Phase 1: Foundation & Systems Architecture (Modules 1-20)

Objective: To design and build the core technical and philosophical infrastructure of the Personal Knowledge Engineering System. This phase focuses on creating a robust, extensible, and future-proof "personal library" using mdBook, which will serve as the central hub for all subsequent learning, creation, and networking activities. The architectural choices made here are paramount, prioritizing open standards, data ownership, and extensibility to create a system that is not merely used, but can be actively developed and customized over time.

Module 1: Defining the Philosophy - From PKM to PKE

  • Tasks: The initial step is to establish a guiding philosophy. This involves reading and synthesizing seminal texts on modern knowledge work. Critically analyze the distinction between methodologies focused on resource management, such as Tiago Forte's Building a Second Brain (BASB), which excels at organizing information for project-based work, and those focused on idea generation, like Niklas Luhmann's Zettelkasten Method (ZKM), which is a system for working with ideas themselves.[1] The BASB approach is explicitly project-oriented, speaking the "language of action," while the ZKM is project-agnostic, speaking the "language of knowledge".[1] Draft a personal "Knowledge Engineering Manifesto" that codifies the principles for this 100-day endeavor. This document should outline primary goals (e.g., "Learn a new technology stack and meet three new developers through a shared project"), core principles (e.g., "Default to learning in public," "Bias for action and rapid failure over perfect planning," "Prioritize connections over collections"), and success metrics (e.g., "Publish one new chapter per month," "Initiate three 'coffee chat' conversations with new contacts").

  • Deliverable: A MANIFESTO.md file, which will serve as the first chapter of the new mdBook project. This document serves as the strategic charter for the entire system.

Module 2: Architecting the Personal Library

  • Tasks: Design the foundational information architecture for your mdBook project. Instead of a freeform network, mdBook encourages a structured, hierarchical approach from the outset. Use the P.A.R.A. method (Projects, Areas, Resources, Archive) as a conceptual guide to organize the top-level chapters and sections within your book's src directory. For example, create main sections for Areas (long-term interests like "AI Engineering") and Projects (short-term efforts). The Zettelkasten concept of atomic notes can be adapted; each self-contained idea or piece of research becomes a .md page within the book's structure, linked hierarchically in the SUMMARY.md file.

  • Deliverable: A defined folder structure within the mdBook's src directory and a METHODOLOGY.md chapter. This document will detail the rules for creating new pages, the strategy for structuring chapters, and the lifecycle of information as it moves from a rough draft to a published chapter.

Module 3: Tool Selection & Core Setup - mdBook as the Core

  • Tasks: Install Rust and mdBook. Initialize a new book project which will become your central PKES. Familiarize yourself with the core components: the book.toml configuration file, the src directory for Markdown content, and the SUMMARY.md file that defines the book's structure. This "publication-first" approach aligns with the goal of moving directly from notes to a shareable format. As part of this module, create an ARCHITECTURE_ROADMAP.md chapter to brainstorm future extensions, such as building custom Rust-based preprocessors for mdBook to add new features (e.g., special syntax for callouts, dynamic content generation) or exploring high-performance stacks like Modular's Mojo/Max platform for future AI integrations.

  • Deliverable: A functional mdBook project, version-controlled with a private GitHub repository, and an ARCHITECTURE_ROADMAP.md chapter outlining future development paths for the PKES itself.

Module 4: Automating Capture - The Editorial Funnel

  • Tasks: Engineer a pipeline to capture external information for potential inclusion in your book. Since mdBook lacks a direct clipper plugin ecosystem, the workflow will be more deliberate. Create a separate inbox directory outside the mdBook src folder. Configure tools like an RSS reader (e.g., Feedly) with IFTTT/Zapier or custom scripts to automatically save interesting articles, paper abstracts, or email newsletters as raw Markdown files into this inbox. This creates an "editorial funnel." The manual process of reviewing these drafts, refining them, and then consciously moving them into the src directory and adding them to SUMMARY.md becomes a key part of the engineering process, ensuring only curated content makes it into the final publication.

  • Deliverable: An automated information capture pipeline that centralizes external content into a dedicated inbox folder, ready for editorial review and integration into the main mdBook project.

Modules 5-6: Building the Public Face - GitHub and HuggingFace

  • Tasks:

    • Day 5 (GitHub): Treat the GitHub profile as a professional landing page. Overhaul the profile README.md to be a dynamic "brag document".[10] Create distinct sections: "Current Focus," "Core Competencies," "Open Source Contributions," and "Let's Connect." Link prominently to your mdBook (once public), LinkedIn, and Hugging Face profile.

    • Day 6 (Hugging Face): Establish a professional presence on Hugging Face.[12] Create a profile mirroring the branding on GitHub. Explore Models, Datasets, and Spaces. Create a placeholder "Space" to demystify the deployment process.

  • Deliverable: Interconnected, professional profiles on GitHub and Hugging Face that serve as the primary public interfaces for the knowledge and artifacts generated by the PKES.

