In January 2026, a computational geometry specialist with twenty-eight years of software development experience had a throwaway idea about building a support chatbot for a client. Nine hours later, he had a working prototype. Sixteen days later, he had a multi-tenant platform with five deployed agents, a paying customer, and a codebase that could onboard a new client from a single URL.
This book is the honest, commit-by-commit story of that sprint–what worked, what broke, and why the decisions that mattered most had nothing to do with AI. It covers security hardening on day two (not day two hundred), a knowledge base built from twelve years of domain expertise rather than a generic document upload, an infrastructure migration that cut hosting costs while improving isolation, and a self-improvement loop where the agents propose their own knowledge base edits for human review. It also covers the parts that didn’t go well: signups that never responded, a shared API budget that took down every agent overnight, and the humbling gap between ”I have a product” and ”anyone knows it exists.”
The tools were new. The judgment behind every architectural decision was not. If you’re an experienced developer wondering what AI-assisted development actually looks like in practice–not the hype, not the demo, but the real engineering tradeoffs–this is that story.
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Paperback. Zustand: new. Paperback. In January 2026, a computational geometry specialist with twenty-eight years of software development experience had a throwaway idea about building a support chatbot for a client. Nine hours later, he had a working prototype. Sixteen days later, he had a multi-tenant platform with five deployed agents, a paying customer, and a codebase that could onboard a new client from a single URL. This book is the honest, commit-by-commit story of that sprint-what worked, what broke, and why the decisions that mattered most had nothing to do with AI. It covers security hardening on day two (not day two hundred), a knowledge base built from twelve years of domain expertise rather than a generic document upload, an infrastructure migration that cut hosting costs while improving isolation, and a self-improvement loop where the agents propose their own knowledge base edits for human review. It also covers the parts that didn't go well: signups that never responded, a shared API budget that took down every agent overnight, and the humbling gap between "I have a product" and "anyone knows it exists." The tools were new. The judgment behind every architectural decision was not. If you're an experienced developer wondering what AI-assisted development actually looks like in practice-not the hype, not the demo, but the real engineering tradeoffs-this is that story. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798249201982
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