That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.AAvi Chawla (@_avichawla):Researchers from AssemblyAI built a state-of-the-art model that:- transcribes speech across 99 languages.- works even if the audio has many speakers.- outperforms Deepgram and OpenAI models.And much more.(2-step setup below) https://t.co/7eg0zpE4pM ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):I've been coding in Python for 9 years now.If I were to start over today, here's a complete roadmap: ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Let's build a multi-agent brand monitoring system (100% local): ...

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):3 expert ways to use GROUP BY in SQL, clearly explained (with code): ...

AI Deployment Tools - Beam is presented as an open-source alternative to Modal for deploying serverless AI workloads [1] - Beam enables turning any workflow into a serverless endpoint by adding a Python decorator [1] Call to Action - The author encourages readers to reshare the content if they found it insightful [1] - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) daily [1]

AI Agent Fundamentals - The document covers agent fundamentals, providing foundational knowledge for understanding AI agents [1] - It differentiates LLM, RAG, and Agents, clarifying their roles and relationships in AI systems [1] - Agentic design patterns are explored, offering insights into structuring and organizing AI agents [1] - Building blocks of agents are outlined, detailing the essential components for constructing AI agents [1] Practical Applications - The document includes 12 hands-on projects for AI Engineers, providing practical experience in building AI agents [1] - It covers building custom tools via MCP (likely referring to a specific methodology or platform), enabling customization and extension of AI agent capabilities [1] Resource Availability - A PDF containing all AI Agents posts is available for download, offering a consolidated resource for learning about AI agents [1]

General Overview - The document is a wrap-up message encouraging readers to reshare the content if they found it insightful [1] - It promotes tutorials and insights on Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAGs) [1] Call to Action - The author, Avi Chawla (@_avichawla), invites readers to find him for more content [1] Specific Topic - The document mentions fine-tuning OpenAI gpt-oss (100% locally) [1]

LLM Evaluation - The industry is focusing on evaluating conversational LLM applications like ChatGPT in a multi-turn context [1] - Unlike single-turn tasks, conversations require LLMs to maintain consistency, compliance, and context-awareness across multiple messages [1] Key Considerations - LLM behavior should be consistent, compliant, and context-aware across turns, not just accurate in one-shot output [1]

Technology & Development - Open-source tools enable building production-grade LLM web apps rapidly [1] - Interactive apps are more suitable for users focused on results rather than code [1] Data Science & Machine Learning - Data scientists and machine learning engineers commonly use Jupyter for data exploration and model building [1] - Tutorials and insights on DS, ML, LLMs, and RAGs are shared regularly [1]