{"title":"Ai \u0026 Technology","description":"","products":[{"product_id":"build-ai-enhanced-web-apps","title":"Build AI-Enhanced Web Apps: How to get reliable results with React, Next.js, and Vercel by Theo Despoudis","description":"\u003ch2\u003eBuild AI-Enhanced Web Apps: How to get reliable results with React, Next.js, and Vercel by Theo Despoudis \u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eBuild AI-Enhanced Web Apps\u003c\/i\u003e is that integrating Large Language Models (LLMs) like GPT, Claude, Llama, or Gemini into production web applications is an engineering challenge, not an algorithmic one. While getting an AI response in a playground terminal is trivial, shipping it to thousands of concurrent users requires solving complex issues like streaming latency, state synchronization, token budget constraints, and model hallucinations. Despoudis establishes that the modern JavaScript ecosystem—anchored by Next.js server actions and the Vercel AI SDK—is perfectly positioned to handle these production requirements.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eRather than teaching abstract data science theories, Despoudis structures the book entirely around full-scale, real-world portfolio projects, including a voice-based interview assistant and a secure, corporate knowledge system. He walks readers through the end-to-end architecture of AI-enhanced systems, demonstrating how to handle real-time streaming, manage complex conversation histories, implement vector embeddings, and construct advanced pipelines using LangChain.js. Crucially, the text bridges the gap between raw AI capabilities and polished User Experience (UX), teaching developers how to design responsible guardrails for safety and cost management.\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eOur local software development ecosystem is experiencing an unprecedented surge, with engineering teams, tech agencies, and startups racing to add intelligence to their products. However, many developers feel stuck in an educational bottleneck—believing they must spend years mastering complex data science, calculus, and machine learning infrastructure before they can build actual AI products. This misconception results in brilliant engineers writing slow, unoptimized API wrappers that are insecure, expensive, and unable to scale beyond a handful of initial users.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eBuild AI-Enhanced Web Apps\u003c\/i\u003e acts as a powerful, liberating catalyst for our community's developers and tech leads. Theo Despoudis’s immense expertise as a Senior Staff Engineer provides a reliable, industry-proven playbook for building software that scales. It shows local teams how to weaponize their existing JavaScript and React skills to build real, market-ready AI solutions—from intelligent customer support engines to voice-activated web services. It is an absolute must-read for any modern engineer ready to stop experimenting in web playgrounds and start shipping resilient, production-ready AI products that capture real market value.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Applied Artificial Intelligence.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47227051966649,"sku":null,"price":300.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Build_AI-Enhanced_Web_Apps..webp?v=1779260071"},{"product_id":"analytics-engineering-with-sql-and-dbt","title":"Analytics Engineering with SQL and dbt: Building Meaningful Data Models at Scale by Rui Machado and Helder Russa","description":"\u003ch2 data-path-to-node=\"6\"\u003e\u003cspan class=\"\"\u003eAnalytics Engineering with SQL and dbt: Building Meaningful Data Models at Scale by Rui Machado and Helder Russa\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003e\u003cspan class=\"\"\u003eThe core thesis of \u003c\/span\u003e\u003ci data-path-to-node=\"6\" data-index-in-node=\"19\" class=\"\"\u003eAnalytics Engineering with SQL and dbt\u003c\/i\u003e\u003cspan class=\"\"\u003e is that data is a product,\u003c\/span\u003e\u003cspan class=\"\"\u003e and it should be built,\u003c\/span\u003e\u003cspan class=\"\"\u003e tested,\u003c\/span\u003e\u003cspan class=\"\"\u003e and maintained with the exact same rigor as customer-facing software.\u003c\/span\u003e\u003cspan class=\"\"\u003e In many modern organizations,\u003c\/span\u003e\u003cspan class=\"\"\u003e data pipelines are notoriously unstable—often consisting of thousand-line,\u003c\/span\u003e\u003cspan class=\"\"\u003e nested SQL queries that nobody understands,\u003c\/span\u003e\u003cspan class=\"\"\u003e leading to conflicting metrics on executive dashboards.\u003c\/span\u003e\u003cspan class=\"\"\u003e The authors introduce \u003c\/span\u003e\u003cb data-path-to-node=\"6\" data-index-in-node=\"414\" class=\"\"\u003eanalytics engineering\u003c\/b\u003e\u003cspan class=\"\"\u003e as the critical missing link that bridges the gap between raw data infrastructure and clean,\u003c\/span\u003e\u003cspan class=\"\"\u003e business-ready analysis.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"\"\u003eRather than discussing abstract database theories,\u003c\/span\u003e\u003cspan class=\"\"\u003e Machado and Russa walk readers through the end-to-end architecture of a scalable data platform.\u003c\/span\u003e\u003cspan class=\"\"\u003e They demonstrate how to take raw,\u003c\/span\u003e\u003cspan class=\"\"\u003e messy data from cloud warehouses like Snowflake,\u003c\/span\u003e\u003cspan class=\"\"\u003e BigQuery,\u003c\/span\u003e\u003cspan class=\"\"\u003e or Databricks and systematically transform it into highly optimized,\u003c\/span\u003e\u003cspan class=\"\"\u003e documented,\u003c\/span\u003e\u003cspan class=\"\"\u003e and fully tested data models.\u003c\/span\u003e\u003cspan class=\"\"\u003e Using dbt as the orchestrator,\u003c\/span\u003e\u003cspan class=\"\"\u003e the book details how to write modular SQL code,\u003c\/span\u003e\u003cspan class=\"\"\u003e manage complex table dependencies (data lineage),\u003c\/span\u003e\u003cspan class=\"\"\u003e implement automated quality guardrails,\u003c\/span\u003e\u003cspan class=\"\"\u003e and safely deploy code changes using Git workflows.\u003c\/span\u003e\u003c\/p\u003e\n\u003cdiv class=\"attachment-container search-images\"\u003e\n\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\n\u003c\/div\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional technology and corporate sectors rapidly digitize, local companies are accumulating unprecedented amounts of transactional, user, and operational data. However, many data teams are struggling to manage this sudden influx. Analysts spend up to 80% of their time manually cleaning spreadsheets, fixing broken dashboards, and arguing over why different departments have completely different numbers for the exact same business metric. This operational friction paralyzes decision-making and wastes immense engineering resources.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003eAnalytics Engineering with SQL and dbt\u003c\/i\u003e provides an exceptionally clear, industry-proven roadmap to solve these exact systemic bottlenecks. Rui Machado and Hélder Russa strip away the confusion around data architecture, providing local engineering leads, startup founders, and business intelligence teams with a highly disciplined blueprint to build absolute reliability into their data platforms. It is an indispensable, must-read technical playbook for any local professional ready to master the modern data stack, eliminate data-team burnout, and transform raw enterprise databases into a powerful corporate asset.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Data Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47227090993337,"sku":null,"price":330.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Analytics_Engineering_with_SQL_and_dbt.jpg?v=1779260424"},{"product_id":"kafka-connect","title":"Kafka Connect: Build and Run Data Pipelines by Mickael Maison and Kate Stanley","description":"\u003ch2\u003eKafka Connect: Build and Run Data Pipelines by Mickael Maison and Kate Stanley\u003c\/h2\u003e\n\u003cp\u003eThe central thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"22\"\u003eKafka Connect\u003c\/i\u003e is that moving data between enterprise data stores should be treated as a standard configuration task rather than a complex programming problem. In many modern software architectures, databases, cloud storage buckets, and search indexes operate as disconnected silos. When engineering teams try to connect these systems using custom-built integration code, they frequently create fragile applications that struggle to scale, drop critical records during network failures, and require constant maintenance.\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"attachment-container search-images\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003cspan\u003eThe authors demonstrate how the open-source Kafka Connect framework solves these systemic issues by providing a standardized, highly scalable execution engine. Rather than teaching abstract streaming theories, Maison and Stanley guide readers through the practical mechanics of deploying \u003cb data-path-to-node=\"7\" data-index-in-node=\"288\"\u003eSource Connectors\u003c\/b\u003e (which ingest real-time events from databases like PostgreSQL or MongoDB) and \u003cb data-path-to-node=\"7\" data-index-in-node=\"384\"\u003eSink Connectors\u003c\/b\u003e (which stream data out to storage layers like Amazon S3, Elasticsearch, or Snowflake). The book provides production-ready strategies for managing data schemas, handling complex transformations on the fly, and scaling cluster topologies to support high-throughput enterprise workloads.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional tech ecosystem undergoes a massive modernization wave, local banks, e-commerce giants, and logistics providers are processing larger volumes of concurrent transactions than ever before. Yet, many local engineering teams still rely on brittle, nightly batch scripts to synchronize their core transactional systems with their analytical data warehouses. This outdated approach limits operational agility, delays fraud detection, and leads to costly sync errors that directly impact customer satisfaction.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003eKafka Connect\u003c\/i\u003e provides an exceptionally precise, production-tested roadmap to eliminate these exact integration bottlenecks. Mickael Maison and Kate Stanley pull from their extensive experience as core maintainers within the Apache Kafka community to deliver an unmatched, practical guide. It shows local system administrators, cloud engineers, and software architects exactly how to replace old-school batch processing with bulletproof, real-time event streams. It is an essential playbook for any local engineering organization ready to break down data silos, scale their cloud infrastructure responsibly, and build a highly responsive, modern data architecture.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Data Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47227112227001,"sku":null,"price":350.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Kafka_Connect.webp?v=1779260746"},{"product_id":"fastapi","title":"FastAPI: Modern Python Web Development by Bill Lubanovic","description":"\u003ch2\u003eFastAPI: Modern Python Web Development by Bill Lubanovic\u003c\/h2\u003e\n\u003cp\u003eThe core thesis of FastAPI: Modern Python Web Development is that backend web development should not force engineers to choose between development speed and application execution performance. Historically, Python developers relied on robust frameworks like Django or Flask, which are exceptionally stable but struggle with high-latency I\/O tasks like database queries or third-party API calls unless heavily modified. Lubanovic establishes that FastAPI completely resolves this trade-off by combining Python’s modern asynchronous (async\/await) ecosystem with two incredibly powerful underlying technologies: Starlette (for high-performance web routing) and Pydantic (for lightning-fast data validation).Rather than teaching abstract code snippets, Lubanovic structures the book as an end-to-end journey from foundational RESTful API design to deploying scalable microservices in cloud environments. He walks readers through the core mechanics of the framework, showing how type hints are used to automatically enforce data safety, prevent runtime errors, and dynamically generate interactive OpenAPI documentation. Beyond the basics, the text addresses critical real-world operational challenges, including dependency injection, relational database integration using SQLModel, security enforcement via OAuth2, and automated testing frameworks.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eOur local technology sector is experiencing an incredible transformation, with corporate enterprise teams, tech agencies, and startups racing to deploy data-rich applications and machine learning features. However, as web traffic explodes, many engineering teams find themselves bottlenecked by older, slow backend legacy code. Developers frequently spend countless hours writing tedious data validation checkers and fighting messy documentation syncing errors between frontend and backend teams.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003eFastAPI: Modern Python Web Development\u003c\/i\u003e delivers an incredibly precise, production-tested playbook to solve these exact systemic engineering challenges. Bill Lubanovic’s immense clarity and practical style—highly celebrated globally from his bestselling \u003ci data-path-to-node=\"31\" data-index-in-node=\"253\"\u003eIntroducing Python\u003c\/i\u003e—makes this book accessible to anyone. It provides local system architects, backend engineers, and computer science graduates with an industry-grade blueprint to build bulletproof APIs that integrate flawlessly with modern frontend frameworks like React and Next.js. 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While standard orchestration frameworks (like basic LangChain) are exceptional for linear, step-by-step tasks, they struggle to model complex real-world workflows that require loops, continuous feedback, and human-in-the-loop decisions. \u003cspan class=\"citation-30\"\u003eMaina establishes \u003c\/span\u003e\u003cb data-path-to-node=\"6\" data-index-in-node=\"414\"\u003e\u003cspan class=\"citation-30\"\u003eLangGraph\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-30\"\u003e as the premier framework that solves this limitation by modeling AI workflows as mathematical graphs containing \u003c\/span\u003e\u003cb data-path-to-node=\"6\" data-index-in-node=\"536\"\u003e\u003cspan class=\"citation-30\"\u003eNodes\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-30\"\u003e (executable functions or LLM tasks), \u003c\/span\u003e\u003cb data-path-to-node=\"6\" data-index-in-node=\"579\"\u003e\u003cspan class=\"citation-30\"\u003eEdges\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-30\"\u003e (routing logic), and a centralized, mutable \u003c\/span\u003e\u003cb data-path-to-node=\"6\" data-index-in-node=\"629\"\u003e\u003cspan class=\"citation-30\"\u003eState\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-30 citation-end-30\"\u003e that preserves operational context across execution cycles.