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AI agents are definitely popular, but the ecosystem gets confusing fast. There are different frameworks, architectures, and even different opinions on what "best practice" means. A few of our books help make sense of it (links in the thread): • AI Agents In Action, Second Edition by @cxbxmxcx • Build a Multi-Agent System (From Scratch) by @_nerdai_ • Essential GraphRAG by Tomaž Bratanič & @oskarhane • Master and Build Large Language Models by @rasbt and @abhinav_kimothi Get access to all of them through the Manning Online annual subscription — 20% off for Memorial Day. Also: • All books 50% off • liveProjects and liveVideos are $10 • 20% off Manning Online Teams Find 'em all at hubs.la/Q04hHC7W0

Best practices only get you so far. Architecture decisions usually live in the gray areas. If you want sharper instincts around tradeoffs, system design, modernization, and distributed architecture, gaining that foundation is crucial. These Manning books are a strong place to start (links in the thread): • Grokking Software Architecture by @codeliftsleep • Kafka for Architects by Katya Gorshkova • Architecture Modernization by Nick Tune and @jgperrin • Microservices Patterns, Second Edition by @crichardson All 50% off in our Memorial Day sale. If you prefer access to everything, Manning Online is 20% off through Monday too. liveProjects and liveVideos are still on sale for $10 too. Find everything at hubs.la/Q04hHYx60

Not every LLM problem needs a bigger model. Sometimes the better answer is a smaller model, smarter retrieval, or stronger production architecture. If you're exploring efficient AI systems, these Manning books are worth a look (links in the thread): • Domain-Specific Small Language Models by @GuglielmoIozzia for targeted models for targeted tasks • LLMs in Production for practical LLMOps and deployment strategy • A Simple Guide to Retrieval Augmented Generation by @abhinav_kimothi for grounding outputs with RAG • Enterprise RAG by @TylerSuard for scaling retrieval systems in production All Manning books are 50% off through Monday for Memorial Day. liveProjects and liveVideos are $10. Check out the catalogue: hubs.la/Q04hHPNg0

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If every new AI project means rebuilding workflows, infrastructure, and deployment patterns from scratch, the problem probably isn't the model; it's the system. These Manning books focus on building AI that actually scales (links in the thread): • Designing AI Systems by @inceptor92 and @SultaniaDewang for AI platforms for repeatable, reusable development • Building Reliable AI Systems by @RushShahani looks at production deployment and maintenance • AI Agents and Applications for practical LLM solutions • Machine Learning Platform Engineering by @bentanweihao & co. looks at ML infrastructure and workflows Memorial Day sale through Monday: • eBooks 50% off • liveProjects and liveVideos are $10 Check our full catalogue out at hubs.la/Q04hz9zM0

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"Move faster" is the default in software engineering. The problem is that repetitive work, constant time pressure, and overload can wear developers down while hurting software quality. Tobias Zwingmann's liveProject series Build Your LLM and Fine-Tune it for Real Tasks takes a practical approach: build and self-host your own Llama 3.2 model, then adapt it for internal workflows like HTML code generation without vendor lock-in. For the Memorial Day sale, this series is $10 today: hubs.la/Q04hpCKJ0 All liveProjects and liveVideos are $10 too.

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