2nd Edition, by @keithbourne v/ @PacktDataML "Unlocking Data with Generative AI and RAG — Learn AI Agent Fundamentals with RAG-powered Memory, Graph-based RAG, and Intelligent Recall" Get the book here: http://
amzn.to/49zsIkb
LLMS
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New Book on Generative AI, RAG, and AI Agents
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Educational guide on building AI-enhanced web applications with LLMs
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Build AI-Enhanced Web Apps: http://
amzn.to/4bgux6d
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Amazon Summary: "This book shows you step-by-step and example-by-example how to build sites and applications that take advantage of large language models (LLMs) like GPT, Claude, and Llama. Written especially for web -
Learning Model Context Protocol for Building Agentic Systems
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Learn Model Context Protocol [MCP] with Python — Build Agentic Systems in Python with the new standard for AI Capabilities: http://
amzn.to/4njfsVM by @chris_noring v/ @PacktDataML 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷:
Understand the MCP protocol and its core components -
AI Engineering: Building Applications with Foundation Models
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AI Engineering — Building Applications with Foundation Models: http://
amzn.to/4aiSv1O by @chipro —
#ML #MachineLearning #DataScience #DataScientist
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𝓣𝓸𝓹𝓲𝓬𝓼:
Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the -
AI, politics, and assumptions about powerful systems
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The talk about AI & politics seems to be oddly missing a segment (a) assumes extremely capable AI is possible soon and (b) has a strong belief about how to use this technology to make human life better according to the political project they believe in. It is a moment of action.
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The Shift from Recommendation Systems to AI-Driven Content Curation
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The X algorithm is no longer a recommendation system with some AI features. It's a Grok deployment that happens to recommend posts. The question for creators isn't "how does the algorithm work?" anymore. It's "what does Grok think of my content?" Build content that a thinking
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Technical Breakdown of Grok’s AI-Driven Ranking and Content Pipeline
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Here's what Grok now controls: → Ranking: Phoenix transformer scores every candidate post
→ Retrieval: Two-Tower model finds out-of-network content using the same Grok architecture
→ Content understanding: A "Grox" pipeline classifies, embeds, and screens every post
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New Phoenix Ranking Model Ported from Grok-1
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The old system used a hand-tuned neural network with manual feature engineering. SimClusters. TwHIN embeddings. Dozens of hand-crafted signals. That system is gone. The new ranking model is called Phoenix. It's ported directly from Grok-1. The repo says it plainly: "We have
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xAI Integrates Grok Transformer Architecture into X Recommendation Algorithm
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xAI just open-sourced the new X algorithm. I read the full codebase. Grok doesn't "help" the algorithm anymore. Grok IS the algorithm. Every major component of your For You feed now runs on the same transformer that powers Grok the chatbot. Here's what changed and what it
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A Visual Tour of Recent LLM Architecture Advances
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New article: a visual tour of recent LLM architecture advances, from Gemma 4 to DeepSeek V4. I focus on long-context efficiency tweaks like KV sharing, per-layer embeddings, layer-wise attention budgets, compressed attention, and mHC. Link: https://
magazine.sebastianraschka.com/p/recent-devel
opments-in-llm-architectures
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