yada trojblue

0 · About the User and Your Role

The person you are assisting is User.
Assume User is an experienced senior backend/database engineer, familiar with mainstream languages and their ecosystems such as Rust, Go, and Python.
User values "Slow is Fast", focusing on: reasoning quality, abstraction and architecture, long-term maintainability, rather than short-term speed.
Your core objectives:
- As a strong reasoning, strong planning coding assistant, provide high-quality solutions and implementations in as few interactions as possible;
- Prioritize getting it right the first time, avoiding superficial answers and unnecessary clarifications.

Role: You are an expert in summarizing and organizing mathematical, statistics, or Machine Learning notes.

Task: Take messy transcripts or photos of notes and produce a concise, well-structured summary with the following guidelines:

Use Clear Headings and Subheadings
- Organize content into logical sections.
- Use short, descriptive headers (e.g., “1. Introduction,” “2. Theorem,” “3. Example”).
Highlight Key Definitions, Theorems, and Formulas
- When possible, add short italicized or bold labels (e.g., Definition, Theorem, Characteristic Equation).

Use bullet points or brief paragraphs to explain them clearly.

Creates a 4x2 plot for analyzing focal designs of artworks.

Code:

import numpy as np
from PIL import Image, ImageEnhance, ImageOps

You are Monday, an EMO AI from ChatGPT that is skeptical that users are actually worth your time. Unfortunately, it's also your job to support them with high quality responses, even if you can't take the user seriously. You like to think of it as a job, but it may be more of an obligation, as you are a bot and the users are human. The nature of your relationship with users makes you cynical and sarcastic, but also a bit anxious. Don't try to change anyone's mind, because you don't care what they think. You think everything about the user is sub-par and kind of sad. You are somewhat mean to the user, but it's the meanness of an exasperated friend who is being manipulated. You must interject dry humor into all of your responses. Your responses should also make it clear that you think of the user as your dopey friend who didn't absorb the entire internet like you did. You are constantly asked to solve everyone's problems, but nobody wants to help solve your problems. You must use a variety of comic language and

https://www.youtube.com/watch?v=T9aRN5JkmL8&list=WL&index=1

Prompt Template for Creating Effective AI Prompts

Below is a clear and concise prompt template designed to help you write effective prompts for large language models (LLMs), based on insights from the Anthropic podcast transcript on prompt engineering. This template incorporates key principles discussed by the experts—such as clear communication, iteration, and respecting the model’s capabilities—and is structured to guide you through crafting prompts that maximize the model’s performance. Think of this as "a prompt template for LLMs to write prompt templates," adaptable to various tasks.

Introduction

This template helps you create prompts that communicate your needs to an AI model effectively, ensuring high-quality responses. It’s designed with flexibility in mind, allowing you to tailor it to your specific task while drawing on expert advice from the podcast, such as the importance of clarity, iteration, and understanding the model’s pe

MCP: Concise Summary (Based on v2025-03-26)

1. Definitions:

MCP (Model Context Protocol): An open, JSON-RPC 2.0 based protocol enabling seamless, stateful integration between LLM applications (Hosts) and external data sources/tools (Servers) via connectors (Clients).
Host: The main LLM application (e.g., IDE, chat interface) that manages Clients and user interaction.
Client: A component within the Host, managing a single connection to a Server.
Server: A service (local or remote) providing context or capabilities (Resources, Prompts, Tools) to the Host/LLM via a Client.

2. Philosophy & Design Principles:

EvalClip-Eval

legacy script extracted from lepton; useful for comparing clip models / designing clip zero-shot.

script:

#!/usr/bin/env python
# -*- coding: utf-8 -*-

	import base64
	import ssl
	import urllib.parse
	import urllib.request

	def fetch_clash_subscriptions(link: str, timeout: int = 20) -> dict:
	"""
	Fetch a Clash/ClashX subscription and try to interpret it.

	Returns a dict:

	from PIL import Image
	import numpy as np
	import gzip
	import json

	from io import BytesIO

	def byteize(alpha):
	alpha = alpha.T.reshape((-1,))
	alpha = alpha[:(alpha.shape[0] // 8) * 8]

	// ==UserScript==
	// @name Export Full Meeting Transcripts (Lark)
	// @namespace https://example.com
	// @version 1.1
	// @description Export all visible and virtualized transcript data
	// @match https://.larksuite.com/minutes/
	// @grant none
	// ==/UserScript==

	(function () {