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60% Positive

Analyzed from 231 words in the discussion.

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#https#com#llm#github#better#gist#wiki#local#karpathy#version

Discussion (13 Comments)Read Original on HackerNews

xnxabout 2 hours ago
superfrankabout 2 hours ago
I've got a couple of LLM wikis running for different purposes. I just pointed Claude at Karpathy's Github Gist and said "do this" and it set up and has maintained them ever since. So far no issue with that.

Can you explain why the version linked is better?

Gist link: https://gist.github.com/karpathy/442a6bf555914893e9891c11519...

jarbusabout 2 hours ago
Where is this 10x number coming from?
dominotwabout 2 hours ago
from llm wiki
esafakabout 2 hours ago
dolebirchwoodabout 2 hours ago
What if I only want 6x better performance? Is there a knob or slider to dial it down?
adithyassekharabout 2 hours ago
Edit: On closer look this looks useful without the coding harness pitch.
nvkabout 2 hours ago
This is not a product, its a foss lib
sppflyabout 2 hours ago
This kind of llm bragging title and AI generated webpage makes me gross.
esafakabout 2 hours ago
How does this differ from https://context7.com/ ?
kordlessagainabout 2 hours ago
LLM Wiki is client-side and local-first (plain Markdown, Obsidian-friendly) designed for deep multi-agent topic research (e.g. automated thesis/counter-thesis runs, local session memory redaction).

Context7 is a hosted SaaS/on-premise MCP server indexing API/library docs (GitHub, Confluence, OpenAPI) to provide coding assistants with fresh, version-specific developer context.

Essentially: LLM Wiki compiles topic research vaults on your local disk, whereas Context7 acts as a semantic doc/API search gateway for programming.

esafakabout 2 hours ago
So the benefit is in caching the resources to avoid web queries, and massaging them to make them amenable to analysis? For intellectual work I imagine it would be useful if it could access gated content, like commercial reports?
kordlessagainabout 1 hour ago
Right, it's the whole search pipeline problem defined. Check this out: https://github.com/deepbluedynamics/lume