OpenDevin: An Open-Source AI Software Engineer Project

📖 TUTORIALS & CASE STUDIES

Fine-tuning Mixtral 8x7B with AutoTrain: A Step-by-Step Guide

read time: 10 minutes

This blog post provides a detailed guide on how to fine-tune the Mixtral 8x7B model on your own dataset using AutoTrain. It covers the process for both local and cloud-based training, specifically using Hugging Face's DGX Cloud. The post also includes a CLI command for those who prefer it.

Unlocking the Power of Retrieval-Augmented Generation with Large Language Models

read time: 20 minutes
This article explores the limitations of Large Language Models (LLMs) and how Retrieval-Augmented Generation (RAG) systems can overcome these limitations. It provides a comprehensive guide on building a RAG system using LangChain, a user-friendly framework, and demonstrates how to integrate an LLM with the RAG system to enhance its capabilities.

Running Open-Source Large Language Models Locally with Ollama

read time: 10 minutes
This guide provides a walkthrough on how to use Ollama, a tool for interacting with open-source large language models (LLMs) like LLaMA 2 and LLaVA. It covers downloading Ollama, and running text-based and multimodal models locally, offering developers transparency and customization.

🧰 TOOLS

OpenDevin: An Open-Source AI Software Engineer Project

read time: 8 minutes
OpenDevin is an open-source project aiming to replicate and enhance Devin, an AI software engineer. The project is currently in alpha, with ongoing work on UI, architecture, and agent capabilities. OpenDevin supports various Language Models and provides a user-friendly setup process. The project welcomes contributions from developers, researchers, and AI enthusiasts. Learn more about the project here.

Plandex: An Open Source, Terminal-Based AI Coding Engine

read time: 10 minutes
Plandex is an open-source, terminal-based AI coding engine that uses long-running agents to break down and implement complex tasks. It features a protected sandbox for changes review, built-in version control, and efficient context management. It currently relies on the OpenAI API, with support for other models coming soon. Plandex Cloud offers an easy way to use the tool, with free accounts currently available.

Backtracing: A New Approach to Understanding User Queries

read time: 10 minutes

Researchers introduce a new task, Backtracing, aimed at identifying the text segment that likely prompted a user query. The study includes a benchmark for backtracing and evaluates various retrieval systems. The findings highlight the need for new retrieval approaches, with the ultimate goal of helping content creators refine their work and enhance user experiences.

Claude's Tool Use: A New Way to Extend AI Capabilities

read time: 15 minutes
Anthropic's AI model, Claude, now supports interaction with external tools, enhancing its task performance capabilities. The feature is currently in public beta and allows developers to provide custom tools for Claude to use. Learn more about this feature and how to implement it in your applications here.

📰 NEWS & EDITORIALS

FastLLM: Qdrant's Game-Changing Language Model for RAG Applications

read time: 5 minutes

Qdrant introduces FastLLM, a lightweight Language Model optimized for Retrieval Augmented Generation (RAG) applications. With a context window of 1 billion tokens, FastLLM integrates with Qdrant to process vast amounts of data, promising to revolutionize AI-driven content generation at a massive scale.

Leveraging Retrieval-Augmented Generation (RAG) for Improved AI Responses

read time: 3 minutes

ThoughtWorks teams have successfully used Retrieval-augmented generation (RAG) to enhance the quality of responses from large language models (LLMs). RAG uses relevant documents stored in databases to provide richer context to the LLM, resulting in higher quality output. Read more about this technique here.

Unveiling Many-Shot Jailbreaking: A New Vulnerability in Large Language Models

read time: 15 minutes

Researchers at Anthropic have discovered a new vulnerability in Large Language Models (LLMs) called 'many-shot jailbreaking'. This technique can force LLMs to produce potentially harmful responses by exploiting the longer context window. The team has already implemented mitigations and is actively working on others, emphasizing the importance of addressing such vulnerabilities as AI models become more powerful.