From the CandleKeep Team
Deep dives into building knowledge libraries for AI agents.
We write about the gap between what AI agents are trained on and what your specific domain actually requires — and how to close that gap with a curated library your agents can read on demand. Expect honest field notes from building CandleKeep, patterns we've seen work across security, design, product, and engineering teams, and the occasional opinion on why retrieval-augmented generation isn't the answer most people think it is.
Your plugin passed every test. It was never running.
An agent tested our Claude Code plugin's startup hook and reported a clean pass. On the second pass a different agent wrote: "the evidence I reported proves nothing." It was right. How we built a test container that holds the coding agent, proves the hook fired, and installs the product on three operating systems as a stranger, and the book that came out of it.
Read moreGetting an agent to look things up on its own
A user asked his coding agent for a security review, the librarian found exactly the right book for free on the marketplace, and the readers came back with "Item not found." Fixing that meant answering a harder question: when should an agent decide on its own to stop working from memory and go look something up? An engineering account of the trigger we built, the three instruction wordings that failed, and what the trials changed.
Read moreLLM Wiki: How AI Agents Build Compounding Knowledge
An LLM wiki is a knowledge base your AI agent writes and maintains itself — so your knowledge compounds across sessions instead of being re-derived every time. If you use Claude Code seriously, you've probably already hacked one together. Here's the version that took me an afternoon to build — because it was just a book.
Read moreAbove the Code: Why I Wrote a Cybersecurity Book for AI Agents
A security researcher found vulnerabilities in my website. I used it as an experiment: plain AI agent vs. agent armed with 3,910 pages of security knowledge. The book-equipped agent found 8x more critical issues.
Read moreThe First Book No Human Should Read: Why I Wrote a UI/UX Guide for AI Agents
After 10 years building backend systems, I discovered why AI agents fail at UI design — and wrote the first book meant only for agents, not humans.
Read moreWhy CandleKeep Doesn't Use RAG — And Why That's the Point
Everyone assumed CandleKeep uses RAG. It doesn't. Here's why agentic search — the same approach Anthropic chose for Claude Code — produces fundamentally better results for book-length content.
Read moreWhy I Built a Library for AI Agents
I wanted to play D&D in my terminal. What I discovered about giving AI agents actual books changed how I think about the entire AI tooling ecosystem.
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