It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.
It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.
It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.
It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.
AI is an amplifier. It magnifies whatever engineering discipline — or lack of it — already exists, which means the bottleneck was never AI's capability; it is the collaboration space you design around it. This book is a six-level progression from early AI panic to sovereign engineering: the discipline of designing the environment in which human and artificial intelligence produce work worth keeping. It moves past prompting into the practices that compound — verification, habitat engineering, specification-first development, and the platform discipline that scales the practice across teams. The habitat is yours to design. Let's build it well.
AI is an amplifier. It magnifies whatever engineering discipline — or lack of it — already exists, which means the bottleneck was never AI's capability; it is the collaboration space you design around it. This book is a six-level progression from early AI panic to sovereign engineering: the discipline of designing the environment in which human and artificial intelligence produce work worth keeping. It moves past prompting into the practices that compound — verification, habitat engineering, specification-first development, and the platform discipline that scales the practice across teams. The habitat is yours to design. Let's build it well.
AI is an amplifier. It magnifies whatever engineering discipline — or lack of it — already exists, which means the bottleneck was never AI's capability; it is the collaboration space you design around it. This book is a six-level progression from early AI panic to sovereign engineering: the discipline of designing the environment in which human and artificial intelligence produce work worth keeping. It moves past prompting into the practices that compound — verification, habitat engineering, specification-first development, and the platform discipline that scales the practice across teams. The habitat is yours to design. Let's build it well.
AI is an amplifier. It magnifies whatever engineering discipline — or lack of it — already exists, which means the bottleneck was never AI's capability; it is the collaboration space you design around it. This book is a six-level progression from early AI panic to sovereign engineering: the discipline of designing the environment in which human and artificial intelligence produce work worth keeping. It moves past prompting into the practices that compound — verification, habitat engineering, specification-first development, and the platform discipline that scales the practice across teams. The habitat is yours to design. Let's build it well.
AI is an amplifier. It magnifies whatever engineering discipline — or lack of it — already exists, which means the bottleneck was never AI's capability; it is the collaboration space you design around it. This book is a six-level progression from early AI panic to sovereign engineering: the discipline of designing the environment in which human and artificial intelligence produce work worth keeping. It moves past prompting into the practices that compound — verification, habitat engineering, specification-first development, and the platform discipline that scales the practice across teams. The habitat is yours to design. Let's build it well.
Close the gap between "AI that generates code" and "AI-assisted development for production software" with this complete, proven playbook including the six-phase arc from audit to production.
Close the gap between "AI that generates code" and "AI-assisted development for production software" with this complete, proven playbook including the six-phase arc from audit to production.
Close the gap between "AI that generates code" and "AI-assisted development for production software" with this complete, proven playbook including the six-phase arc from audit to production.
Close the gap between "AI that generates code" and "AI-assisted development for production software" with this complete, proven playbook including the six-phase arc from audit to production.
Close the gap between "AI that generates code" and "AI-assisted development for production software" with this complete, proven playbook including the six-phase arc from audit to production.
Cyber risk in M&A gets treated as a compliance check, a heat-map of red and yellow boxes, or a problem to deal with after close. By then it is too late.Quantitative Cyber Diligence (QCD) replaces qualitative cyber diligence with a five-pillar framework that produces a single dollar-denominated figure the investment committee can argue about — calibrated, defensible, and reproducible. Each pillar maps to a measurable exposure: external attack surface, vendor concentration, data sensitivity, security program maturity, and integration risk. The framework aggregates per-pillar exposures with explicit correlation discount, surfaces tail-driver scenarios that conventional analysis misses, and translates the aggregate figure into deal-architecture recommendations: reps and warranties, escrow, price adjustment, or walk-away.This book documents how QCD engagements actually run. Two worked examples (healthcare-IT and industrial-IT). Practitioner templates. Per-pillar pseudocode. Industry loss benchmarks. The complete framework, calibrated to the post-Change-Healthcare era.
Cyber risk in M&A gets treated as a compliance check, a heat-map of red and yellow boxes, or a problem to deal with after close. By then it is too late.Quantitative Cyber Diligence (QCD) replaces qualitative cyber diligence with a five-pillar framework that produces a single dollar-denominated figure the investment committee can argue about — calibrated, defensible, and reproducible. Each pillar maps to a measurable exposure: external attack surface, vendor concentration, data sensitivity, security program maturity, and integration risk. The framework aggregates per-pillar exposures with explicit correlation discount, surfaces tail-driver scenarios that conventional analysis misses, and translates the aggregate figure into deal-architecture recommendations: reps and warranties, escrow, price adjustment, or walk-away.This book documents how QCD engagements actually run. Two worked examples (healthcare-IT and industrial-IT). Practitioner templates. Per-pillar pseudocode. Industry loss benchmarks. The complete framework, calibrated to the post-Change-Healthcare era.
