not knowledge for knowledge's sake -- we want or need better information for continuous self-improvement... to improve our investing and better investments, not just money but of our time ... to improve how we spend our time making progress with better business opportunities or better employment ... to improve our stewardship of our time, everything in our lives, our attention, energy, ambitions ... to improve how we align our time, resources, energies with our Creator's purpose or will for our lives.
The whole PKM thing is geared toward managing knowledge to have better, more relevant information at the time we need it ... which involves personal transformation and renewal ... transcending just accepting what information one gets just from different extraneous recommendation engines [which are part of our tracked lives], but instead being more proactive and 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:
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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.
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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.
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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.
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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.
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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.