@Asteri_eth: Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and ov…
Summary
Karpathy's 'Wiki Layer' method reduces LLM token usage by up to 90% by having the model clean, structure, and link data into a local Markdown knowledge base, eliminating repeated reading of raw files.
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@rohit4verse: karpathy moved his token budget from writing code to building a knowledge base. markdown files an llm writes and mainta…
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@neil_xbt: Someone turned 383 scattered files and 100 meeting transcripts into a compact wiki and cut their Claude token usage by …
A technique called the LLM Wiki pattern compiles raw documents into a structured wiki with an index, cutting Claude token usage by 95% by paying structural understanding costs only once during compilation instead of on every query.
After a month on Karpathy's LLM Wiki, the bottleneck isn't setup. It's maintenance
A developer shares a month-long experience building an LLM-powered wiki based on Andrej Karpathy's idea, discovering that while setup is easy, ongoing maintenance—like handling stale sources, cost, and integration—poses the real challenge.
@DataScienceDojo: Andrej Karpathy's LLM Wiki is a pattern for building a personal knowledge base that actually compounds over time. The d…
Andrej Karpathy's LLM Wiki pattern enables building a persistent, structured knowledge base that compounds over time, unlike stateless RAG systems. The tutorial shows how to create one in under 30 minutes using LLMs to compile and link markdown pages.
@DeRonin_: This 2-hour Andrej Karpathy lecture will teach you more about using LLMs than most AI twitter will ever figure out.. no…
Andrej Karpathy's lecture reveals an 'LLM Wiki' pattern to transform past content into a self-updating knowledge base, helping creators discover patterns in their writing.