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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. |
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#!/usr/bin/env python3 | |
import os | |
import json | |
import kfp | |
import kfp.dsl | |
import kfp.components | |
from kubernetes.client.models import V1EnvVar |
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# Load libraries | |
import re | |
import time | |
import operator | |
import numpy as np | |
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer | |
from scipy.sparse import csr_matrix | |
import pandas as pd |
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#!/bin/bash | |
INSTALL_PATH=<your_local_install_path_goes_here> | |
curl -LO https://mirrors.kernel.org/gnu/gcc/gcc-4.8.5/gcc-4.8.5.tar.bz2 | |
tar xf gcc-4.8.5.tar.bz2 | |
mkdir build |
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""" | |
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 |
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Preface: | |
I have no idea if any of this is the "right" way of doing it. This is just how I got my unit working. | |
Good luck. | |
WARNING: Do not use a V1 Waveshare display module with a stock pwnagotchi! The pwnagotchi expects a V2 module, and may irreparably damage a V1 module! | |
A V1 display module *can* work, but you must modify `waveshare.py` and `display.py` beforehand. For the time being, this is left as an exercise for the reader. | |
If you have already burnt a V1 display, try disconnecting it from the Pi and leaving it overnight. This may rejuvenate the display somewhat, but it will likely still display signs of damage. | |
Installation: | |
1. `dd` Raspbian Lite to an SD card. |
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# https://hakibenita.com/fast-load-data-python-postgresql | |
from typing import Iterator, Dict, Any, Optional | |
from urllib.parse import urlencode | |
import datetime | |
#------------------------ Profile | |
import time |
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import pyodbc | |
from subprocess import run, CalledProcessError | |
import os | |
from string import Template | |
import pandas as pd | |
''' ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~''' | |
''' DATABASE CONNECTION FILE (keep it simple) ''' | |
''' ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~''' |
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# Install R | |
sudo apt update | |
sudo apt install gdebi libxml2-dev libssl-dev libcurl4-openssl-dev libopenblas-dev r-base r-base-dev | |
# Install RStudio | |
cd ~/Downloads | |
wget https://download1.rstudio.org/desktop/bionic/amd64/rstudio-1.2.5001-amd64.deb | |
sudo gdebi rstudio-1.2.5001-amd64.deb | |
printf '\nexport QT_STYLE_OVERRIDE=gtk\n' | sudo tee -a ~/.profile |
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