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C++ Mastery to LLM Engineering Bundle
From your first C++ program to running your own LLM inference engine — this is the complete path to modern C++ mastery.
Master algorithms through 1000 progressive challenges, then apply that knowledge to build the core technologies powering today's AI models.
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$27.00
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The following 2 books are included in this bundle...
These books have a total suggested price of $32.00. Get them now for only $27.00!
About
About the Bundle
C++ Mastery to LLM Engineering Bundle is a complete journey from learning C++ fundamentals to building a real LLM inference engine from scratch.
Start with 1000 carefully designed algorithmic challenges that guide you through C++ programming, data structures, algorithms, and advanced problem solving — progressing from absolute beginner concepts to competitive programming and expert-level techniques.
Then take your skills into the world of modern AI systems by building a complete LLM inference engine in C++. Learn how real language models run internally by implementing tensors, memory management, transformer components, attention mechanisms, quantization, model loading, and performance optimizations.
This bundle bridges the gap between writing C++ code and engineering high-performance AI systems.
Two books. One complete path: Learn the language. Master the algorithms. Build the engine behind modern AI.
Books
About the Books
C++ Algorithmic Mastery: 1000 Challenges from Beginner to Legendary
Solved and Tested — A Complete 8-Volume Journey from Beginner to Advanced Algorithms
1000 problems. Eight volumes. One complete journey from your very first line of C++ to the frontiers of modern algorithms.
This is the complete collection: 1000 carefully designed C++ problems, organized into 8 progressive volumes that take you from absolute beginner to advanced algorithmic mastery — with every single solution compiled and tested on g++.
What you get:
- 1000 problems, carefully ordered by difficulty across 8 volumes
- A complete, working C++ solution for every problem
- Clear explanations, hints, and complexity analysis throughout
- A structured path, so you never have to guess what to learn next
Every problem includes:
- A clear statement with examples and constraints
- A hint to point you in the right direction
- A full, tested C++ solution
- Time and space complexity
- A plain-language explanation of how it works
The 8 volumes:
1. Absolute Beginner — I/O, Variables, Conditions, Loops, Functions, Arrays
2. Beginner — Strings, Recursion, Basic Math, Simple Data Structures
3. Elementary — Sorting, Searching, Core Algorithms, the STL
4. Upper-Intermediate — Data Structures, Two Pointers, Prefix Sums, Greedy
5. Advanced — Graphs, Dynamic Programming, Trees, Shortest Paths
6. Expert — Advanced Strings, Heavy Graph Machinery, Computational Geometry
7. Master — Suffix Structures, Flow & Matching, Number Theory, Combinatorics
8. Genius — Polynomials & Transforms, Advanced Graphs, Capstone Challenges
Who it's for:
Anyone learning C++ who wants a structured, guided path — students, self-taught programmers, and anyone preparing for coding interviews or competitive programming who wants every step explained, not just a list of problems.
One thousand problems. Eight volumes. A complete roadmap from foundations to mastery.
Build an LLM Inference Engine in C++
A Challenge-Driven Guide to Building a CPU-First Inference Engine in C++20
Build an LLM Inference Engine in C++ — Through Challenges
You don't truly understand how large language models run
until you've built the engine yourself.
This book takes you from a blank C++ project to a complete,
working inference engine that loads a real Llama-family model
and generates text — one challenge at a time.
What you'll build:
- A strided tensor system with zero-copy views and arena allocation
- Math kernels: RMSNorm, SwiGLU, softmax, GEMM with SIMD
- A byte-level BPE tokenizer
- A full Transformer: RoPE, GQA, Flash Attention, KV Cache
- int8/int4 quantization with direct block multiplication
- GGUF model loading with mmap
- Sampling, streaming, speculative decoding, and continuous batching
- An optional CUDA capstone for the heaviest kernels
By Unit 14, the engine runs a real model on CPU. Every concept
earns its place right after you've built the thing it improves.
Who this is for: C++20 developers comfortable with algorithms
and memory layout who want to understand what actually happens
inside an LLM runtime — not by reading, but by building.
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