Bundles

All 6 Guides to Kalman Filters, MPC, Battery Systems, Sensor Fusion, Control Systems, and System Identification — One Library, One Price

The Internet is not magic. It is a machine built from protocols, routing decisions, networks and systems that have to work together every second. This four-book bundle takes you behind the scenes, from the fundamentals of packets and addressing to cloud connectivity, IPv6 and the modern Internet edge. Learn what happens when a packet leaves your machine, crosses the world and reaches its destination, then go deeper into the systems that keep modern infrastructure connected, secure and fast.

Go beyond simply asking AI to write your code. This four-book bundle shows you how to use Claude Code across the software lifecycle, from building full-stack applications and understanding existing codebases to static analysis and MCP-powered workflows. Whether you are learning the basics or looking to improve the way you already work, these practical guides give you the knowledge, techniques and hands-on examples to put Claude Code to work on real engineering problems.

Six great books on building agile teams: Games and self-assessments, reteaming, mindset shifts and agile principles, 101 ideas, tips from the trenches, and suggestions for forming teams. All in one buy for a largely reduced price!

The complete LLM Engineering collection—from a single weight to production systems that fail silently. Explore how LLMs work, how to build and optimise them, and how to diagnose the failures that matter.

Two forensic collections. 110 real investigations into C++ and LLM systems that compile, run, and quietly lie. Trace the evidence, uncover the root cause, and see how each failure was fixed.

A comprehensive 3-book toolkit for modern Python developers to build, test, and ship production-grade applications. Master Linux & Docker containerization, write rock-solid test suites with pytest, and automate your entire software delivery workflow using GitHub Actions.

# Master AI Engineering & High-Performance Python from Scratch! The ultimate 5-book collection designed to bridge the gap between abstract mathematical formulas, low-level Python algorithms, and production-grade AI systems. Instead of relying solely on high-level black-box frameworks, this bundle gives you complete architectural visibility into how AI models, vector search systems, and neural networks actually work under the hood. --- ### What You'll Learn: * **Solid Mathematical Foundations:** Move seamlessly from probability, vector calculus, and linear algebra equations to clean, runnable Python code. * **Zero-Framework Deep Learning:** Build custom neural network architectures and backpropagation engines from scratch using pure Python & NumPy. * **Semantic Search Architecture:** Design and implement end-to-end semantic search systems and vector similarity indexers without external APIs. * **Extreme Python Optimization:** Uncover performance bottlenecks in pure Python loops and apply vectorization, memory management, and algorithmic tuning for massive AI workloads. --- ### Books Included in This Bundle: 1. **Applied Mathematics & Artificial Intelligence:** Your primary bridge from theoretical formulas to algorithm implementation. 2. **The Ultimate AI Math Cheat Sheet:** Quick-reference mathematical formulas paired with production-ready NumPy code snippet equivalents. 3. **Behind the Black Box: Building Neural Networks From Scratch:** Step-by-step guidance on constructing deep learning components without heavy libraries. 4. **Semantic Search from Scratch:** A practical guide to building scalable, vector-based semantic search tools using pure Python. 5. **The Hyper-Drive Algorithms:** Master algorithm optimization techniques to turn sluggish Python loops into blazing-fast routines. --- ### Who This Bundle Is For: * **Software Engineers & Backend Developers** stepping into AI/ML engineering who want low-level control over their code. * **Data Scientists & AI Practitioners** looking to sharpen their mathematical implementation and pure Python performance skills. * **Computer Science Students & Researchers** who learn best by building systems from scratch.

Master the complete AI journey—from foundational concepts and professional AI practice to autonomous agents, real-world implementation, and deployment.Three handbooks. One complete operational journey.ROY-OS¹ — Learn AI. Build the conceptual and practical foundations for working effectively with AI.ROY-OS² — Build Agentic AI. Move from AI usage to intelligent systems, autonomous agents, workflows, tools, knowledge, and orchestration.ROY-OS³ AI Lab — Apply AI. Turn those capabilities into real-world solutions through implementation, testing, evaluation, deployment, and continuous improvement.From conceptual frameworks to autonomous agents, from AI capability to real-world deployment—the ROY-OS™ trilogy provides a progressive path from learning to building to implementation and measurable impact.Learn. Build. Apply. Transform. 

智能时代的开发进阶思维

Software tools change fast and keeping up with them can be a challenge. This bundle brings together three practical guides covering modern Python, JavaScript and Linux tooling in 2026. Learn how today’s tools work, why developers use them and how they fit into real projects. From package managers and testing tools to build systems, command line utilities and production workflows, these books give you a practical look at the tools that can make your work faster, cleaner and easier.

Four handbooks for people who run Claude Code every day: what it costs and how to cut that, what to do when a release changes the rules under you, the guards worth installing before anything else, and how to keep one instruction file working across nine tools. Over 460 pages, written from an operator's own logs and incidents rather than from the documentation. Every book has a free sample, and updates are free to buyers.

Seventeen books, one rule: nothing asserted, everything demonstrated — build it, break it, check it yourself.

Almost everything written about large language models is advice. This series measures it. Nine volumes, 609 pages, built and run on a single CPU core with no access to a large model —and that constraint produced the method rather than limiting it. Several results contradictedthe advice they were testing: hybrid retrieval lost in every condition, neither reranker beatthe noise floor, and unstructured pruning saved not one byte. One experiment turned out to be confounded. It is in the book alongside its correction, becausethe mistake was more instructive than the finding.