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Martin D: Bears & Mosquitoes

Bears & Mosquitoes

Building Trusted AI from the Inside Out - A Field Guide to AI That Earns Its Keep

Running a large language model on hardware you control is no longer exotic, but the gap between a model that loads and a service that holds up under load is where most self-hosting efforts stall. This book closes that gap by building the thing, one working system that grows across all fifteen chapters.You begin with an 8B model on a single card, the kind of hardware you can rent for pennies an hour or already own. From there the book adds what real traffic demands: an inference engine chosen on its merits, a control plane that provisions GPUs and tears them down again, a router and queue that decide what runs next, and cache-aware routing that stops a conversation being re-read from scratch on every turn. Part III moves past what one card can hold, placing a 70B deployment across a pool of GPUs selected on the network between them rather than the sum of their memory, and shopping for that capacity across several providers at once.The final part goes to the frontier. You will serve a 753-billion-parameter sparse model on eight rented H200s, then measure what it costs per session as concurrency climbs, and find the point where a larger batch stops making each session cheaper. That result explains how commercial inference providers price what they sell, and it tells you when self-hosting wins and when it does not.Costs are measured rather than estimated throughout. Every figure in the final part is regenerated from a committed data file, and the book says plainly which numbers are measurements and which are predictions. The complete control plane is available as an open-source companion repository, written in Go, so you can run what you read.You Will:<ul><li>Build a working inference control plane in Go that provisions GPUs across providers, deploys engines onto them, routes and queues requests, and reports what it is doing.</li><li>Learn how inference actually consumes a GPU, covering KV cache growth, quantization formats and their real cost, tensor and expert parallelism, and why a fast interconnect matters more than total memory.</li><li>Measure the economics from real runs, working out per-session cost as batch size and context length grow, and identifying the point where self-hosting stops beating a commercial API.</li></ul>This book is for:Backend, systems and platform engineers who want to run LLMs on infrastructure they control. Also useful to infrastructure and DevOps engineers building internal AI platforms, and to technical founders with data-residency or cost constraints that rule out third-party APIs. No machine learning background is assumed. Readers should be comfortable with a systems language such as Go or Python, with containers, and with HTTP APIs.

Running a large language model on hardware you control is no longer exotic, but the gap between a model that loads and a service that holds up under load is where most self-hosting efforts stall. This book closes that gap by building the thing, one working system that grows across all fifteen chapters.You begin with an 8B model on a single card, the kind of hardware you can rent for pennies an hour or already own. From there the book adds what real traffic demands: an inference engine chosen on its merits, a control plane that provisions GPUs and tears them down again, a router and queue that decide what runs next, and cache-aware routing that stops a conversation being re-read from scratch on every turn. Part III moves past what one card can hold, placing a 70B deployment across a pool of GPUs selected on the network between them rather than the sum of their memory, and shopping for that capacity across several providers at once.The final part goes to the frontier. You will serve a 753-billion-parameter sparse model on eight rented H200s, then measure what it costs per session as concurrency climbs, and find the point where a larger batch stops making each session cheaper. That result explains how commercial inference providers price what they sell, and it tells you when self-hosting wins and when it does not.Costs are measured rather than estimated throughout. Every figure in the final part is regenerated from a committed data file, and the book says plainly which numbers are measurements and which are predictions. The complete control plane is available as an open-source companion repository, written in Go, so you can run what you read.You Will:<ul><li>Build a working inference control plane in Go that provisions GPUs across providers, deploys engines onto them, routes and queues requests, and reports what it is doing.</li><li>Learn how inference actually consumes a GPU, covering KV cache growth, quantization formats and their real cost, tensor and expert parallelism, and why a fast interconnect matters more than total memory.</li><li>Measure the economics from real runs, working out per-session cost as batch size and context length grow, and identifying the point where self-hosting stops beating a commercial API.</li></ul>This book is for:Backend, systems and platform engineers who want to run LLMs on infrastructure they control. Also useful to infrastructure and DevOps engineers building internal AI platforms, and to technical founders with data-residency or cost constraints that rule out third-party APIs. No machine learning background is assumed. Readers should be comfortable with a systems language such as Go or Python, with containers, and with HTTP APIs.

