social-ai VS langchain

I wrote some code to annotate the image with the text of the prompt. I couldn't find a convenient way to do it in EXIF.

For Midjourney: https://github.com/ernop/social-ai/tree/main/SocialAI

Yeah, it's really strange how hard they make it to manage, download, and get full prompts for your images on all these platforms. I made this discord bot for midjourney which with some easy configuration can download and annotate all your images, including as much info as I could grab about version, etc. https://github.com/ernop/social-ai/tree/main/SocialAI

Even then it's not perfect since I'm getting info off of the command you send, which may have fallen into whatever the defaults were at the time, and so when interpreted today, not easily possible to reconstruct the version/seed/etc. from that point in the past, if you didn't include it in the prompt. But still, I just like having a folder of 30k images that I can never lose, with at least the prompt, so I can go through and re-run them later (even manually) to get comparisons over time.

Repo for my C# bot which you can use to download (and annotate) all your creations. https://github.com/ernop/social-ai/

LangChain is one of the most used frameworks to create AI agents with, if not THE most used. With nearly 90.000 stars and almost 14000 forks on GitHub at the time of this article, we can safely say it is popular.

LangChain - Python and JS libraries, provides convenient functions for interacting with Amazon Bedrock’s models and related services like vector databases

Undoubtedly, LangChain is the most popular framework for AI application development at the moment. The advent of LangChain has greatly simplified the construction of AI applications based on Large Language Models (LLM). If we compare an AI application to a person, the LLM would be the "brain," while LangChain acts as the "limbs" by providing various tools and abstractions. Combined, they enable the creation of AI applications capable of "thinking." However, this article does not delve into the specific usage of LangChain but aims to discuss with readers the last-mile issue in LangChain application development—how to deploy LangChain applications, using AWS as an example. Why deploy on AWS? The free tier is simply too appealing for daily use.

The number of tools and functions that aim to enhance the abilities of language models (LMs) is growing rapidly. For example, the popular LM framework LangChain grew its tool catalog from three to seventy-seven in the last 15 months. However, this approach of building tools for every little thing may be misguided and ultimately counterproductive. Instead, providing AI with direct access to a terminal, where it can use the many command line tools already created, and even create its own tools, will lead to more powerful, flexible, and future-proof systems.

Limited by the current packaging method of Pluto, it does not yet support LangChain's Template Ecosystem. Coming soon

Big fan of Aider.

We are interesting in integrating Aider as a tool for Dosu https://dosu.dev/ to help it navigate and modify a codebase on issues like this https://github.com/langchain-ai/langchain/issues/8263#issuec...

Developed using Langchain and Streamlit technologies for enhanced performance.

Tip: Kenton Blacutt, an AWS Associate Cloud App Developer, collaborated with Langchain, creating the Amazon Dynamodb based memory class that allows us to store the history of a langchain agent in an Amazon DynamoDB.