Best of LLM — 2024

  1. 1
    Article
    Avatar of lobstersLobsters·2y

    The Death of the Junior Developer

    The rise of AI tools like ChatGPT is reshaping the software development landscape, significantly impacting junior developer roles. These language models are becoming highly competent at tasks traditionally reserved for junior programmers, lawyers, and writers, raising concerns about job displacement. Senior developers are adapting by using AI to accelerate their work, shifting into roles that focus on prompt engineering and code review. The article urges junior developers to upskill rapidly and stay ahead of these technological advancements to remain competitive in the evolving job market.

  2. 2
    Article
    Avatar of devtoDEV·2y

    I'm tired of it

    AI-generated content is pervasive, often creating bland, inaccurate articles that lack true value. The author criticizes this trend, emphasizing the importance of human-crafted content that showcases effort and unique perspectives. Highlighting examples of pointless AI-generated articles and the inefficiency of email communication due to AI, the appeal is to maintain authenticity and personal touch in writing.

  3. 3
    Article
    Avatar of communityCommunity Picks·2y

    OpenDevin/OpenDevin: 🐚 OpenDevin: Code Less, Make More

    OpenDevin is an open-source project aiming to replicate Devin, an autonomous AI software engineer. It allows users to run commands, generate scripts, and contribute to advancing software engineering with AI.

  4. 4
    Article
    Avatar of lobstersLobsters·2y

    ChatGPT is bullshit

    Large language models like ChatGPT are producing falsehoods more accurately described as 'bullshit' rather than 'hallucinations'. These models generate human-like text by analyzing probabilities rather than aiming for truth. Describing their inaccuracies as bullshit is argued to be a more useful framework for understanding and discussing their behavior, particularly since these models are designed to produce convincing text rather than accurate information.

  5. 5
    Article
    Avatar of medium_jsMedium·2y

    Prompt Engineering 101 : Understanding the Basics

    Prompt engineering is the art of crafting effective prompts to interact seamlessly with Large Language Models (LLMs) like ChatGPT. By understanding key components such as instruction, context, input data, and output indicators, one can create high-quality prompts. Various prompting techniques like zero-shot, few-shot, and chain-of-thought prompting can drastically influence the results. Iteratively experimenting with different prompts helps refine the results for better outcomes.

  6. 6
    Article
    Avatar of medium_jsMedium·2y

    Start Building These Projects to Become an LLM Engineer

    To become an LLM engineer, start by building practical projects that showcase skills in API usage and real-world applications, like chatbots for WhatsApp, Discord, or Telegram. Initial projects could include summarizing YouTube videos or handling various user queries via chatbots. The post also introduces a project-based course to help you build LLM applications and serve them as WhatsApp chatbots.

  7. 7
    Video
    Avatar of 3blue1brown3Blue1Brown·1y

    Large Language Models explained briefly

    The post explains large language models (LLMs), how they function, and the complexities behind their training. LLMs predict the next word in a sequence based on probabilities, using vast amounts of text data for training. The introduction of transformers in 2017 allowed for parallel processing of text, enhancing computation efficiency. Pre-training is supplemented by reinforcement learning with human feedback to refine model predictions. The sheer scale of data and computation involved is formidable, taking advantage of specialized hardware like GPUs.

  8. 8
    Article
    Avatar of developingdevThe Developing Dev·2y

    A New Era of Writing Code

    Large language models (LLMs) are revolutionizing coding by automating repetitive tasks, making debugging easier, and providing more efficient ways to carry out coding tasks. However, relying on LLMs can lead to a loss of understanding of the code, and they struggle with open-ended tasks and complex environment setups. Despite these limitations, they’re valuable for focused changes, basic UI, and transpiling. Knowing how to query and troubleshoot LLMs effectively is crucial for maximizing their benefits.