\u003csup class=\"superscript\" data-turn-source-index=\"2\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"citation-30 citation-end-30\"\u003e\u003cspan class=\"citation-29 citation-end-29\"\u003eRather than remaining tangled in abstract theory, Maina delivers a massive project catalog featuring over 50 business-focused AI agents.\u003csup class=\"superscript\" data-turn-source-index=\"3\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-28 citation-end-28\"\u003eThe book guides readers through the practical mechanics of setting up fully production-grade systems, including automated customer support helpdesks, continuous data-mining crawlers, complex financial analytics platforms, and automated developer QA tracks.\u003csup class=\"superscript\" data-turn-source-index=\"4\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-27\"\u003eBeyond isolated tasks, the text serves as a masterclass in advanced agent design patterns, including \u003c\/span\u003e\u003cb data-path-to-node=\"7\" data-index-in-node=\"495\"\u003e\u003cspan class=\"citation-27\"\u003ePlan-and-Execute systems\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-27\"\u003e, \u003c\/span\u003e\u003cb data-path-to-node=\"7\" data-index-in-node=\"521\"\u003e\u003cspan class=\"citation-27\"\u003eCorrective RAG (CRAG)\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-27 citation-end-27\"\u003e architectures, and hierarchical multi-agent structures where a supervisor agent dynamically delegates workloads across specialized worker agents\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional software ecosystem continues its rapid push into enterprise automation, local tech firms, financial institutions, and digital agencies are running into a frustrating wall with first-generation generative AI. Many teams are finding that standard chat boxes and basic sequential prompt scripts fail miserably when pushed out into messy, real-world corporate environments. They are prone to wild data hallucinations, break down when APIs experience brief hiccups, and lack the structure needed to mirror complex business processes safely.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\" id=\"p-rc_3fcf1b71fcf852e8-26\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eThe Complete LangGraph Blueprint\u003c\/i\u003e provides the exact, code-rich antidote to these costly automation bottlenecks. \u003cspan class=\"citation-26 citation-end-26\"\u003eJames Karanja Maina strips away the dense academic jargon surrounding agentic AI and hands developers a highly practical, down-to-earth engineering guide.\u003csup class=\"superscript\" data-turn-source-index=\"6\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e He demonstrates exactly how to build enterprise-grade, graph-based systems that can handle multi-step reasoning, self-heal from API errors, and maintain strict data governance via human oversight gates. It is an absolutely essential playbook for any local systems architect, backend engineer, or computer science graduate ready to transition from playing with basic AI novelties and start shipping industrial-strength, revenue-generating autonomous products.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Artificial Intelligence.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47227640840377,"sku":null,"price":380.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/The_Complete_LangGraph_Blueprint.jpg?v=1779263666"},{"product_id":"python-automation-for-dummies","title":"Python Automation For Dummies (For Dummies (Computer\/Tech) by Alan Simpson","description":"\u003ch2\u003ePython Automation For Dummies (For Dummies (Computer\/Tech) by Alan Simpson \u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003e\u003cspan class=\"\"\u003eThe core thesis of \u003c\/span\u003e\u003ci data-path-to-node=\"6\" data-index-in-node=\"19\" class=\"\"\u003ePython Automation For Dummies\u003c\/i\u003e\u003cspan class=\"\"\u003e is that you do not need a computer science degree to unlock the massive time-saving benefits of programming.\u003c\/span\u003e\u003cspan class=\"\"\u003e Every single day,\u003c\/span\u003e\u003cspan class=\"\"\u003e millions of office workers waste countless hours manually copying data from websites,\u003c\/span\u003e\u003cspan class=\"\"\u003e rewriting messy Excel rows,\u003c\/span\u003e\u003cspan class=\"\"\u003e renaming hundreds of PDFs,\u003c\/span\u003e\u003cspan class=\"\"\u003e and sending out repetitive emails.\u003c\/span\u003e\u003cspan class=\"\"\u003e Simpson establishes that Python is the perfect \"software multi-tool\" to cure this workplace inefficiency.\u003c\/span\u003e\u003cspan class=\"\"\u003e Because Python features clean,\u003c\/span\u003e\u003cspan class=\"\"\u003e readable syntax that resembles plain English,\u003c\/span\u003e\u003cspan class=\"\"\u003e beginners can learn the basics quickly and start writing functional scripts in their very first week.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eRather than boring readers with abstract mathematical puzzles, Simpson focuses entirely on immediate, real-world utility. He guides the reader step-by-step through setting up a free coding workspace and diving into targeted, practical scripts. The book covers how to search for and rename massive collections of local desktop folders, automatically extract table text from PDFs, pull product prices directly from web pages using scraping tools, and control mouse clicks and keyboard inputs to handle stubborn legacy software apps. It is a comprehensive roadmap for transforming tedious manual workflows into automated, single-click routines.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eAcross our regional corporate offices, tech startups, and banking systems, thousands of administrative professionals, financial analysts, and small business owners are trapped in an exhausting loop of repetitive digital work. Teams spend hours every week manually matching spreadsheet rows, creating invoice templates, and tracking online market data by hand. These tedious chores frequently cause severe processing delays, expensive clerical mistakes, and deep workplace burnout, draining energy that should be spent on growing businesses.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003ePython Automation For Dummies\u003c\/i\u003e provides the perfect, stress-free escape hatch from these daily workflow bottlenecks. Alan Simpson’s legendary ability to turn complex technical topics into clear, easy-to-digest instructions makes this the ultimate guide for absolute beginners. He completely removes the intimidation factor from programming, providing local professionals and students with a direct blueprint to reclaim their time and build valuable workplace skills. It is an absolute must-own guide for anyone ready to eliminate tech anxiety, automate stressful daily office chores, and turn their desktop computer into a highly efficient, self-operating assistant.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Tech Education.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47227688812729,"sku":null,"price":300.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Python_Automation_For_Dummies.jpg?v=1779263974"},{"product_id":"data-contracts","title":"Data Contracts: Developing Production-Grade Pipelines at Scale by Chad Sanderson, Mark Freeman and B. E. Schmidt","description":"\u003ch2\u003eData Contracts: Developing Production-Grade Pipelines at Scale by Chad Sanderson, Mark Freeman and B. E. Schmidt\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eData Contracts\u003c\/i\u003e is that the modern data industry is facing a massive data quality crisis caused by structural silos. In most enterprise tech organizations, software engineers frequently modify application code or alter production database schemas to roll out new features. However, downstream data engineering pipelines are silently pulling from those exact same databases. When a software developer renames a database column or drops an old table field, the data lake breaks instantly. Downstream analytics dashboards go blank, machine learning models output corrupt predictions, and business intelligence teams are left scrambling to hunt down the root cause.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan class=\"attachment-container search-images\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003cspan\u003eSanderson, Freeman, and Schmidt establish \u003cb data-path-to-node=\"7\" data-index-in-node=\"42\"\u003eData Contracts\u003c\/b\u003e as the ultimate technical framework to enforce a shared, legally binding agreement between data \u003ci data-path-to-node=\"7\" data-index-in-node=\"153\"\u003eproviders\u003c\/i\u003e (software engineers) and data \u003ci data-path-to-node=\"7\" data-index-in-node=\"193\"\u003econsumers\u003c\/i\u003e (data analysts and ML teams). The authors guide readers through the practical execution of defining explicit structural schemas, SLA (Service Level Agreement) latencies, and semantic definitions in standardized formats like YAML or JSON Schema. By embedding these contracts directly inside continuous integration pipelines, organizations can completely eliminate silent data breakages. If an application change violates an active data contract, the system automatically blocks the software deployment before it ever hits production.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional enterprise ecosystem embraces digital transformation, cloud migrations, and predictive AI modeling, companies are building vast data engineering departments. Yet, a massive systemic issue persists behind the scenes: a constant, costly battle against broken internal data feeds. Local data engineering teams routinely waste half their workweeks debugging broken pipelines, fixing corrupted reports, and re-running expensive cloud transformations because upstream engineering groups changed an app setting without telling anyone. This silent friction burns out elite technical talent and shatters executive trust in business metrics.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003eData Contracts\u003c\/i\u003e addresses this exact, multi-million dollar operational vulnerability with surgical precision. Chad Sanderson, Mark Freeman, and B.E. Schmidt leverage their immense prestige as world-renowned experts to provide an incredibly practical, code-rich operational masterclass. They give local CTOs, platform team leads, and backend engineers a clear, standardized playbook to stop treating data like an accidental byproduct and start managing it as an institutional-grade asset. It is an absolute must-read guide for any local enterprise tech professional ready to establish unshakeable platform reliability, eliminate pipeline fires, and deploy bulletproof automated architectures that operate flawlessly at massive corporate scale.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Data Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47227734819001,"sku":null,"price":300.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Data_Contracts.jpg?v=1779265130"},{"product_id":"fundamentals-of-data-engineering","title":"Fundamentals of Data Engineering: Plan and Build Robust Data Systems by Joe Reis, Matt Housley","description":"\u003ch2\u003eFundamentals of Data Engineering: Plan and Build Robust Data Systems by Joe Reis, Matt Housley\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eFundamentals of Data Engineering\u003c\/i\u003e is that data tools are secondary to data architecture. In a rapidly changing tech market, engineers frequently chase after trendy software libraries or database vendors, only to build fragile, convoluted pipelines that collapse under heavy production loads. Reis and Housley introduce a rigorous, comprehensive framework: the \u003cb data-path-to-node=\"6\" data-index-in-node=\"378\"\u003eData Engineering Lifecycle\u003c\/b\u003e. They demonstrate that whether you are using open-source tools or proprietary enterprise clouds, data must always flow smoothly through five immutable stages: generation, ingestion, storage, transformation, and serving.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan class=\"attachment-container search-images\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003cspan\u003eRather than advocating for a specific software stack, the authors teach readers how to evaluate technologies based on fundamental engineering trade-offs. The book details how to assess systems across critical vectors like cost, speed, scalability, and security. The text provides a deep architectural breakdown of data streaming versus batch processing, relational versus non-relational storage layers, and data modeling styles. Crucially, Reis and Housley weave across this lifecycle a set of universal \"undercurrents\"—including security, data governance, orchestration, and software engineering best practices—ensuring that readers build highly dependable, future-proof systems.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional tech ecosystem undergoes a massive, data-driven transformation, organizations across fintech, e-commerce, and enterprise software are racing to build data platforms. However, many engineering teams run into a massive structural wall: they pour huge amounts of capital into expensive cloud tools, only to build chaotic, unmanageable pipeline tangles. Data pipelines break daily, analytics dashboards report incorrect figures, and computing bills skyrocket out of control because teams lack a foundational understanding of data architecture.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003eFundamentals of Data Engineering\u003c\/i\u003e provides the ultimate, authoritative antidote to this widespread operational confusion. Joe Reis and Matt Housley deliver an exceptionally clear, rigorous framework that cuts through the marketing noise of tool vendors. They arm local systems architects, backend developers, and data engineers with the structural thinking required to plan, build, and maintain institutional-grade data systems. It is an absolute must-read technical blueprint for any professional ready to eliminate chaotic data practices, control cloud infrastructure expenses, and build highly secure, self-healing data assets that drive genuine corporate value at massive enterprise scale.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Data Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47227760410809,"sku":null,"price":370.