TechbyHimalayas’ Post

Building AI apps is like assembling a complex puzzle. Each piece—whether it’s inferencing services, vector databases, or knowledge graphs—needs to fit together perfectly. But these pieces aren’t static; they’re dynamic, containerized, and accessed via APIs, making the operational load heavier than ever. As AI becomes more mainstream, understanding the intricacies of these components is key to maintaining secure and efficient systems. Discover how these elements come together and why they’re crucial for the future of AI applications in our latest blog: https://lnkd.in/ewuWKi-P #AI #ApplicationArchitecture #APIs

Building AI apps is like assembling a complex puzzle. Each piece—whether it’s inferencing services, vector databases, or knowledge graphs—needs to fit together perfectly. But these pieces aren’t static; they’re dynamic, containerized, and accessed via APIs, making the operational load heavier than ever. As AI becomes more mainstream, understanding the intricacies of these components is key to maintaining secure and efficient systems. Discover how these elements come together and why they’re crucial for the future of AI applications in our latest blog: https://lnkd.in/gfr9fCY9 #AI #ApplicationArchitecture #APIs

Are you a history buff?! Imagine creating a query system that can answer all questions about key historical events like the World Wars. Today, advanced AI technologies make this possible and we will show you how... 🚀 👇 We are excited to share a comprehensive guide on building Retrieval-Augmented Generation (RAG) applications using DSpy and Llama, featured on Superteams.ai. In this in-depth tutorial, you will discover: 1. RAG Architecture Fundamentals: Explore how combining retrieval and generation boosts accuracy and efficiency in AI applications. 2. Setting Up DSPy, Llama 3 and Indexify: Follow clear, step-by-step instructions for seamless installation and configuration. 3. Developing and Deploying RAG Applications: Gain practical insights on constructing a RAG app from scratch, illustrated with real-world use cases. For AI professionals and enthusiasts aiming to advance their solutions, this guide is a must-read. Dive into the full article here: https://lnkd.in/gnr2nx8H #AI #MachineLearning #RAG #DSpy #Llama3 #TechInnovation #AIDevelopment

Why Do AI Agents Fail? As Dave Ebbelaar pointed out, LLM orchestration frameworks often fall short because they're not versatile tools - they're complex solutions designed for specific use cases. This complexity makes it difficult to understand and debug your code. However, there's good news: Pydantic has released a new agent framework called Pydantic-AI that addresses these issues. It offers simplicity and transparency, making it much easier to track what your code is doing. Here's an example; I built an Email Subject Analyzer using Pydantic-AI. Code : https://lnkd.in/e5q6KYVY Thank you to Dave Ebbelaar for inspiring this project through his insightful video on AI agents. #AIAgents #Pydantic #PythonDevelopment #LLM #ArtificialIntelligence #SoftwareEngineering #OpenSource #MachineLearning #PydanticAI

Maximize efficiency and savings in your Spark data pipelines with Chicory AI. The platform uses Generative AI to optimize your sparksql and transformations, helping you reclaim valuable resources and reduce costs. By addressing inefficiencies early in the development process, Chicory AI not only boosts developer productivity but also contributes to a more sustainable tech environment. Explore how you can enhance your data workflows at www.chicory.ai. Would love to get as much feedback as possible. #DataPipelines #SparkSQL #PySpark #CostSavings #Sustainability #ChicoryAI Chicory

Anthropic has released Claude 3 setting so many benchmarks in LLMs. There are three powerful models: Haiku, Sonnet, and Opus. Opus is the most intelligent model that has expert knowledge, language fluency in Spanish, French and Japanese and reasoning abilities. Claude 3 offers live chats, auto-completions, and real-time data extraction. It has strong vision as it processes various visual formats, charts, graphs, and more. It improved contextual understanding, reducing unnecessary refusals. Claude 3 Opus shows a twofold improvement in challenging open-ended questions. It offers a 200K context window, capable of exceeding 1 million tokens. It is responsibly designed addressing biases, ensuring safety, and transparency in AI models. It is better at following complex instructions, developing customer-facing experiences. Model Details: Opus: Intelligence beyond comparison, optimal for advanced tasks. Sonnet: Balances intelligence and speed, cost-effective for enterprise workloads. Haiku: Fastest, most compact model for near-instant responsiveness. Model Availability: Opus and Sonnet available now in the Claude API. Haiku coming soon. Explore the future of AI with Claude 3! Visit https://lnkd.in/gfMPTJvr to start building. #Claude3 #AIInnovation #Anthropic #ArtificialIntelligence #TechNews #Innovation #MachineLearning #llms #genai

