The Weekly Roundup: July 11—17

Half off all print books and more ends tomorrow! ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏  ͏ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­  

View in browser The Weekly Roundup: July 11—17  HALF OFF all print books, including all preorders in MEAP! Renew or get Manning Online Pro annual for just $199.99—SAVE $50! Save 37% on everything else Offer ends midnight PT July 19th. Only at manning.com. Here's your opportunity to catch up on Manning's latest releases, MEAP updates, bestsellers and more!  New MEAP! LLM Customization and Fine-Tuning LLM Customization and Fine-Tuning is a hands-on playbook for turning a general-purpose open-weights model into a focused, cost-efficient system that’s tailored to your business. You’ll explore the complete adaptation spectrum, from prompting and RAG, through LoRA and QLoRA, to fully supervised fine-tuning, knowledge distillation, and preference alignment with DPO.    Everything you learn is fully reproducible on cost-effective and easy-to-access hardware. You’ll train LoRA and QLoRA adapters on a modest consumer card and perform full fine-tuning on a  single 24 GB card such as an A30 or RTX 4090. All with no requirement for a datacenter. [Read more] 5 chapters of this new MEAP are available now with more to follow soon! SAVE HALF until July 24 eBook $47.99$23.99 print $59.99$29.99 Be sure to also check out Building Reliable AI Systems  Rearchitecting LLMs  Build Your LLM and Fine-Tune it for Real Tasks  Now in print! Reinforcement Learning from Human Feedback When ChatGPT proved that Reinforcement Learning from Human Feedback (RLHF) could make AI production-ready, the technique transformed the field overnight. In Reinforcement Learning from Human Feedback, post-training expert Nathan Lambert offers an insider's guide to building AI that is safer, smarter, and aligned with human values. Drawing on his real-world experience building models like Olmo, Lambert shares hard-won insights on preventing over-optimization, crafting model character, and performing rigorous model evaluation.   Through hands-on experiments and mini- implementations, Lambert walks you through the full post-training pipeline that emerged from RLHF, from foundational theory and landmark papers to the optimization tools that turn a generic base model into a capable AI assistant. You'll explore reward models, instruction tuning, direct alignment algorithms, and the mathematics behind the latest reinforcement learning techniques with LLMs. Then go further with advanced techniques including designing asynchronous systems for reinforcement learning with verifiable rewards (RLVR), constitutional AI, synthetic data, and tool-use. [Read more] SAVE HALF until July 30 eBook $47.99$34.64 print $59.99$29.99 Be sure to also check out Grokking Deep Reinforcement Learning  Applied Reinforcement Learning  Deep Reinforcement Learning for Self-Driving Robots  Now in print! Sutskever's List What did Ilya see? Read through Sutskever’s List and you’ll uncover the breakthroughs, ideas, and mental models that shaped the vision of AI guru Ilya Sutskever, all clearly explained and interpreted by veteran author and teacher Richard Heimann.   OpenAI co-founder Ilya Sutskever is one of the most important figures in modern deep learning. Along with developing the GPT models, he is the originator of the famous “Sutskever’s List”—a reading list of 30 research papers that cover “90% of what matters” in AI. In Sutskever’s List,  AI expert Rich Heimann dives into each of these influential and vitally-important papers, revealing the breakthroughs that shaped Ilya’s thinking and the entire AI field. [Read more] SAVE HALF until July 30 eBook $39.99$19.99 print $49.99$24.99 Be sure to also check out Inside Deep Learning  How Large Language Models Work  Inside AI  More new MEAPs Spring Start Here, Second Edition  Build Applications with Local AI Models on a Mac  Context Engineering  Building LLM Applications with DSPy  Designing AI Agents  Web Component Development with Modern Libraries and Tooling Crack Any Codebase with AI  Embedded Software with Rust  Building Agentic Applications with CrewAI and MCP  Machines that Think  More new print books Build A Reasoning Model (From Scratch)  Software Security for Developers  Domain-Specific Small Language Models  Build Python Web Apps with Streamlit  Deep Learning with R, Third Edition  Architecting an Apache Iceberg Lakehouse Kubernetes in Action, Second Edition  Grokking AI Algorithms, Second Edition  AI Agents in Action, Second Edition  Math for Web Design  Manning Online Pro annual subscription—Just $199.99 A Manning Online Pro subscription unlocks our library with special bonuses and a free eBook to keep every month! This week, we’re offering a $50 discount on an annual Manning Online Pro subscription! Don't miss out on this great offer!   