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Fundamentals_of_Data_Engineering.jpg?v=1779265508"},{"product_id":"deep-learning-with-pytorch-second-edition","title":"Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models by Luca Antiga, Eli Stevens, Howard Huang and Thomas Viehmann","description":"\u003ch2\u003eDeep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models by Luca Antiga, Eli Stevens, Howard Huang and Thomas Viehmann\u003c\/h2\u003e\n\u003cp\u003eThe central thesis of the second edition remains unchanged but profoundly modernized: deep learning is fundamentally an engineering discipline of data tensor manipulation, gradient tracking, and numerical optimization. While many developer books treat neural networks as opaque, \"black-box\" abstractions where you simply change string parameters, \u003ci data-path-to-node=\"6\" data-index-in-node=\"347\"\u003eDeep Learning with PyTorch\u003c\/i\u003e forces the programmer to look under the hood. \u003cspan class=\"citation-73 citation-end-73\"\u003eIt teaches you exactly how data structures are stored in GPU memory, how automatic differentiation functions, and how code scales out to multi-node training clusters.\u003csup class=\"superscript\" data-turn-source-index=\"3\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"citation-73 citation-end-73\"\u003e\u003cspan class=\"citation-72 citation-end-72\"\u003eHoward Huang preserves the original book's brilliant project-driven backbone: a multi-chapter, hands-on journey constructing a real-world, clinical-grade medical image classifier to detect lung tumors from raw CT scans.\u003csup class=\"superscript\" data-turn-source-index=\"4\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-71 citation-end-71\"\u003eHowever, the Second Edition injects massive upgrades to address the generative AI revolution.\u003csup class=\"superscript\" data-turn-source-index=\"5\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-70\"\u003eHuang introduces entirely new sections on the structural building blocks of the \u003c\/span\u003e\u003cb data-path-to-node=\"7\" data-index-in-node=\"394\"\u003e\u003cspan class=\"citation-70\"\u003eTransformer architecture\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-70 citation-end-70\"\u003e, walking through tokenization, multi-head attention mechanisms, and context windows.\u003csup class=\"superscript\" data-turn-source-index=\"6\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-69 citation-end-69\"\u003eReaders don't just call pretrained models; they build text-generation pipelines and image diffusion models step-by-step using pure PyTorch syntax.\u003csup class=\"superscript\" data-turn-source-index=\"7\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our local technology sector races to build in-house AI capabilities, automated fintech pipelines, and digital health diagnostic platforms, the demand for true machine learning expertise has skyrocketed. However, many local software engineers and data science graduates find themselves hitting a technical ceiling. Relying on simple tutorials or fragile third-party wrappers, developers struggle to build production-grade systems that can converge on complex data or scale efficiently within limited GPU budgets.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eThe \u003cb data-path-to-node=\"30\" data-index-in-node=\"4\"\u003eSecond Edition\u003c\/b\u003e of \u003ci data-path-to-node=\"30\" data-index-in-node=\"22\"\u003eDeep Learning with PyTorch\u003c\/i\u003e delivers the exact, rigorous medicine our advanced engineering ecosystem needs. By combining the deep systems background of the original authors with Howard Huang's elite insights as a core PyTorch library developer, this book sets a new standard for technical education. It transforms developers from basic code-consumers into master AI architects capable of building enterprise-grade, self-correcting neural pipelines. It is an absolute must-read blueprint for any local technical leader, cloud engineer, or machine learning researcher determined to dominate modern AI infrastructure and ship highly optimized, scalable intelligent software.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Artificial Intelligence.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong style=\"font-size: 0.875rem;\"\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228046704825,"sku":null,"price":390.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Deep_Learning_with_PyTorch_Second_Edition.jpg?v=1779270853"},{"product_id":"privacy-and-security-for-large-language-models","title":"Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI  by Baihan Lin","description":"\u003ch2\u003ePrivacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI  by Baihan Lin\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003ePrivacy and Security for Large Language Models\u003c\/i\u003e is that foundation models introduce entirely new attack surfaces that traditional cybersecurity architectures are fundamentally ill-equipped to handle. Large Language Models are highly fluid, probabilistic systems. They do not just execute explicit code paths; they interpret natural language prompts, memorize training data characteristics, and rely on external retrieval tools (like RAG databases) that can be easily manipulated. Lin establishes that securing production AI requires a comprehensive \u003cb data-path-to-node=\"6\" data-index-in-node=\"567\"\u003edefense-in-depth framework\u003c\/b\u003e that guards the model across its entire lifecycle: from raw training datasets to runtime token inferences.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan class=\"attachment-container search-images\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan\u003eRather than offering generic security tips, Lin walks readers through the exact mechanics of advanced AI exploitation and defense. The book outlines how attackers manipulate context windows through automated \u003cb data-path-to-node=\"7\" data-index-in-node=\"208\"\u003eindirect prompt injections\u003c\/b\u003e, extract confidential enterprise secrets via \u003cb data-path-to-node=\"7\" data-index-in-node=\"280\"\u003edata leakage exploits\u003c\/b\u003e, and force corporate chatbots to bypass safety protocols using complex \u003cb data-path-to-node=\"7\" data-index-in-node=\"373\"\u003ejailbreaking vectors\u003c\/b\u003e. To counter these threats, the text serves as an operational masterclass in cutting-edge mitigation frameworks. It details how to mathematically guarantee user anonymity using \u003cb data-path-to-node=\"7\" data-index-in-node=\"570\"\u003eDifferential Privacy (DP)\u003c\/b\u003e, run computations on untrusted clouds using \u003cb data-path-to-node=\"7\" data-index-in-node=\"640\"\u003eHomomorphic Encryption\u003c\/b\u003e, and build real-time guardrail networks that actively sanitize user inputs and model outputs.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-59 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQhQE\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-59\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-59\"\u003e\n\u003c!----\u003e\n\u003cpre class=\"ng-tns-c3299913081-59\"\u003e\u003ccode role=\"text\" data-test-id=\"code-content\" class=\"code-container formatted ng-tns-c3299913081-59 no-decoration-radius\"\u003e\u003c\/code\u003e\u003cbr\u003e\u003c\/pre\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional enterprise ecosystem rapidly adopts corporate AI solutions, fintech automation, and intelligent database integrations, security and compliance are becoming top priorities. However, many engineering leads and technical teams are moving fast without a clear safety net. They deploy live chatbots and autonomous agents that pull directly from proprietary company data, leaving themselves highly vulnerable to sophisticated cyber exploits. A single successful prompt injection attack or data leakage incident can compromise customer trust, expose corporate secrets, and result in massive regulatory fines.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e \u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003ePrivacy and Security for Large Language Models\u003c\/i\u003e provides the precise, technical blueprint needed to secure these systems. Baihan Lin bypasses high-level philosophical debates and delivers an immensely practical, code-driven security guide. He provides local CTOs, security engineers, and DevOps leads with a clear, step-by-step roadmap to harden their systems against modern threats, prevent expensive data breaches, and build production-grade AI platforms that pass strict institutional audits. It is an indispensable technical playbook for anyone ready to eliminate AI vulnerabilities and deploy secure, robust intelligent software at scale.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Artificial Intelligence.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228067315897,"sku":null,"price":290.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Privacy_and_Security_for_Large_Language_Models.webp?v=1779271202"},{"product_id":"building-machine-learning-systems-with-a-feature-store","title":"Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems by Jim Dowling","description":"\u003ch2\u003eBuilding Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems by Jim Dowling\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eBuilding Machine Learning Systems with a Feature Store\u003c\/i\u003e is that operational machine learning is fundamentally a data engineering challenge, not an algorithmic modeling challenge. In most enterprise tech teams, data scientists spend up to 80% of their time writing repetitive data transformation code to clean features for training. Worse, when these models go live, software backend engineers are forced to rewrite those exact same data pipelines in a different language (like Java or C++) to serve real-time predictions. This structural fragmentation leads to catastrophic \u003cb data-path-to-node=\"6\" data-index-in-node=\"592\"\u003etraining-serving skew\u003c\/b\u003e, where a model performs beautifully in training but fails instantly in production due to mismatched data calculations.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan class=\"attachment-container search-images\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan\u003eJim Dowling establishes the modern \u003cb data-path-to-node=\"7\" data-index-in-node=\"35\"\u003eFeature Store\u003c\/b\u003e as the ultimate technical architectural layer that bridges this divide. Acting as a specialized data warehouse for machine learning, a feature store provides a dual-database design: a \u003cb data-path-to-node=\"7\" data-index-in-node=\"233\"\u003eTime-Travel Offline Store\u003c\/b\u003e optimized for processing massive, cost-efficient batch data for model training, and a \u003cb data-path-to-node=\"7\" data-index-in-node=\"345\"\u003eLow-Latency Online Store\u003c\/b\u003e (operating at sub-millisecond speeds) to serve real-time feature variables during live production inferences. Dowling walks readers through building complete F-O-M (Feature, Training, Serving) pipelines, structuring streaming features using Apache Kafka and Flink, and utilizing the feature store as a robust semantic foundation to power Large Language Model (LLM) agents via real-time context injections.\u003c\/span\u003e\u003c\/span\u003e\u003ccode role=\"text\" data-test-id=\"code-content\" class=\"code-container formatted ng-tns-c3299913081-66 no-decoration-radius\"\u003e\u003c\/code\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-66 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQqgE\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-66\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-66\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional tech ecosystem enters a sophisticated phase of scaling automated banking apps, personalized e-commerce engines, and high-throughput fintech platforms, local companies are running into a painful structural roadblock. While training an AI model in a Jupyter Notebook is relatively easy, keeping that model supplied with clean, real-time, unpolluted data feeds in production is notoriously difficult. Many local engineering teams watch their expensive AI models fail on deployment because they lack the data platform architecture needed to feed their systems accurately on autopilot.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003eBuilding Machine Learning Systems with a Feature Store\u003c\/i\u003e provides the precise, infrastructure-level blueprint to break through this multi-million dollar bottleneck. Jim Dowling uses his immense prestige as an enterprise system creator to deliver a highly practical, code-rich masterclass. He demonstrates exactly how to decouple fragile code blocks, construct reliable offline\/online data lakes, and bridge the dangerous gap between data engineering and machine learning teams. It is an absolutely essential playbook for any local CTO, MLOps professional, or data engineer determined to control cloud infrastructure expenses, eliminate pipeline errors, and build bulletproof, real-time intelligent software that scales flawlessly.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Data Platform Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e \u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228083142841,"sku":null,"price":390.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Building_Machine_Learning_Systems_with_a_Feature_Stor.jpg?v=1779271451"},{"product_id":"build-a-large-language-model","title":"Build a Large Language Model by Sebastian Raschka","description":"\u003ch2\u003eBuild a Large Language Model by Sebastian Raschka\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eBuild a Large Language Model (From Scratch)\u003c\/i\u003e is that the absolute best way to truly understand complex transformer-based architectures is to build them from the ground up (Raschka 2024). In an industry dominated by massive cloud infrastructure, developers frequently implement AI models without understanding the underlying math, data flows, and attention mechanisms. Raschka breaks down this technical barrier by providing a clear, step-by-step educational pathway. He strips away unnecessary mathematical abstractions, replacing them with clean, executable PyTorch code that demonstrates exactly how text is tokenized, vectorized, and processed through complex multi-head attention blocks.\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAcross our regional technology centers, research laboratories, and software companies, teams are racing to adopt generative AI. However, a major technical gap has emerged: many developers only know how to connect to external, black-box cloud APIs. This heavy reliance on foreign infrastructure introduces severe data privacy risks, expensive subscription fees, and unpredictable performance shifts. To build true technological independence, our software market needs engineers who can design, train, and deploy custom, localized language models tailored to specialized domains—like secure banking, local language translation, or private healthcare analysis.