  9. 9
    Article
    Avatar of langchainLangChain·2y

    LangGraph Studio: The first agent IDE

    LangGraph Studio, an IDE tailored for developing agentic applications, is now in open beta. This specialized tool helps visualize and interact with agent graphs, making it easier to debug and iterate on complex LLM applications. LangGraph, the underlying orchestration framework, offers a stable and open-source solution for building domain-specific cognitive architectures in Python and Javascript. The studio integrates with LangSmith and aims to augment traditional code editors by providing additional tools for agent development.

  10. 10
    Article
    Avatar of mlmMachine Learning Mastery·2y

    7 Free Resource to Master LLMs

    Large Language Models (LLMs) are increasingly popular, with many companies seeking expertise in this area for AI-driven automation and optimization. This post reviews seven free resources, including courses from Cohere, Stanford, and Microsoft, as well as roadmaps and tutorials on GitHub and DataCamp. These resources aim to equip learners with the skills needed to understand, build, and deploy LLMs in various applications.

  11. 11
    Article
    Avatar of mlmMachine Learning Mastery·2y

    5 Influential Machine Learning Papers You Should Read

    Discover five influential machine learning papers that have shaped the field. Highlights include the introduction of the Transformer model in 'Attention is All You Need,' the interpretation of neural networks as decision trees, the impact of unsupervised preprocessing on cross-validation bias, low-rank adaptations for large language models with LoRA, and insights into overcoming overfitting on small datasets with 'grokking.' These papers have significantly advanced model architecture, evaluation, adaptation, and generalization in machine learning.

  12. 12
    Video
    Avatar of youtubeYouTube·1y

    This is how I scrape 99% websites via LLM

    Explore how advancements in AI, particularly large language models (LLMs), are revolutionizing web scraping in 2024. Learn the best practices for scripting internet data at a large scale, building autonomous web scrapers, and handling complex web interactions. The post demonstrates various kinds of web scraping tasks, including scraping public websites, handling complex web manipulations, and more sophisticated tasks that require reasoning. It also includes details about services like OpenAI, AgentQL, and SpiderCloud that facilitate optimized web content extraction.

  13. 13
    Article
    Avatar of mlmMachine Learning Mastery·1y

    5 Free Courses for Mastering LLMs

    Large Language Models (LLMs) have become a significant breakthrough in AI, excelling in understanding and generating human-like text. This post highlights five free courses to help learners master LLMs. Courses include an introduction by Google, an AI for Educators course by Microsoft, a technical deep dive from Cohere’s LLM University, prompt engineering courses by Anthropic, and a detailed LLM agents course by UC Berkeley and Google DeepMind. These courses cater to a range of learners from beginners to those looking to develop expertise in LLM applications and prompt engineering.

  14. 14
    Article
    Avatar of mlmMachine Learning Mastery·2y

    7 LLM Projects to Boost Your Machine Learning Portfolio

    Explore seven interesting projects designed to enhance your machine learning portfolio with large language models (LLMs). From creating a retrieval-based Q&A app and an LLM-powered workflow automation agent to developing a text-to-SQL query generator and an AI-powered documentation generator for codebases, the guide covers essential components and integration requirements. Gain hands-on experience with vector databases, frameworks, and APIs, and build innovative applications that simplify complex tasks.

  15. 15
    Article
    Avatar of devtoDEV·2y

    Choose Your Own Coding Assistant

    This post reports the results of experimenting with four leading Large Language Models to evaluate which one reigns supreme as a coding assistant. GPT-4 emerged as the overall victor, offering the most accurate and comprehensive assistance across all tasks. Smaller models may present viable alternatives depending on specific needs. Google's new LLM, Gemini Advance, shows significant improvements and is a serious contender to the crown of 'best LLM overall'.

  16. 16
    Article
    Avatar of watercoolerWatercooler·2y

    ChatGPT got our backs 😂

    ChatGPT is a language model that can be used for various purposes but has limitations.

  17. 17
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    A Crash Course on Building RAG Systems – Part 4

    Part 4 of the crash course on building RAG systems focuses on implementing RAG on multimodal data, specifically complex documents with tables, texts, and images. This series covers foundational components, evaluation methods, optimization techniques, and handling large data sets, making it highly beginner-friendly. Understanding how to build reliable RAG systems can reduce costs and enhance scalability for enterprises, bypassing the need for fine-tuning large language models (LLMs).

