After Andrej Karpathy outlined the idea of “LLM Knowledge Bases” on April 3, 2026, developers built Graphify in under 48 hours. Graphify turns any folder of code, docs, and images into an interlinked markdown wiki you can query without vector databases or complex setup. It persists across sessions, builds backlinks and concept maps, and cuts future query costs.

• Karpathy's April 3, 2026 pitch for "LLM Knowledge Bases" proposed replacing RAG's fetch-at-query-time approach with an LLM-generated, persistent markdown wiki.
• Developers built Graphify from that idea in under 48 hours, and it racked up hundreds of GitHub stars within hours of posting.
• Graphify converts any folder (code, docs, images) into an interlinked markdown wiki—summaries, backlinks, concept maps, index—with one command, no vector database or retrieval pipeline needed.
• Because the wiki is plain markdown that compounds with each update, it's transparent, Git-versionable, and cuts future query costs compared to traditional embedding-based retrieval.

This article argues that using Markdown prompts and LLMs as a runtime interpreter for business rules is slow, expensive, unreliable, and insecure. It shows how mixing regular code, human approvals, and LLMs without a solid framework leads teams to overuse LLMs instead of writing proper code and handling state continuations.

• Using LLMs to interpret Markdown business logic runs ~10,000x slower, costs more, and invites hallucinations, privacy leaks, and prompt-injection attacks than writing regular code.
• The real cause is engineering friction: without a framework to pause/resume code and store continuations, teams dump logic into prompts and let the LLM manage state via conversation history instead of building proper state machines.
• ~90% of support queries follow predictable flows that deterministic code handles better and near-instantly, so LLMs should be reserved for genuinely unstructured tasks like image recognition, sentiment scoring, or free-form-to-query translation.