Here’s what you get with Manning Online Pro for just $249.99 $199.99! All books online via liveBook All Manning Early Access Program (MEAP) Exclusive to Pro! All liveProjects Exclusive to Pro! All liveVideos Exclusive to Pro! All liveAudio audiobooks Exclusive to Pro! 50% discount on all print purchases Exclusive to Pro! 1 FREE eBook download monthly Get an annual subscription for$249.99$199.99! Recent MEAP updates Secrets of the JavaScript Ninja, Third Edition >> Chapter 12, “Wrangling regular expressions” >> Learn what a JavaScript Ninja knows about regular expressions (including why they rock) as you get a regex refresher, examine complex parsing patterns that regular expressions can’t handle, and take a look at advanced features added by the latest ECMAScript specs.   Vibe Engineering >> Chapter 9, “Economics of vibe engineering: cost optimization at scale” & Chapter 10, “Your codebase is the bottleneck: code organization for the agent era” >> Explore what happens when the “enthusiasm that fueled the prototype phase meets the gravitational pull of unit economics” and learn why your API bill is the least interesting number on your invoice. Then follow a single narrative through six problems that  surface in sequence when you scale AI-assisted development beyond one developer, and discover what the authors mean when they say your codebase is the bottleneck…     Web Component Development with Modern Libraries and Tooling >> Chapter 7, “Stencil compiler”, Chapter 9, “wrec Library”, Chapter 10, “Using in SPAs” >> Discover a highly popular way to create web components (rather than framework-locked widgets), and gain an understanding of why this approach is different—and better—than some others. Then explore a web component library that features automatic reactivity and follows best  practices while requiring less code, and learn how to use web components in your web framework of choice.    Learn AI Data Engineering >> Chapter 12, “AI-powered web scraping data pipelines in action”, Appendix A, “Setting Up Your Environment”, Appendix B, “Prompt Engineering Reference”, Appendix C, “Using the OpenAI API”, Appendix D, “Troubleshooting Common Errors”, Part 1, “Core Concepts of Data Engineering with AI”, Part 2, “Data Cleaning and Transformation Pipelines with AI”, & Part 3, “Generating Data with AI” >> Learn  how to close the gap between a pile of useful snippets where most data works starts and a pipeline that works across many products, and that you can actually trust. Then take a look at the appendixes, now readily available for your reference, that walk you through setting up your environment, troubleshooting the most common errors, and more. And check out the new focus-setting part openers that prepare you for what you’ll learn in the coming chapters.     Build Applications with Local AI Models on a Mac >> Chapter 13, “System Prompts and Parameter Tuning”, Chapter 14, “Offline LLMs and Their Benefits”, Chapter 15, “Common Errors and Troubleshooting”, & Chapter 16, “Advancing to RAG, Web UI, and Fine-Tuning” >> Learn how to leverage your prompts to tailor your chatbot’s “personality” to emulate anything from a strict coding mentor to a salty pirate tutor. Next, walk  through verifying that your LLM works offline (and why that’s more important than you might think), and start building your diagnostic toolkit. Then explore the possibilities for expanding on the foundation you’ve built so far.   Our bestselling Summer print books Build a Large Language Model (From Scratch) Build a Reasoning Model (From Scratch) AI Agents in Action, Second Edition Deep Learning with Python, Third Edition LLM Customization and Fine-Tuning Grokking Deep Reinforcement Learning  Domain-Specific Small Language Models Build a Multi-Agent System (from Scratch)  Spring Start Here, Second Edition Context Engineering  Our bestselling Summer MEAPs LLM Customization and Fine-Tuning Build Applications with Local AI Models on a Mac Context Engineering Crack Any Codebase with AI Designing AI Agents Build an AI Agent (From Scratch) Spring Start Here, Second Edition  Timeless Algorithms: The Seminal Papers CUDA for Deep Learning  Vanilla Web Our bestselling Summer eBooks Build a Reasoning Model (From Scratch) Build a Large Language Model (From Scratch) AI Agents in Action, Second Edition Domain-Specific Small Language Models AI Agents and Applications Sutskever's List Effective Platform Engineering Software Security for Developers  Reinforcement Learning for Human Feedback  Deep Learning with Python, Third Edition  Our bestselling Summer liveProjects Build an Agentic RAG Application Build Your LLM and Fine-Tune it for Real Tasks From Prompts to an Agentic System Mastering Algorithms: From Smart Search to Stock Trading Open Source LLMs on Your Own Computer Build a Small Dockerized Data Mesh Vision Models for Classification and YOLO Segmentation Deep Learning for Multimodal Image Retrieval Hands-on with C++ 20: Ranges, Coroutines, Concepts, and Modules Modern CSS for a Portfolio Page Our bestselling Summer liveVideos Master and Build Large Language Models Rust in Motion Machine Learning for Mere Mortals A Complete Plan for AI Security Angular 16 and RxJS: Build modern Single Page Applications Modern Threat Modeling for Cybersecurity Certified Ethical Hacker Boot Camp C++ for Beginners: Mastering C++ programming essentials Get Programming with Python in Motion Grokking Deep Learning in Motion You're receiving this email because you're subscribed to our newsletters. 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