Storia AI’s Post

The lifecycle of a production-grade AI code assistant to generate code completions: A great insight on what goes through the nuances of AI code completion. This is covered across 4 stages: 1) Planning, where the code context is analyzed to set the approach; 2) Retrieval, which collects relevant code snippets and contextual data; 3) Generation, where the LLM produces the code based on the provided context; and 4) Post-processing, where the generated code is refined and filtered to ensure relevance and quality. A great resource that highlights the complexities involved in developing an AI system that not only generates code but also integrates deeply with user expectations and sophisticated language understanding tools. https://lnkd.in/eCdrmrV5 -- If you liked this article you can join 60,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata

📢 𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗡𝗲𝘄𝘀! We are thrilled to welcome Sage Elliott, AI engineer and developer advocate at Union, as a distinguished guest speaker at our upcoming LLM Bootcamp! Sage is a seasoned expert in AI and machine learning, renowned for his educational workshops on how to get started with Python, machine learning, computer vision, and AI observability. His session will focus on building scalable workflows for Large Language Models (LLMs), ensuring you gain practical and cutting-edge knowledge to build AI workflows for fine tuning LLMs - it's perfect for anyone eager to build scalable AI applications with LLMs 🔍 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗹𝗼𝗼𝗸 𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝘁𝗼: 🔹 MLOps/LLMOps Basics 🔹 Fine-tuning a Hugging Face LLM model 🔹 Building scalable, reproducible workflows. 🔹 Saving versioned models 🔹 Interacting with fine-tuned LLMs By the end of this workshop, you'll be equipped with the skills to harness modern MLOps tools, streamline your AI processes, and enhance reproducibility. 💡 Seats are rapidly filling up! Don’t miss your chance to become a trailblazer in the next wave of technological innovation -🔗Secure your spot now: https://hubs.ly/Q02JGdcj0 #llmdojo #largelanguagemodels #machinelearning #ai #generativeai #finetuning #LLMOps #MLOps #aiworkflows #huggingface #aitraining

The lifecycle of a production-grade AI code assistant to generate code completions: A great insight on what goes through the nuances of AI code completion. This is covered across 4 stages: 1) Planning, where the code context is analyzed to set the approach; 2) Retrieval, which collects relevant code snippets and contextual data; 3) Generation, where the LLM produces the code based on the provided context; and 4) Post-processing, where the generated code is refined and filtered to ensure relevance and quality. A great resource that highlights the complexities involved in developing an AI system that not only generates code but also integrates deeply with user expectations and sophisticated language understanding tools. https://lnkd.in/ejzWihxT -- If you liked this article you can join 60,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata

🚀 Unlock the Future with LangChain! 🚀 Are you ready to revolutionize your AI capabilities? Dive into the world of LangChain with our comprehensive guide, "LangChain Python for RAG Beginners." This book is your gateway to mastering advanced AI workflows and building powerful AI agents. 🌟 Why LangChain? - **Modular Framework**: Seamlessly integrate various AI models and automate complex tasks. - **State-of-the-Art Integration**: Work with leading AI models like OpenAI's GPT and Anthropic's Claude. - **Advanced Features**: From memory management to Retrieval Augmented Generation, LangChain has you covered【4:1†source】【4:6†source】【4:11†source】. 📚 What You'll Learn: - Develop sophisticated AI agents capable of autonomous decision-making. - Implement Retrieval Augmented Generation to enhance AI interactions. - Understand and utilize vector databases for efficient data handling【4:2†source】【4:6†source】【4:19†source】. This isn't just a book; it's your toolkit for creating the next generation of AI applications. Whether you're a developer, business professional, or an AI enthusiast, LangChain opens up a world of possibilities【4:10†source】. 🔗 Grab your copy now and start building the future: [LangChain on Amazon](https://lnkd.in/gCTV6hB2) #AI #LangChain #MachineLearning #AIDevelopment #FutureOfWork

