Security

Factual findings from grepping Claude Code’s production bundle: KAIROS, Operon, permission modes, scheduled tasks, trust, and teleport.

What RAG Is and Why Anyone Bothered Retrieval-Augmented Generation, or RAG, was the answer to a straightforward problem: LLMs are smart but amnesiac. They know a lot, but only up to their training cutoff, and they know nothing about your data. RAG solved this by bolting on an external retrieval layer (typically a vector store) that chunks your documents, embeds them into a high-dimensional space, and retrieves semantically relevant chunks at query time to inject into the model’s context window. The LLM never actually “learns” your data. It just gets handed relevant pieces of it right before it answers. Think of it less as teaching the model and more as handing it a briefing document every single time it walks into the room. ...

At both of my last two jobs I was faced with a task that seemed insurmountable: identify and block fraudulent activity. Many commercial tools exist, but none are particularly suited to the job. At Ticketmaster I learned the lessons I would carry to Weedmaps—that success requires customized workflows for identifying and remediating malicious, non-human traffic. At both Ticketmaster and Weedmaps, we faced the same adversaries: bots, scrapers, fraudulent reviews, and other forms of malicious automation. While the request seemed deceptively simple, the task was Sisyphean at best and Kafkaesque the rest of the time. How do you find a needle in a haystack? Easy—burn the hay and use a magnet. But this was more like finding one particular needle in a stack of needles, without knowing what made that needle unique. ...