AI Python for Beginners - Unlock the Power of Python and AI

📖 TUTORIALS & CASE STUDIES

AI Python for Beginners - Unlock the Power of Python and AI

Class time: 4 - 5 Hours

This new course from DeepLearning.AI teaches software developers how to leverage Python and AI tools to build practical applications. You'll learn Python fundamentals while creating AI-powered tools like recipe generators, to-do lists, and vacation planners. With support from an AI chatbot, you'll gain essential coding skills and the ability to interact with large language models - valuable skills across industries.

How to Fine-Tune Chat Models from OpenAI's Cookbook

Read time: 10 minutes

This article from the OpenAI Cookbook provides a step-by-step guide for fine-tuning OpenAI's GPT-4o mini language model for a recipe ingredient extraction task. It covers setting up the dataset, preparing training and validation data, fine-tuning the model, and using the fine-tuned model for inference. This tutorial equips software developers with the knowledge to leverage generative AI in their applications, helping them stay competitive in the evolving job market.

Learn RAG with Langchain 🦜⛓️‍

Read time: 10 minutes

This comprehensive guide teaches software developers how to master Retrieval-Augmented Generation (RAG) using the powerful LangChain framework. It covers the RAG pipeline in depth, including query transformation, hypothetical document embeddings, routing, indexing, and generation - providing the practical knowledge needed to leverage RAG in real-world applications and stay competitive in the evolving AI landscape.

Learn RAG Fundamentals and Advanced Techniques

Course time: 1.5 hours

This article provides a comprehensive overview of Retrieval-Augmented Generation (RAG), a powerful AI technique that combines retrieval-based methods with generative models to create more accurate and contextually relevant responses. The article covers the fundamental concepts of RAG, guides you through building a RAG system for chatting with documents, and explores advanced techniques like query expansion to improve system performance. By the end, you'll have a solid understanding of RAG and the skills to build and enhance your own RAG systems.

🧰 TOOLS

LLaMA Factory: Unified Efficient Fine-Tuning of 100+ Language Models

Read time: 12 minutes

LLaMA Factory is a powerful framework that enables software developers to efficiently fine-tune over 100 large language models, including LLaMA, ChatGLM, Baichuan, and Qwen. The framework supports a wide range of training approaches like supervised fine-tuning, reward modeling, and reinforcement learning. It also provides advanced algorithms, practical tricks, and experiment monitoring tools to boost the performance and efficiency of the fine-tuned models. LLaMA Factory aims to democratize access to powerful AI technology and help developers stay competitive in the evolving software market.

SmolLM - Blazingly Fast and Remarkably Powerful

Read time: 11 minutes

This article introduces SmolLM, a family of state-of-the-art small language models with 135M, 360M, and 1.7B parameters, trained on a high-quality curated dataset called SmolLM-Corpus. The models demonstrate impressive performance across common sense reasoning and world knowledge benchmarks, outperforming other small models in their size categories. The article covers the data curation process, model training, and evaluation, highlighting the advantage of small yet powerful models that can run on a variety of devices.

JetBrains AI Assistant 2024.2: Unlocking Next-Gen Productivity

Read time: 10 minutes

This article highlights the latest updates to the JetBrains AI Assistant, including smarter code completion, enhanced in-editor code generation, improved AI-powered chat features, and new capabilities for resolving Git conflicts and generating unit tests. The release also introduces customizable documentation prompts and AI integration for database management, helping software developers boost their productivity and stay competitive in the AI-driven landscape.

Agents | Mistral AI Large Language Models

Read time: 12 minutes

Mistral AI's Agents are autonomous AI systems powered by large language models (LLMs) that can plan, use tools, and execute complex tasks to achieve specific goals. Developers can create custom agents through Mistral's user-friendly agent builder or the programmatic Agent API. The article showcases use cases like a French-speaking agent, a Python code-generating agent, and multi-agent analytical workflows - demonstrating the versatility of Mistral's agent capabilities for software developers.

LangGraph Engineer: An Alpha Tool to Bootstrap LangGraph Applications

Read time: 6 minutes

LangGraph Engineer is an alpha-stage agent that can help software developers bootstrap LangGraph applications. It focuses on creating the correct nodes and edges for a LangGraph system, leaving the implementation details to the developer. The agent gathers requirements, generates a draft, runs checks, and iterates until a viable solution is produced. While currently limited, future plans include more sophisticated checks, code execution, and generation of node/edge logic.

Streamlining Prompt Design with Prompt Poet

Read Time: 6 minutes

Prompt Poet is a tool that simplifies prompt design for both developers and non-technical users. It takes a low-code approach to streamlining the prompt creation process for generative AI models, making it easier to leverage these powerful tools in software applications. Prompt Poet's features include a modular prompt structure, parameter tuning, and integration with popular language models, enabling developers to quickly build and test prompts without extensive coding.

Introducing Structured Outputs in the OpenAI API

Read Time: 10 minutes

OpenAI has introduced Structured Outputs, a new feature that ensures model-generated outputs will exactly match JSON Schemas provided by developers. This allows software developers to build more reliable applications that leverage OpenAI's language models, such as integrating them into agent systems or using them as code assistants. The article covers the technical details behind Structured Outputs and how it can be used to generate structured data, extract information from unstructured inputs, and more.

📰 NEWS & EDITORIALS

Call for Applications: Llama 3.1 Impact Grants

Read Time: 6 minutes

Meta AI is seeking applications for the Llama 3.1 Impact Grants program, which will award up to $2 million to organizations using the new features of Llama 3.1 to address social challenges. This builds on their successful Llama Impact Grants program, which has already funded innovative projects using Llama in areas like education, entertainment, and medical research. By investing in open-source AI tools, Meta aims to democratize access to powerful generative AI capabilities.

OpenAI co-founder Schulman leaves for Anthropic, Brockman takes extended leave

Read Time: 8 minutes

This article reports that OpenAI co-founder John Schulman has left the company to join rival AI startup Anthropic. Additionally, OpenAI co-founder and president Greg Brockman is taking an extended leave through the end of the year. These leadership changes at OpenAI will be of interest to software developers leveraging generative AI tools like ChatGPT in their applications.

The Adoption of ChatGPT

Read time: 7 minutes

This article reveals that ChatGPT has already seen widespread adoption, with half of workers in relevant occupations using the AI chatbot. However, a significant gender gap exists, with women 20 percentage points less likely to use ChatGPT than men. Researchers find that workers see substantial productivity potential in ChatGPT but certain factors, beyond just awareness, are preventing more widespread adoption, especially among female workers.

Replacing my Right Hand with AI

Read time: 10 minutes

This article explores the author's experience using AI coding assistants like Claude and GitHub Copilot to write most of his code after breaking his hand. He shares lessons on effectively prompting AI models, leveraging their strengths while understanding their limitations, and envisions a future where AI becomes a seamless "AI Engineer" partner for software developers, elevating their creativity and focus on high-level problems.