🚀 Excited to announce major updates to Grami AI - The Modern Async AI Agent Framework! introducing Grami AI 0.2.0, bringing powerful async-first AI capabilities to Python developers. 🔥 What's New: • Comprehensive documentation at https://lnkd.in/dkqk2ntV • Production-ready async tools system • Flexible memory management with Redis support • Multi-provider LLM integration (Gemini, GPT, Ollama) • Type-safe interfaces throughout 💡 Key Features: • Built async-first for high performance • Modular, plug-and-play architecture • Production-grade error handling • Extensive tool ecosystem • Comprehensive type hints 🛠️ Perfect for building: • AI-powered research assistants • Data analysis pipelines • Multi-tool workflows • Custom AI agents 🔗 Resources: • Docs: https://lnkd.in/dkqk2ntV • GitHub: https://lnkd.in/d_buWVxp • GitHub Pages: https://lnkd.in/dPY3hHZM Join us in building the future of AI agents! Star us on GitHub and share your feedback. #AI #Python #OpenSource #AsyncProgramming #ArtificialIntelligence #Development #Innovation

Wow. I am a little behind the every-week-schedule for my publishing. But here we are, in the middle of tons of changes those days, I am happy to share with you my last post on Substack. Let me know what you think! How to scale the serving of LLMs in production? A Ray of light in scaled Generative AI! Building, customizing, and deploying Large Language Models using Ray.io framework https://lnkd.in/diUwi2S7

🌐🤖 Excited to share my latest side project: The Alice Retrieval-Augmented Generation (RAG), a proof-of-concept application designed to answer user queries about the timeless classic "Alice’s Adventures in Wonderland" by Lewis Carroll. 🔍 What? Alice RAG combines the strengths of retrieval-based and generation-based approaches, leveraging the power of modern AI. By integrating a customized knowledge store with Google's Gemini language models, it can accurately and efficiently retrieve information from the book and generate responses related to Alice’s Adventures in Wonderland. 🔧 How? You only need a Gemini API key (free tier available at https://ai.google.dev/), and then you can run the application container using Docker right now: `docker run -p 8501:8501 -e GEMINI_API_TOKEN="SECRET_TOKEN" pmiron/alice-rag-llm` 💡 Why? This project showcases my ability to implement advanced AI techniques, containerization, CI/CD, documentation, infrastructure as code (IoC), and web-based interaction using Streamlit. It was also a good opportunity to apply best practices in software development and DevOps. 📜 Details Blog Post: https://lnkd.in/dWn3AhWc GitHub: https://lnkd.in/dVcxDXci Documentation: https://lnkd.in/d4xXiDs8 #AI #MachineLearning #NLP #Containerization #SoftwareDevelopment #Azure #Streamlit #AliceInWonderland #AIInnovation #TechBlog

With a $1M prize pool and the fact that the seemingly trivial puzzles get the better of even OpenAI's O(1) models (only 18% accuracy), François Chollet's ARC-AGI Challenge was bound to pique our research team's interest. While the IQ-style colored grids of ARC may seem far removed from automated software generation, the fundamental challenges of designing a solution (reasoning, planning, knowledge distillation, inference-time optimizations) are perfectly aligned with our research agenda for building the underlying reasoning engine powering CodeWords. With the building blocks already in place for an agentic system that reasons, generates, and validates code, we couldn't resist having a go. In part 1 of our ARC Challenge deep dive, Jack Hogan, Founding AI Research Scientist at agemo, explains our approach and early results, including: 📍 How we designed an object-centric Python framework for constraining the search space of solutions 📍 How, using a seed set of only 3 hand-written solutions, our solver agent autonomously generated correct solutions to ~350 of the 400 ARC training tasks 📍 How we plan to use these solutions and their reasoning traces to train our own model This research directly strengthens CodeWords, our platform for transforming natural language into ready-to-use tools, taking us another step closer to making sophisticated software development accessible to everyone. Read the full technical deep dive here: https://lnkd.in/dHEwV9QM #arc #ai #softwarecreation

Over the summer, we decided to apply the same intuition we used for our AI reasoning engine to solve the ARC-AGI benchmark which, to this day, remains a challenge for state-of-the-art models like OpenAI o1 or Claude Sonnet. Simply put, ARC-AGI is a set of visual puzzles that big models have never been trained on so they cannot rely on their sophisticated pattern-matching capabilities to solve them. So we need to come up with something new. These are the kind of complex research problems our team gets excited by, where creativity and thinking outside of the box are required. What's exciting is that this challenge is still unsolved and the team behind ARC-AGI will come back with a v2 next year (and more consistency across training and test datasets!). In the next few weeks, we are planning to open-source our solver, release a paper and share learnings on topics such as core primitive designs, test-time finetuning, reimplementing preference optimization algorithms to run on limited compute... cc Jack Hogan Osman Ramadan Read the first part of the technical deep dive here: https://lnkd.in/dHEwV9QM