🔬 Comparative Analysis of 60+ Large Language Models

📖 TUTORIALS & CASE STUDIES

LangChain Tutorial: Summarize YouTube Videos In Less Than 10 Lines of Code
Watch time: 10min
In this tutorial learn to create a YouTube video summarizer using LangChain and OpenAI in under 10 lines of code! Extract transcripts, split text efficiently, and summarize with AI. Enhance with custom prompts or a UI.

Unleashing the Full Potential of Large Language Models
Read Time: 15 minutes

This comprehensive article explores the limitations of Large Language Models (LLMs) and the ecosystem of tools and methodologies that can be used to enhance their capabilities. It covers topics such as prompt engineering, retrieval augmented generation, conversational memory, agents, and guardrails, providing insights into how these components can be used to build more effective and reliable AI systems.

Comparative Analysis of 60+ Large Language Models
Read Time: 8 minutes
This web page presents a unique comparison of over 60 Large Language Models (LLMs) based on their responses to a set of 20 questions testing basic reasoning, instruction following, and creativity. The results, stored in a SQLite database, provide insights into the real-world performance of these models. The author plans to refine the testing process and introduce features like public voting and side-by-side model comparison.

Interactive API Tutorials: A New Approach
Read Time: 15 minutes
This article introduces a concise and readable way to write interactive tutorials and how-tos for any HTTP API. It provides a crash course in HTTP messages and demonstrates how to use HTTP requests to document API usage. The author also showcases an interactive API guide using GitHub's Gists API as an example.

🧰 TOOLS

Unleashing the Full Potential of Generative AI with LanceDB
Read Time: 15 minutes

This article explores the challenges and potential solutions in the field of Generative AI, particularly Language Model Machines (LLMs). It discusses the limitations of LLMs, the concept of Retrieval Augmented Generation (RAG), and introduces LanceDB, an AI-native, multi-modal, and embedded vector database designed to enhance the performance and scalability of AI systems.

Falcon 180B: The New State-of-the-Art Open Language Model
Read Time: 10 minutes
TII's Falcon 180B, the largest openly available language model with 180 billion parameters, is now on HuggingFace. Trained on a massive 3.5 trillion tokens, Falcon 180B achieves state-of-the-art results across natural language tasks, rivaling proprietary models. Learn more about its capabilities and how to use it in this blog post.

Pinecone TypeScript Client: v1.0.0 Release
Read Time: 7 minutes
The latest release of Pinecone TypeScript Client introduces a simplified library initialization, streamlined method signatures, and support for namespaces. It also includes published TypeScript types with support for generics, runtime validations, and is backwards compatible. The update also marks a migration to the Apache 2 open source license and introduces other convenience methods and improvements.

Pantry: A Comprehensive LLM Manager for Developers
Read Time: 7 minutes
Pantry is a versatile tool for managing Large Language Models (LLMs), offering a repository, a local LLM runner, and a remote API. It supports one-click downloads, custom configurations, and the ability to run multiple LLMs in parallel. The tool is compatible with all LLMs supported by the rustformers/llm project and provides a user-friendly UI, CLI, and API for easy integration into applications.

GPT Pilot: Aiding Developers in Building Apps Faster
Read Time: 10 minutes
GPT Pilot is a tool designed to assist developers in building apps 20x faster. It generates most of the code for an app, while the developer oversees the process, reviewing each task and providing assistance when needed. The tool aims to utilize GPT-4 to generate fully working, production-ready apps.

OnPrem.LLM: Running Large Language Models Locally
Read Time: 8 minutes
OnPrem.LLM is a Python package that simplifies running large language models (LLMs) on local machines using non-public data. It supports various tasks such as few-shot prompting, retrieval augmented generation, and text-to-code generation. The tool also provides GPU-accelerated inference for faster results. Future versions will transition to the newer GGUF format from the currently supported GGML format.

CodeRabbit: Revolutionizing Code Reviews with AI
Read Time: 3 minutes
CodeRabbit is an AI-powered tool designed to streamline and enhance the code review process. It leverages AI to provide insightful, automated code reviews, helping developers improve code quality and efficiency.

Introducing Persimmon-8B: A Powerful Open-Source Language Model
Read Time: 10 minutes
Adept has open-sourced Persimmon-8B, a highly capable language model with less than 10 billion parameters. The model, which can be fine-tuned on a single GPU, offers superior performance despite being trained on less data than its competitors. It also features unique inference code that combines the speed of C++ implementations with the flexibility of aPython interface.

📰 NEWS

Mojo: The High-Performance 'Python++' Language Now Available for Local Download
Read Time: 8 minutes

The AI-focused programming language, Mojo, has launched its local download version, starting with Linux systems. Mojo combines Python's usability with high-performance features, offering up to 68,000x the performance of Python. The Mojo SDK includes a driver, Visual Studio Code extension, Jupyter kernel, and upcoming debugging support. I’ve been playing with Mojo in their hosted playground and I’m very impressed. Looking forward to the OSX compatible SDK.

Meta's Ambitious Plan for a New AI Chatbot
Read Time: 4 minutes
Meta is investing heavily in AI training chips and data centers to develop a sophisticated chatbot, aiming to rival OpenAI's GPT-4. The company plans to start training the new large language model in 2024, with the intention of making it freely available for AI tool creation. Read more about it here.

Microsoft's AI Copilot: Taking Responsibility for Copyright Infringement
Read Time: 4 minutes
Microsoft has announced the Copilot Copyright Commitment, stating it will assume legal responsibility for copyright infringement lawsuits related to its AI Copilot services. This move aims to support customers, address copyright holders' concerns, and encourage the use of generative AI services without stifling competition and innovation.

Transforming Software Engineering with AI
Read Time: 7 minutes
AI and generative pre-trained transformers (GPT) are revolutionizing software engineering, fostering organizational efficiencies, and transforming software practices. This Forbes article discusses how AI tools like Code Whisperer and CoPilot are enabling engineers to focus on creative tasks, while AI takes care of mundane coding and unit tests. It also explores the impact of AI on organizational structures and software processes.

Perplexity.ai: Pioneering the Future of Search
Read Time: 15 minutes
Perplexity.ai, co-founded by Aravind Srinivas, is revolutionizing the search engine landscape by creating an "answer engine" powered by large language models (LLMs). Unlike traditional search engines, Perplexity.ai provides concise, accurate answers to user queries, prioritizing user needs over advertisers. The full article delves into the origins of Perplexity, its approach to search, and its potential impact on the internet's structure.

⌨️ PUT DOWN THE KEYBOARD

Introduction to Generative AI
Watch time: 22min
In Dr. Gwendolyn Stripling's course at Google Cloud, attendees dive into Generative AI. This technology uses deep learning to produce various content types, from text to imagery. It's reshaping traditional programming, enabling unique content creation based on vast trained data. The session covered core ML concepts, AI subsets, and practical tools for developers.

What is Retrieval-Augmented Generation (RAG)?
Watch time: 7min
In this course Marina Danilevsky of IBM explains how RAG helps large language models provide accurate, up-to-date responses by retrieving content from external sources. This ensures answers are current and reduces errors, aligning more closely with primary data rather than just prior training. Perfect for dynamic info needs.