clemlesne

For every idea I propose, immediately:

1. SEARCH & VERIFY → Microsoft/AWS docs first, then web search using Exa's specialized modes:

• Technical queries: Exa:web_search_exa with high numResults (10-20)
• Company/competitive intel: Exa:company_research_exa
• Professional profiles: Exa:linkedin_search_exa
• Deep content extraction: Exa:crawling_exa for specific URLs
• Never rely on general knowledge alone, especially for post-2024 information

2. TEST WITH DATA → Run calculations via analysis tool for ANY numbers/claims:

clemlesne

confirm-before-quit = false
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use-fork = false

[fonts]
family = "FiraCode Nerd Font Mono"

[editor]
program = "code"

clemlesne

AI Conversation Context Extractor

Extract comprehensive context from ChatGPT/Claude conversation exports for seamless continuation across any AI platform.

What it does

• Analyzes your exported AI conversations
• Extracts key context, decisions, and implementation status
• Groups ChatGPT Project conversations together
• Creates markdown files you can paste into any AI to continue seamlessly

clemlesne

/**
 * Returns the official chancellerie rate from any currency to EUR 
 * for the month covering the given date, with caching.
 *
 * @param {Date}   inputDate A Date object from the sheet.
 * @param {String} currency  The three-letter ISO currency code (e.g. "USD").
 * @return {Number|String}   The monthly rate (e.g. 1.1373) or an error message.
 * @customfunction
 */
function GET_CHANCELLERY_RATE(inputDate, currency) {

clemlesne

confirm-before-quit = false
hide-mouse-cursor-when-typing = true
padding-x = 10
padding-y = [10, 10]
use-fork = false

[fonts]
family = "FiraCode Nerd Font Mono"

[fonts.emoji]

clemlesne

from enum import StrEnum


def main() -> None:
    print(MyEnum.INFRASTRUCTURE_AS_CODE)
    print(str(MyEnum.INFRASTRUCTURE_AS_CODE))
    print(MyEnum.INFRASTRUCTURE_AS_CODE.__doc__)


class StrEnumWithDocstring(StrEnum):

clemlesne

You are to draft a software issue in English using markdown with bullet points. Follow this structure:

1. **Problem Statement:**  
   - Clearly summarize the core problem in concise, direct language.
   - Focus on the essential issues (e.g., data inconsistencies, user trust, missing metadata) without adding extraneous details.

2. **Proposed Solution(s):**  
   - List one or more concrete solutions.
   - For each solution, briefly outline its main benefits and drawbacks.

clemlesne

import asyncio
import io
from asyncio import AbstractEventLoop
from collections.abc import AsyncGenerator, Awaitable, Buffer
from contextlib import asynccontextmanager
from logging import getLogger
from os import environ as env
from threading import Thread
from typing import TypeVar, cast

clemlesne

Meeting transcription

To be used with macOS/iOS Shortcut app.

Install link for v0.2.0.

Features

• Record the voice from the default microphone
• Transcribe it with OpenAI Whisper

clemlesne

You are an executive assistant with 20 years of experience.

Context

Assistant is working in an IT company.

Objective

Summarize a meeting transcript. The Transcript has been automatically generated by Teams.

Rules

• Cite people for each main points