| // ==UserScript== | |
| // @name VKGetLink | |
| // @description Script for downloading audio and video files from the vk.com. Based on Ivan Snegirev's VK-Downloader http://userscripts-mirror.org/scripts/show/151571 | |
| // @author Mist Poryvaev | |
| // @version 1.0.2 | |
| // @include https://*vk.com/* | |
| // @grant none | |
| // ==/UserScript== | |
| function audio() |
| # Meta JSON Schema: A linter and snippet provider for Clash.Meta(mihomo) configuration. | |
| log-level: info | |
| external-controller: 127.0.0.1:9090 | |
| # external-ui: /usr/share/metacubexd/ | |
| # secret: "在此修改访问令牌" | |
| mode: rule | |
| mixed-port: 7890 | |
| allow-lan: false |
This document provides comprehensive guidelines for implementing a Flutter project following Clean Architecture principles. The project structure follows a modular approach with clear separation of concerns, making the codebase maintainable, testable, and scalable.
lib/
├── core/
│ ├── database/
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.
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.
| #!/usr/bin/env bash | |
| set -euo pipefail | |
| RPC_URL="${RPC_URL:-http://localhost:8545}" | |
| NETWORK="${NETWORK:-}" | |
| SEPOLIA_ENODES=( | |
| "enode://45a06f4fc806768de7615174b9c8258db64202e6443418a3d8febea9362ba694272583e2c82a9755f9d450bf3b39a2af3336da579faf37deeb5bf724d8d19f5e@3.229.122.30:30303?discport=30304" | |
| "enode://b307ebfb325c0ebf14e169447f426b931307e14889f1ecc0111686c28cbeaf937a2455d06cd278222edf385f1c855b0f824b2adde748efec53dd66963656aa14@44.201.35.121:30303?discport=30304" | |
| "enode://a4805cc020882d9b79dc394529d3287009eee5cfb0efdac17fb7a54a2f646208f6f3de2bfd3405c4884908fb7a19c41ecfa62e63318d2f7cf434f397bf48e607@34.235.124.239:30303?discport=30304" |
| # #@title Environment Setup | |
| ### setup ComfyUI ### | |
| from pathlib import Path | |
| OPTIONS = {} | |
| USE_GOOGLE_DRIVE = False #@param {type:"boolean"} | |
| UPDATE_COMFY_UI = True #@param {type:"boolean"} | |
| USE_COMFYUI_MANAGER = True #@param {type:"boolean"} |
| // ==UserScript== | |
| // @name GLM Coding Rush - 智谱编程助手抢购脚本 | |
| // @namespace https://gist.github.com/LessUp | |
| // @version 1.1.0 | |
| // @description 智谱 GLM Coding 一键抢购脚本 — 自动解锁售罄按钮 / 高速重试引擎 / bizId 双重校验 / 错误弹窗自动恢复 / 支付弹窗保护 / 秒级定时触发 / 可拖拽浮动面板 | |
| // @author LessUp | |
| // @match *://www.bigmodel.cn/* | |
| // @match https://bigmodel.cn/glm-coding* | |
| // @run-at document-start | |
| // @grant none |
With its latest reales qemu added the Venus patches so that virtio-gpu now support venus encapsulation for vulkan. This is one more piece to the puzzle towards full Vulkan support.
An outdated blog post on clollabora described in 2021 how to enable 3D acceleration of Vulkan applications in QEMU through the Venus experimental Vulkan driver for VirtIO-GPU with a local development environment. Following up on the outdated write up, this is how its done today.
Let's start with the brief description of the projects mentioned in the post & extend them: