glOriginMind

What the LLM Wiki actually fixes

The LLM Wiki idea has gained traction quickly because it offers a compelling answer to stateless AI. Instead of repeatedly querying raw files and reconstructing understanding from scratch, the system compiles knowledge into a persistent, interlinked body of Markdown. Context compounds. Links persist. Contradictions can be tracked. The cost of repeating the same cognitive setup work begins to fall, which is the difference between disposable chat sessions and a memory system that can keep up with serious work.

The limit of treating the wiki as the destination

The enthusiasm is justified, but it also reveals a deeper misunderstanding. Many people are treating the LLM Wiki as the endpoint, when it is primarily infrastructure. Persistent memory, organization, and reduced maintenance all matter. None of them, alone, solve the harder problem. A creator does not merely need a system that stores and retrieves ideas. A creator needs a system that preserves continuity from the f

rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

AlgorithmsAreCool

using System.Threading.Channels;

using CommunityToolkit.Diagnostics;

using Orleans.Concurrency;

namespace Orleans.Grains.Utility;

public sealed class MessageChannelGrain<T> : Grain, IMessageChannelGrain<T>
{

guest271314

• V8

V8 is Google’s open source high-performance JavaScript and WebAssembly engine, written in C++. It is used in Chrome and in Node.js, among others. It implements ECMAScript and WebAssembly, and runs on Windows 7 or later, macOS 10.12+, and Linux systems that use x64, IA-32, ARM, or MIPS processors. V8 can run standalone, or can be embedded into any C++ application.

• SpiderMonkey

SpiderMonkey is Mozilla’s JavaScript and WebAssembly Engine, used in Firefox, Servo and various other projects. It is written in C++, Rust and JavaScript. You can embed it into C++ and Rust projects, and it can be run as a stand-alone shell. It can also be [compiled](https://bytecodealliance.org/articles/making-javascript-run-fast-on

reduz

During the past days, this great article by Sam Pruden has been making the rounds around the gamedev community. While the article provides an in-depth analysis, its a bit easy to miss the point and exert the wrong conclusions from it. As such, and in many cases, users unfamiliar with Godot internals have used it points such as following:

• Godot C# support is inefficient
• Godot API and binding system is designed around GDScript
• Godot is not production ready

In this brief article, I will shed a bit more light about how the Godot binding system works and some detail on the Godot

CandyMi

测试描述

本次对比常用库包括:

• zlib
• lz4
• snappy

压缩测试

montasim

KFZUS-F3JGV-T95Y7-BXGAS-5NHHP
T3ZWQ-P2738-3FJWS-YE7HT-6NA3K
KFZUS-F3JGV-T95Y7-BXGAS-5NHHP
65Z2L-P36BY-YWJYC-TMJZL-YDZ2S
SFZHH-2Y246-Z483L-EU92B-LNYUA
GSZVS-5W4WA-T9F2E-L3XUX-68473
FTZ8A-R3CP8-AVHYW-KKRMQ-SYDLS
Q3ZWN-QWLZG-32G22-SCJXZ-9B5S4
DAZPH-G39D3-R4QY7-9PVAY-VQ6BU
KLZ5G-X37YY-65ZYN-EUSV7-WPPBS

rminderhoud

# This is a super **SIMPLE** example of how to create a very basic powershell webserver
# 2019-05-18 UPDATE — Created by me and and evalued by @jakobii and the comunity.

# Http Server
$http = [System.Net.HttpListener]::new() 

# Hostname and port to listen on
$http.Prefixes.Add("http://localhost:8080/")

# Start the Http Server