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Artificial Intelligence.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228241051833,"sku":null,"price":350.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Build_a_Large_Language_Model.jpg?v=1779275242"},{"product_id":"generative-ai-on-kubernetes","title":"Generative AI on Kubernetes: Operationalizing Large Language Models by Roland Huß, Daniele Zonca","description":"\u003ch2\u003eGenerative AI on Kubernetes: Operationalizing Large Language Models by Roland Huß, Daniele Zonca\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eGenerative AI on Kubernetes\u003c\/i\u003e is that the biggest challenge facing generative AI today is no longer model design, but orchestration at scale. Large Language Models (LLMs) are uniquely demanding: they consume massive amounts of GPU memory, require specialized hardware acceleration, incur incredibly high cloud compute bills, and exhibit non-deterministic runtime behaviors. Running these workloads on rigid, traditional virtual machines leads to wasted hardware power, slow scaling, and frequent production outages.\u003c\/p\u003e\n\u003cp data-path-to-node=\"6\"\u003e\u003cspan class=\"citation-144 citation-end-144\"\u003eHuß and Zonca position Kubernetes as the ultimate control plane for production-grade AI.\u003csup class=\"superscript\" data-turn-source-index=\"3\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e They provide a practical roadmap for training, fine-tuning, deploying, and auto-scaling generative models. The authors walk readers through configuring advanced GPU scheduling, setting up fractional hardware virtualization, and minimizing the cost of idle cluster space. Crucially, the text details how to run open runtimes like vLLM and TGI within a cloud-native mesh, connect secure agent networks to real-time external tools, and establish monitoring pipelines to track deep LLM health metrics.\u003c\/p\u003e\n\u003cp data-path-to-node=\"6\"\u003eAs our regional software ecosystem rapidly transitions from simple AI experiments to launching live, production-grade applications, teams are hitting a massive infrastructure wall. Companies are burning through capital on unmanaged, always-running cloud servers that sit idle for hours but crash instantly under sudden user traffic surges. \u003cspan class=\"citation-139 citation-end-139\"\u003eMLOps teams are struggling to manage GPU allocations, protect underlying data from security vulnerabilities, and balance compute costs against real-time application speeds.\u003csup class=\"superscript\" data-turn-source-index=\"8\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Systems Architecture.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\" id=\"p-rc_d1897ac610724bdb-66\"\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228276342969,"sku":null,"price":350.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Generative_AI_on_Kubernetes.jpg?v=1779275801"},{"product_id":"designing-machine-learning-systems","title":"Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen","description":"\u003ch2\u003eDesigning Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eDesigning Machine Learning Systems\u003c\/i\u003e is that deploying a model is the easy part—maintaining it in production while user behaviors actively shift is the real engineering challenge. Traditional software development handles static, deterministic logic. Machine learning, however, is deeply non-deterministic and entirely dependent on live data flows. Huyen introduces a holistic, end-to-end framework that views ML engineering as an iterative loop spanning data engineering, model development, deployment, infrastructure scaling, and continuous real-time monitoring.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan class=\"attachment-container search-images\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan\u003eRather than diving into specific software libraries, Huyen guides readers through the complex architectural trade-offs that dictate production success. She breaks down how to choose between batch prediction and low-latency \u003cb data-path-to-node=\"7\" data-index-in-node=\"223\"\u003estreaming inference\u003c\/b\u003e, how to avoid catastrophic \u003cb data-path-to-node=\"7\" data-index-in-node=\"270\"\u003edata leakage\u003c\/b\u003e during feature engineering, and how to detect the silent killer of AI applications: \u003cb data-path-to-node=\"7\" data-index-in-node=\"367\"\u003edata drift\u003c\/b\u003e and \u003cb data-path-to-node=\"7\" data-index-in-node=\"382\"\u003econcept drift\u003c\/b\u003e. From selecting training data sizes to deploying models across edge devices or multi-cloud infrastructures, the text provides structured, real-world case studies from major tech enterprises to show how resilient, self-correcting ML systems are engineered.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-87 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQoQI\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-87\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-87\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional tech ecosystem enters a highly sophisticated phase—with local software teams rolling out automated financial apps, real-time e-commerce recommendation engines, and digital logistics platforms—engineers are hitting a massive operational wall. While many local developers can easily train a predictive model in an isolated notebook, very few know how to scale that model to serve millions of requests without breaking corporate cloud budgets or suffering severe performance degradation.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Software Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e \u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228302131385,"sku":null,"price":350.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Designing_Machine_Learning_Systems.jpg?v=1779276022"},{"product_id":"communicating-with-data","title":"Communicating with Data: Making Your Case With Data by Carl Allchin","description":"\u003ch2\u003eCommunicating with Data: Making Your Case With Data by Carl Allchin\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eCommunicating with Data\u003c\/i\u003e is that even the most brilliant, advanced data analysis is completely worthless if your organization's decision-makers cannot understand or use your findings. In most enterprise tech and analytics teams, a massive communication gap exists. Technical teams often present their conclusions using confusing, over-complicated dashboards stuffed with messy charts, excessive color schemes, and unedited data columns. This overwhelming wall of information frustrates non-technical executives and frequently leads to costly strategic mistakes.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eCarl Allchin establishes that true data communication is a deliberate, audience-focused design discipline. He walks readers through his step-by-step structural framework for clarifying data presentation. Rather than tying his lessons to a single specific software tool like Tableau, Power BI, or Python's matplotlib, Allchin teaches the timeless, universal principles of information architecture. Readers learn exactly how to define their central audience, strip out distracting visual clutter, choose the correct graph style to match their data, and craft a clear, narrative-driven presentation that guides a business stakeholder seamlessly from raw observation to decisive strategic action.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-94 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQvAI\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-94\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-94\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional business landscape embraces digital transformation, local banks, corporate enterprises, and booming tech startups are gathering more data than ever before. However, many organizations are drowning in metrics they simply do not know how to present. Data analysts, financial modelers, and business intelligence engineers frequently watch their excellent research get ignored by executive boards because their findings are trapped inside confusing, messy data readouts.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eCommunicating with Data\u003c\/i\u003e provides the precise, practical blueprint to bridge this corporate communication gap. Carl Allchin uses his immense global prestige as a world-class Tableau coach to deliver an exceptionally clear, highly practical guide. He cuts through academic design fluff, arming local analysts and technical leads with the core storytelling skills needed to build clean, intuitive, and high-impact visual presentations. It is an indispensable manual for any technical professional determined to elevate their corporate influence, land their message clearly in executive meetings, and turn raw numbers into decisive business growth.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Business Intelligence.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228323102905,"sku":null,"price":330.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Communicating_with_Data.jpg?v=1779276246"},{"product_id":"visual-introduction-to-deep-learning","title":"Visual Introduction to Deep Learning by Meor Amer","description":"\u003ch2\u003eVisual Introduction to Deep Learning by Meor Amer\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eA Visual Introduction to Deep Learning\u003c\/i\u003e is that deep learning is fundamentally an exercise in geometric transformations, not just abstract, robotic numerical calculation. Most textbooks introduce neural networks through dense multi-variable calculus and linear algebra proofs. While mathematically rigorous, this traditional approach often leaves students memorizing formulas without actually understanding what happens to data as it passes through hidden layers. Amer flips this paradigm by translating abstract mathematics into clear, visual concepts.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan class=\"attachment-container search-images\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan\u003eThe book acts as an illuminated roadmap through a neural network's architecture. Amer walks readers through the absolute basics of a single artificial neuron, showing how input features are scaled by weights, combined with a bias, and reshaped by activation functions. From there, the book expands into deep feedforward networks, uncovering the true nature of \u003cb data-path-to-node=\"7\" data-index-in-node=\"360\"\u003eForward Propagation\u003c\/b\u003e and \u003cb data-path-to-node=\"7\" data-index-in-node=\"384\"\u003eBackpropagation\u003c\/b\u003e. Instead of drowning the reader in calculus code, the text uses clean, step-by-step vector diagrams to illustrate how gradient descent actively navigates error surfaces to minimize loss and optimize neural weights.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-101 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQ3QI\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-101\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-101\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional tech sector experiences an explosion of interest in machine learning, thousands of local software engineers, university students, and business analysts are trying to transition into AI. However, many hit an immediate wall because traditional learning materials are overly academic and math-heavy. This barrier prevents brilliant programmers from entering the field simply because they don't have an advanced background in multivariable calculus.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Data Science Fundamentals.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228346433721,"sku":null,"price":300.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Visual_Introduction_to_Deep_Learning.jpg?v=1779276534"},{"product_id":"ux-for-ai","title":"UX for AI: A Framework for Designing AI-Driven Products by Greg Nudelman, Daria Kempka","description":"\u003ch2\u003eUX for AI: A Framework for Designing AI-Driven Products by Greg Nudelman, Daria Kempka\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eUX for AI\u003c\/i\u003e is that an AI model's raw mathematical capabilities mean nothing if human users cannot easily understand, navigate, and trust its outputs. Traditional software engineering deals with predictable, deterministic logic—if a user clicks button A, the system always executes action B. Artificial intelligence, however, is inherently probabilistic, experimental, and prone to hallucinations or errors. Nudelman argues that slapping a generic chatbot window onto a complex data tool is a lazy design shortcut that alienates everyday users and reduces overall retention.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan class=\"attachment-container search-images\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan\u003eThe authors introduce a highly structured, multi-layered design framework specifically tailored to the unique complexities of machine learning. They move far beyond simple visual interfaces, guiding readers through the complex psychological trade-offs of building user trust. The text details how to handle slow backend latencies using clever system feedback loops, how to design intentional onboarding paths that manage user expectations, and how to create clean \u003cb data-path-to-node=\"7\" data-index-in-node=\"464\"\u003esemantic UI elements\u003c\/b\u003e that clearly explain \u003ci data-path-to-node=\"7\" data-index-in-node=\"506\"\u003ewhy\u003c\/i\u003e an AI generated a specific recommendation. Through clear, real-world case studies spanning autonomous agents, prediction engines, and generative systems, the book shows how to transform alienating AI systems into valuable, highly collaborative human tools.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-108 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQ-gI\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-108\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-108\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional tech ecosystem goes through a massive shift—with engineering teams rolling out custom enterprise platforms, intelligent SaaS solutions, and local mobile apps—product teams are hitting a painful user adoption bottleneck. Local developers are spending immense capital training powerful models, only to see users abandon the final apps within minutes. The core issue isn't the underlying math; it is that the software interfaces are frustrating, confusing, and overly difficult for everyday non-technical users to control.