25 verified solution paths
Start from the symptom you are seeing and trace it to evidence, mechanism, chapter and repository diagnostic.
Find a solution →Read complete books on the web, understand the mechanisms from first principles, and apply them to real code through verified solutions and AI diagnostics.
Programmer.ie is organized around books rather than publication dates. Each book is available as a structured web edition now, with ebook and video editions built from the same source.
Evidence, distributed execution, reliability, authority, durable workflows, replay, security, verification and explicit control planes.
Read 46 chapters → 10 Agent systemsPlanning, structured actions, tools, state, memory, search, revision and external verification assembled into a working agent architecture.
Read 10 chapters → 09 Model architectureScorers, value functions, policies, hierarchical state, recursive compute and model selection through explicit architectural decomposition.
Read 9 chapters → 11 FoundationsTensors, autograd, neural networks, data pipelines, attention, training diagnostics and a small language model from first principles.
Read 11 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 →Build a Production AI Agent From First Principles: The Complete Reference Architecture We have spent this series adding mechanisms only when a specific failure demanded them. We …
Read →Treat agent behavior like a production interface: define behavioral contracts, test compatibility across models, prompts, tools, memory, routers and verifiers, then promote changes through explicit release gates.
Read →Learn how to detect silent behavioral regressions in agent systems caused by model, prompt, router, verifier, retrieval, policy and data-distribution drift, then roll back safely using evidence rather than intuition.
Read →Learn how to contain failing model, browser, retrieval, verifier, database and API dependencies with circuit breakers, bulkheads, bounded retries and graceful degradation.
Read →Learn how to schedule many concurrent AI agent runs across shared models, GPUs, browsers, tools and external APIs using admission control, quotas, fairness, priorities, reservations and backpressure.
Read →Can Your Agent Coordinate Across Machines Without Duplicating Work? A single-process agent can already be complicated. It can plan. It can search. It can launch speculative …
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