Add this file to your AI assistant's system prompt or context to help it avoid common AI writing patterns. Source: tropes.fyi by ossama.is
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| """List Python modules that appear unused according to Ruff's import graph. | |
| The result is intentionally conservative: modules used as scripts or mentioned | |
| by non-Python files are excluded. Use ``--delete`` only after reviewing the | |
| default output. | |
| """ | |
| from __future__ import annotations | |
| import argparse |
Construí un MVP de buscador para un corpus completo de y sobre Jorge Luis Borges, usando Python 3.14, FastAPI, SQLAlchemy 2 async, Alembic, PostgreSQL en Neon y despliegue en FastAPI Cloud.
Objetivo:
- descargar libros de Borges
- Importar libros EPUB de Borges y también textos “sobre Borges”.
- Preservar estructura editorial y bibliográfica.
- Permitir búsqueda textual, semántica e híbrida.
- Mostrar fragmentos relevantes, ampliar contexto y abrir el texto completo.
- Filtrar/facetar por autor, obra, libro/edición, año, década, tipo de texto y naturaleza del documento.
- Sugerir fragmentos y obras relacionadas.
Complementa Arquitectura. Referencias al código apuntan a
developen GitHub.
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| #!/usr/bin/env bash | |
| # Android phone as webcam for Linux via scrcpy + v4l2loopback. | |
| # | |
| # Initial setup recap: | |
| # 1. Install build/runtime dependencies if needed: | |
| # sudo apt install ffmpeg libsdl2-2.0-0 adb wget \ | |
| # gcc git pkg-config meson ninja-build libsdl2-dev \ | |
| # libavcodec-dev libavdevice-dev libavformat-dev libavutil-dev \ | |
| # libswresample-dev libusb-1.0-0 libusb-1.0-0-dev \ |
Este proyecto existe para probar consultas sobre Datos Abiertos de Argentina usando CKAN MCP con el menor nivel posible de alucinación.
La prioridad es:
- responder sólo con evidencia trazable a datasets y recursos concretos;
- distinguir claramente entre hechos observados, cómputos derivados e inferencias;
- rechazar o limitar respuestas cuando el portal no alcance para sostenerlas.
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| # /// script | |
| # requires-python = ">=3.12" | |
| # dependencies = [ | |
| # "tl-parser==0.7.11", | |
| # "beautifulsoup4", | |
| # "lxml", | |
| # "pyquery", | |
| # "matplotlib", | |
| # ] | |
| # |
$ llm \
--schema '{
"type": "object",
"properties": {
"customer": {
"type": "string",
"description": "Nombre del cliente"
},
"items": {
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