Programmer.ie books turn the learning paths into complete technical products: read them on the web now, with ebook, print-ready and video formats following from the same source.
The books are the primary publishing units of Programmer.ie. Every finished book should lead to an applied solution: a durable page and implementation where the mechanism becomes something inspectable, testable or usable.
Engineering AI Systems That Turn Past Experience Into Better Present Behaviour
Build AI memory from storage and retrieval through provenance, temporal state, unfinished work, context selection, behavioural evaluation, trust, and the boundary with learning — adding only the mechanisms that measurement earns.
Read on the webEPUB plannedStore edition available
What Happens When Language Stops Being the Whole Interface
A first-principles investigation of how AI can transform language from the primary carrier of communication into one representation within a larger system of semantic mediation, attention, personalisation and delegated action.
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Engineering What an AI Gets to Know
Context as a compiled, observable artefact: see what a model actually receives, learn what capacity, content, representation, time, authority and scope each demand, then build a deterministic Context Compiler, deliver its output to a runtime, observe that it arrived, and test whether it helped.
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Engineering Intelligence Into a Reliable Process
Build from a callable model to explicit context, durable state, controlled actions, independent verification, and a deterministic runtime that decides what happens next.
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Geometry, Retrieval, Alignment, and the Hidden Representation Layer of AI
Explore what it means for information to become geometry, when that geometry can be trusted, and how representation, similarity, retrieval, calibration, cross-space alignment, and compression combine into an embedding runtime that knows its own limits.
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Build browser-native AI from the capability boundary upward: local models, observability, WebMCP tools, agent security, and a user-owned browser policy engine.
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Turn language-model behavior into an experimental variable, then learn when optimization is measurable, when improvement is trustworthy, and what DSPy contributes to the process.
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An Experimental Search for Life-Like Organization in Computation
An experimental search for life-like organization in computational systems — through emergence, causality, persistence, material turnover, hidden state and finite computation.
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From Python Exceptions to Models, Hallucinations, and Agents
Progress from deterministic debugging to diagnosing models, evidence, trajectories, and AI-generated work.
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Explore why language models hallucinate, how hallucination can be measured and evaluated, where individual detection methods fail, and how evidence, verification, policy, abstention, and memory gates can be combined to build reliable systems around stochastic models.
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Understand PyTorch from first principles — from tensor geometry and autograd to model structure, transforms, attention, debugging, performance, compilation, reproducibility, and a GPT-style model built from scratch.
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Learn to read modern AI models by decomposing them into smaller mechanisms, then rebuilding quality scorers, value and policy heads, recurrent reasoning systems, optimizers, and preference rankers in PyTorch.
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Build reliable AI agents by understanding the mechanisms underneath them: control loops, actions, validation, planning, state, tools, memory, search, and external verification.
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Go beyond basic agent loops into orchestration, evaluation, reliability, multi-step reasoning, memory, and production-grade agent systems.
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Think, create, research, write, and build with AI through open conversation, clear intent, structured reflection, iterative collaboration, and amplified creation.
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From Local Rules to Emergence, Computation, Artificial Life and Learned Systems
Build cellular automata from the smallest local rules, then follow them through measurement, computation, continuous artificial life, learned dynamics and reproducible engineering. A first-principles investigation of what simple local systems can do — and how to tell what the evidence actually supports.
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Explore how LLM agents grow from simple conversations into reflective, tool-using, memory-enabled and multi-agent systems—and how those systems can become collaborators, companions, interfaces, and extensions of human agency.
Read on the webEPUB plannedStore edition available
The web chapters remain freely readable. The same underlying work can become diagnostics, capstones, demos and working systems, while book editions preserve the full explanation. The book is the understanding layer, not the final stop.
