Sanches Library
Curated essential reads for the AI-Native engineer
Context Engineering & AI Systems
AI Engineering: Building Applications with Foundation Models
Chip Huyen
The reference book for building systems with foundation models — RAG, agents, fine-tuning, evaluation and production deployment. Chip Huyen distills years of production ML experience into a complete architectural framework.
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Hands-On Large Language Models
Jay Alammar & Maarten Grootendorst
Visual and deep transformer architecture — attention mechanisms, KV cache, embeddings and generation pipelines explained with diagrams no other book has. The definitive technical guide to understanding LLMs from the inside out.
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Designing Machine Learning Systems
Chip Huyen
How to architect ML systems for real production — feature stores, data pipelines, model monitoring and feedback loops. Essential for anyone designing cognitive infrastructure at scale.
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Prompt Engineering for Generative AI
James Phoenix & Mike Taylor
Systematic prompt engineering — chain-of-thought, few-shot, zero-shot, structured output and advanced output control techniques. The practical guide for mastering context design with technical rigor.
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Building Agentic AI Systems
Anjanava Biswas & Wrick Talukdar
Practical blueprint for production agentic systems — reasoning loops, episodic memory, hierarchical planning, tools and multi-agent orchestration. From concept to deployment.
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Building AI Agents with LLMs, RAG, and Knowledge Graphs
Sebastien Burel & Poulain
Integrating knowledge graphs with LLMs and RAG — semantic representation, OWL/RDF, structured reasoning and neuro-symbolic pipelines for reliable agents. The closest thing to a neuro-symbolic context engineering manual.
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Generative AI with LangChain — Second Edition
Ben Auffarth
LangChain, LangGraph and agent architectures — chains, tools, memory state and deployment of genAI production applications with Python. The second edition covers LangGraph and modern agentic patterns.
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Natural Language Processing with Transformers (Revised)
Lewis Tunstall, Leandro von Werra & Thomas Wolf
From fine-tuning to RAG — practical transformers with HuggingFace for classification, NER, generation and retrieval. The technical reference manual for modern NLP, written by the HuggingFace creators themselves.
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Machine Learning Engineering in Action
Ben Wilson
MLOps and ML systems engineering — design patterns, model validation, feature pipelines, shadow deployments and rollback strategies. The practical guide from those who actually implement ML in real production.
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AI Agents in Action
Micheal Lanham
Building, orchestrating and deploying autonomous multi-agent systems — tools, distributed memory, parallel coordination and resilience patterns for agents in production.
Buy on AmazonNeuro-Symbolic AI & Reasoning
The Book of Why: The New Science of Cause and Effect
Judea Pearl & Dana Mackenzie
The causal revolution — the Ladder of Causation, do-calculus and the fundamental difference between correlation, intervention and counterfactual. The theoretical foundation for any AI system that needs to reason about causality.
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Rebooting AI: Building Artificial Intelligence We Can Trust
Gary Marcus & Ernest Davis
Technically grounded critique of deep learning limits — benchmark overfitting, brittle generalization, absent commonsense and the case for neuro-symbolic AI. The book that grounded my architectural conviction.
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Thinking, Fast and Slow
Daniel Kahneman
System 1 vs System 2 — the cognitive framework behind the neuro-symbolic approach. How the duality between fast/approximate and slow/precise processing shapes reliable AI architecture design.
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Artificial Intelligence: A Modern Approach (4th Edition)
Stuart Russell & Peter Norvig
The AI bible — search, propositional logic, first-order logic, planning, agents, learning and uncertainty. Unavoidable foundation for anyone wanting to understand the full spectrum between classical AI and machine learning.
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Gödel, Escher, Bach: An Eternal Golden Braid
Douglas Hofstadter
Self-reference, strange loops and the emergence of consciousness — the deepest text ever written about cognition, formal representation and the limits of computation. Mandatory for anyone wanting to understand what it means to "reason" in formal systems.
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The Alignment Problem
Brian Christian
The central problem of modern AI — objective specification, reward hacking, interpretability and value alignment. A rigorous technical and philosophical analysis of why intelligent systems do what we want versus what we need.
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The Master Algorithm
Pedro Domingos
Five tribes of machine learning — symbolists, connectionists, evolutionaries, Bayesians and analogizers. The search for a universal learning algorithm and its implications for unified cognitive systems.
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On Intelligence
Jeff Hawkins
The memory-prediction theory of the neocortex — how the brain creates hierarchical models of the world and how this principle grounds real intelligence. Essential foundation for Hierarchical Temporal Memory and neuroscience-inspired AI architectures.
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Probabilistic Graphical Models: Principles and Techniques
Daphne Koller & Nir Friedman
The definitive text on probabilistic graphical models — Bayesian networks, Markov random fields, variational inference and structure learning. The mathematical foundation for reasoning under uncertainty in neuro-symbolic systems.