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Human-Computer Interaction (HCI) .\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228364226745,"sku":null,"price":350.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/UX_for_AI.jpg?v=1779276835"},{"product_id":"llms-in-production","title":"LLMs in Production: From language models to successful products by Christopher Brousseau, Matt Sharp","description":"\u003ch2\u003eLLMs in Production: From language models to successful products by Christopher Brousseau, Matt Sharp\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eLLMs in Production\u003c\/i\u003e is that a fine-tuned neural network or a clever prompt template is not an actual software product. Moving a Large Language Model (LLM) into production creates a mountain of operational complexity: text models are naturally prone to hallucinations, data leakage risks, unpredictable computing costs, and high inference latency. Brousseau and Sharp introduce an end-to-end framework that treats an LLM as just one component within a larger, data-intensive corporate architecture.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan class=\"attachment-container search-images\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003cspan\u003eThe authors guide readers through the practical realities of building \u003cb data-path-to-node=\"7\" data-index-in-node=\"70\"\u003eRetrieval-Augmented Generation (RAG)\u003c\/b\u003e systems, creating automated semantic parsing layers, and managing state across long-running autonomous agent networks. Rather than focusing purely on abstract math or simple tool wrappers, the text details the concrete engineering trade-offs between hosting open-source models (like Llama or Mistral) locally versus consuming closed cloud models. From implementing robust guardrails to protect user privacy to scaling semantic cache systems to optimize infrastructure budgets, the book provides clear, step-by-step corporate case studies showing how high-performance AI software is built.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"text-block-with-attachment\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-115 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQlwM\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-115\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-115\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional software market undergoes rapid digital modernization—with local software houses, banking institutions, and e-commerce platforms rushing to integrate generative AI—engineering teams are hitting a painful production wall. While many local developers can easily build a simple chatbot using a basic API call, very few know how to design an enterprise-grade RAG pipeline that stays accurate, respects data privacy laws, and operates without causing massive cloud cost overruns.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003eLLMs in Production\u003c\/i\u003e delivers the exact, battle-tested engineering blueprint needed to break through this operational bottleneck. Christopher Brousseau and Matthew Sharp combine their immense prestige as senior enterprise data consultants to provide an exceptionally practical, code-rich masterclass. They strip away industry hype and abstract math, giving local tech leads, data platform architects, and software engineers the concrete infrastructure patterns required to build secure, low-latency, and highly profitable AI applications. It is an absolute must-read manual for any technical professional serious about mastering the business-critical world of MLOps.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Enterprise Software Architecture.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228596748473,"sku":null,"price":400.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/LLMs_in_Production.jpg?v=1779283150"},{"product_id":"learning-github-copilot","title":"Learning GitHub Copilot: Multiplying Your Coding Productivity Using AI by Brent Laster","description":"\u003ch2\u003eLearning GitHub Copilot: Multiplying Your Coding Productivity Using AI by Brent Laster\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eLearning GitHub Copilot\u003c\/i\u003e is that generative AI is shifting the bottleneck of software engineering from mechanical syntax writing to architectural intent. 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He skips the basic marketing fluff, arming local developers, team leads, and software architects with the advanced prompt structures and Agent workflows needed to double their coding speed, automate testing pipelines, and deploy rock-solid applications. 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Autonomous agents, however, are non-deterministic, heavily reliant on tool usage, and notoriously fragile when left unguided. \u003cspan class=\"citation-168 citation-end-168\"\u003eKouri details how to bridge this gap, asserting that a staggering 89% of enterprise AI initiatives collapse because developers skip foundational state-management and orchestration safeguards.\u003csup class=\"superscript\" data-turn-source-index=\"3\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"citation-168 citation-end-168\"\u003eKouri walks readers through an iterative journey to absolute agentic mastery using the latest iterations of LangChain. \u003cspan class=\"citation-167 citation-end-167\"\u003eRather than drowning developers in abstract machine learning theory, this book is intensely practical, delivering a 90-day architectural blueprint.\u003csup class=\"superscript\" data-turn-source-index=\"4\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e It explores how to establish explicit stateful graphs, execute complex \u003cb data-path-to-node=\"7\" data-index-in-node=\"338\"\u003eRouter and Supervisor design patterns\u003c\/b\u003e, handle non-deterministic tool calling gracefully, and connect robust \u003cb data-path-to-node=\"7\" data-index-in-node=\"446\"\u003eRetrieval-Augmented Generation (RAG)\u003c\/b\u003e nodes. \u003cspan class=\"citation-166 citation-end-166\"\u003eFrom stopping infinite agent loops to managing token limits via smart conversational memory windowing, Kouri arms builders with the precise patterns Fortune 500 corporations pay premier consulting firms hundreds of thousands of dollars to implement.\u003csup class=\"superscript\" data-turn-source-index=\"5\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional software development industry makes a major pivot toward building autonomous internal tools, intelligent customer support agents, and automated data processing engines, engineering teams are encountering a brutal reality check. 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Rather than relying on fragile prompt tweaks, the book implements structured development patterns to stop infinite model loops, isolate external tool calling within sandboxed execution environments, and orchestrate deterministic routing logic across decentralized networks.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-83 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwiHkOWS_ceUAxUAAAAAHQAAAAAQgQI\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-83\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-83\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"31\"\u003eAs our regional tech ecosystems, enterprise software houses, and offshore development sectors rapidly shift from building basic web wrappers to constructing sophisticated autonomous business tools, engineering teams are hitting a massive deployment wall. 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It is a mandatory addition to any serious software engineer's active reference library.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: AI Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47229194338489,"sku":null,"price":270.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Patterns_for_Building_AI_Agents.jpg?v=1779302187"},{"product_id":"mastering-ai-agents-by-marcus-lighthaven","title":"Mastering AI Agents by Marcus Lighthaven","description":"\u003ch2\u003eMastering AI Agents by Marcus Lighthaven\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eMastering AI Agents\u003c\/i\u003e is that the true power of the artificial intelligence revolution doesn't belong exclusively to elite Silicon Valley software engineers; it belongs to the forward-thinking operators who know how to stitch autonomous systems together to drive business efficiency. Lighthaven highlights a massive paradigm shift: traditional software automation relies on rigid, unchanging, pre-programmed \"if\/then\" code blocks. If a vendor changes a spreadsheet format by a single column, old automation scripts crash. LLM-powered AI agents, however, possess a crucial differentiator: \u003cb data-path-to-node=\"6\" data-index-in-node=\"605\"\u003ethe ability to interpret intent, dynamically problem-solve, and adapt to changing data environments.\u003c\/b\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eLighthaven guides the reader through the foundational architecture of autonomous systems without burying them under code fences. He breaks down how an agent actually works—balancing communication, tool access, and dynamic memory. 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From automating complete digital content lifecycles to building self-improving customer support operations, Lighthaven provides a clear, democratic blueprint for scaling business output.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-90 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwiHkOWS_ceUAxUAAAAAHQAAAAAQngI\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-90\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-90\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional business landscape, digital agency ecosystem, and startup communities face intense pressure to maximize operational efficiency, scaling output while managing tight budgets has become a critical challenge. Many local entrepreneurs, freelance professionals, and small business owners want to leverage the power of artificial intelligence but feel entirely locked out by advanced programming jargon, Python environments, and complex machine learning setups.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eMastering AI Agents\u003c\/i\u003e serves as the perfect, accessible gateway to true automation. Marcus Lighthaven strips away the gatekeeping technical complexity to deliver a highly practical, no-code and low-code blueprint. He focuses on real-world tool stacks like Make and n8n to equip local professionals, managers, and non-technical founders with the concrete strategies needed to eliminate administrative friction, build resilient digital workflows, and scale client delivery effortlessly. It is an absolute must-read handbook for anyone wanting to build a highly competitive, modern business.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre:  AI Business Automation.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47229198434489,"sku":null,"price":270.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/79d09f1f-baf0-474e-aa30-ac2f77d59064.jpg?v=1779556663"},{"product_id":"ai-projects-in-pytorch","title":"Ai Projects in Pytorch: Hands-on Projects in Vision, Text, and Generative Models by Siddhesh Prashant Chaubal","description":"\u003ch2\u003eAi Projects in Pytorch: Hands-on Projects in Vision, Text, and Generative Models by Siddhesh Prashant Chaubal\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core philosophy of \u003ci data-path-to-node=\"6\" data-index-in-node=\"23\"\u003eAI Projects in PyTorch\u003c\/i\u003e is that deep learning mastery cannot be achieved by reading equations; it must be forged by writing production code. Chaubal identifies a major gap in modern tech education: many developers understand basic machine learning concepts like feature engineering and overfitting, but they hit a wall when trying to architect end-to-end pipelines that handle raw, messy multi-modal datasets. This book bridges that gap by walking the reader through a series of complete, production-grade projects.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eChaubal begins by building a rock-solid foundation in PyTorch internals, detailing how tensor operations, custom dataset abstractions, optimized data loaders, and automatic gradient computations work under the hood. From there, the book shifts into rapid, high-velocity engineering. Rather than isolating domains, Chaubal challenges developers to build across the entire spectrum of modern AI. Readers dive deep into building advanced \u003cb data-path-to-node=\"7\" data-index-in-node=\"435\"\u003eComputer Vision\u003c\/b\u003e systems with \u003ccode data-path-to-node=\"7\" data-index-in-node=\"464\"\u003etorchvision\u003c\/code\u003e, processing textual data with sequence models, transforming raw audio waves into image-like Mel spectrograms for \u003cb data-path-to-node=\"7\" data-index-in-node=\"589\"\u003eAudio Classification\u003c\/b\u003e, and orchestrating complex multi-modal architectures that blend text and vision together.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-97 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwiHkOWS_ceUAxUAAAAAHQAAAAAQuAI\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-97\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-97\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional software ecosystem continues to evolve, local software companies, offshore engineering labs, and technology startups are moving away from simply calling external web APIs and are focusing heavily on training proprietary, in-house deep learning models. 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While standard large language models are often treated as simple Q\u0026amp;A search engines, Microsoft Copilot functions as an active, integrated digital partner operating directly inside your proprietary database and office file layers. The guide addresses a widespread corporate challenge: many professionals face software fatigue because they input vague, shallow requests, resulting in generic or inaccurate outputs that require extensive manual rewriting.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eTo solve this operational bottleneck, this comprehensive playbook teaches users how to construct prompts utilizing a highly systematic, four-part ingredient matrix. By breaking down the specific structures required to interact with Word, Excel, PowerPoint, Teams, and Outlook, the guide demonstrates how simple natural language instructions can analyze financial trends, draft detailed project outlines, and convert long data reports into beautiful slide decks in seconds. It bridges the gap between raw technological capability and real-world business output, ensuring your organization saves massive amounts of time while maintaining strict data governance.