minimaxir

Agent Guidelines for Python Code Quality

This document provides guidelines for maintaining high-quality Python code. These rules MUST be followed by all AI coding agents and contributors.

Your Core Principles

All code you write MUST be fully optimized.

"Fully optimized" includes:

gd3kr

/*
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?
*/

harshildarji

import json
import os
import string
import zipfile

from lxml import etree
from nltk.tokenize import RegexpTokenizer
from tqdm import tqdm

import docx

EcZachly

-- first query all the users 
WITH offsets AS (SELECT a.*,
                        EXTRACT(hour FROM ptn.utc_offset) AS utc_offset
                 FROM bootcamp.attendees a
                          JOIN pg_timezone_names ptn ON a.timezone = ptn.name
                 WHERE a.bootcamp_version = 3
                   AND a.timezone IS NOT NULL
                   AND a.content_delivery = 'Live'::text
                  ),
-- then aggregate the users by track and offset, we want matching timezones to fill up first

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

vrslev

import os

import arel
from fastapi import FastAPI, Request
from fastapi.templating import Jinja2Templates

app = FastAPI()
templates = Jinja2Templates("templates")

if _debug := os.getenv("DEBUG"):

bendichter

# MIT LICENSE


import datetime
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import numpy as np
import dash_core_components as dcc
import dash_html_components as html
import dash

containeroo-gists

version: '3'

services:
  traefik:
    image: traefik:v2.0
    container_name: traefik
    restart: unless-stopped
    security_opt:
      - no-new-privileges:true
    networks:

tvst

import streamlit.ReportThread as ReportThread
from streamlit.server.Server import Server


def get_session_id():
    # Hack to get the session object from Streamlit.

    ctx = ReportThread.get_report_ctx()

    this_session = None

ines

"""
Example of a Streamlit app for an interactive Prodigy dataset viewer that also lets you
run simple training experiments for NER and text classification.

Requires the Prodigy annotation tool to be installed: https://prodi.gy
See here for details on Streamlit: https://streamlit.io.
"""
import streamlit as st
from prodigy.components.db import connect
from prodigy.models.ner import EntityRecognizer, merge_spans, guess_batch_size