nash_su nashsu

title

FaultLine — GBrain-Inspired Vulnerability Scanner

type

project

created

2026-04-08

updated

2026-04-08

FaultLine

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 10K Stars ⭐️, 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.

升级小米路由器3 SSH + Shadowsocks

起因
升级到开发版
破解ssh登录
前提，下面描述的方法针对的条件如下：

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	import gzip

	def gzip_search(query: str, candidate_chunks: list[str], top_k: int=1):
	"""
	文字列ベースで類似したテキストチャンクを推定するアルゴリズム.
	`query`, `chunk`, および`query + " " + chunk`をそれぞれgzipで圧縮し、編集距離のようなものをベースに評価する.

	Parameters:
	query (str): 検索クエリとして使用する文字列.
	top_k (int, optional): 返される類似チャンクの上位k個を指定する (default: 1).

	<?xml version="1.0" encoding="UTF-8"?>
	<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
	<plist version="1.0">
	<dict>
	<!-- not sure which one it is, so set both -->
	<key>Ensemble</key>
	<dict>
	<key>Enabled</key>
	<true/>
	</dict>

	#
	# This small example shows you how to access JS-based requests via Selenium
	# Like this, one can access raw data for scraping,
	# for example on many JS-intensive/React-based websites
	#

	from time import sleep

	from selenium import webdriver
	from selenium.webdriver import DesiredCapabilities

	from PIL import Image, ImageFont, ImageDraw
	import numpy as np
	from keras import backend as K
	from keras.models import load_model
	from keras.layers import Input
	from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
	from yolo3.utils import letterbox_image
	import os
	import tensorflow as tf
	from timeit import default_timer as timer

	HTTPBindAddress 0.0.0.0
	HTTPPort 8090
	CustomLog -

	<Feed feed.ffm>
	File ./feed.ffm
	FileMaxSize 256M
	ACL ALLOW localhost
	</Feed>

	//wearversion
	//wearlog
	//wearvoiceinputenable
	//wearvoiceinputdisable
	//weargoogleapi
	//assert
	//pushassert
	//uplog
	//upcrash
	//switchnotificationstatus