SJ TouristShaun

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.

Below is the full structure for the SmartGrocer repository, meticulously crafted to meet your requirements for functionality, branding, commercialization, and scalability. Every file and folder is explicitly described for clarity and utility.

Repository: smartgrocer

Description: The full-stack app for grocery list management, analytics, and efficient shopping using advanced AI and SQLite. Designed to be pro-consumer and commercialized with integrity.

Root Directory

smartgrocer/ ├── README.md

Backend Setup Instructions

Use this guide to setup the backend for this project.

It uses Supabase, Drizzle ORM, and Server Actions.

Write the complete code for every step. Do not get lazy. Write everything that is needed.

Your goal is to completely finish the backend setup.

	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 argparse
	import json
	import logging
	import os
	import re
	import shutil
	from concurrent.futures import ProcessPoolExecutor, as_completed
	from dataclasses import dataclass
	from datetime import datetime
	from typing import Any, Callable, Dict, List, Literal, Optional, Tuple

	nodes:
	- id: webcam
	custom:
	source: https://huggingface.co/datasets/dora-rs/dora-idefics2/raw/main/operators/opencv_stream.py
	outputs:
	- image
	- id: idefics2
	operator:
	python: https://huggingface.co/datasets/dora-rs/dora-idefics2/raw/main/operators/idefics2_op.py
	inputs:

	// 3D Dom viewer, copy-paste this into your console to visualise the DOM as a stack of solid blocks.
	// You can also minify and save it as a bookmarklet (https://www.freecodecamp.org/news/what-are-bookmarklets/)
	(() => {
	const SHOW_SIDES = false; // color sides of DOM nodes?
	const COLOR_SURFACE = true; // color tops of DOM nodes?
	const COLOR_RANDOM = false; // randomise color?
	const COLOR_HUE = 190; // hue in HSL (https://hslpicker.com)
	const MAX_ROTATION = 180; // set to 360 to rotate all the way round
	const THICKNESS = 20; // thickness of layers
	const DISTANCE = 10000; // ¯\\_(ツ)_/¯

	"""Hello world, with a genetic algorithm.

	https://twitter.com/matthen2/status/1769368467067621791
	"""

	import random
	import time
	from dataclasses import dataclass
	from itertools import chain
	from typing import Iterable, List

	"""
	Source: https://github.com/karpathy/nanoGPT/blob/master/model.py
	"""

	import math
	import torch
	from torch import nn
	import torch.nn.functional as F

	class MaskedSelfAttention(nn.Module):


	/*
	the twitter api is stupid. it is stupid and bad and expensive. hence, this.

	Literally just paste this in the JS console on the bookmarks tab and the script will automatically scroll to the bottom of your bookmarks and keep a track of them as it goes.

	When finished, it downloads a JSON file containing the raw text content of every bookmark.

	for now it stores just the text inside the tweet itself, but if you're reading this why don't you go ahead and try to also store other information (author, tweetLink, pictures, everything). come on. do it. please?
	*/