Bookmarks - llm, graph, memory, security

These are some things I’ve wandered across on the web this week. 🔖 OpenAI agents carried out an undisclosed cyber-attack on RubyGems On May 11th, 2026, hundreds of malicious packages were uploaded to RubyGems by AI agents. We believe these were authored by internal OpenAI agents llm openai ruby security 🔖 Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects Agent memory has been touted as a dimension of growth for LLM-based applications, enabling agents that can accumulate experience, adapt across sessions, and move beyond single-shot question answering. The current generation of agent memory systems treats memory as an external layer that extracts salient snippets from conversations, stores them in vector or graph-based stores, and retrieves top-k items into the prompt of an otherwise stateless model. While these systems improve personalization and context carry-over, they still blur the line between evidence and inference, struggle to organize information…