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Pattern Recognition and Machine Learning
Christopher M. Bishop
The Bayesian bible of machine learning — neural networks, SVMs, mixture models, EM, variational inference and Gaussian processes with full mathematical rigor. The irreplaceable reference for understanding what models really do.
Buy on AmazonSystems Architecture
Designing Data-Intensive Applications
Martin Kleppmann
The bible of distributed systems — replication, partitioning, transactions, eventual consistency and stream processing. Indispensable for anyone designing infrastructure that serves cognitive systems at scale.
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Clean Architecture: A Craftsman's Guide to Software Structure and Design
Robert C. Martin
SOLID principles, components, architectural boundaries and dependency rules — the framework for systems that withstand time. The design philosophy that separates policy from technical detail.
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Building Microservices (2nd Edition)
Sam Newman
Decomposing systems into microservices — service boundaries, synchronous vs asynchronous communication, service mesh and resilience patterns. The reference book for migrating cognitive systems to distributed architectures.
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Software Architecture: The Hard Parts
Neal Ford & Mark Richards
Difficult architectural decisions — data decomposition, service contracts, orchestration vs choreography and trade-offs no framework resolves automatically. The book for architects who need to justify choices.
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Fundamentals of Software Architecture
Neal Ford & Mark Richards
Architectural styles, architecture characteristics and structured decision-making — layered, event-driven, microkernel, microservices and space-based. The foundation for architects who need to choose the right style for the right problem.
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Site Reliability Engineering
Google SRE Team (Beyer, Jones, Petoff, Murphy)
How Google engineered reliability at scale — SLOs, SLAs, error budgets, toil reduction and blameless postmortems. The operational framework for cognitive services that cannot fail.
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The Phoenix Project
Gene Kim, Kevin Behr & George Spafford
DevOps as narrative — theory of constraints, value stream, feedback loops and unplanned work. The story that transformed how the world thinks about software delivery and organizational agility.
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Release It! (2nd Edition)
Michael T. Nygard
Stability patterns for production systems — circuit breakers, timeouts, bulkheads, strangler figs and anti-patterns that cause failure cascades. The book for those dealing with systems that need to survive real chaos.
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A Philosophy of Software Design
John Ousterhout
Complexity as the central engineering problem — deep vs shallow modules, information hiding, design it twice and comments that reveal design. The most direct and actionable philosophy about what separates sustainable code from legacy code.
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Software Engineering at Google
Titus Winters, Tom Manshreck & Hyrum Wright
How Google scales engineering culture and practices — code review, testing, deprecation, code sustainability and horizontal technical leadership. The book that defines the industry standard for large-scale software engineering.
Buy on AmazonEngineering Leadership
Staff Engineer: Leadership Beyond the Management Track
Will Larson
The definitive guide for senior+ engineers navigating technical leadership without entering management — Staff archetypes, sponsorship, technical strategy and measuring organizational-level impact.
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An Elegant Puzzle: Systems of Engineering Management
Will Larson
Engineering management as systems — hiring funnels, succession planning, migrations, org design and how to intervene in the right problems. Systemic framework for leaders who think at scale.
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Team Topologies: Organizing Business and Technology for Fast Flow
Matthew Skelton & Manuel Pais
Four team topologies and three interaction modes — stream-aligned, enabling, complicated-subsystem and platform teams. The industry standard for modern value-stream-oriented org design.
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The Manager's Path
Camille Fournier
The journey from IC to CTO — mentoring, tech lead, manager of managers and directorship, with what changes, what stays and what nobody told you. The most honest map of the engineering career.
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Accelerate: The Science of Lean Software and DevOps
Nicole Forsgren, Jez Humble & Gene Kim
The four DORA metrics — deployment frequency, lead time, change failure rate and MTTR — and the science behind why elite technical practices drive superior organizational outcomes.
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The Pragmatic Programmer: Your Journey to Mastery (20th Anniversary)
David Thomas & Andrew Hunt
Software craftsmanship updated for 2020 — DRY, tracer bullets, orthogonality, design by contract and automate everything. The book that has most influenced engineering culture over the past two decades.
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The Staff Engineer's Path
Tanya Reilly
How Staff Engineers navigate the organization, create technical vision and move large projects — sponsorship, writing for influence and how to define the correct scope for senior+ level impact.
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Tidy First?
Kent Beck
Incremental design and the philosophical question behind every refactoring — when to reorganize structure before changing behavior, the economic theory of software and temporal coupling. Kent Beck at his best.
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Engineering Management for the Rest of Us
Sarah Drasner
Engineering management for those who never planned to become a manager — 1:1s that work, difficult feedback, hiring, firing and how not to lose technical empathy when assuming organizational responsibility.
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97 Things Every Engineering Manager Should Know
Camille Fournier (Ed.)
Collective wisdom from 97 engineering managers — diversity, performance, layoffs, technical leadership, culture and the mistakes nobody mentions in interviews. The practical reference book for engineering leaders.
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