--- title: Best GoPenAI posts — May 2024 | daily.dev description: The most upvoted GoPenAI posts from May 2024, curated by the daily.dev community. canonical: https://daily.dev/sources/gopenai/best-of/2024/05 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/sources/gopenai/best-of/2024/05 og:type: website og:site_name: daily.dev og:title: Best GoPenAI posts — May 2024 | daily.dev og:description: The most upvoted GoPenAI posts from May 2024, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of GoPenAI — May 2024 1. 1 [](https://daily.dev/posts/langflow-zero-code-platform-k6jebik4k "Langflow: Zero Code Platform") Article GoPenAI·2y Langflow: Zero Code Platform Langflow is an easy-to-use, open-source platform for building AI applications without coding. It offers a graphical interface for working with Large Language Models (LLMs), and provides various components such as Agents, LLMs, Prompts, Memory, and Chains. 59 2 2. 2 [](https://daily.dev/posts/advanced-rag-with-self-correction-langgraph-no-hallucination-agents-groq-vjhghhzcs "Advanced RAG with Self-Correction | LangGraph | No Hallucination | Agents | GROQ") Article GoPenAI·2y Advanced RAG with Self-Correction | LangGraph | No Hallucination | Agents | GROQ Learn how to make Large Language Models smarter and more reliable with Advanced Retrieval-Augmented Generation (RAG) using LangGraph. Build an Adaptive RAG application that auto-critiques itself, integrates powerful agents, and reduces latency on LLM responses leveraging GROQ. 52 1 3. 3 [](https://daily.dev/posts/how-to-build-neural-network-with-real-world-dataset-using-pytorch-rl7qe7wgd "How to build Neural Network with real-world dataset using PyTorch") Article GoPenAI·2y How to build Neural Network with real-world dataset using PyTorch Learn how to build and train a neural network model using the FitBit Fitness Tracker Dataset and PyTorch. The post provides a step-by-step guide and covers topics such as importing libraries, loading and preparing the data, defining the model, training and evaluating the model, and making predictions on new data. By following the post, readers can build and train their own neural network models for various use cases. 52 4. 4 [](https://daily.dev/posts/building-a-rag-chatbot-using-llamaindex-groq-with-llama3-chainlit-mskaaihv8 "Building a RAG Chatbot using Llamaindex, Groq with Llama3 & Chainlit") Article GoPenAI·2y Building a RAG Chatbot using Llamaindex, Groq with Llama3 & Chainlit Retrieval Augmented Generation (RAG) is a language model that combines retrieval-based and generation-based approaches to generate high-quality text. It has advantages such as improved accuracy, flexibility, and scalability. RAG can be used for question answering, text summarization, text generation, and chatbots. 27 5. 5 [](https://daily.dev/posts/from-text-to-action-building-llm-applications-g6qy8neoq "From Text to Action: Building LLM Applications") Article GoPenAI·2y From Text to Action: Building LLM Applications Explore the key issues hindering LLM applications and techniques to overcome them, including RAG and chain-of-thought prompting. 20 6. 6 [](https://daily.dev/posts/fine-tuning-llama-in-practices-rz1cy8s7a "Fine-Tuning Llama in practices") Article GoPenAI·2y Fine-Tuning Llama in practices This blog post provides practical tips on fine-tuning Llama-2 models, including methods like LoRA and QLoRA that reduce memory requirements while maintaining performance. It also guides readers through the fine-tuning process with code examples. 16 7. 7 [](https://daily.dev/posts/leveraging-langchain-and-streamlit-for-interactive-csv-analysis-1h2ltk68h "Leveraging LangChain and Streamlit for Interactive CSV Analysis") Article GoPenAI·2y Leveraging LangChain and Streamlit for Interactive CSV Analysis Learn how to leverage LangChain and Streamlit to build an interactive CSV analysis tool. Simplify and expedite CSV data analysis while enhancing productivity. 15 8. 8 [](https://daily.dev/posts/implementing-a-local-rag-with-langchain-and-llama3-a-quick-guide-lbrnc60i7 "Implementing A Local RAG with LangChain and Llama3: A Quick Guide") Article GoPenAI·2y Implementing A Local RAG with LangChain and Llama3: A Quick Guide This post explores the implementation of a local RAG using LangChain and Llama3\. It discusses the advantages of Llama3 and the process of extracting metadata from a user query for vector store filtering. 14 1 9. 