Manning Publications Co.

For developers today, understanding how to build secure systems isn't optional, but figuring out what actually applies to your day-to-day work can be. That's where Software Security for Developers by Adib Saikali and Laurentiu Spilca comes in. It focuses on practical application, not theory overload. It shows how to use built-in security features in tools you're already working with, like Java and Spring. If you want a clearer picture of how that plays out in practice, Laurențiu breaks it down in a GOTO Conferences Book Club conversation with Thomas Vitale. The discussion: https://hubs.la/Q04cTzX50 Explore the book: https://hubs.la/Q04cY3Ky0

Some concepts don't really stick until you try them yourself. That's what makes Mesut Oezdil's walkthrough of CUDA for Deep Learning stand out. You're not just reading what he learns from Elliot Arledge's book — you can follow along in his repo and work through it in parallel. It's a simple loop: learn the concept, apply it, validate your understanding. The book provides the foundation, and the repo gives you a way to pressure-test it in practice. He's also added a few incentives along the way, including the chance to win prizes. See how he's approaching it: https://hubs.la/Q04cTnwH0 Explore the book: https://hubs.la/Q04cTsB70

AI agents aren't one idea — they're where multiple parts of AI come together into real systems. That's part of what makes them powerful. And harder to navigate. You're not just learning a tool. You're learning how models, orchestration, data, and decision-making all connect. Today's Deal of the Day is built for that. A range of books at different levels, so you can move from foundational concepts to building and deploying agents in practice, without getting stuck in one layer of the stack. Included titles: • AI Agents and Applications by Roberto Infante • AI Agents in Action, Second Edition by Micheal Lanham • Build an AI Agent (From Scratch) by 허정준 (Jungjun Hur) and Younghee Song • Building AI Agents in .NET by Daniel Costea • Grokking AI Applications by Andrea De Mauro • Hugging Face in Action by Wei-Meng Lee All half off today, April 22nd: https://hubs.la/Q04cYlRv0

Systems programming has a reputation: powerful, but not always approachable. Garrison Hinson-Hasty is working to shift that. His book, Systems Programming with Zig isn't just about syntax or low-level mechanics — it's about making the process feel intuitive and even enjoyable. The book shows how far you can go with Zig without needing deep prior expertise, while still building a solid mental model for how systems actually work. If systems programming has felt out of reach or just not worth the effort, his book is a more accessible way in. Hear his thought process: https://hubs.la/Q04cPxjb0 Explore the book: https://hubs.la/Q04cPNpZ0

Some approaches look outdated. Until you understand what they're optimizing for. In a recent review, Oleksandr Kaleniuk reflected on Vanilla Web by Maximiliano Firtman through an unexpected lens: growing up in the USSR. His analogy cuts through the usual "framework vs. no framework" debate and reframes minimalism as a deliberate, practical choice — not a limitation. It's a useful reminder that simpler approaches often prioritize control, performance, and longevity in ways that aren't obvious at first glance. Read his full comparison: https://hubs.la/Q04cWlyh0 Explore the book: https://hubs.la/Q04cWfbJ0

Conferences are great for learning, but the moments people remember tend to be more personal. At Spring I/O, one of our newest authors, Daniel Garnier-Moiroux, teamed up with VMware Tanzu and Broadcom to be a part of their Tanzu Card Game. Attendees could collect cards — including one featuring Daniel and another highlighting his book, Testing Spring Boot Applications. A few even walked away with a free copy. It's a simple idea, but it changes the dynamic. Instead of just hearing from an author, you interact with them, connect the work to a face, and take something tangible with you. Explore the book: https://hubs.la/Q04cV-ky0 See how the card game worked at Devnexus: https://hubs.la/Q04cVXDt0

Long hours in tech aren't new, but the reasons behind them are shifting. Data science, software engineering, and ML have long had a reputation for intense workloads. What's changing is why people are putting in those hours. In a recent conversation with Lex Fridman, Nathan Lambert and Sebastian Raschka, PhD discuss how, in some cases, the drive isn't external pressure — it's genuine interest in contributing to the progress of AI. That doesn't make the tradeoffs simple. But it does change how we think about motivation, burnout, and what sustainable work actually looks like in fast-moving fields. Watch the discussion here: https://hubs.la/Q04cB_3z0 If you want to go deeper into their thinking, you can get their books half off here: https://hubs.la/Q04cC4T80

Finding the right balance in technical learning isn't easy. If it's too theoretical, it's often hard to apply. And if it's too simplified, it can lack depth. That tension is something we think about with every book we publish. Some series go deeper into theory, like our "...in Depth" titles, while others focus on accessibility and intuition, like the Grokking series. The goal isn't to pick one approach — it's to meet you where you are and help you keep progressing. Sayan Biswas shared a thoughtful perspective on how that balance played out in his own experience: https://hubs.la/Q04cB-PH0 Explore more about our approach and series: https://hubs.la/Q04cC6LF0

Most conversations about bias in LLMs stay at the surface: outputs. There's toxic responses, skewed predictions, and things you can filter or prompt around. But that assumes the issue starts there. Pere Martra's Medium article takes a different angle. It shows how bias is embedded in the model's internal structure. Certain layers and pathways don't just reflect bias from training data; they can amplify it. Which means you can't fully "fix" it from the outside. Read the article to see where bias actually lives: https://hubs.la/Q04cBXKx0 That's the focus of his book, Rearchitecting LLMs — how to move beyond black-box models and shape systems that behave the way you need: https://hubs.la/Q04cBS8v0

So, you walk out of an interview feeling confident. Then get a short rejection with no explanation... What actually went wrong? Next week, Barbara Limmer and Laura Browne will unpack that gap at the Women Who Go (London) Meetup. They'll break down what interview questions are really designed to reveal, how employers evaluate your answers, and how to respond in a way that's clear, relevant, and memorable. It's a practical look at how to approach interviews with less guesswork and more control over how you're perceived. Register for the April 21st event: https://hubs.la/Q04cs1C70 Not in London? Their book, Interview Speak, covers the same approach — available at 45% off with wwglapr26: https://hubs.la/Q04cs4720