17twenty

For those of you who want to try out systemd-networkd, you can read on, and find out in this tutorial how to switch from NetworkManager to systemd-networkd on Linux.

Requirement systemd-networkd is available in systemd version 210 and higher. Check the version of your systemd before proceeding.

$ systemctl --version

Switch from Network Manager to Systemd-Networkd

It is relatively straightforward to switch from Network Manager to systemd-networkd (and vice versa).

dori4n

Office 2024 ISO images

updated 2024-09-20

Version 2409? (Build 16.0.17928.20148)

---

English

nakamuraos

#!/bin/bash
set -euo pipefail

# Author: NakamuraOS <https://github.com/nakamuraos>
# Latest update: 03/19/2025
# Tested with Navicat 15.x, 16.x, and 17.x on Debian and Ubuntu.

BGRED="\e[1;97;41m"
ENDCOLOR="\e[0m"

HugsLibRecordKeeper

Log uploaded on Sunday, September 27, 2026, 1:05:49 AM
Loaded mods:
Harmony(brrainz.harmony)[mv:2.4.2.0]: 0Harmony(2.4.1), HarmonyMod(2.4.2)
Loading Progress(ilyvion.LoadingProgress): ilyvion.LoadingProgress(0.14.0)
PurePatcher(Vortex.PurePatcher): PurePatcher.Annotations(1.8.0), PurePatcher(1.0.0)
Faster Game Loading - Continued(Taranchuk.FasterGameLoading): FasterGameLoading(1.0.0)
Core(Ludeon.RimWorld): (no assemblies)
Royalty(Ludeon.RimWorld.Royalty): (no assemblies)
Ideology(Ludeon.RimWorld.Ideology): (no assemblies)

CrossGen-ai

ruvector 2026: Batched HNSW with Shared Candidate Frontier — High-Performance Rust Vector Search

Summary (≤150 chars): Rust HNSW backend that shares candidate vectors across correlated query batches — saves 71-97% vector loads, 1.24× faster, zero recall loss.

Introduction

If your Rust vector search workload is a RAG loop with multi-turn queries, an MMR / diverse-beam reranker, or a multi-vector retrieval (ColBERT / MaxSim / MuVera), you are running correlated query batches — and every mainstream HNSW library (FAISS, Milvus, Qdrant, Weaviate, Pinecone, LanceDB) treats those queries as independent, re-loading each candidate vector's cache lines Q times. This ruvector nightly research (2026-09-26, ADR-347) publishes a Rust HNSW search backend that shares distance computations across the batch: 71 – 97 % fewer vector loads and 1.24 × faster wall clock at Q = 16 on Apple M4 Max, with recall bit-for-bit identical to per-query search.

Keywords: rust vector search, HNSW, ANN, ruvector, batc

forkyau

choco-bot

<?xml version="1.0" encoding="utf-8"?>
<fileSnapshot xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <files>
    <file path="C:\ProgramData\chocolatey\lib\nexa-coffee\nexa-coffee-0.1.0-windows-x64.zip.txt" checksum="66AD980C6AEEA8A346EC3CB1B72AAB7E" />
    <file path="C:\ProgramData\chocolatey\lib\nexa-coffee\nexa-coffee.nupkg" checksum="F1700985C21E631377F91085A36E807F" />
    <file path="C:\ProgramData\chocolatey\lib\nexa-coffee\nexa-coffee.nuspec" checksum="2B92B962E76EAE90C8212B8D21B2356F" />
    <file path="C:\ProgramData\chocolatey\lib\nexa-coffee\tools\chocolateybeforemodify.ps1" checksum="84AE4378D945164BBD935DE393C63609" />
    <file path="C:\ProgramData\chocolatey\lib\nexa-coffee\tools\chocolateyinstall.ps1" checksum="968BE0AD278B40A164B2992062D1029D" />
    <file path="C:\ProgramData\chocolatey\lib\nexa-coffee\tools\LICENSE" checksum="75DB5BAD2DE4E892C773302E910F1CF0" />
    <file path="C:\ProgramData\chocolatey\lib\nexa-coffee\tools\ne

inawrath

import re
from easysnmp import Session
from pprint import pprint
from struct import pack, unpack

printer_ip = "10.0.0.222"


session = Session(hostname=printer_ip, community="public", version=1, timeout=1)
password = [74, 54]

jinjier

使用JAV金鸡儿奖官网附带的工具JAV SQL 查询器，可查询各种类别的JavDB TOP250影片：

• JavDB TOP250
• JavDB 有码 TOP250
• JavDB 无码 TOP250
• JavDB 欧美 TOP250
• JavDB FC2 TOP250

及分年数据（存在部分重复影片，原始数据的问题）：

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.