Code247 ai tri cong (16 Ergebnisse)
Sprache: Englisch
Verlag: Independently published, 2026
Serie: Buch 1 von 1 - THE AI-NATIVE ARCHITECT FOR SECURITIES FIRMS SERIES
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Sprache: Englisch
Verlag: Independently published, 2026
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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Sprache: Englisch
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Sprache: Englisch
Verlag: Independently published, 2026
Serie: Buch 2 von 1 - THE AI-NATIVE ARCHITECT FOR SECURITIES FIRMS SERIES
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Sprache: Englisch
Verlag: Independently published, 2026
Serie: Buch 2 von 2 - THE AI-NATIVE ARCHITECT FOR BANK SERIES
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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Sprache: Englisch
Verlag: Independently published, 2026
- Softcover
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Sprache: Englisch
Verlag: Amazon Digital Services LLC - Kdp Aug 2026, 2026
Serie: Buch 1 von 1 - THE AI-NATIVE ARCHITECT FOR SECURITIES FIRMS SERIES
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Neuware - 'Why was I sold out at 10:47, at that price ' The firm took four days and five systems to answer - approximately. Then it settled. That letter opens this book, and answering it in under a minute, to the tick, is what the book builds. On the way it names the seven design errors sitting on almo…st every margin desk - an advance-rate table nobody owns, a call ladder with no liquidity term, concentration measured per client but never per firm, a risk grade that enters no formula, one rate for every collateral book, ratios computed on yesterday's close, and a close-out order improvised under stress. None is a technology failure. Each is a business decision that was never put to anyone who could take it. The repair is a limit that is the minimum of five independently computed ceilings: lending value, client capacity, concentration - per stock, per sector and across the whole firm - firm limits, and the liquidity horizon. A worked account runs through all five: a portfolio worth $1,560,000 on paper supports a $594,000 loan by value - and only $494,000 by exit, because one holding needs six days to sell. That $100,000 gap between paper value and exit value is where margin books die, and the fifth ceiling is the only one that sees it. Inside: - Five capability layers, from routing to close-out, each passing one test: switch off every model and the layer still works;- A call ladder whose triggers rise and whose grace shrinks as a portfolio's exit time lengthens - and a committed close-out that sold 4,600 liquid shares where the improvised desk sold 150,000 illiquid ones;- Seven playbooks and a case map with proved reductions: 27,648 combinations to 51 cells to a 335-case acceptance suite that Book Two must pass;- Findings you cannot see without the sweep - including why the per-stock concentration cap mathematically cannot protect you on illiquid collateral, and why at institutional size every name is a small name;- Governance for a machine that decides intraday, personal data by design, decisions that replay to the cent - and information barriers built into the data model, assistant included;- The full engine in an appendix: six small files, 72 columns, offline, with tests reproducing every number in the book. The honest position, stated on page one. Every parameter is marked illustrative. A signature page names what your head of margin risk, your trading desk and your board must review before any figure is used - and the appendix ends with that table, waiting for initials. The method transfers as it stands; the numbers are yours to set. For chief executives, heads of risk, architects and regulators of securities firms - and for anyone who wants to see what 'AI-native' means when the output is a forced sale of a client's property and 'usually right' is not good enough.
Sprache: Englisch
Verlag: Amazon Digital Services LLC - Kdp Jul 2026, 2026
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Neuware - Your AI assistant forgets everything. Every conversation, every preference, every decision - gone the instant the call ends.Large language models are stateless by design. Everything an AI system appears to 'remember' - the case discussed last week, the user's preferences, the plan agreed an h…our ago - is engineered, deliberately, outside the model. That engineering layer decides what enters the context window on every single turn, and it is where AI systems quietly succeed or silently fail.This book teaches you to build that layer properly.While most teams throw bigger models and bigger windows at the problem, quality keeps degrading: context rot, lost-in-the-middle, cross-user memory leaks, agents that drift off course after twenty steps. The root cause is almost never the model. It is an unmanaged context and memory layer - and this book gives you the complete discipline to fix it.Inside, a full engineering system: - The Five Pillars - Direction & Token Budget, Memory Tiering, Selection & Assembly, Writing & Consolidation, and Forgetting, Freshness & Safety - expanded into eighteen working principles- Fifteen practical strategies, from sliding windows and compaction to agentic memory and hybrid tiered architectures, with a decision tree for choosing among them- A seven-step design process that takes you from problem statement to a running, measurable system- Five hands-on design guides: window assembly, memory store design, compaction & summarization, retrieval-for-memory, and lifecycle & governance- Two complete end-to-end projects at opposite poles: an enterprise multi-tenant agent copilot, and a personalized tutor that remembers one learner across weeks- Fourteen anti-patterns mapped to the pillars they violate - a diagnostic chart for when your system 'answers poorly for no clear reason'- A measurement framework: ten quality criteria, concrete metrics, golden task sets, and a directed improvement loop Every chapter ends with a self-check quiz. The appendices pack a glossary, cheat-sheet, a 100+ item design checklist, a 100+ entry pattern library, and starter templates for developers, business analysts and AI engineers.Book 3 of the AI-Native Architecture series. Book 1 covered RAG - exploiting knowledge. Book 2 covered Knowledge Graphs - constructing knowledge. This volume operates the layer where both converge: seen from here, RAG is long-term semantic memory, the knowledge graph is structured memory, and the quality of any agent is upper-bounded by the context and memory layer it stands on.If you build AI systems that must remember what matters - this is the missing manual.
