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.
Unblocked is an AI tool that augments code with knowledge from systems like GitHub, Slack, Confluence, and Jira to provide quick, accurate answers about your application.
Jelled.ai utilizes AI-powered digital twins to revolutionize workplace communication. These twins distill, prioritize, and automate communication, providing contextual insights, drafting responses, and ensuring continuity of knowledge.
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.
A practical guide to Architecture Decision Records (ADRs) covering what they are, why they're important, how to use them, tools for managing them, writing good ones, when to use them, and organizing them.
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.
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.
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.
