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"

Brandon7CC

function speak_ollama() {
    if ! command -v ollama &> /dev/null; then
        echo "Error: ollama is not installed."
        return 1
    fi

    if [ "$#" -ne 2 ]; then
        echo "Usage: speak_ollama <file_path> <model>"
        return 1
    fi

vivgui

Here are my favorite tech newsletters:

• Frontend Focus: A once–weekly roundup of the best front-end news, articles and tutorials. HTML, CSS, WebGL, Canvas, browser tech, and more.
• This Week In React: The latest news directly in your inbox!
• Web Tools Weekly: A weekly newsletter for front-end developers with a specific focus on tools.
• NEXT.JS Weekly: A newsletter of the best Next.js articles, tools and projects
• JavaScript Weekly: A newsletter of JavaScript articles, news and cool projects
• Friday Front-End: Front-end development links tweeted daily, emailed weekly.
• CSS Weekly: Weekly e-mail roundup of CSS articles, tutorials, experiments, and tools curated by Zoran Jambor
• Liquid Weekly: Your one-stop shop for all things Shopify Development

willbchang

#!/bin/zsh
# #popclip
# name: Azure TTS
# icon: symbol:message.and.waveform

# Please apply for your own key
AZURE_REGION=
AZURE_SUBSCRIPTION_KEY=

# Create a temporary audio file

mrjk

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Version: 08-2024

# Migrated to: https://github.com/mrjk/python_snippets/blob/main/examples/typer/cli-app-typer.py

"""MyApp CLI interface

This CLI provides a similar experience as the git CLI, but in Python with Typer.

hwchase17

from langchain.chat_models import ChatOpenAI
from pydantic import BaseModel, Field
from langchain.document_loaders import UnstructuredURLLoader
from langchain.chains.openai_functions import create_extraction_chain_pydantic

class LLMItem(BaseModel):
    title: str = Field(description="The simple and concise title of the product")
    description: str = Field(description="The description of the product")

def main():

kylemcdonald

CharlesNepote

#!/usr/bin/env bash
# sudo ln -s "$(pwd)/csv2datasette" /usr/bin/csv2datasette

# csv2datasette is meant to explore CSV data. It is not meant to create a sustainable DB.
# csv2datasette is a bash script which open CSV files directly in Datasette. It offers
# a number of options for reading and exploring CSV files, such as --stats, inspired by WTFCsv.
#
# `--stats` option includes, for each column: the column name, the number of unique values,
# the number of filled rows, the number of missing values, the mininmum value, the maximum value, 
# the average, the sum, the shortest string, the longest string, the number of numeric values, 

hwchase17

from langchain.document_loaders import YoutubeLoader
from langchain.indexes import VectorstoreIndexCreator
urls = [
    ("https://www.youtube.com/watch?v=fP6vRNkNEt0", "Prompt Injection"),
    ("https://www.youtube.com/watch?v=qWv2vyOX0tk", "Low Code-No Code"),
    ("https://www.youtube.com/watch?v=k8GNCCs16F4", "Agents In Production"),
    ("https://www.youtube.com/watch?v=1gRlCjy18m4", "Agents"),
    ("https://www.youtube.com/watch?v=fLn-WqliEQU", "Output Parsing"),
    ("https://www.youtube.com/watch?v=ywT-5yKDtDg", "Document QA"),
    ("https://www.youtube.com/watch?v=GrCFyyyAxCU", "SQL"),

hwchase17

from langchain.document_loaders import YoutubeLoader
from langchain.indexes import VectorstoreIndexCreator

loader = YoutubeLoader.from_youtube_url("https://www.youtube.com/watch?v=fLn-WqliEQU&lc=UgyOc6oNr_4-YLGCL2R4AaABAg", add_video_info=False)

docs = loader.load()

index = VectorstoreIndexCreator()

index = index.from_documents(docs)