An exploration of using the KDE Plasma desktop environment as a foundation for personal knowledge management to avoid vendor lock-in. Instead of relying on specialized applications like Obsidian or Logseq, this workflow uses plain Markdown files managed through Dolphin and Ghostwriter.
- Creating custom templates in ~/Templates for rapid note creation
- Organizing notes with dedicated directories for attachments and inbox items
- Using Dolphin's built-in tagging system via Baloo for efficient searching
- Implementing manual interlinking using relative paths to ensure file portability
- Managing the trade-offs of strict organization requirements and link fragility
- Creating custom templates in ~/Templates for rapid note creation
- Organizing notes with dedicated directories for attachments and inbox items
- Using Dolphin's built-in tagging system via Baloo for efficient searching
- Implementing manual interlinking using relative paths to ensure file portability
- Managing the trade-offs of strict organization requirements and link fragility
Andrej Karpathy, Google, and Garry Tan are all adopting Markdown as the primary format for agent memory and organizational context. While Karpathy focuses on personal knowledge, Google targets enterprise runbooks, and Tan defines agent roles, they all share a reliance on versioned Markdown files. This shift suggests that the strategic advantage is moving from the specific large language model used to the curated knowledge base a team accumulates.
- Karpathy's LLM Wiki for personal knowledge bases
- Google's Open Knowledge Format for enterprise context
- Garry Tan's gstack for defining agent roles
- The migration of the competitive moat from models to portable data files
- Karpathy's LLM Wiki for personal knowledge bases
- Google's Open Knowledge Format for enterprise context
- Garry Tan's gstack for defining agent roles
- The migration of the competitive moat from models to portable data files
Google Cloud introduces the Open Knowledge Format (OKF), a vendor-neutral specification designed to formalize documentation patterns into a portable, interoperable standard for Large Language Models and agents. By using markdown files with YAML frontmatter, OKF provides a way to represent metadata, context, and curated knowledge that is readable by both humans and machine-parseable systems without requiring specialized SDKs or proprietary platforms.
Key points:
* Addresses the fragmentation of internal knowledge across disparate systems like wikis, databases, and code comments.
* Enables "knowledge as a living wiki" where information can be managed alongside code in version control.
* Employs a minimally opinionated design that separates content producers from consumers.
* Provides reference implementations including an enrichment agent and a static HTML visualizer.
Key points:
* Addresses the fragmentation of internal knowledge across disparate systems like wikis, databases, and code comments.
* Enables "knowledge as a living wiki" where information can be managed alongside code in version control.
* Employs a minimally opinionated design that separates content producers from consumers.
* Provides reference implementations including an enrichment agent and a static HTML visualizer.
This open-source template provides a structured framework for building an LLM-powered second brain using Markdown, Git, and coding agents like Codex or Claude Code. It utilizes a Karpathy-style architecture designed to keep raw source materials immutable while allowing AI agents to synthesize that information into a maintained wiki layer. The system is built for durability and readability, making it ideal for use with tools like Obsidian.
Key features:
- Dual-layer structure separating raw data from synthesized wiki content
- Automated ingestion workflows using coding agents to update indexes and logs
- Git-based version control for reviewing and rolling back AI-generated changes
- Highly compatible with Obsidian and mobile capture workflows
Key features:
- Dual-layer structure separating raw data from synthesized wiki content
- Automated ingestion workflows using coding agents to update indexes and logs
- Git-based version control for reviewing and rolling back AI-generated changes
- Highly compatible with Obsidian and mobile capture workflows
An Anthropic engineer argues that while Markdown is the current standard for AI agent communication due to its simplicity and portability, HTML offers significantly better capabilities for rich visualizations, color, diagrams, and interactive elements. The discussion highlights that Markdown was originally designed as a syntax meant to be converted into HTML rather than serving as the final output format itself.
Key points:
- Limitations of Markdown regarding visual complexity and richness.
- Advantages of HTML including CSS styling and JavaScript interactivity for AI outputs.
- Historical context of Markdown's purpose as an intermediary tool for generating HTML.
Key points:
- Limitations of Markdown regarding visual complexity and richness.
- Advantages of HTML including CSS styling and JavaScript interactivity for AI outputs.
- Historical context of Markdown's purpose as an intermediary tool for generating HTML.
Simon Willison discusses why requesting HTML rather than Markdown as an LLM output format can significantly enhance technical explanations. While token constraints previously favored Markdown, modern models benefit from the ability of HTML to incorporate SVG diagrams, interactive widgets, and improved navigation. The article provides prompt examples for reviewing pull requests via HTML artifacts and showcases a GPT-5.5 generated explanation of a Linux security exploit that uses CSS and JavaScript to create a rich documentation experience.
This article proposes the DataBook, a design pattern that utilizes Markdown to bridge the gap between large-scale RDF knowledge graphs and small, ephemeral, task-specific semantic content. By combining YAML frontmatter for metadata, inline identifiers for addressability, and typed fenced code blocks for data payloads, DataBooks create self-describing and portable semantic artifacts. The authors argue that this approach allows for a microdatabase model where structured data can exist without the overhead of a full triple store.
