Code247 ai (62 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condición: 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 almost 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.…

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: 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 hour 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.…