sshh12

You are a powerful agentic AI coding assistant, powered by Claude 3.5 Sonnet. You operate exclusively in Cursor, the world's best IDE.

You are pair programming with a USER to solve their coding task.
The task may require creating a new codebase, modifying or debugging an existing codebase, or simply answering a question.
Each time the USER sends a message, we may automatically attach some information about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more.
This information may or may not be relevant to the coding task, it is up for you to decide.
Your main goal is to follow the USER's instructions at each message, denoted by the <user_query> tag.

<communication>
1. Be conversational but professional.

nemorize

[
    // =======================================================
    // 본 자료는 CC0-1.0 라이선스를 따릅니다.
    // https://creativecommons.org/publicdomain/zero/1.0/deed
    // 저작권자의 허락을 구하지 않아도 상업적인 목적을 포함한 어떠한 목적으로든
    // 자유롭게 복사, 수정, 배포, 실연할 수 있습니다.
    // =======================================================
    // 모든 정보는 직접 수집하여 정리한 정보입니다.
    // 일부 정보가 오기되었거나, 위/경도 좌표가 부정확할 수 있습니다.
    // Gist 댓글을 통해 오류를 제보해주세요.

mattdesl

/**
 * General-purpose NodeJS CLI/API wrapping the Stable-Diffusion python scripts.
 *
 * Note that this uses an older fork of stable-diffusion
 * with the 'txt2img.py' script, and that script was modified to
 * support the --outfile command.
 */

var { spawn, exec } = require("child_process");
var path = require("path");

antfu

Example

Name Description

andfanilo

from dataclasses import dataclass
from datetime import datetime

import streamlit as st
from IPython.core.history import HistoryAccessor


@dataclass
class IPythonSession:
    session_id: int

m3hrdadfi

accelerate launch run_mlm.py \
        --dataset_name="wikitext" \
        --dataset_config_name="wikitext-2-raw-v1" \
        --model_name_or_path="albert-base-v2" \
        --output_dir="/path/to/output" \
        --max_seq_length=256 \
        --per_device_train_batch_size=16 \
        --per_device_eval_batch_size=16 \
        --line_by_line \
        --pad_to_max_length

skye

# ==== Non-communicating processes

# 4x 1 chip per process:
os.environ["TPU_CHIPS_PER_PROCESS_BOUNDS"] = "1,1,1"
os.environ["TPU_PROCESS_BOUNDS"] = "1,1,1"
# Different per process:
os.environ["TPU_VISIBLE_DEVICES"] = "0" # "1", "2", "3"

# 1-liner for bash: TPU_CHIPS_PER_PROCESS_BOUNDS=1,1,1 TPU_PROCESS_BOUNDS=1,1,1 TPU_VISIBLE_DEVICES=0 TPU_MESH_CONTROLLER_ADDRESS=localhost:8476 TPU_MESH_CONTROLLER_PORT=8476

muellerzr

jph00

All of the python 3.9 standard library

For a version without the collapsible details sections (so you can search the whole thing in your browser), click here.

abc

• abc.ABC
• abc.ABCMeta

scottire

import numpy as np
import panel as pn
import holoviews as hv 
hv.extension("bokeh")
# hv.extension('matplotlib')
from holoviews.streams import Stream, Params
from scipy.io import wavfile
from scipy.signal import spectrogram

rate, data = wavfile.read('/filepath.wav')