TIL @anthropic also has a great Agent Skills course! → anthropic.skilljar.com/introduction-to-agent-skills thanks @Chikker96 for sharing this! [Translated from EN to English]
GENERATIVE AI
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13 Free AI Courses and Certificates from Claude and Anthropic
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Did you know @claudeai has 13 AI courses and certificates available COMPLETELY FREE? You can jump in and start learning right away. #1 Claude 101 → anthropic.skilljar.com/claud… #2 AI Fluency: Frameworks & Foundations → anthropic.skilljar.com/ai-fl… #3 Introduction to Agent Skills → anthropic.skilljar.com/intro… #4 Building with the Claude API → anthropic.skilljar.com/claud… #5 Claude Code in Action → anthropic.skilljar.com/claud… #6 Intro to Model Context Protocol → anthropic.skilljar.com/intro… #7 MCP: Advanced Topics → anthropic.skilljar.com/model… #8 AI Fluency for Students → anthropic.skilljar.com/ai-fl… #9 AI Fluency for Educators → anthropic.skilljar.com/ai-fl… #10 Teaching AI Fluency → anthropic.skilljar.com/teach… #11 AI Fluency for Nonprofits → anthropic.skilljar.com/ai-fl… #12 Claude with Amazon Bedrock → anthropic.skilljar.com/claud… #13 Claude with Google Cloud Vertex AI → anthropic.skilljar.com/claud…
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LangChain 2nd Edition: Build Production-Ready Agentic AI Applications
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The 2nd Edition of this book has arrived, with Agentic AI updates: "Generative AI with LangChain — Build Production-ready LLM Applications and Advanced Agents using Python and LangGraph" at amzn.to/3JEeS6K v/ @PacktDataML 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: 🟠Design and implement multi-agent systems using LangGraph 🟠Implement testing strategies that identify issues before deployment 🟠Deploy observability and monitoring solutions for production environments 🟠Build agentic RAG systems with re-ranking capabilities 🟠Architect scalable, production-ready AI agents using LangGraph and MCP 🟠Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-mini 🟠Design secure, compliant AI systems aligned with modern ethical practices
→ View original post on X — @kirkdborne, 2026-04-06 05:47 UTC
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Generative AI and RAG for Beginners: LLM Applications Guide
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"Generative AI and RAG for Beginners: A Practical Step-by-Step Guide to Building LLM and RAG Applications with LangChain and Python" Get your copy at amzn.to/3MZZ9R5 Independently published: December 2025 Print length: 255 pages
→ View original post on X — @kirkdborne, 2026-04-06 05:46 UTC
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LangChain Crash Course: Build OpenAI LLM Apps with Python
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LangChain Crash Course — Fast track to building OpenAI LLM-powered Apps using Python: amzn.to/3TFlQJT ————— #LLMs #AI #ML #MachineLearning #LLMOps #DataScience #DataScientist
→ View original post on X — @kirkdborne, 2026-04-06 05:46 UTC
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Generative AI on Google Cloud with LangChain and Vertex AI
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Generative AI on Google Cloud with LangChain — Design scalable Generative AI solutions with Python, LangChain, and Vertex AI on Google Cloud: amzn.to/4frbkPA v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🔴Turn challenges into opportunities by learning advanced techniques for text generation, summarization, and question answering using LangChain and Google Cloud tools 🔵Solve real-world business problems with hands-on examples of GenAI applications on Google Cloud 🟡Learn repeatable design patterns for Gen AI on Google Cloud with a focus on architecture and AI ethics 🔴Build and implement GenAI agents and workflows, such as RAG and NL2SQL, using LangChain and Vertex AI 🔵Purchase of the print or Kindle book includes a free PDF eBook
→ View original post on X — @kirkdborne, 2026-04-06 05:27 UTC
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Machine Learning Architecture Handbook: Practical MLOps AI Strategies
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Machine Learning Solutions Architect Handbook — Practical Strategies and Best Practices in the ML Lifecycle, System Design, MLOps, and Generative AI: amzn.to/4bx8t6b v/ @PacktDataML [Translated from EN to English]
→ View original post on X — @kirkdborne, 2026-04-06 05:22 UTC
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Siri Cofounder Announces Major AI App Update Coming June
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Day Kittlaus, cofounder of Siri, the first AI consumer app, says a major new Siri comes in June. Talking now in a space. @Dagk
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IMMACULATE: Auditing LLM Providers with Verifiable Computation
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Can you really trust your black-box LLM provider with correct inference and honest billing? Researchers from NUS, NTU, and UC Berkeley introduce IMMACULATE. This practical auditing framework uses verifiable computation to randomly check a small fraction of LLM requests. It detects economically motivated cheats like model substitution, quality degradation, and token overbilling without needing trusted hardware or internal model access. IMMACULATE reliably distinguishes honest vs. malicious LLM execution in dense and MoE models, adding less than 1% throughput overhead. IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation Paper: arxiv.org/pdf/2602.22700 Code: github.com/guo-yanpei/Immacu… Our report: mp.weixin.qq.com/s/WR9nXudXT… 📬 #PapersAccepted by Jiqizhixin
→ View original post on X — @jiqizhixin, 2026-04-06 05:13 UTC
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When Everyone Uses AI, Only Different Thinkers Stand Out
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I published an episode on @ivoox: "#1104: When Everyone Uses AI, Only Different Thinkers Stand Out #podcast go.ivoox.com/rf/170014617?ut… [Translated from EN to English]
→ View original post on X — @juanmerodio, 2026-04-06 05:02 UTC