This essay argues that the economics of context engineering expose a gap in the Brynjolfsson-Hitzig framework that changes its practical implications: for how enterprises build with AI, which firms centralize successfully, and whether the AI economy will be as centralized as their framework suggests. It explores how the cost and effort required to make knowledge usable by AI—context engineering—creates a bottleneck that prevents complete centralization, preserving the importance of local knowledge and human judgment. The article discusses the implications for SaaS companies, knowledge workers, and the future of work in an AI-driven economy, predicting that those who invest in context engineering capabilities will see the highest ROI.
The author experimented with feeding a month's worth of daily journal entries (originally in Obsidian) into NotebookLM. The AI was able to analyze the entries, summarize personal growth, and allow for conversational querying of the journal data, providing insights and context with citations to the original entries.
Standard browser bookmarks and reading lists often become cluttered and difficult to navigate over time. This article explains how the Raindrop.io extension can transform these messy collections into an organized, searchable research database. The tool allows for better categorization through nested collections and tags, provides full-text search across web pages and PDFs, and enables active learning via text highlighting and annotations that can be synced to note-taking apps like Obsidian or Notion.
An article detailing the benefits of Capacities as a Personal Knowledge Management (PKM) tool, highlighting its features such as cross-platform availability, object-oriented approach, linking notes, and powerful tables with databases.
"KaraKeep is a note-taking app designed to help you build and connect your ideas using the power of AI. It goes beyond simple note storage by enabling you to create a 'second brain' – a personal knowledge management system.
Key features include AI-powered summarization, insightful connections between notes, and a focus on long-term knowledge retention. KaraKeep allows you to easily capture thoughts, organize information, and discover hidden patterns in your notes. It aims to be more than just a tool for taking notes; it's a system for thinking and learning.
The app is designed for individuals looking to improve their productivity, creativity, and overall knowledge management."
Key features include AI-powered summarization, insightful connections between notes, and a focus on long-term knowledge retention. KaraKeep allows you to easily capture thoughts, organize information, and discover hidden patterns in your notes. It aims to be more than just a tool for taking notes; it's a system for thinking and learning.
The app is designed for individuals looking to improve their productivity, creativity, and overall knowledge management."
In this paper, the authors discuss the challenges faced in developing the knowledge stack for the Companion cognitive architecture and share the tools, representations, and practices they have developed to overcome these challenges. They also outline potential next steps to allow Companion agents to manage their own knowledge more effectively.
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
Sparse Priming Representations (SPR) is a research project focused on developing and sharing techniques for efficiently representing complex ideas, memories, or concepts using a minimal set of keywords, phrases, or statements, enabling language models or subject matter experts to quickly reconstruct the original idea with minimal context.
