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.
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.
Read 16 chapters → 20Turn 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 20 chapters → 24Build browser-native AI from the capability boundary upward: local models, observability, WebMCP tools, agent security, and a user-owned browser policy engine.
Read 24 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 → 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 → 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 → 12Learn 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 12 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 → 34Think, create, research, write, and build with AI through open conversation, clear intent, structured reflection, iterative collaboration, and amplified creation.
Read 34 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 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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