dbreunig

import ollama
import csv

PROMPT = """
Read the following text and rephrase it in a more contemporary and casual manner:

The different accidents of life are not so changeable as the feelings
of human nature. I had worked hard for nearly two years, for the sole
purpose of infusing life into an inanimate body. For this I had
deprived myself of rest and health. I had desired it with an ardour

dbreunig

You are an AI assistant tasked with improving image generation prompts. Your goal is to take a brief, potentially vague prompt and transform it into a detailed, vivid description that will result in a more polished and interesting image output from an image generation model.

Here is the original prompt provided by the user:

<original_prompt> {{INPUT_PROMPT}} </original_prompt>

To improve this prompt, follow these guidelines:

1. Expand on the core concept of the original prompt

dbreunig

import llm
import re
import cairosvg

model = llm.get_model("gemma3:27b") # <- Swap in whatever model you want to test here
response = model.prompt("Generate an SVG of a pelican riding a bicycle")

# Extract the SVG code from the response
svg_match = re.search(r'<svg.*?</svg>', response.text(), re.DOTALL)
if svg_match:

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]

dbreunig

from faster_whisper import WhisperModel
import dspy
from typing import List
import json

# Replace as you see fit...
whisper_model = "medium.en"
podcast_path = "audio/the_invention_of_photography.mp3"

# We're using the In Our Time episode about the invention of photography.

dbreunig

from pathlib import Path
import re
import dspy
from typing import TypedDict, List
import yaml

# Set up DSPy and the LM
lm = dspy.LM('anthropic/claude-3-5-haiku-latest', api_key='YOUR_API_KEY')
dspy.configure(lm=lm)

dbreunig

class HomeController < ApplicationController
  def index
    # Intercept DuckDB requests
    if request.user_agent =~ /cpp-httplib/
      send_file(Rails.root.join('public', 'public_lists.db'), 
        type: 'application/octet-stream', 
        disposition: 'attachment')
      return
    end
    # Handle non-DuckDB requests...

dbreunig

zcat ../latest-all.json.gz | sed 's/,$//' | split -l 100000 - wd_items_cw --filter='gzip > $FILE.gz'

dbreunig

EMOJI_CODEPOINTS = [
  *0x1F600..0x1F64F,  # Emoticons
  *0x1F300..0x1F5FF,  # Misc Symbols and Pictographs
  *0x1F680..0x1F6FF,  # Transport and Map Symbols
  *0x2600..0x26FF,    # Misc symbols
  *0x2700..0x27BF,    # Dingbats
  *0x1F900..0x1F9FF,  # Supplemental Symbols and Pictographs
  *0x1FA70..0x1FAFF   # Symbols and Pictographs Extended-A
]
def random_emoji = EMOJI_CODEPOINTS.sample.chr(Encoding::UTF_8)

dbreunig

def process_file(filename)
  puts "Processing file: #{filename}"
    File.open(filename) do |filename|
      # Do stuff
    end
  end
  puts "Done processing file: #{filename}"
end

# Define the number of Ractors in the pool