Programmer.ie turns deep technical work into books, diagnostics and working systems.
The books work through difficult subjects from first principles. The goal is not to stop at explanation: each path should lead toward something that can be built, tested or used.
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 19 chapters → 24Build context systems from first principles: observe what a model actually receives, measure when more information hurts, then earn pruning, compaction, externalization, recall, caching, policy, budgeting, and a Context Compiler through controlled experiments.
Read 24 chapters → 30Build from a callable model to explicit context, durable state, controlled actions, independent verification, and a deterministic runtime that decides what happens next.
Read 30 chapters → 26Explore 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.
Read 26 chapters → 25Build browser-native AI from the capability boundary upward: local models, observability, WebMCP tools, agent security, and a user-owned browser policy engine.
Read 25 chapters → 21Turn language-model behavior into an experimental variable, then learn when optimization is measurable, when improvement is trustworthy, and what DSPy contributes to the process.
Read 21 chapters → 19 Artificial life, complex systems and computational emergenceAn experimental search for life-like organization in computational systems — through emergence, causality, persistence, material turnover, hidden state and finite computation.
Read 19 chapters → 60Progress from deterministic debugging to diagnosing models, evidence, trajectories, and AI-generated work.
Read 60 chapters → 16Explore 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.
Read 16 chapters → 16Understand 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.
Read 16 chapters → 13Learn 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.
Read 13 chapters → 11Build reliable AI agents by understanding the mechanisms underneath them: control loops, actions, validation, planning, state, tools, memory, search, and external verification.
Read 11 chapters → 47Go beyond basic agent loops into orchestration, evaluation, reliability, multi-step reasoning, memory, and production-grade agent systems.
Read 47 chapters → 32Think, create, research, write, and build with AI through open conversation, clear intent, structured reflection, iterative collaboration, and amplified creation.
Read 32 chapters → 60Explore cellular automata by building the rules, dynamics, patterns, and computational ideas from the smallest possible mechanisms.
Read 60 chapters → 12Explore 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 12 chapters →The practical destination of the work: projects, diagnostics and tools that turn the underlying ideas into something usable.
A standalone memory runtime for AI agents that separates retrieval, historical recall, temporal validity, current work, trust, decisive selection, traceability, unfinished work, and explicit memory writes from permission to influence present behaviour.
Explore solution →Evidence-first AI systems where claims, decisions, policy boundaries, failure states and replay remain explicit.
Explore solution →A relation-aware embedding runtime that measures identity, compatibility, preservation, and policy-scoped usability instead of trusting raw vector similarity.
Explore solution →ZeroModel is an attempt to compile useful parts of intelligence into identified, portable, inspectable decision artifacts — and to preserve enough evidence around those artifacts …
Read →Embeddings are everywhere in modern AI. They power semantic search, retrieval-augmented generation, recommendations, clustering, duplicate detection, code search, memory systems, …
Read →For decades, the technological singularity has been presented as a future event. At some point, an artificial intelligence would become as intelligent as a human being, improve …
Read →What happens when a system stops asking an AI for the same directions repeatedly, and starts placing signs where decisions need to be made? ZeroModel in One Image Most AI systems …
Read →We Trained a Model on Editorial Choices to See Whether It Learned What Happened Next Most preference-learning systems use a choice to change the future. A model produces two …
Read →Evidence that the alternative a writer rejects can help predict the future stability of what they retain.
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