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Candy corn, M&Ms, tootsie rolls—some flavors are always popular for a reason! These Manning books are some of the best-loved treats of our readers, and we think you’ll love them too!
Get better results from AI coding agents by making your intent crystal clear. Learn to turn specifications into the foundation of software development, guiding AI from planning through implementation, testing, and deployment while keeping quality and architectural decisions firmly in your hands. [Read more]
3 chapters of this MEAP are available now, with more to follow soon!
Build your own GPT-style language model! Explore attention mechanisms, transformer architecture, pretraining, and fine-tuning through hands-on Python projects that take you from foundational concepts to a working chatbot. [Read more]
How do AI models learn to solve complex problems step by step? Discover the techniques behind modern reasoning models, from inference-time strategies and evaluation to reinforcement learning and distillation. [Read more]
Modern web applications don’t always need the overhead of a framework! Using plain JavaScript, HTML, and CSS, learn to create reusable components, implement routing, and develop progressive web apps. Embrace the browser's built-in capabilities to deliver fast, maintainable experiences with fewer dependencies. [Read more]
13 chapters of this MEAP are available now, with more to follow soon!
Effective AI software development with Claude Code and Claude Cowork
Turn AI into a collaborative partner for every part of the software development lifecycle. Learn to use Claude Code and Claude Cowork for coding, documentation, testing, and architecture, then explore how custom agents can automate operational tasks—all whilst keeping engineering quality and human oversight at the center of how you work. [Read more]
6 chapters of this MEAP are available now, with more to follow soon!
Move beyond basic AI assistants and build agents that can reason, use tools, retrieve knowledge, and coordinate complex workflows. With practical Python examples, explore modern frameworks, multi-agent collaboration, evaluation, and deployment to develop AI systems ready for real-world applications. [Read more]
Author unmasked! Suhas Suresha Author of Designing AI Systems
This Halloween, we’re unmasking the authors behind amazing Manning books! We’re continuing our series of under-the-mask interviews with Suhas Suresha, author of Designing AI Systems.
Suhas Suresha is a Senior Machine Learning Engineer at Adobe, where he builds large-scale generative AI platforms across the full machine
learning lifecycle. He previously co-founded QALY, where he helped deploy real-time ECG analysis models to more than 100,000 users.
What are you afraid of right now?
I'm afraid that we're outsourcing not just our thinking to AI, but also our understanding. It's quite a paradox: AI is probably the best tutor any of us has ever had access to, yet my observation is that it often has the opposite effect. People stop trying to understand how things work because they can hand the question to a model. I worry about a world with fewer and fewer people who actually want to understand how things work, and the long-term effect that will have on society.
Make one prediction.
My bold prediction is that AI will actually increase the number of software engineering jobs, not reduce them. Modern AI systems are getting very good at hard problems, but the problems they solve best are the ones with clear evaluations that tell you whether the answer is right. Most real software engineering isn't like that. The requirements are uncertain and keep shifting with customers, with how people behave, and with how the world changes. As AI makes building software cheaper, we'll find many more problems worth solving with software, and we'll need more people to build and improve those systems, not fewer.
Explore the architecture behind modern AI systems, from data preparation and model selection to deployment, monitoring, and continuous improvement. Learn to balance performance, scalability, and cost while building AI solutions designed to deliver lasting value. [Read more]
Take your container skills into production with a practical guide to Kubernetes! Learn to deploy applications across clusters, manage configuration and storage, scale workloads, and perform updates safely. Build the confidence to operate resilient cloud-native applications in real-world environments. [Read more]
Make unfamiliar codebases easier to understand with AI-powered analysis techniques. Learn to map architectures, trace dependencies, uncover design decisions, and generate useful diagrams with practical prompts and workflows. Whether you're navigating legacy software or AI-generated code, develop the insight to make changes confidently. [Read more]
9 chapters of this MEAP are available now, with more to follow soon!
A friendly guide for programmers and other curious people
Make better architectural decisions without getting lost in theory. Through visual explanations, practical examples, and engaging scenarios, explore APIs, microservices, event-driven systems, and cloud-native design. Develop a structured approach to evaluating trade-offs and building software that can evolve as requirements change. [Read more]
13 chapters of this MEAP are available now, with more to follow soon!
What if, instead of a custom or specific database, you could…just use Postgres? Discover how PostgreSQL can support transactional applications, full-text search, geospatial data, time-series workloads, and AI-powered features. Learn to use modern SQL, indexing, and powerful extensions to simplify your architecture without sacrificing capability. [Read more]
Bigger isn't always better when it comes to AI. Learn to adapt pretrained language models to specialized tasks using custom datasets, fine-tuning, and optimization techniques. Build efficient small language models that deliver focused results while reducing deployment costs and running on everyday hardware. [Read more]
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