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-104 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwiHkOWS_ceUAxUAAAAAHQAAAAAQzgI\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-104\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-104\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"31\"\u003eAs our regional corporate sectors, financial institutions, and digital agencies rapidly adopt cloud productivity suites, maximizing daily output while reducing administrative overhead has become a critical priority. 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The authors point out a harsh economic reality: if any competitor can input the exact same prompt into the same public cloud model, that tool cannot provide a sustainable competitive advantage. It simply becomes a baseline utility. True value creation occurs when an enterprise treats its unique internal data as a critical strategic asset, feeding it through specialized architectural pipelines to drive real, long-term business growth.\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional economies, corporate groups, and financial sectors undergo a massive wave of digital transformation, executive boards are facing intense pressure to successfully integrate artificial intelligence into their business models. However, many local managing directors, operations leaders, and technology consultants are finding that their early AI projects are stalling out as expensive, disconnected experiments that fail to deliver a clear, measurable return on investment.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Corporate Leadership.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47229260660921,"sku":null,"price":300.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/AI_Value_Creators.jpg?v=1779304291"},{"product_id":"building-ai-powered-products","title":"Building AI-Powered Products: The Essential Guide to AI and GenAI Product Management by Marily Nika","description":"\u003ch2\u003eBuilding AI-Powered Products: The Essential Guide to AI and GenAI Product Management by Marily Nika\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eBuilding AI-Powered Products\u003c\/i\u003e is that traditional, deterministic software product management frameworks are completely broken when applied to artificial intelligence. In classical software development, a product manager (PM) maps out explicit \"if\/then\" features, engineers write deterministic code, and the system behaves exactly the same way every single time. AI systems, however, are inherently \u003cb data-path-to-node=\"6\" data-index-in-node=\"416\"\u003eprobabilistic\u003c\/b\u003e—they rely on statistical data patterns, continuously evolve based on user interactions, and occasionally output unexpected predictions. Dr. Nika argues that to survive this shift, modern product leaders must completely retrain their mental models to manage uncertainty, data dependency, and evolving model accuracy.\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional tech startups, software engineering agencies, and digital product labs push hard to capture global market share, local product managers and founders face a massive challenge. While any developer can quickly wire up a basic chat wrapper API, local product teams are discovering that these projects routinely fail in production because they lack a systematic, strategic framework to calculate ROI, handle model non-determinism, and measure true user value.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Technology Strategy.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\" id=\"p-rc_514e3dad861ee110-389\"\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47229264494777,"sku":null,"price":270.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/228023948.jpg?v=1779304520"},{"product_id":"building-ai-agent-platforms","title":"Building AI Agent Platforms by Ben O'Mahony, Fabian Nonnenmacher","description":"\u003ch2\u003eBuilding AI Agent Platforms by Ben O'Mahony, Fabian Nonnenmacher\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core philosophy of \u003ci data-path-to-node=\"6\" data-index-in-node=\"23\"\u003eBuilding AI Agent Platforms\u003c\/i\u003e is that individual AI agents are highly impressive in a vacuum, but \u003cb data-path-to-node=\"6\" data-index-in-node=\"119\"\u003ethe organizations that will dominate the market are those that can manage them at scale.\u003c\/b\u003e The authors address a critical bottleneck across the modern technology landscape: building a simple script with an open-source framework is relatively straightforward, but deploying and managing AI to build, test, and maintain corporate software systems is an entirely different battle. When an organization scales up from a few experimental sandboxes to thousands of active agents modifying data paths simultaneously, standard IT infrastructures crumble under unpredictable runtime control flows, massive token costs, and catastrophic security vulnerabilities.\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional technology corridors, offshore engineering ecosystems, and corporate software laboratories move rapidly from basic exploratory data analytics to building fully autonomous operational systems, technical leads are hitting a major wall. While intermediate programmers can easily spin up a basic LangChain or CrewAI template, enterprise projects routinely fail when shifting to production because they lack a systematic, enterprise-grade platform framework to handle unexpected agent loops, monitor data privacy, and control runaway cloud costs.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Platform Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47229273243833,"sku":null,"price":220.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/building_ai_agent_platforms.png?v=1779304684"},{"product_id":"an-illustrated-guide-to-ai-age","title":"An Illustrated Guide to AI Age by Maarten Grootendorst, Jay Alammar","description":"\u003ch2\u003eAn Illustrated Guide to AI Age by Maarten Grootendorst, Jay Alammar\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eAn Illustrated Guide to AI Agents\u003c\/i\u003e is that large language models are fundamentally static, forgetful text predictors, but \u003cb data-path-to-node=\"6\" data-index-in-node=\"140\"\u003ean AI agent is a dynamic, closed-loop engine that actively interacts with its environment.\u003c\/b\u003e Grootendorst and Alammar address a massive educational bottleneck in the modern tech landscape: while there are endless code tutorials teaching developers how to spin up simple agent frameworks, very few resources explain the \u003ci data-path-to-node=\"6\" data-index-in-node=\"457\"\u003ewhy\u003c\/i\u003e behind underlying agent behavior. When software teams build complex systems without an unshakeable mental model of how an LLM routes data to a tool or compresses context, their production loops inevitably break.\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional software ecosystems, university IT programs, and corporate tech startups shift away from simply calling public chat APIs toward building fully autonomous digital systems, developers are running into an optimization wall. While intermediate programmers can easily stack up basic code blocks, projects frequently stall out because teams lack a true structural intuition of how memory layers, reasoning prompts, and tool registries align under high traffic.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: AI Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47229279633593,"sku":null,"price":390.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/An_Illustrated_Guide_to_AI_Age.jpg?v=1779305139"},{"product_id":"prompting-guide-101","title":"Prompting Guide 101 by Gemini for Google Workspace ","description":"\u003ch2\u003ePrompting Guide 101 by Gemini for Google Workspace \u003c\/h2\u003e\n\u003cp\u003eThe core principle behind the \u003ci data-path-to-node=\"6\" data-index-in-node=\"30\"\u003ePrompting Guide 101\u003c\/i\u003e is that you do not need to be a formal prompt engineer to use generative AI effectively at work. \u003cspan class=\"citation-84\"\u003eGoogle shifts the focus away from technical jargon and treats prompts simply as \u003c\/span\u003e\u003cb data-path-to-node=\"6\" data-index-in-node=\"227\"\u003e\u003cspan class=\"citation-84 citation-end-84\"\u003econversation starters with your AI assistant.\u003csup class=\"superscript\" data-turn-source-index=\"2\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/b\u003e The book addresses a common friction point in corporate workflows: the average user tries to interact with an AI model using short, vague search queries (often fewer than nine words), which leads to generic, unhelpful responses. Google's internal data shows that the most successful and actionable AI completions come from collaborative prompts that average around 21 words, providing clear direction and setting explicit boundaries.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003eAs local corporate groups, creative marketing labs, and administrative departments rapidly upgrade to modern cloud setups, teams face an unexpected challenge. While buying an AI upgrade is easy, companies are finding that their employees often struggle to get real value out of the software, wasting time typing unclear queries that lead to uninspired or inaccurate results.\u003c\/p\u003e\n\u003cp data-path-to-node=\"32\"\u003e\u003ci data-path-to-node=\"32\" data-index-in-node=\"0\"\u003ePrompting Guide 101\u003c\/i\u003e serves as the perfect practical cure for digital transformation hurdles. 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LangChain solves this infrastructure crisis by providing a unified, modular framework of abstractions.\u003c\/p\u003e\n\u003cp data-path-to-node=\"6\"\u003eWritten by a founding engineer of the framework itself, this book skips superficial prompt-engineering tricks and dives straight into structural software engineering. \u003cspan class=\"citation-123\"\u003eIt introduces readers to the \u003c\/span\u003e\u003cb data-path-to-node=\"7\" data-index-in-node=\"196\"\u003e\u003cspan class=\"citation-123\"\u003eLangChain Expression Language (LCEL)\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-123 citation-end-123\"\u003e—a powerful, declarative syntax built to handle streaming data, parallel execution steps, and automatic fallback behaviors seamlessly.\u003csup class=\"superscript\" data-turn-source-index=\"3\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-122 citation-end-122\"\u003eThrough clear, side-by-side Python and JavaScript architectures, the text guides developers through building data indexing systems, complex Retrieval-Augmented Generation (RAG) loops, and stateful multi-actor agent networks using LangGraph.\u003csup class=\"superscript\" data-turn-source-index=\"4\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional software export firms, tech startups, and freelance engineering ecosystems rush to fulfill the massive international demand for production-grade artificial intelligence systems, backend developers are hitting a major architectural wall. 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By laying out clear, concrete blueprints for LCEL pipeline composition, automated vector indexing, and stateful LangGraph agent orchestrations in both Python and JavaScript, it equips local systems engineers, full-stack developers, and technical architects with the practical expertise required to build resilient, global-scale AI applications. It is a mandatory desktop companion for the modern software engineer.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: AI Engineering\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\" id=\"p-rc_22239127ed1c9f4c-66\"\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\" id=\"p-rc_22239127ed1c9f4c-62\"\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47231420268729,"sku":null,"price":290.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Learning_LangChain_Building_AI_and_LLM_Applications_with_LangChain_and_LangGraph.jpg?v=1779345066"},{"product_id":"ai-assisted-programming","title":"AI-Assisted Programming: Better Planning, Coding, Testing, and Deployment by Tom Taulli","description":"\u003ch2\u003eAI-Assisted Programming: Better Planning, Coding, Testing, and Deployment by Tom Taulli\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe central thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"22\"\u003eAI-Assisted Programming\u003c\/i\u003e is that \u003cb data-path-to-node=\"6\" data-index-in-node=\"54\"\u003ethe role of the software developer is rapidly shifting from a syntax writer to a system architect.\u003c\/b\u003e Taulli addresses an anxious bottleneck spreading through the global tech workforce: many developers fear being replaced by generative AI models, while others dismiss the technology entirely due to model hallucinations and poor, unsecure code suggestions. The book outlines a pragmatic middle ground, proving that developers who learn to master context orchestration, prompt patterns, and real-time model auditing will easily outpace those who resist the change.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eInstead of filling the pages with abstract machine learning math, Taulli provides an immediate, project-based curriculum centered around dominant industry tools like \u003cb data-path-to-node=\"7\" data-index-in-node=\"166\"\u003eGitHub Copilot\u003c\/b\u003e, \u003cb data-path-to-node=\"7\" data-index-in-node=\"182\"\u003eChatGPT\u003c\/b\u003e, \u003cb data-path-to-node=\"7\" data-index-in-node=\"191\"\u003eCursor\u003c\/b\u003e, and \u003cb data-path-to-node=\"7\" data-index-in-node=\"203\"\u003eAmazon Q\u003c\/b\u003e. The text systematically breaks down how to feed clear context to an IDE (Integrated Development Environment) extension, write precise coding prompts, auto-generate robust unit tests, quickly decipher massive legacy codebases, and safely handle security vulnerabilities. By teaching engineers how to strategically guide the AI, Taulli helps them automate tedious boilerplate tasks so they can focus on high-level system design.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3878679226-64 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjP-v3438mUAxUAAAAAHQAAAAAQ0wE\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3878679226-64\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3878679226-64\"\u003e\n\u003c!