Modules 7-10: The AI-Powered Research Assistant

  • Tasks:

    • Day 7 (arXiv & Alerting): Systematize research monitoring. Use tools like ArXiv Sanity Preserver or a Python script for keyword alerts (e.g., "agentic AI," "neuromorphic computing").[14, 15] Configure these alerts to be saved into your inbox directory from Module 4.

    • Day 8 (AI Summarization): Build a summarization tool with an LLM API (e.g., Gemini). Write a Python script that processes a URL or PDF, extracts key sections, and generates a concise summary in Markdown format, ready to be moved into your book.

    • Day 9 (Papers with Code Integration): Automate tracking state-of-the-art advancements. Use the Papers With Code API to write a script that generates a weekly digest of trending papers in your field as a new Markdown file in your inbox.

    • Day 10 (Building the Research Dashboard): Create a Research Dashboard.md chapter in your mdBook. Since there's no dynamic plugin like Dataview, write a simple Python or shell script that scans your inbox directory for new files or files with a #summarize tag in their frontmatter, and generates a summary list. This script can be run manually to update the dashboard page.

  • Deliverable: A semi-automated system for identifying, capturing, summarizing, and tracking relevant scientific literature, feeding a structured editorial pipeline for your knowledge book.

Modules 11-15: Skill Refreshment & Foundational Tooling

  • Tasks:

    • Day 11 (Docker, containerization, setting up Python environments, k8s orchestration, buildah, cloudkernel, Modular platform, MLIR compiler frameworks): Create a standardized, but minimal Dockerfile build process for a data science container (Python, common libraries, PyTorch) to ensure all future projects are harmoniously pythonic and reproducible.

    • Day 12 (Pythonic ecosystem): Explore the pythonic ecosystem, including: a) NumPy, the library for numerical computing and tools for handling large, multi-dimensional arrays and matrices, as well as functions for mathematical operations b) pandas, the library for data manipulation and analysis, providing data structures for handling tabular data, time series data, and more. pandas also includes functions for data cleaning, merging, and reshaping c) SciPy, the library for scientific computing in Python, including tools for optimization, integration, interpolation, and more d) statsmodels, the library for statistical modeling in Python; SciPy provides tools for regression analysis, time series analysis, and more. e) scikit-learn, the library for machine learning in Python. It provides tools for supervised and unsupervised learning, as well as tools for data preprocessing and model selection. f) Matplotlib, library for creating visualizations which provides tools for creating line plots, scatter plots, histograms, and more. g) seaborn, the library for creating statistical visualizations which provides tools for creating heatmaps, scatter plots, and more.

    • Day 13 (Mathematica Deep Dive, complement Pythoic ecosystem): Refresh foundational math concepts (Linear Algebra, Calculus, Probability) using Wolfram Mathematica. Create dedicated notebooks and export key visualizations and formulas as images to be embedded in new chapters of your mdBook; in the future this might involve extending mdBook or GitHub Actions to develop a seamless "write, commit, publish" workflow.

    • Day 14 (Git commands, GitHub, advanced Git, Jujutsu): Review basic Git commands including GitHub Actions, essential for open-source collaboration: interactive rebasing, cherry-picking, submodules.

    • Day 15 (Git workflows, GitButler branching workflows): Master advanced DVCS flow, complex Git/Jujutsu workflows, including GitButler and the role of semantic versioning and conventional commit messages.

  • Deliverable: New mdBook chapters documenting refreshed mathematical knowledge, most likely using Python, but possibly also looking at the path for similar investigations with Mathematica and using Wolfram notebooks; a reusable Docker image for ML projects; and demonstrated proficiency in advanced Git workflows.

Modules 16-20: Establishing the Content & Networking Foundation

  • Tasks:

    • Day 16 (Technical Blog Setup): Your mdBook project is your technical blog. Looking into extending the GitHub Actions workflow used to automatically build and deploy your mdBook to GitHub Pages on every push to the main branch. Don't just create a seamless "write, commit, publish" workflow but understand how to extend, alter that infrastructure-as-code.

    • Day 17 (LinkedIn & Professional Framing): Revamp your LinkedIn profile to align with the "Practitioner-Scholar" persona, framing your career as a narrative. Perhaps publish a short article announcing the 100-day learning journey and linking to your newly deployed mdBook.

    • Day 18 (Identifying Communities): Research and identify 3-5 high-signal online communities (subreddits, Discord servers, etc.). Join and observe the culture before participating.

    • Day 19 (Crafting a Mentorship / Partnership Strategy): Develop a dual-pronged mentorship/partnership plan: identify 25-50 potential partners/mentors to learn from, and outline a plan for mentoring others based on your extensive experience.

    • Day 20 (Phase 1 Review & Planning): Conduct a formal review of the first 20 modules. Write a new chapter in your mdBook evaluating the system's architecture. Create a detailed plan for Phase 2, outlining the specific technology domains for deep dives and project objectives.

  • Deliverable: A live technical book deployed via GitHub Pages; a professionally framed LinkedIn profile; a curated list of target communities; a formal mentorship strategy chapter; and a detailed, actionable plan for Phase 2.