Key points include:
The use of Markdown as a substrate for semantic infrastructure.
Defining the microdatabase for small-scale, non-indexed knowledge work.
Inverting the LLM role to act as a transformation engine within a DataBook pipeline.
Implementing provenance through process stamps in YAML metadata.
Managing complex dependencies via manifest DataBooks and build graphs.
Supporting secure data transfer through designed-in encryption profiles.
Key points include:
The use of Markdown as a substrate for semantic infrastructure.
Defining the microdatabase for small-scale, non-indexed knowledge work.
Inverting the LLM role to act as a transformation engine within a DataBook pipeline.
Implementing provenance through process stamps in YAML metadata.
Managing complex dependencies via manifest DataBooks and build graphs.
Supporting secure data transfer through designed-in encryption profiles.
This article details Andrej Karpathy’s innovative approach to managing knowledge for AI projects, dubbed "LLM Knowledge Bases." This system aims to overcome the limitations of traditional Retrieval-Augmented Generation (RAG) and the frustrating context limits of "stateless" AI development.
**Key takeaways:**
* **Beyond RAG:** Karpathy proposes an alternative to vector databases and RAG, utilizing the LLM itself as a constantly updating "research librarian."
* **Markdown as Core:** The system centers around maintaining a structured knowledge base using Markdown files, which are easily readable, editable, and auditable.
* **Three-Stage Process:** The system involves: 1) **Data Ingest** (raw data to Markdown), 2) **Compilation** (LLM generates summaries, backlinks, and a structured wiki), and 3) **Active Maintenance** (LLM "lints" the wiki for consistency and new connections).
* **Self-Healing & Auditable:** The LLM actively maintains the knowledge base, ensuring it's self-healing and providing full traceability of information.
* **Enterprise Potential:** This approach could be a game-changer for businesses struggling with unstructured data, allowing them to create a dynamic, "Company Bible" of knowledge.
* **Scaling & Future:** While currently a "hacky collection of scripts," the system shows promise for scaling, potentially leading to synthetic data generation and fine-tuning of custom AI models.
The article highlights a shift towards treating LLMs not just as tools to *access* knowledge, but as agents actively *managing* and *improving* it. This philosophy prioritizes a "file-over-app" approach, giving users ownership of their data.
**Key takeaways:**
* **Beyond RAG:** Karpathy proposes an alternative to vector databases and RAG, utilizing the LLM itself as a constantly updating "research librarian."
* **Markdown as Core:** The system centers around maintaining a structured knowledge base using Markdown files, which are easily readable, editable, and auditable.
* **Three-Stage Process:** The system involves: 1) **Data Ingest** (raw data to Markdown), 2) **Compilation** (LLM generates summaries, backlinks, and a structured wiki), and 3) **Active Maintenance** (LLM "lints" the wiki for consistency and new connections).
* **Self-Healing & Auditable:** The LLM actively maintains the knowledge base, ensuring it's self-healing and providing full traceability of information.
* **Enterprise Potential:** This approach could be a game-changer for businesses struggling with unstructured data, allowing them to create a dynamic, "Company Bible" of knowledge.
* **Scaling & Future:** While currently a "hacky collection of scripts," the system shows promise for scaling, potentially leading to synthetic data generation and fine-tuning of custom AI models.
The article highlights a shift towards treating LLMs not just as tools to *access* knowledge, but as agents actively *managing* and *improving* it. This philosophy prioritizes a "file-over-app" approach, giving users ownership of their data.
The /llms.txt file is a proposal to standardize a method for providing LLMs with concise, expert-level information about a website. It addresses the limitations of LLM context windows by offering a dedicated markdown file containing background information, guidance, and links to detailed documentation. The format is designed to be both human and machine readable, enabling fixed processing methods. The proposal includes generating markdown versions of existing HTML pages (appending .md to the URL). This initiative aims to improve LLM performance in various applications, from software documentation to complex legal analysis, and is already being implemented in projects like FastHTML and nbdev.
Typeui.sh offers a curated collection of design skills available as 'skill.md' files. These files are designed to be integrated into agentic AI tools, allowing users to instruct Large Language Models (LLMs) to create websites with specific designs.
Users can obtain these skill files using the command 'npx typeui.sh pull name » ' or by directly copying/downloading them from the website. These hand-crafted designs enable both developers and AI agents, such as those built with OpenClaw, to build websites based on pre-defined aesthetic principles. A newsletter subscription is available for updates on features and design system tips.
Users can obtain these skill files using the command 'npx typeui.sh pull name » ' or by directly copying/downloading them from the website. These hand-crafted designs enable both developers and AI agents, such as those built with OpenClaw, to build websites based on pre-defined aesthetic principles. A newsletter subscription is available for updates on features and design system tips.