----\u003e\n\u003cpre class=\"ng-tns-c3878679226-64\"\u003e\u003ccode role=\"text\" data-test-id=\"code-content\" class=\"code-container formatted ng-tns-c3878679226-64 no-decoration-radius\"\u003e\u003c\/code\u003e\u003c\/pre\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional software export firms, tech startups, and freelance developer communities face intense global pressure to ship software faster and more cost-effectively, engineers are hitting a major productivity wall. Many local developers waste significant portions of their day writing mundane boilerplate scripts, hunting down basic runtime errors, or rewriting undocumented legacy systems.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eAI-Assisted Programming\u003c\/i\u003e provides the perfect, practical cure for this development bottleneck. Tom Taulli skips theoretical hype to deliver a completely hands-on, tool-focused guide. By providing clear, step-by-step recipes for configuring IDE extensions, writing optimized code prompts, and automating testing frameworks, this book gives system engineers, full-stack developers, and technical leads the exact skills needed to eliminate development friction and dramatically accelerate their release cycles. It is a mandatory desktop reference for any modern builder.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Software Engineering.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cpre class=\"ng-tns-c3878679226-64\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/pre\u003e\n\u003cdiv class=\"code-block ng-tns-c3878679226-64 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjP-v3438mUAxUAAAAAHQAAAAAQ0wE\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3878679226-64\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3878679226-64\"\u003e\n\u003c!----\u003e\n\u003cpre class=\"ng-tns-c3878679226-64\"\u003e\u003ccode role=\"text\" data-test-id=\"code-content\" class=\"code-container formatted ng-tns-c3878679226-64 no-decoration-radius\"\u003e\u003c\/code\u003e\u003c\/pre\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47231479152825,"sku":null,"price":280.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/AI-Assisted_Programming.jpg?v=1779345307"},{"product_id":"hackers-guide-to-machine-learning-in-python","title":"Hacker's Guide to Machine Learning in Python by Venelin Valkov","description":"\u003ch2\u003eHacker's Guide to Machine Learning in Python by Venelin Valkov\u003cbr\u003e\n\u003c\/h2\u003e\n\u003cp\u003eThe core philosophy of the \u003ci data-path-to-node=\"6\" data-index-in-node=\"27\"\u003eHacker's Guide to Machine Learning with Python\u003c\/i\u003e is that \u003cb data-path-to-node=\"6\" data-index-in-node=\"82\"\u003ethe fastest way to master machine learning is by breaking and fixing code in real-world scenarios.\u003c\/b\u003e Valkov targets a universal frustration in technical education: many textbooks bog developers down in abstract linear algebra formulas and statistical calculus before they ever write a line of code. For a practical programmer (\"hacker\"), this creates a wall. Valkov flips this model completely upside down. \u003cspan class=\"citation-159 citation-end-159\"\u003eEvery single chapter begins with a messy, real-world problem—such as predicting highly volatile cryptocurrency prices, recognizing specific text intents, or detecting credit card anomalies—and uses Python libraries to engineer an immediate solution.\u003csup class=\"superscript\" data-turn-source-index=\"1\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional software development hubs, offshore engineering groups, and independent tech labs work to upgrade their service rosters from standard web and mobile applications to AI-integrated enterprise software, engineers are hitting a steep learning curve. Many local programmers want to add deep learning capabilities to their toolkits but find themselves trapped in academic choice paralysis or confused by math-heavy lecture series that offer little guidance on real-world coding.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Applied Machine Learning.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47231495635129,"sku":null,"price":290.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/c2309777-099c-4174-adb7-1af017fad840.jpg?v=1779556162"},{"product_id":"web-performance-engineering-in-the-age-of-ai","title":"Web Performance Engineering in the Age of AI: Mastering Speed and Quality for AI-Generated Applications by Addy Osmani","description":"\u003ch2\u003eWeb Performance Engineering in the Age of AI: Mastering Speed and Quality for AI-Generated Applications by Addy Osmani\u003c\/h2\u003e\n\u003cp\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eWeb Performance Engineering in the Age of AI\u003c\/i\u003e is that \u003cb data-path-to-node=\"6\" data-index-in-node=\"72\"\u003eAI coding assistants are highly efficient at generating code that is functional, but they are fundamentally blind to runtime performance.\u003c\/b\u003e Osmani addresses what he terms the \"Performance Paradox\": software teams are using AI to ship features faster than ever, yet they are inadvertently introducing massive technical debt. \u003cspan class=\"citation-218 citation-end-218\"\u003eAI models lean heavily on repetitive boilerplate, generate bloated JavaScript bundles, and build complex component structures that ignore critical browser rendering patterns.\u003csup class=\"superscript\" data-turn-source-index=\"4\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e When pushed to production at scale, these unoptimized patterns cause layout shifts, delay user interactions, and compromise the user experience.\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our local software export houses, tech startups, and full-stack development agencies lean heavily into automated coding tools to meet aggressive international deadlines, teams are hitting a severe technical wall. While junior and senior engineers alike can easily generate complete web interfaces using AI prompts, projects are routinely stalling out or facing client rejections upon deployment because the raw, unvetted code collapses under real-world traffic, resulting in sluggish load times and erratic layout behaviors.\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eWeb Performance Engineering in the Age of AI\u003c\/i\u003e provides the exact, code-forward cure the industry needs right now. \u003cspan class=\"citation-212 citation-end-212\"\u003eAddy Osmani bridges his unparalleled, decade-long legacy on the Google Chrome team with his cutting-edge insights as an AI director to deliver an incredibly practical, masterfully written field guide.\u003csup class=\"superscript\" data-turn-source-index=\"10\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e By providing clear, step-by-step blueprints for diagnosing bundle bloat, profiling main-thread bottlenecks, and building a performance-first engineering culture, this book equips systems developers, frontend architects, and engineering managers with the precise skills needed to ship blazing-fast, stable, and highly responsive modern applications. It is an absolute must-read manual for the next generation of web engineering.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Web Performance Optimization.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\" id=\"p-rc_52bbd1307dca6cf2-109\"\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47231503368377,"sku":null,"price":270.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/247940561.jpg?v=1779345726"},{"product_id":"agentic-design-patterns","title":"Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems by Antonio Gulli","description":"\u003ch2\u003eAgentic Design Patterns: A Hands-On Guide to Building Intelligent Systems by Antonio Gulli\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe central thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"22\"\u003eAgentic Design Patterns\u003c\/i\u003e is that \u003cb data-path-to-node=\"6\" data-index-in-node=\"54\"\u003ebuilding an effective autonomous system requires structure and design, not just a more powerful foundation model.\u003c\/b\u003e Gullí addresses an industry-wide frustration: while intermediate developers can easily orchestrate a basic single-call prompt or tool-calling loop, those scripts inevitably break down when exposed to chaotic, real-world data at scale. Without structural engineering patterns—like transaction safety, fallback logic, and intentional memory management—autonomous agents risk falling into endless execution loops, exhausting their token contexts, or generating unvetted database writes.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eGullí cures this architectural uncertainty by breaking down autonomous software design into 21 discrete, battle-tested patterns. To ground these theoretical principles, the book provides functional, hands-on code notebooks written across three premier execution environments: \u003cb data-path-to-node=\"7\" data-index-in-node=\"276\"\u003eLangChain\/LangGraph\u003c\/b\u003e for handling complex state sequences, \u003cb data-path-to-node=\"7\" data-index-in-node=\"334\"\u003eCrewAI\u003c\/b\u003e for orchestrating multi-role collaboration networks, and \u003cb data-path-to-node=\"7\" data-index-in-node=\"398\"\u003eGoogle’s Agent tooling\u003c\/b\u003e. By demonstrating patterns uniformly across these distinct frameworks, Gullí teaches systems engineers how to separate abstract agentic logic from underlying code tools, allowing them to build resilient, self-correcting digital entities.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3878679226-86 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjP-v3438mUAxUAAAAAHQAAAAAQxwI\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3878679226-86\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3878679226-86\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional software development firms, enterprise IT hubs, and advanced tech startups move aggressively away from basic AI wrappers toward shipping multi-step autonomous workflows, development teams are running into a production wall. 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This creates a steep learning curve and causes user fatigue, as humans project unrealistic capabilities onto the software and end up frustrated by unexpected model errors.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eRather than approaching design as a purely aesthetic layout task, Macfadyen focuses heavily on closing the \"conceptual distance\" between what a language model actually does and what a user imagines it can do. The book outlines a highly tactical, three-part \u003cb data-path-to-node=\"7\" data-index-in-node=\"257\"\u003eInput-Computation-Output framework\u003c\/b\u003e. It provides real-world design patterns for managing user attention during high-latency deep reasoning tasks, creating clear \"affordances\" (visual cues) that show users how to modify prompts, establishing strict content moderation warnings, and implementing \"interruptibility\" controls that let users stop a running agentic task mid-stream before it writes to a database.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3878679226-93 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjP-v3438mUAxUAAAAAHQAAAAAQ5QI\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3878679226-93\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3878679226-93\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs local software agencies, digital design firms, and enterprise product labs rapidly add generative features to their client offerings, product teams are running into a serious usability crisis. While developers can easily connect an app to a model API, projects are frequently struggling during user testing because customers find plain chat boxes confusing, get frustrated by long loading delays, or abandon tools due to a lack of clear visual guidance.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eDesigning AI Interfaces\u003c\/i\u003e offers the exact, practice-oriented solution our market needs right now. Louise Macfadyen combines her extensive experience on Google's Material Design and Microsoft's Fluent AI teams to deliver a beautiful, highly actionable field manual. By providing clear UI blueprints for handling component latency, visualizing model thought patterns, and designing safe autonomous workflows, this book arms UX designers, product managers, and frontend developers with the precise skills needed to ship engaging, accessible, and user-friendly software. It is an indispensable guide for anyone shaping the future of digital interaction.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Human-Computer Interaction (HCI) .\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47231518867641,"sku":null,"price":250.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/243354865.jpg?v=1779346113"},{"product_id":"grokking-ai-algorithms-second-edition","title":"Grokking AI Algorithms, Second Edition: How AI solves complex problems by Rishal Hurbans","description":"\u003ch2\u003eGrokking AI Algorithms, Second Edition: How AI solves complex problems by Rishal Hurbans\u003c\/h2\u003e\n\u003cp data-path-to-node=\"7\"\u003eThe core philosophy of \u003ci data-path-to-node=\"7\" data-index-in-node=\"23\"\u003eGrokking AI Algorithms\u003c\/i\u003e is that \u003cb data-path-to-node=\"7\" data-index-in-node=\"54\"\u003eif you can visualize a concept, you can program it.\u003c\/b\u003e Hurbans addresses a massive structural problem in computer science education: most machine learning and AI literature drops readers directly into a sea of dense multivariable calculus, linear algebra, and complex greek notation (\u003cspan class=\"math-inline\" data-math=\"W^T X + b\" data-index-in-node=\"335\"\u003e$W^T X + b$\u003c\/span\u003e). For many self-taught software engineers and system developers, this dense theoretical barrier turns practical engineering into a black box. This book solves that issue by breaking down advanced computational logic into clear, relatable concepts—like comparing a neural network's weights to adjusting knobs on an audio mixer.\u003c\/p\u003e\n\u003cp data-path-to-node=\"8\"\u003eRather than treating AI as a synonym for Large Language Models, the second edition explores the full spectrum of artificial intelligence. It takes developers on a journey through foundational search algorithms, evolutionary computing, swarm intelligence, optimization routines, and deep neural networks. By showing readers how to build these complex engines from scratch using standard Python code (without hiding behind monolithic cloud APIs), Hurbans transforms abstract data patterns into tangible, line-by-line coding logic.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs our regional software export firms, tech startups, and university computing departments push to upgrade their skills for the international AI market, developers are hitting a major academic wall. Many local programmers want to expand their expertise into machine learning, but they feel discouraged by dry, math-heavy textbooks that offer little guidance on how to write actual code.