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Security Checklist (What can be done in CursorAI & software-level not infrastructure!)

Configuration Security

Detect secrets in code
Identify secrets committed to version control
Flag hardcoded credentials

Authentication & Authorization

Identify missing authentication checks
Detect improper authorization patterns

Meta Prompting

In the Generative AI Age your ability to generate prompts is your ability to generate results.

Guide

Claude 3.5 Sonnet and o1 series models are recommended for meta prompting.

Replace {{user-input}} with your own input to generate prompts.

Use mp_*.txt as example user-inputs to see how to generate high quality prompts.

🤗Huggingface Model Downloader

Considering the lack of multi-threaded download support in the official huggingface-cli, and the inadequate error handling in hf_transfer, this command-line tool smartly utilizes wget or aria2 for LFS files and git clone for the rest.

Features

⏯️ Resume from breakpoint: You can re-run it or Ctrl+C anytime.
🚀 Multi-threaded Download: Utilize multiple threads to speed up the download process.
🚫 File Exclusion: Use --exclude or --include to skip or specify files, save time for models with duplicate formats (e.g., *.bin or *.safetensors).
🔐 Auth Support: For gated models that require Huggingface login, use --hf_username and --hf_token to authenticate.
🪞 Mirror Site Support: Set up with HF_ENDPOINT environment variable.

edit traefik.yml

entryPoints:
  web:
    address: ":80"
  websecure:
    address: ":443"
  dot: # <- ADD THIS
    address: ":853"  # <- ADD THIS

	---
	description: Guidelines and best practices for creating .mdc (Markdown Configuration) files in Cursor, including structure, metadata annotations, and formatting rules
	globs: ["*/.mdc"]
	---

	# Cursor MDC File Guidelines

	@context {
	"type": "documentation",
	"purpose": "cursor_rules",

	import asyncio
	import json

	from livekit import rtc
	from livekit.agents import JobContext, WorkerOptions, cli, JobProcess
	from livekit.agents.llm import (
	ChatContext,
	ChatMessage,
	)
	from livekit.agents.voice_assistant import VoiceAssistant

	llmc() {
	local system_prompt='Output a command that I can run in a ZSH terminal on macOS to accomplish the following task. Try to make the command self-documenting, using the long version of flags where possible. Output the command first enclosed in a "```zsh" codeblock followed by a concise explanation of how it accomplishes it.'
	local temp_file=$(mktemp)
	local capturing=true
	local command_buffer=""
	local first_line=true
	local cleaned_up=false # Flag to indicate whether cleanup has been run

	cleanup() {
	# Only run cleanup if it hasn't been done yet

	version: '3.2'

	services:
	changedetection:
	image: ghcr.io/dgtlmoon/changedetection.io:latest
	container_name: changedetection
	hostname: changedetection
	volumes:
	- changedetection-data:/datastore
	environment:

	{
	"LoRA_type": "Standard",
	"adaptive_noise_scale": 0.006,
	"additional_parameters": "",
	"block_alphas": "",
	"block_dims": "",
	"block_lr_zero_threshold": "",
	"bucket_no_upscale": true,
	"bucket_reso_steps": 64,
	"cache_latents": true,

	import numpy as np
	import cv2
	import os
	import argparse
	from pathlib import Path

	from cv2.ximgproc import guidedFilter

	def main(args):
	input = Path(args.input_file_or_dir)