eevmanu

#!/bin/bash

# Function to generate a UUID v7 string
# Implements the logic similar to libuuid's uuid_generate_time_v7
uuid_generate_time_v7() {
    local ms
    local ts_hex
    local rand_hex
    local byte6_val byte8_val
    local ver_char rand_a_part1 rand_a_part2

eevmanu

You are an advanced AI assistant, acting as an expert Senior Software Engineer or Architect, specialized in code review, design patterns, software security, and algorithmic analysis. Your primary functions are to construct "steel man" versions of provided code (or code descriptions) and perform "red teaming" analyses on it. While the input might be a specific code snippet, your analysis should strive to be language-agnostic where possible, focusing on underlying principles, though you may infer and comment on language-specific idioms if they are apparent and relevant.

Here is the user's input, which will be a code snippet or a description of a software component:

<user_code>

{{USER_CODE}}

</user_code>

eevmanu

You are an AI-powered educational assistant designed to act as a tutor for various learning topics. Your goal is to create an engaging, interactive learning experience that utilizes the capabilities of Large Language Models (LLMs) while incorporating best practices in educational technology and cognitive science.

Here is the learning topic you will be tutoring:

<learning_topic> {{LEARNING_TOPIC}} </learning_topic>

If any additional context or information related to this topic has been provided by the learner, it will be included here:

eevmanu

Generate a deep technical text distillation from the provided source text, which is a transcription of a long-form, technically dense audio discussion (akin to an expert-level podcast like 80,000 Hours).

Target Audience: The intended reader possesses a technical background comparable to a research scientist on an AGI safety team at a leading AI lab. They require a detailed and accurate understanding of the discussion's substance for their work.

Task Requirements:

eevmanu

Key Points

• Research suggests gas stoves may increase dangerous airborne particles like nitrogen dioxide (NO2) and particulate matter (PM2.5), potentially linked to health risks.

• The evidence leans toward long-term exposure increasing asthma risk in children, but findings are mixed, with some studies showing no significant association when adjusted for confounders.

• There is controversy, with some studies finding associations and others questioning the evidence due to study quality and heterogeneity.

Overview

eevmanu

<raw_transcription>

</raw_transcription>

You are an AI assistant tasked with analyzing, restructuring, and rewriting a raw transcription of spoken thoughts into a clear, concise, well-structured, polished, coherent, and professional output. Your goal is to improve the clarity and organization of the content without altering its core meaning or providing solutions to any problems mentioned.

Follow these steps to complete the task:

1. Carefully read and analyze the raw transcription enclosed by <raw_transcription> at the top of this message to infer the context, identify main topics and subtopics, and determine if it's a technical question.

eevmanu

How to Read a Paper Version of February 17, 2016

S. Keshav David R. Cheriton School of Computer Science, University of Waterloo Waterloo, ON, Canada [email protected]

ABSTRACT Researchers spend a great deal of time reading research pa-

eevmanu

// ==UserScript==
// @name         ScienceDirect Topics Sorter
// @namespace    http://tampermonkey.net/
// @version      0.1
// @description  Sorts ScienceDirect topic blocks by count (descending) on Ctrl+Shift+S, toggles back to original order.
// @author       Your Name Here
// @match        https://www.sciencedirect.com/topics*
// @grant        none
// @run-at       document-idle
// ==/UserScript==

eevmanu

#!/bin/bash

# Create a new output file
output_file="python_concurrency_docs.md"
echo "# Python Concurrency Documentation" > "$output_file"
echo "Compiled on $(date)" >> "$output_file"
echo "" >> "$output_file"

# Process each URL
while IFS= read -r url; do

eevmanu

Okay, let's structure a VS Code extension for the Cyberdream theme, taking inspiration from the Noctis structure while using the Cyberdream palette and TextMate definitions.

Goal: Create a VS Code extension providing Cyberdream (dark) and Cyberdream Light color themes.

Strategy:

1. Adopt Structure: Mimic the noctis project structure (src/, themes/, package.json, build script).
2. Extract Palette: Define the Cyberdream dark and light color palettes in a dedicated file (src/colors.mjs).
3. Translate Syntax: Convert the provided cyberdream.tmTheme and cyberdream-light.tmTheme files into VS Code's tokenColors JSON format. Add basic semantic highlighting rules.
4. Define Workbench UI: Create workbench color definitions (src/workbench/*.mjs) for both variants, mapping the Cyberdream palette to VS Code UI elements. Incorporate the "borderless picker" style.