25 verified solution paths
Start from the symptom you are seeing and trace it to evidence, mechanism, chapter and repository diagnostic.
Find a solution →Programmer.ie is an independent publishing project turning fast-moving developments in AI, software, finance and technology into durable knowledge: books, experiments, explanations and practical tools.
Books are the highest-value output of Programmer.ie: a place to take a difficult subject, work through the mechanisms and evidence, and turn scattered ideas into coherent, durable knowledge. Every book is available as a structured web edition.
Build measurable, optimizable language-model programs from first principles: signatures, modules, examples, metrics, compilation, reflection, tools, context, experimental isolation, deployment, and evidence-driven improvement.
Read 18 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 → 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 → 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 → 11Build a working understanding of modern AI models by tracing the mechanisms from representations and learning through attention and transformer architectures.
Read 11 chapters → 47Go beyond basic agent loops into orchestration, evaluation, reliability, multi-step reasoning, memory, and production-grade agent systems.
Read 47 chapters → 60Explore cellular automata by building the rules, dynamics, patterns, and computational ideas from the smallest possible mechanisms.
Read 60 chapters →The books explain the mechanisms. The solution library turns those mechanisms into problem-first diagnostics, and the AI prompts let you apply them directly to your own repository.
Start from the symptom you are seeing and trace it to evidence, mechanism, chapter and repository diagnostic.
Find a solution →Every book remains fully navigable as a free web learning path with chapter progress and connected application material.
Open learning paths →Evidence-driven repository prompts turn the material into audits for PyTorch, model design, agents and production AI systems.
Browse AI prompts →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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