Cyber risk in M&A gets treated as a compliance check, a heat-map of red and yellow boxes, or a problem to deal with after close. By then it is too late.Quantitative Cyber Diligence (QCD) replaces qualitative cyber diligence with a five-pillar framework that produces a single dollar-denominated figure the investment committee can argue about — calibrated, defensible, and reproducible. Each pillar maps to a measurable exposure: external attack surface, vendor concentration, data sensitivity, security program maturity, and integration risk. The framework aggregates per-pillar exposures with explicit correlation discount, surfaces tail-driver scenarios that conventional analysis misses, and translates the aggregate figure into deal-architecture recommendations: reps and warranties, escrow, price adjustment, or walk-away.This book documents how QCD engagements actually run. Two worked examples (healthcare-IT and industrial-IT). Practitioner templates. Per-pillar pseudocode. Industry loss benchmarks. The complete framework, calibrated to the post-Change-Healthcare era.
Cyber risk in M&A gets treated as a compliance check, a heat-map of red and yellow boxes, or a problem to deal with after close. By then it is too late.Quantitative Cyber Diligence (QCD) replaces qualitative cyber diligence with a five-pillar framework that produces a single dollar-denominated figure the investment committee can argue about — calibrated, defensible, and reproducible. Each pillar maps to a measurable exposure: external attack surface, vendor concentration, data sensitivity, security program maturity, and integration risk. The framework aggregates per-pillar exposures with explicit correlation discount, surfaces tail-driver scenarios that conventional analysis misses, and translates the aggregate figure into deal-architecture recommendations: reps and warranties, escrow, price adjustment, or walk-away.This book documents how QCD engagements actually run. Two worked examples (healthcare-IT and industrial-IT). Practitioner templates. Per-pillar pseudocode. Industry loss benchmarks. The complete framework, calibrated to the post-Change-Healthcare era.
Cyber risk in M&A gets treated as a compliance check, a heat-map of red and yellow boxes, or a problem to deal with after close. By then it is too late.Quantitative Cyber Diligence (QCD) replaces qualitative cyber diligence with a five-pillar framework that produces a single dollar-denominated figure the investment committee can argue about — calibrated, defensible, and reproducible. Each pillar maps to a measurable exposure: external attack surface, vendor concentration, data sensitivity, security program maturity, and integration risk. The framework aggregates per-pillar exposures with explicit correlation discount, surfaces tail-driver scenarios that conventional analysis misses, and translates the aggregate figure into deal-architecture recommendations: reps and warranties, escrow, price adjustment, or walk-away.This book documents how QCD engagements actually run. Two worked examples (healthcare-IT and industrial-IT). Practitioner templates. Per-pillar pseudocode. Industry loss benchmarks. The complete framework, calibrated to the post-Change-Healthcare era.
Fifty RF‑101 Voodoos arrived in Vietnam. Forty‑seven were shot down in a year. The pilots who survived didn't fly faster aircraft — they cycled through the OODA loop faster than the missiles chasing them. Jeff Sutherland was one of them. He went on to co‑create Scrum. Now, six decades later, he is running AI agents through daily sprints at machine speed — and this book is the playbook for how to do it without getting shot down.
Fifty RF‑101 Voodoos arrived in Vietnam. Forty‑seven were shot down in a year. The pilots who survived didn't fly faster aircraft — they cycled through the OODA loop faster than the missiles chasing them. Jeff Sutherland was one of them. He went on to co‑create Scrum. Now, six decades later, he is running AI agents through daily sprints at machine speed — and this book is the playbook for how to do it without getting shot down.
Fifty RF‑101 Voodoos arrived in Vietnam. Forty‑seven were shot down in a year. The pilots who survived didn't fly faster aircraft — they cycled through the OODA loop faster than the missiles chasing them. Jeff Sutherland was one of them. He went on to co‑create Scrum. Now, six decades later, he is running AI agents through daily sprints at machine speed — and this book is the playbook for how to do it without getting shot down.
Fifty RF‑101 Voodoos arrived in Vietnam. Forty‑seven were shot down in a year. The pilots who survived didn't fly faster aircraft — they cycled through the OODA loop faster than the missiles chasing them. Jeff Sutherland was one of them. He went on to co‑create Scrum. Now, six decades later, he is running AI agents through daily sprints at machine speed — and this book is the playbook for how to do it without getting shot down.
Fifty RF‑101 Voodoos arrived in Vietnam. Forty‑seven were shot down in a year. The pilots who survived didn't fly faster aircraft — they cycled through the OODA loop faster than the missiles chasing them. Jeff Sutherland was one of them. He went on to co‑create Scrum. Now, six decades later, he is running AI agents through daily sprints at machine speed — and this book is the playbook for how to do it without getting shot down.
Build GPT-2, Llama 3, and DeepSeek from scratch in PyTorch. Every chapter has runnable end-to-end code and loads real pretrained weights. Goes well past where most LLM tutorials stop.