🚀 Checkout this short course on #AutoGen from Deeplearning.AI:🚀 https://lnkd.in/dvk9s8ru 🤖 What is #AutoGen? #AutoGen is cutting edge technology created out of collaborative research from Microsoft, Penn State University, and the University of Washington. Aimed at revolutionizing the landscape of Large Language Model (LLM) applications. At its core, #AutoGenempowers developers to create dynamic applications using multiple agents capable of conversing with each other to tackle complex tasks. 💡 Key Features: ✨ Customizable & Conversable Agents: #AutoGen agents are not only adaptable to various tasks but also seamlessly integrate human participation into the conversation flow. ✨ Multi-Mode Operation: With #AutoGen, developers can harness the power of LLMs, human inputs, and tools, optimizing workflows and maximizing performance. ✨ Diverse Conversation Patterns: From conversation autonomy to agent conversation topology, #AutoGen supports a wide array of conversation patterns, catering to diverse application needs. ✨ Ready-to-Use Systems: #AutoGen offers a collection of pre-built systems spanning various domains and complexities, showcasing its versatility in supporting different conversation patterns. 🔍 Enhanced LLM Inference: #AutoGen goes beyond just facilitating conversations; it enhances LLM inference through API unification, caching mechanisms, and advanced usage patterns like error handling and context programming. #AutoGen #MultiAgentSystems #LLMApplications #CollaborativeResearch #Innovation #Technology #AI #LLM

Reminder: Our first of many AI Innovators Labs is this Monday. In this session, we'll be exploring as a group Retrieval Augmented Generation, but I already have an entire roadmap of topics covering LLMs, RAG, Agents, LangChain, and Computer Vision just to name a few. https://lnkd.in/gYbaS7gp For those who have registered and can't attend, PLEASE update your RSVP. We have a very large waitlist of people who wish to attend. Bring your laptop and be ready to collaborate. Exciting times ahead for those wishing to gain knowledge and learn about AI hands-on from the experts at Lab651 | Custom Software Development & Process / Recursive Awesome & Applied AI! #llms #rag #ai #genai

In the rapidly evolving Gen AI landscape, selecting the right framework for agent development is crucial. Let’s delve into the three most used Gen AI agent frameworks: Autogen, LanGraph, and Crew AI. Each offers unique strengths and challenges. - Autogen is a solid and robust framework, providing a great foundation for multi-agent systems. Some features include: ** Real-time data processing through streaming output. ** Containerized code execution for safety and isolation. In our experience, we have seen some challenges with fine-tuning randomness and navigating a slow UI. * Crew AI leverages Langchain and compatibility with major platforms like OpenAI, Google, Azure, and HuggingFace. Some features include: ** Hierarchical agent structure for organized task management. ** Compatibility with both local and global large language models (LLMs). Crew AI's flexibility and integration capabilities make it an excellent choice for building proof-of-concept Gen AI applications. - LanGraph offers an exciting approach using directed acyclic graphs (DAGs). DAGs are typically used for Task Scheduling, Data Processing, and Version Control. It also provides an intuitive design. LanGraph makes it appealing for data scientists seeking to implement Gen AI agent projects. #AI #MachineLearning #ArtificialIntelligence #TechInnovation #AIDevelopment #AITools #Autogen #LanGraph #CrewAI #DataScience

Has anyone used Anthropic for writing and debugging code? Their Claude model is highly capable and efficient. I used a combination of Claude's Haiku and Opus variants to build a website in just one day: 1. Claude 3 Haiku is super fast and more affordable 2. Claude 3 Opus is the most intelligent and comprehensive By leveraging these AI models, I dramatically reduced the time it typically takes to build a website from months to a single day. It's amazing how far the technology has come! #genAI #LLM