Building and Operating Production AI Systems from a Single GPU to Multi-Node Frontier Models

Self-Hosted LLM Inference

This executive book is a standalone work, not a second edition of Agentic by Design. It is intentionally vendor-neutral and references product names only as examples of categories. It includes no code, configuration, or implementation plan. This book is intended for decision-makers, not implementers.

How AI Governance Became the Growth Engine

Governed Autonomy

How the rise of AI agents--and other forms of autonomous intelligence--will transform every aspect of how companies create value and compete.Companies have long operated with a basic assumption: intelligence is scarce. Talent is limited, expertise is expensive, and good judgment is hard to scale. But that world is ending.AI agents are bringing a new form of labor into the enterprise--one that can work autonomously, learn continuously, and scale instantly. Their embodied counterparts, from drones to humanoid robots, are doing the same in the physical world. Together, these systems are introducing a new economic reality: cognition is becoming cheap, elastic, and abundant.In Abundant Intelligence, Deloitte's Nitin Mittal and futurist Mike Walsh show what happens when intelligence becomes cheap and widely available. Drawing on inside access to the people leading AI transformations at the world's most powerful companies, they uncover how this shift is challenging every assumption about strategy, productivity, and leadership, and what organizations must do now to stay ahead.As abundance becomes the new normal, the leader's job shifts from collecting intelligence to directing it. Those who learn to harness this expanding workforce of digital labor will unlock new levels of speed, creativity, and advantage. For everyone else, the gap will widen fast.

How Digital Labor Will Rewrite the Rules of Business

Abundant Intelligence

Turn AI investment into scalable business impact by enabling your workforce to build the solutions they actually need. From AI Users to Solution Builders is a strategic guide for senior AI, technology, and transformation leaders who want to move beyond underperforming, centralized AI initiatives, and unlock real enterprise value. As organizations struggle to translate AI into measurable results, this book shows how shifting from top-down deployment to workforce-led solution building creates faster adoption, stronger ROI, and sustained innovation. Written by experienced AI leader Wei Manfredi, this book combines strategic insight with practical execution. It draws clear parallels between failed digital transformation efforts and current AI adoption challenges, while demonstrating how natural language interfaces are reshaping access to AI. By enabling non-technical professionals to design their own tools, organizations can embed AI directly into workflows, decision-making, and daily operations. You'll learn how to: - Shift from centralized AI models to workforce-driven innovation - Enable employees to build AI solutions for real workflows - Design scalable infrastructure for AI solution development - Apply change management to overcome resistance and drive adoption &gt;Featuring real-world examples from organizations including Lululemon, Google, Oracle, and JPMorgan Chase, this book provides a proven framework for embedding AI into everyday collaboration, accelerating innovation, and future-proofing your organization's competitive advantage. Themes include: AI strategy, AI adoption, workforce transformation, AI governance, enterprise AI, change management

Scaling AI Across Your Organization

From AI Users to Solution Builders

AI and quantum computing are two disruptive forces about to push financial services and the banking industry over the edge, demanding an unprecedented transformation. They are not fleeting trends but fundamental disruptors that will reshape the core of banking and finance. Authored by a senior Swiss banking technology executive, this practical guide helps board members and senior leaders turn disruption into a disciplined strategic agenda. It offers a rare insider perspective and a roadmap for navigating these seismic shifts. Bridging the gap between technological innovation and practical application, this book equips leaders with the tools to address the challenges and opportunities posed by AI and quantum computing. It features a thorough review of the latest business opportunities and threats associated with these technologies, alongside foresight-based strategic decision frameworks to guide prioritisation. Real-world case studies and an online companion with 125 AI use cases tailored for banks provide actionable insights. It also introduces an original AI Adoption Maturity Model and a Hybrid Enterprise Reference Architecture to ensure safe integration of internal systems with external frontier models. Finally, it provides two detailed multi-year roadmaps, offering a clear path to harness AI's potential and to prepare for a post-quantum future through crypto agility.Rather than offering abstract predictions or generic solutions, this book presents financial-services leaders with the frameworks, choices, and implementation path they need to act with confidence at a moment when waiting is no longer an option.

