Raycast has become an indispensable tool in my workflow. The ability to quickly automate tasks and create custom integrations boosts my productivity daily. One common need I have is getting key summaries from long blocks of text. For example, summarize a long blog article I’m too lazy to read. Or quickly understand what is going on based on a long email thread. While there are pre-built Raycast AI extensions, I prefer to use Amazon Bedrock for privacy and security.
In the past, I used to copy the text and paste it into an AI chatbot, but this context switching was cumbersome and interrupted my flow. Raycast is a more natural way to summarize a piece of text I just copied.
I have been using Joplin for work for two years now, and I love it. Joplin is a free and open source note-taking app that lets you create notes in Markdown format, sync them across devices, and encrypt them for privacy. Joplin is also very flexible and customizable, allowing you to use plugins, themes, templates, and more to suit your needs.
One of the features that I recently discovered in Joplin is linking to local files and folders on my computer. This allows me to access my work documents without storing them in Joplin’s database, which can get too large and slow down the app. It also allows me to organize my files and folders in my own way, and take advantage of file sync services like Dropbox or Amazon WorkDocs. In this blog post, I will show you how to link to local files and folders in Joplin, and how to quickly copy the full file path from your file explorer.
Llm-chain is a Rust crate that help you create advanced LLM applications such as chatbots, agents, and more. It supports various drivers, such as OpenAI, llama.cpp, and llm, that can connect to different APIs or run models locally. llm-chain allows you to easily switch between different drivers and options without a complete rewrite of your code.
One of the challenges of using llm-chain is the cost associated with invoking the LLMs. Depending on the driver you use, you may incur either an API fee or a compute resource cost for running your own model. The local models usually requires pretty powerful machines, and the setup process might be a little complicated.
Hi, everyone! I hope you are all doing well and enjoying Rust as much as I do. I have some exciting news to share with you today: I have just published my new book, Practical Rust Projects, 2nd Edition.
I would like to acknowledge the contribution of my co-author, Andrew Rzeznik, who took over the project after I had to stop for personal circumstances. He did a fantastic job in finishing the book and making it even better than I could have imagined. I am very happy with the final outcome of our collaboration, and I hope you will find our book useful and enjoyable. Thank you, Andrew Rzeznik, for your hard work and dedication!
Recently I’ve been contributing to llm-chain, a Rust library for working with large language models (LLMs). It’s similar to Python’s LangChain. I contributed this tutorial to the official website for setting up a simple llm-chain using LLaMA models running locally using the llama.cpp project. I’m also publishing the same tutorial in this blog for archiving.
Tutorial: Getting Started using the LLAMA driver
In this tutorial, you will learn how to set up an llm-project using the LLAMA drive. If you wish to use the other drivers you can skip this part of the tutorial.