Sprache: Englisch
Verlag: Amazon Digital Services LLC - Kdp Aug 2026, 2026
Serie: Buch 2 von 1 - THE AI-NATIVE ARCHITECT FOR SECURITIES FIRMS SERIES
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Neuware - The specification was Book One. This is the build. Book One ended with a contract: five ceilings on every grant, a call ladder with a liquidity term, a committed close-out plan, and a 311-case acceptance map. This volume builds that machine - components, code, checkpoints, go-live - and print…s every line of it in the appendices. - Nine components, four boundaries, and five requirements that cannot be retrofitted - time as a parameter first, because the question to answer is why was I sold at 10:47:12.480;- The feedback loop, computed: on the same shock, sell-everything turns one breach into four forced accounts and -26.5%; the committed plan sells one capped slice and stops at -2.5% - with three of the four forced accounts solvent before the shock;- Eight runnable demos whose transcripts are generated by executing the code while the book is typeset: the golden memo, the tick-level replay, the fact-checked call notice, the acceptance runner;- Four checkpoints passed by running, never by presenting; a migration queue that walks every legacy loan down to its engine number on a schedule, not a hope;- A decision book of eight pages only the board can write - each with a named default that operates if the board stays silent;- The complete source in the appendices: build modules, demos, and the 57-test suite that is also the spec. Nothing to download. The method catches its own author in print: Chapter 38 tells how the acceptance runner refused to construct six cells of the draft case map, forcing a fourth theorem into Book One before publication. A map written in prose would have carried those cells forever; a map that must be built cannot. An honest position: every rate, threshold and count is marked illustrative. This is a book of methods with working code, not a parameter set for your market - the signature page in Book One names what your reviewers must ink first, and the decisions only your board can take are Chapter 49 here. Read Book One for the specification. Read this volume to build it - and to hand your auditor a machine that answers in rows, not recollections.
Sprache: Englisch
Verlag: Amazon Digital Services LLC - Kdp Aug 2026, 2026
Serie: Buch 1 von 2 - THE AI-NATIVE ARCHITECT FOR BANK SERIES
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Neuware.
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Neuware.
Sprache: Englisch
Verlag: Amazon Digital Services LLC - Kdp Aug 2026, 2026
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Neuware - Your dashboards show you the number. Who explains the why Every BI system built in the last twenty years was designed to answer 'what happened.' The AI era demands more: systems that investigate causes on their own, explain their answers, and act - without losing the discipline that made clas…sical BI trustworthy. This book builds that bridge, end to end.AI-Native Business Intelligence, Volume 1 is a complete, practice-first course in three parts: PART I - Classical BI, rebuilt properly. Inmon vs. Kimball, dimensional modeling, slowly changing dimensions, ETL/ELT, data quality, MDM, and governance - the foundation your future agents will stand on, taught through one continuous retail case.PART II - The AI-Native architecture. The six-layer reference model with the semantic layer at its heart: metrics as code, MCP as the controlled door, conversational and agentic BI, RAG and knowledge graphs, hallucination control, compile-time security, and FinOps for agent workloads. You'll learn why 'wrap an LLM around the database and hope' fails - and what to design instead.PART III - Transformation in practice. Greenfield, brownfield, and hybrid playbooks with roadmaps, risk registers, and rollback criteria - capped by a full multinational, multi-currency capstone (Truong Son Group) you can rebuild for your own organization, exercises and reference solutions included.Inside this volume- 35 chapters, 60+ original architecture figures, and chapter quizzes with full answer keys- Ready-to-adapt templates: semantic-layer YAML, MCP server configuration, eval suites with 'trap' questions, Bus Matrix, design checklists- A 90-day / 6-month / 12-month migration playbook with quantitative stop-and-rollback criteria- 30/60/90-day self-study paths for newcomers, BI veterans, and decision-makersWritten for data engineers, architects, analytics leaders, and CTOs who refuse to choose between AI capability and engineering discipline. No prior AI experience required; every concept is built from the ground up.Volume 1 is the thinking book - what to build and why. Volume 2 (Design & Implementation) is the building book. They pair, but each stands alone.
- Softcover
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Taschenbuch. Zustand: Neu. Neuware - Your search box can find documents. It cannot answer questions. Somewhere in your organization there is a question no single passage contains: 'Which form does this process need, and who signs it ' - 'How many products share this component ' - 'What changed since yesterday ' Classic RAG fails… on all of them - and it fails quietly, with answers that sound confident and are wrong. GraphRAG is the engineering discipline that fixes this - and this book teaches it end to end: not as theory, but as a complete, measurable method you can defend in front of your team. What you will be able to do after reading: - Decide with evidence, not fashion - classify your real questions, walk a 35-point decision matrix, and know exactly when you need a graph (and when you don't)- Build the graph from any source - LLM extraction, rules, database import, and images; entity resolution that never merges the wrong people- Master the five retrieval patterns - local, global, DRIFT, path traversal, and Text2Cypher - and route every question to the right one- Ship answers people can trust - citations that open, reasoning paths that are recorded (never invented), and refusals that are honest- Evaluate and operate for real - golden sets, three-tier diagnosis, incremental updates, cost budgets, and access control that never leaks- Go agentic when it pays - multi-step research loops with tools, budgets, and guards What makes this book different: - Three complete projects built chapter by chapter on realistic synthetic data - an internal knowledge assistant, a support chatbot, and a research assistant - with real token bills, real failures, and real fixes- Nine industry deep-dives: code assistants, legal contracts, BI, CRM, e-commerce, medicine, fraud detection, news intelligence, and technical manuals- Runnable companion datasets with 45 expert-keyed test questions and planted traps - so you can reproduce every number in the book- Self-check quizzes in every chapter, with answer keys Written for engineers, architects, and technical leaders building AI systems over private data. Python examples throughout; works with NetworkX for learning and Neo4j for production. Measure first. Decide with criteria. Cite everything. Refuse honestly. That is GraphRAG as this book teaches it - 31 chapters, one method, and a system you can keep honest for years.