Evolutions at the Bleeding Edge

How AI and Quantum Computing Will Make or Break the Bank

Risk, Governance and Society

From Data Analytics to Global Risk Governance

Evolution of Anomaly Detection

Most AI governance is built around the wrong object.

Enterprises watch the model. They should be governing the crossing-the moment-generated language asks to become institutional reality.

The Bounding Gap exposes the substitutions that pass for AI safety: observation for control, behavior modification for enforcement, explanation for proof, and human review after the decisive movement has already begun. It relocates governance from the model itself to the boundary where generated possibility becomes institutional consequence.

Through a series of scientific inversions, the book develops a new architecture of authority. Gödel shows why a system cannot certify itself. Turing is inverted to ask not whether machines can imitate human judgment but whether their outputs possess standing to enter institutional life. Wilson's correlation geometry explains why modern AI cannot be governed through the old imagination of local logic and linear control. Hooke and Payne-Gaposchkin reveal why observation without the correct interpretive structure remains blind.

These arguments converge on one claim:

Authority cannot live inside the generative field. It must be external, deterministic, and enforceable before consequence becomes real.

The book also names the danger created by speed. Synthesis entropy and governance dilation describe what happens when machine-generated states multiply faster than institutions can establish admissibility. A rationale can now be produced in milliseconds, while responsibility, evidence, and authority remain unresolved.

For enterprise architects, Chief Compliance Officers, General Counsels, Chief Risk Officers, AI founders, and regulators, The Bounding Gap offers more than a critique of current governance. It identifies the missing boundary and shows why observation, monitoring, alignment, and review cannot substitute for executable refusal.

The future of institutional AI will belong to those who can govern the crossing before machine-generated possibility becomes consequence.

Why Institutions Must Govern Before AI Becomes Consequence

THE BOUNDING GAP

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.
Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.
You Will:
<ul>
<li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li>
<li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li>
<li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li>
</ul>
&nbsp;
This book is for : Software Architects &amp; System Designers
&nbsp;

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.You Will:<ul><li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li><li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li><li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li></ul>&nbsp;This book is for : Software Architects &amp; System Designers&nbsp;

Architecting Scalable, Ethical, and Trustworthy Systems for Enterprise

Agentic AI Systems in Practice

Security, Audit and Leadership Series

Organizational Debt and AI Failure: Why Your AI Project Fails challenges the assumption that unsuccessful artificial intelligence initiatives are primarily caused by weak models, insufficient computing power, or poor technology choices. Instead, Scott Andersen argues that AI often fails because it is introduced into organizations carrying years of unresolved structural debt.The book presents a practical framework for identifying and managing five interconnected forms of organizational debt: technical, data, personnel, security, and embedded debt. Technical debt creates fragile architectures and brittle integrations. Data debt undermines meaning, lineage, context, and trust. Personnel debt appears through skill gaps, unclear responsibilities, burnout, and dependence on a few critical individuals. Security debt weakens identity, governance, assurance, and accountability. Embedded debt connects them through undocumented assumptions, informal processes, workarounds, and hidden dependencies.AI does not necessarily create these weaknesses-it exposes and accelerates them. Processes that remained stable through human judgment and manual intervention can collapse when automated systems begin operating continuously and at machine speed. What appeared to be resilience may have depended on people quietly compensating for organizational problems.Drawing on decades of enterprise architecture and technology experience, Andersen explains why promising pilots become trapped in "pilot purgatory," why local improvements can increase systemwide risk, and why failures frequently emerge at the seams between technologies, teams, data, and decision authorities. He introduces practical concepts and measures-including Total AI Debt Exposure and Total AI Operational Pressure-to help leaders make risk visible and actionable.This book is written for executives, architects, technology leaders, security professionals, data teams, program managers, and organizations preparing to scale AI. Its central message is direct: successful AI transformation does not begin with adoption. It begins with organizational truth. By identifying, measuring, and governing the debt already present, organizations can move beyond experimentation and build AI systems that achieve delivery, adoption, and sustained operational value.