  18. 18
    Article
    Avatar of devtoDEV·1y

    Llama 3.3 vs OpenAI O1

    Llama 3.3 and OpenAI O1 are two advanced AI models offering enhanced reasoning, scalability, and versatile applications. Llama 3.3 stands out with its open-source flexibility and cost-effective solution, while OpenAI O1 offers a user-friendly API and robust security. Apidog is recommended for integrating these AI models, simplifying API development with its intuitive interface.

  19. 19
    Article
    Avatar of kdnuggetsKDnuggets·2y

    10 Free Resources to Learn LLMs

    Large Language Models (LLMs) are pivotal in the current AI landscape, essential for various data-centric roles. This guide provides 10 free resources from organizations like Deeplearning.AI, Microsoft, and AWS to help you learn about LLMs. These include video tutorials, full courses, and practical guides covering topics from basic LLM concepts to advanced tasks like fine-tuning and deployment. Various resources cater to beginners as well as those with some prior knowledge in AI and NLP.

  20. 20
    Article
    Avatar of golangGo·2y

    Building LLM-powered applications in Go

    As Large Language Models (LLMs) and embedding models improve, more developers are integrating LLMs into their applications. Go excels in building LLM-powered applications due to its support for REST/RPC protocols, concurrency, and performance. This post demonstrates creating a Retrieval Augmented Generation (RAG) server in Go, which uses HTTP endpoints to add documents to a knowledge base and answer user questions. It explores implementing this with tools like Google Gemini API, Weaviate, LangChainGo, and Genkit for Go, highlighting Go's strengths in cloud-native application development.

  21. 21
    Article
    Avatar of itnextITNEXT·1y

    How to Use an LLM-Powered Boilerplate for Building Your Own Node.js API

    Discover how an enhanced Node.js API boilerplate utilizes LLM Codegen to generate module code based on text descriptions. This feature automates the creation of E2E tests, database migrations, and business logic. The boilerplate supports OpenAI and Claude LLM clients and adheres to vertical slicing architecture and Clean Code principles, ensuring the generated code is clean and maintainable.

  22. 22
    Article
    Avatar of tdsTowards Data Science·2y

    Using LLMs to Learn From YouTube

    Learn how to build a chatbot using LangChain, Pinecone, Flask, and React that allows users to ask questions about YouTube videos. The chatbot uses the RAG framework to generate answers that take the conversation history into account.

  23. 23
    Article
    Avatar of freecodecampfreeCodeCamp·2y

    Mastering RAG from Scratch

    Learn how to implement Retrieval-Augmented Generation (RAG) from scratch with an in-depth course on the freeCodeCamp.org YouTube channel. RAG combines retrieval systems with advanced natural language generation and is valuable in chatbot development and other fields.

  24. 24
    Article
    Avatar of uberUber Engineering·1y

    Introducing the Prompt Engineering Toolkit

    A well-crafted prompt is vital for obtaining accurate outputs from Large Language Models (LLMs). To streamline this process, Uber developed a Prompt Engineering Toolkit that centralizes prompt template creation, management, and evaluation. The toolkit supports context enrichment, batch generation, version control, and safety measures to ensure responsible AI use. It includes a GenAI Playground for prompt exploration and advanced guidance techniques to enhance prompt quality. The toolkit is designed to facilitate LLM usage across development and production stages, offering a robust framework for effective prompt engineering.

  25. 25
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    RAG vs Agentic RAG

    Agentic RAG systems introduce dynamic, adaptable behaviors into the traditional RAG workflow. Unlike traditional RAG, which retrieves and generates once, agentic RAGs iteratively refine queries and context, adapting based on the problem's complexity. This makes them more effective for complex queries and problem-solving. The open-source tool Opik by CometML supports the evaluation, testing, and monitoring of LLM applications from development to production, offering features like logging traces and detecting hallucinations.

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