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# Project Policy

This policy provides a single, authoritative, and machine-readable source of truth for AI coding agents and humans, ensuring that all work is governed by clear, unambiguous rules and workflows. It aims to eliminate ambiguity, reduce supervision needs, and facilitate automation while maintaining accountability and compliance with best practices.

# 1. Introduction

> Rationale: Sets the context, actors, and compliance requirements for the policy, ensuring all participants understand their roles and responsibilities.

## 1.1 Actors

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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

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function deleteSavedItems() {
	var query = document.querySelectorAll("#sc-saved-cart input[value=Delete]")
	if (query.length) {
		query[0].click();
	}
	if (query.length > 1) {
		setTimeout(deleteSavedItems,100);
	}
	else {
		console.log('Finished');

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import transformers

model_name = 'Intel/neural-chat-7b-v3-1'
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)


def generate_response(system_input, user_input):
  
    # Format the input using the provided template

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import transformers

model_name = 'Intel/neural-chat-7b-v3-1'
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)


def generate_response(system_input, user_input):
  
    # Format the input using the provided template

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!pip install transformers optimum
!pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name_or_path = "TheBloke/zephyr-7B-beta-GPTQ"

model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,

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How to install CUDA & cuDNN on Ubuntu 22.04

Install NVIDIA drivers

Update & upgrade

sudo apt update && sudo apt upgrade

Remove previous NVIDIA installation

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#!/bin/sh

set -x

NAME=thingy
MIRROR=ftp.at.debian.org/debian

tmpdir=$(mktemp -p "${TMPDIR:-/tmp/}" -d $NAME-XXXX) || exit 1
#tmpdir=/tmp/thingy
#mkdir -p "$tmpdir"

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// gcc aml-upgrade-package-extract.c -o aml-upgrade-package-extract
// ./aml-upgrade-package-extract update-usb-burning-mode.img

// /dev/sdX - fat32 sdcard

// Make bootable Android update:

// dd if=aml_sdc_burn.UBOOT bs=1 count=442 of=/dev/sdX
// dd if=aml_sdc_burn.UBOOT seek=1 skip=1 bs=512 of=/dev/sdX
// sync

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Install the dependecies

sudo apt install qemu-system-arm qemu-system-mips qemu-efi-aarch64 qemu-kvm qemu-efi cloud-image-utils

Prepare the EFI partition

dd if=/dev/zero of=flash0.img bs=1M count=64
dd if=/usr/share/qemu-efi/QEMU_EFI.fd of=flash0.img conv=notrunc