Stop building "fancy RAG" and start compiling your knowledge. The Problem: Senior researchers and CTOs face an "information explosion" where data integrity and retrieval-at-scale become the primary bottlenecks for R&D. The Solution: A "Knowledge-as-Code" pipeline that treats a Markdown directory as a compiled target, managed by LLM agents.In this episode of the Neural Intel podcast, we conduct a technical teardown of Andrej Karpathy’s personal research infrastructure. We move past the abstract and look at the actual engineering components:

• The Compiler Pipeline: Using LLMs to incrementally "compile" raw articles into a directory structure with auto-generated summaries and backlinks.
• The Scaling Limit: Why Karpathy finds this method effective for knowledge bases up to 400,000 words without reaching for complex RAG architectures.
• Data Integrity & Linting: How "health checks" are used to find inconsistencies and impute missing data through web searchers.
• Obsidian as an IDE: Using Marp and Matplotlib for visual knowledge exploration.
• The Weight Horizon: The transition from context-window reliance to synthetic data generation and finetuning.

Neural Signal Check: This development matters because it hints at a new product category-one that replaces "hacky scripts" with a sovereign, structured knowledge engine that lives on your local machine, not in a vendor's black-box database.Tell us your take: Are you still relying on manual wikis, or are you ready to let an LLM "compile" your research? Drop your thoughts in the comments.

Links: 

🌐 Full Analysis: neuralintel.org 

🐦 X/Twitter: @neuralintelorg 

🎧 Also available on Apple Podcasts and Youtube.