9 [](https://daily.dev/posts/building-ai-powered-apps-with-langchain-a-2024-guide-5qubrdmwt "Building AI-Powered Apps with LangChain: A 2024 Guide") Article GoPenAI·2y Building AI-Powered Apps with LangChain: A 2024 Guide Learn about LangChain, a powerful framework for building AI-powered apps with large language models. Understand the basics of LLMs and the transformer architecture. Explore the key concepts of LangChain, including chains and links. Get started with LangChain by following the installation and setup guide. 13 10. 10 [](https://daily.dev/posts/speak-your-way-to-success-ai-voice-assistant-for-tech-interview-prep-gktrxumes "Speak Your Way to Success: AI Voice Assistant for Tech Interview Prep") Article GoPenAI·2y Speak Your Way to Success: AI Voice Assistant for Tech Interview Prep A powerful AI Voice Assistant for tech interview preparation that provides real-time assistance, syntax highlighting, and a natural conversational experience. 11 11. 11 [](https://daily.dev/posts/understanding-the-magic-deconstructing-langchain-s-sql-agent-m6petbjul "Understanding the Magic: Deconstructing Langchain’s SQL Agent") Article GoPenAI·2y Understanding the Magic: Deconstructing Langchain’s SQL Agent This post explores the components and inner workings of Langchain's SQL Agent, including the Prompt Template, SQL Database Toolkit, and Parser. 11 12. 12 [](https://daily.dev/posts/customer-lifetime-value-clv-prediction-with-machine-learning-and-db-querying-with-llm-8ttlbbjes "Customer Lifetime Value (CLV) Prediction With Machine Learning and DB Querying With LLM") Article GoPenAI·2y Customer Lifetime Value (CLV) Prediction With Machine Learning and DB Querying With LLM This post discusses the prediction of Customer Lifetime Value (CLV) for auto insurance clients using machine learning and database querying with LLM. It covers data extraction and cleaning, exploratory data analysis, machine learning model selection and optimization, user interface for CLV prediction, and a Q&A interface for data retrieval. The project aims to improve customer retention, enhance marketing effectiveness, facilitate data-driven decision-making, and provide a user-friendly experience. 11 13. 13 [](https://daily.dev/posts/advanced-rag-corrective-retrieval-augmented-generation-crag-with-langgraph-kmbx4h2jv "Advanced RAG: Corrective Retrieval Augmented Generation (CRAG) with LangGraph") Article GoPenAI·2y Advanced RAG: Corrective Retrieval Augmented Generation (CRAG) with LangGraph CRAG enhances the traditional RAG by introducing a retrieval evaluator to assess the relationship between the retrieved documents and the query. It also introduces a knowledge refinement algorithm and knowledge searching to improve the accuracy of the generated response. LangGraph is an extension of the LangChain ecosystem that allows the building of AI apps as a graph, treating workflows as a cyclic Graph structure. 10 14. 14 [](https://daily.dev/posts/langfuse-opensource-llm-tracking-tool-6gbt4lnn5 "Langfuse : OpenSource LLM Tracking Tool") Article GoPenAI·2y Langfuse : OpenSource LLM Tracking Tool Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications. It offers simplified self-hosting, custom dashboards, prompt management, traces and sessions, monitoring, integrations, exports, and datasets. 10 15. 15 [](https://daily.dev/posts/how-to-run-ollama-llama3-llm-on-google-colab-using-colab-xterm-vehwdwb31 "How to Run Ollama Llama3 LLM on Google Colab using colab-xterm") Article GoPenAI·2y How to Run Ollama Llama3 LLM on Google Colab using colab-xterm Discover how to run the Ollama Llama3 LLM on Google Colab using colab-xterm. Learn about the improvements in Llama 3 compared to Llama 2 and how to install and use Ollama with popular tooling such as LangChain. 10 16. 16 [](https://daily.dev/posts/deep-dive-into-langgraph-building-stateful-and-multi-agent-language-models-jspyh5flr "Deep Dive into LangGraph: Building Stateful and Multi-Agent Language Models") Article GoPenAI·2y Deep Dive into LangGraph: Building Stateful and Multi-Agent Language Models This post explores LangGraph, a library for building stateful and multi-agent language models. It discusses the techniques used, such as stateful graphs and defining tools, as well as the concepts of nodes, edges, and graph representation. The post also highlights the practical applications of LangGraph in dialogue systems, interactive narratives, intelligent tutors, and generative art/music. Additionally, it covers advanced considerations such as error handling, scalability, performance, and integration with external systems. 10 [See all GoPenAI archives](/sources/gopenai/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} 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