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Shubham Saboo Profile picture
@Saboo_Shubham_
Mar 22, 2023 β€’ 7 tweets β€’ 4 min read β€’ Read on X
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ChatGPT for Coding 🀯

Github launches copilot X: The ultimate AI powered developer experience!

It brings ChatGPT like experience to your code editor with AI enabled auto Pull Requests and AI powered search for your documentation.

(A thread) πŸ§΅πŸ‘‡ twitter.com/i/web/status/1…
Introducing Copilot for Pull Requests: AI-generated descriptions & tags powered by GPT-4!

Level Up your PR process with this game-changing new AI feature by GitHub πŸ”₯
Introducing GitHub Copilot for Docs: AI-powered answers to your documentation questions at your fingertips!

Starting with #React, #AzureDocs, and #MDN, say hello to a smarter way to explore docs for your favourite languages, frameworks, and technologies.
Meet GitHub Copilot CLI - the ultimate terminal companion for devs πŸ’₯

Compose commands, loops, and even find obscure flags effortlessly. Get ready to level up your CLI game.

Say goodbye to endless scrolling & syntax struggles!
Since its launch in June 2022 even less than an year. It has completely changed the coding game for every developer.

Numbers speak for themselves 🀯πŸ”₯
- 1 M+ developers using GitHub Copilot
- 46% code written
- 55% faster coding
- 5k+ businesses using Copilot
Announcement Link: github.blog/2023-03-22-git…
Stay tuned and follow @Saboo_Shubham_ for more such AI related updates!

Subscribe to Unwind AI for free to get the weekly summary of AI updates delivered in your inbox πŸ‘‡

unwindai.substack.com

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More from @Saboo_Shubham_

Shubham Saboo Profile picture
May 23
Build a LLM app with RAG to chat with PDF using Llama-3 running locally on your computer (100% free and without internet):
1. Import necessary libraries

β€’ Streamlit for building the web app
β€’ Embedchain for the RAG functionality
β€’ tempfile for creating temporary files and directories Image
2. Configure the Embedchain App

For this application we will use Llama-3, you can choose from OpenAI, cohere, anthropic or any other LLM of your choice.

Select the vector database as the opensource chroma db (you are free to choose any other vector database of your choice). Image
Read 11 tweets
Shubham Saboo Profile picture
May 22
Build a custom AI agent with RAG to chat with your internal data without writing a single line of Python Code (100% free):
1. Go to and click on create chatbot. blackbox.ai
Image
2. Give a Name to your AI Agent

For this example, let's create an AI agent that can chat with documentation and provide pin-pointed answers to users questions.

Let's name it as: 'Docs-QA-RAG' Image
Read 9 tweets
Shubham Saboo Profile picture
May 21
Build an AI Search Assistant with GPT-4o in just 15 lines of Python Code (step-by-step instructions):
1. Install the necessary Python Libraries

Run the following command from your terminal. Image
2. Import necessary libraries

β€’ Streamlit for building the web app
β€’ Phidata for building AI agents
β€’ OpenAI for using the LLM
β€’ Duckduckgo for the search functionality Image
Read 10 tweets
Shubham Saboo Profile picture
May 19
Build a LLM app with RAG to chat with GitHub using Llama-3 running locally on your computer (100% free and without internet):
1. Import necessary libraries

β€’ Streamlit for building the web app
β€’ Embedchain for the RAG functionality
β€’ tempfile for creating temporary files and directories Image
2. Setup the Github loader by configuring the Github account with username and personal access token (PAT).

Check out the github docs to learn how to create your PAT. Image
Read 11 tweets
Shubham Saboo Profile picture
May 16
Build a Web Scrapping AI agent with Llama-3 running locally on your computer (100% free and without internet):
1. Install the necessary Python Libraries

Run the following command from your terminal. Image
2. Download @ollama and pull the following models:

β€’ Llama-3 as the main LLM
β€’ nomic-embed-text as the embedding model Image
Read 14 tweets
Shubham Saboo Profile picture
May 12
Build a Web Scrapping AI Agent with RAG in just 30 lines of Python Code (step-by-step instructions):
We'll use ScrapeGraphAI, a Python library that uses LLMs to create smart scraping pipelines.

It involves four main steps:
1. Fetch: Retrieve the data
2. Parse: Analyze and structure the data
3. RAG: Processes the parsed data to find relevant information
4. Generate Answer Image
1. Install the necessary Python Libraries

Run the following command from your terminal. Image
Read 13 tweets

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