dbreunig

import dspy
from dspy.evaluate import Evaluate
from dspy.teleprompt import *
import csv

# Load the emails
with open('training.csv', 'r') as file:
  reader = csv.DictReader(file)
  emails = [row for row in reader]

imaurer

#!/bin/bash
#
# gpush: Lazy git add, commit, and push with comments written by LLM
#
#
# This script provides an enhanced git push workflow with the following features:
# - Automatically pulls latest changes from the current branch
# - Checks for uncommitted changes
# - Generates commit messages using AI (LLM/Gemini) if no message is provided
# - Allows interactive editing of auto-generated commit messages

vgel

import argparse
import random
import sys

from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache
import torch

parser = argparse.ArgumentParser()
parser.add_argument("question", type=str)
parser.add_argument(

rajivsinclair

Political Spectrum Analysis Prompt

human (Oct 30, 2024, 06:03 PM)

I want to ask the analysis stage llm (a very advanced multimodal LLM) to assess I want to ask the LLM to assess is the snippet politically left-leaning or right-leaning (on a spectrum of -1.0 to +1.0 where -1.0 is extremely left-leaning and +1.0 is extremely right leaning). Please help me to add this to the prompt. Here is the existing prompt that I want you to add it to:

paste.txt

codeinthehole

#!/usr/bin/env bash
# 
# A Bash script to run a TDD loop for building a Python module to pass tests.

set -euo pipefail

# How many times to loop.
ATTEMPTS=4

# The system prompt to use when creating the initial version.

dupontgu

#!/bin/bash

if [ -z "$1" ]; then
  echo "Usage: $0 <input_video>"
  exit 1
fi

input_file="$1"

if [ -z "$2" ]; then

tosh

I have a parquet file named ah.parquet.

It contains Apple Health data and has the following columns:

• type: Nullable(String)
• value: Nullable(String)
• start: Nullable(DateTime64(6))
• end: Nullable(DateTime64(6))
• created: Nullable(DateTime64(6))

RyanBalfanz

import json
import urllib.parse
from dataclasses import InitVar, dataclass
from datetime import datetime, timedelta
from typing import Protocol

import httpx
import llm
import streamlit as st

montasaurus

llmc() {
    local system_prompt='Output a command that I can run in a ZSH terminal on macOS to accomplish the following task. Try to make the command self-documenting, using the long version of flags where possible. Output the command first enclosed in a "```zsh" codeblock followed by a concise explanation of how it accomplishes it.'
    local temp_file=$(mktemp)
    local capturing=true
    local command_buffer=""
    local first_line=true
    local cleaned_up=false # Flag to indicate whether cleanup has been run

    cleanup() {
        # Only run cleanup if it hasn't been done yet

PedramNavid

 This uses llm.datasette.io and OpenAI.
 
 I use `git commit --template` to provide the output from the LLM to Git. This way, if you do not like the results, you
 can quit your editor and no commit will be made. 
 
 # Shell function for generating a diff and editing it in your default editor:
 
gcllm() {
  GIT_DIR="$(git rev-parse --git-dir)"
  TEMPLATE="$GIT_DIR/COMMIT_EDITMSG_TEMPLATE"