Why your AI projects fail

Organizational Debt

Non-human identities now outnumber people, and someone must sign for what the machines do.
Defensible Identity Architecture is a professional reference by Professor Kai London. It covers non-human identities, agentic AI, the regulatory crosswalk, machine accountability, decision rights architecture, the identity control plane, SPIFFE and workload identity, ITDR and cyber-insurance posture.
Written for boards, executives, CISOs, engineers, risk and compliance leaders and practitioners who need clear, defensible guidance they can put to work, the book combines explanation with practical frameworks, checklists and decision aids.
Key topics include: non-human identities, agentic AI, the regulatory crosswalk, machine accountability, decision rights architecture, the identity control plane, SPIFFE, workload identity, ITDR, cyber-insurance posture.

Defensible Identity Architecture

Turn enterprise AI into a source of strategic insight, not just productivity gains.The AI Gap by leading AI expert and keynote speaker Richard Foster-Fletcher shows business and technology leaders how to unlock greater value from enterprise AI by changing the structures, processes and culture surrounding it. Organizations invest in AI expecting new intelligence and transformative insights. Yet AI is often introduced through procurement, security, legal and compliance systems designed for standard software, restricting its potential before it can deliver on that promise. This book explains why this gap persists and what organizations must change to close it. You'll learn how to: - Identify organizational barriers limiting the value of AI - Move beyond productivity-focused AI implementation - Create conditions where AI can generate deeper insights - Enable employees to explore, experiment and challenge established thinking &gt;The AI Gap equips leaders to rethink AI implementation, create space for experimentation and turn artificial intelligence into a powerful driver of organizational insight and long-term business value. Themes include: enterprise AI, AI implementation, AI strategy, business transformation, organizational culture, AI innovation

How Business Decisions Expand What AI Can Do

The AI Gap

Studies in International Trade and Investment Law

This open access book examines the evolving role of international economic law in addressing two interrelated challenges of the 21st century: enabling digital development and bridging the digital divide. The rapid digitalisation of the economy has generated significant challenges, including a deepening global digital and data divide and a persistent lack of inclusive digital development. Confronting these challenges head on, this pioneering book brings together experts across different legal subfields, geographies, and disciplines to examine the role and relevance of international economic law in bridging the global digital divide and enabling digital development, particularly from the perspective of the Global South. The contributions are anchored in a distinctive analytical framework that traces future directions through which international economic law interacts with digital development, demonstrating how it may function as a constraint, fade into irrelevance, or eventually act as an enabler. The chapters focus on a broad range of areas, including international trade, investment, tax, development, competition, finance and related regulatory and policy domains; they consider multilateral, regional, and plurilateral frameworks; and draw on a plurality of perspectives from Africa, Asia, the Middle East, and Latin America. The authors employ a wide range of methodological approaches to address issues such as digital sovereignty and digital industrial policy, cross-border data flows and digital global value chains, competition in data markets, AI governance, financial inclusion, and the sustainability-digitalisation nexus. This book speaks to scholars, policymakers, and practitioners in international economic law, development studies, and technology law who seek theoretical and pragmatic pathways for reorienting international economic law as a tool for equitable, sustainable, and meaningful digital development. The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com.

Towards Digital Development

Reorienting International Economic Law for the Digital Economy

Bears & Mosquitoes

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