Books

Not Artificial, Not Intelligent cuts through the hype and doom to show what AI really is: powerful pattern-matching infrastructure, not thinking machines. Forget sci-fi fantasies - this is AI realism, explaining how machine learning is already reshaping business and culture.

Sharpen your knowledge for the CIPP/E exam with 90 practice questions and detailed explanations. Updated September 2025, this guide covers all exam domains and helps you succeed with confidence.

Learn a step-by-step, evidence-based approach that gives new bug hunters a realistic path to success. Stop relying on luck—get the system to land your first bounty in 6–8 months.

Go from idea to value at scale. Leading a tech portfolio is rarely straightforward. Ideas outstrip capacity, funding is finite, and priorities collide, all while teams push for clarity that leadership struggles to provide. The real challenge isn’t ambition or talent — it’s turning ideas into value through clarity, alignment, and the right actions. The Tech Portfolio Playbook is a practical, principle-based guide for navigating that complexity. It draws on field-tested experience to show how strategy, funding, and delivery can connect to move ideas to value. Inside, you’ll find over 30 principles and examples — from connecting the dots between idea to value, to aligning senior leaders around priorities, to creating space for delivery teams to accelerate without burning out. This isn’t another framework to adopt. It’s a playbook for the messy, complex reality of enterprise technology — built to help you make better decisions, align faster, and go from idea to value.

This handbook is a practical, hands-on guide for Master’s students in Business and Marketing. It provides step-by-step support for every stage of the research journey: from choosing a topic and reviewing the literature to designing frameworks, selecting methods, analyzing data, and writing up results. Unlike generic textbooks, this guide focuses on practical tools, examples, and checklists that students can apply immediately. With integrated resources on SPSS, NVivo, MAXQDA, SmartPLS, Jamovi, R, and AI research tools (Consensus, Elicit, ResearchRabbit), it bridges theory and practice, helping students move from confusion to clarity. A must-have companion for anyone who wants to successfully complete a thesis in business and marketing.

Born from year one of The Coder Cafe, the newsletter trusted by 3,000+ readers, this contains distills timeless concepts to help you master the fundamentals of software engineering: code health, testing, distributed systems, critical thinking, and more.Written by Teiva Harsanyi, Google SWE and published author, it supports your growth as an engineer, one concept at a time.

What does it really mean to be an architect in the age of AI? If you are a software architect or stepping into the role of AI architect, this question is already shaping your path. Just as we once moved from monoliths to services and from infrastructure to the cloud, we are now designing for intelligent systems that must be explained and trusted. Dear Software and AI Architect is the guide the industry has been missing. Drawing on two decades of practice, field stories, and lessons learned, it shows you how to navigate ambiguity, balance trade-offs, and build trust that scales. You will gain real-world insight into architecting with both software craftsmanship and AI intelligence, use AI as a partner in your craft, and apply enduring principles, patterns, and practices across teams and enterprises. With 50+ caselets, expert voices, and the Architect’s V-Impact Canvas, this book equips you to lead with clarity, build with context, and thrive in the era of AI engineering.

How to represent knowledge for LLMs and build memory for agents, I discovered Mark's work on semantic spacetimes. It's more of a theoretical framework from someone who came from physics. But actually, jumping to knowledge representation and reasoning, and trying to answer the question of how to build dynamic and complex systems—semantic spacetimes and promise theories are crucial for the future of agentic systems, in my belief.The semantic spacetime approach gives us answers on how to organize better memory and how to have better knowledge representation that could be understood quite well by LLMs. Vector embeddings actually create a lot of challenges—some spaces and some relations in vector embeddings simply don't exist. We all have this problem where "love my wife " and "hate my wife" while actually quite distant in practice, and also time and dynamics matter