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003eGrokking AI Algorithms (2nd Edition)\u003c\/i\u003e provides the perfect, visual remedy for this training gap. Rishal Hurbans skips academic pretense to deliver an incredibly accessible, project-driven masterclass. By laying out clear, visual blueprints for pathfinding routines, evolutionary computing, and neural network mechanics using plain Python, this book equips system developers, full-stack engineers, and tech leads with the fundamental expertise needed to confidently build intelligent applications. It is a mandatory addition to the desk of any modern programmer.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Artificial Intelligence Foundations.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47231542689977,"sku":null,"price":450.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Grokking_AI_Algorithms_Second_Edition.jpg?v=1779346373"},{"product_id":"a-beginner-s-guide-to-generative-ai","title":"A Beginner’s Guide to Generative AI: An Introductory Path to Diffusion Models, ChatGPT, and LLMs by Deepshikha Bhati, Fnu Neha, Angela Guercio, Md Amiruzzaman, Aloysius Bathi Kasturiarachi","description":"\u003ch2\u003eA Beginner’s Guide to Generative AI: An Introductory Path to Diffusion Models, ChatGPT, and LLMs by Deepshikha Bhati, Fnu Neha, Angela Guercio, Md Amiruzzaman, Aloysius Bathi Kasturiarachi\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core philosophy of \u003ci data-path-to-node=\"6\" data-index-in-node=\"23\"\u003eA Beginner's Guide to Generative AI\u003c\/i\u003e is that \u003cb data-path-to-node=\"6\" data-index-in-node=\"67\"\u003eunderstanding modern AI requires trace-level evolutionary context, not just simple interface prompting.\u003c\/b\u003e The authors address a critical gap in contemporary tech education: while many entry-level guides focus exclusively on writing prompts inside a web browser, and advanced manuals jump straight into deep vector matrices, beginners are left without a firm grasp of underlying model mechanics. This textbook demystifies the technology by breaking down exactly how machines learn to generate coherent text and pixels, treating generative AI as a natural progression of neural networks.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eRather than limiting the discussion to a single tool or company, the book provides a sweeping overview of the modern generative spectrum. It takes readers from early probabilistic language approaches (like n-grams) up through \u003cb data-path-to-node=\"7\" data-index-in-node=\"226\"\u003eTransformers\u003c\/b\u003e, the multi-layered \u003cb data-path-to-node=\"7\" data-index-in-node=\"258\"\u003eChatGPT architecture\u003c\/b\u003e, \u003cb data-path-to-node=\"7\" data-index-in-node=\"280\"\u003eGoogle's Gemini ecosystem\u003c\/b\u003e, and deep \u003cb data-path-to-node=\"7\" data-index-in-node=\"316\"\u003eDiffusion Models\u003c\/b\u003e for visual processing. By pairing clear architectural breakdowns with real-world industry case studies across healthcare, legal technology, and business analytics, the authors ensure readers build a solid conceptual foundation capable of adapting to rapid industry shifts.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3878679226-107 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjP-v3438mUAxUAAAAAHQAAAAAQngM\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3878679226-107\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3878679226-107\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional universities, computing academies, and tech upskilling centers scramble to update their computer science curricula for an increasingly automated market, students are facing a difficult training gap. While there is plenty of online material, it tends to split into two extremes: trivial clickbait tutorials that teach very little, or intense research papers packed with advanced multivariable calculus that overwhelm newcomers.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eA Beginner's Guide to Generative AI\u003c\/i\u003e provides the exact structural bridge our academic programs need. Deepshikha Bhati and her co-authors balance academic rigor with welcoming clarity. By breaking down the complex mechanics of LLMs, ChatGPT, and image generation into clear, manageable concepts, this Springer text gives students, junior developers, and tech managers the precise conceptual framework required to confidently enter the field of AI engineering. It is an indispensable guide for any modern technical classroom.\u003cspan\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Foundational Computer Science..\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47231769411769,"sku":null,"price":250.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/A_Beginner_s_Guide_to_Generative_AI.jpg?v=1779350925"},{"product_id":"financial-data-engineering","title":"Financial Data Engineering: Design and Build Data-Driven Financial Products by Tamer Khraisha","description":"\u003ch2\u003eFinancial Data Engineering: Design and Build Data-Driven Financial Products by Tamer Khraisha\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe central thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"22\"\u003eFinancial Data Engineering\u003c\/i\u003e is that \u003cb data-path-to-node=\"6\" data-index-in-node=\"57\"\u003efinancial data is fundamentally different from generic web or enterprise data, requiring unique infrastructure safeguards and deep domain context.\u003c\/b\u003e Khraisha targets a structural failure point in many modern fintech startups and banking systems: engineers try to apply standard, off-the-shelf data-intensive patterns to financial pipelines, completely neglecting the domain's strict rules around time-series synchronicity, highly fragmented identification schemas, and rigid regulatory compliance. 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These traditional methodologies frequently trap traders in \"the confidence game,\" spitting out precise numeric predictions (point estimates) that look highly accurate on paper but collapse during structural market shifts because they treat probability as a long-run limiting frequency rather than an evolving state of knowledge.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eInstead of hiding behind abstract, monolithic black boxes, Kanungo shifts the paradigm toward Bayesian frameworks and generative ensembles. He shows developers how to treat uncertainties as core features of the system by outputting comprehensive probability distributions. The book walks readers through encoding subjective, empirical, and institutional knowledge directly into models, utilizing \u003cb data-path-to-node=\"7\" data-index-in-node=\"396\"\u003eMarkov Chain Monte Carlo (MCMC)\u003c\/b\u003e simulations, and deploying \u003cb data-path-to-node=\"7\" data-index-in-node=\"455\"\u003eMetropolis Sampling\u003c\/b\u003e routines to map earnings expectations and portfolio default risks. This approach enables financial systems to act with \"uncertainty awareness\"—explicitly flagging when market shifts have rendered their predictions unreliable.\u003ccode role=\"text\" data-test-id=\"code-content\" class=\"code-container formatted ng-tns-c3878679226-121 no-decoration-radius\"\u003e\u003c\/code\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3878679226-121 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjP-v3438mUAxUAAAAAHQAAAAAQ0gM\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3878679226-121\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3878679226-121\"\u003e\n\u003c!----\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional fintech developers, quantitative investment houses, and algorithmic prop-trading groups build automated systems to compete in international financial markets, engineering teams are hitting a severe operational wall. While developers can easily train standard predictive models on historical price charts, these systems regularly collapse during real-world market shocks because their underlying code is blind to tail risk and structural environment shifts.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eProbabilistic Machine Learning for Finance and Investing\u003c\/i\u003e provides the exact, code-forward solution our trading infrastructure requires. Deepak K. Kanungo perfectly balances his decades of real-world derivatives trading with highly practical Python development patterns. By cutting through academic pretense and providing clear, line-by-line implementations of Monte Carlo paths, Bayesian logic, and MCMC networks, this book gives financial software engineers, quantitative analysts, and systems architects the precise tools needed to ship highly resilient, self-monitoring, and risk-managed platforms. It is a mandatory addition to any serious financial engineering library.\u003c\/p\u003e\n\u003csection id=\"shopify-section-template--21334189375673__main\" class=\"shopify-section section\"\u003e\n\u003cdiv class=\"page-width\"\u003e\n\u003cdiv class=\"product product--medium product--left product--thumbnail product--mobile-hide grid grid--1-col grid--2-col-tablet\"\u003e\n\u003cdiv class=\"product__info-wrapper grid__item scroll-trigger animate--slide-in\"\u003e\n\u003csection id=\"ProductInfo-template--21334189375673__main\" class=\"product__info-container product__column-sticky\"\u003e\n\u003cdiv class=\"product__description rte quick-add-hidden\"\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-108 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQ-gI\"\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-108\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-108\"\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: \u003cb data-path-to-node=\"32,1,0\" data-index-in-node=\"0\"\u003e:\u003c\/b\u003e Quantitative Finance Architecture.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv id=\"shopify-block-AbTNjUDdGSldnOEd5a__judge_me_reviews_preview_badge_MPK6Xc\" class=\"shopify-block shopify-app-block\"\u003e\u003c\/div\u003e\n\u003c\/section\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/section\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47231833997497,"sku":null,"price":250.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Probabilistic_Machine_Learning_for_Finance_and_Investing.jpg?v=1779351322"},{"product_id":"vibe-coding-for-beginners-with-python-and-chatgpt","title":"Vibe Coding for Beginners with Python and ChatGPT by Greg Lim","description":"\u003ch2\u003eVibe Coding for Beginners with Python and ChatGPT by Greg Lim\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe core philosophy of \u003ci data-path-to-node=\"6\" data-index-in-node=\"23\"\u003eVibe Coding for Beginners with Python \u0026amp; ChatGPT\u003c\/i\u003e is that \u003cb data-path-to-node=\"6\" data-index-in-node=\"79\"\u003ethe ultimate programming language is no longer Python, Java, or C++—it is plain English.\u003c\/b\u003e Lim addresses a classic bottleneck in technology education: millions of aspiring creators want to build software, but they get immediately discouraged by missing semicolons, confusing indentation errors, and the intimidating learning curve of traditional syntax. \"Vibe coding\" shifts the developer's role from a low-level code typewriter to a high-level creative director.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eRather than teaching programming through boring abstract theory, Lim guides readers through building real, working projects immediately. The book shows how to use ChatGPT as an interactive pair-programmer to generate clean Python scripts, build user interfaces, scrape web data, and automate repetitive office tasks. By teaching readers how to clearly communicate intent, handle model hallucinations, and break complex apps down into simple conversational prompts, Lim transforms raw software ideas into production-ready code with minimal friction.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3878679226-128 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjP-v3438mUAxUAAAAAHQAAAAAQ7QM\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3878679226-128\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3878679226-128\"\u003e\n\u003c!----\u003e\n\u003cpre class=\"ng-tns-c3878679226-128\"\u003e\u003ccode role=\"text\" data-test-id=\"code-content\" class=\"code-container formatted ng-tns-c3878679226-128 no-decoration-radius\"\u003e\u003c\/code\u003e\u003cbr\u003e\u003c\/pre\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional entrepreneurship hubs, non-technical business professionals, and digital design studios rush to build custom software, prototypes, and automated business tools, they frequently hit a hard technical wall. Hiring expensive software agencies is out of reach for many, and learning traditional programming from scratch takes more time than busy professionals can afford.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eVibe Coding for Beginners with Python \u0026amp; ChatGPT\u003c\/i\u003e provides the ultimate, project-driven solution for this modern skill gap. Greg Lim skips dense, dry academic lectures to deliver an incredibly friendly, welcoming, and high-velocity field guide. By packing every single chapter with practical, real-world mini-apps—from automated data processors to sleek custom interfaces—this book equips absolute beginners, product managers, and creative professionals with the immediate confidence and skill required to build automated software tools overnight. It is a mandatory playbook for the modern digital workspace.\u003c\/p\u003e\n\u003csection id=\"shopify-section-template--21334189375673__main\" class=\"shopify-section section\"\u003e\n\u003cdiv class=\"page-width\"\u003e\n\u003cdiv class=\"product product--medium product--left product--thumbnail product--mobile-hide grid grid--1-col grid--2-col-tablet\"\u003e\n\u003cdiv class=\"product__info-wrapper grid__item scroll-trigger animate--slide-in\"\u003e\n\u003csection id=\"ProductInfo-template--21334189375673__main\" class=\"product__info-container product__column-sticky\"\u003e\n\u003cdiv class=\"product__description rte quick-add-hidden\"\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-108 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQ-gI\"\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-108\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-108\"\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: AI-Assisted Programming.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv id=\"shopify-block-AbTNjUDdGSldnOEd5a__judge_me_reviews_preview_badge_MPK6Xc\" class=\"shopify-block shopify-app-block\"\u003e\u003c\/div\u003e\n\u003c\/section\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/section\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47231898714297,"sku":null,"price":250.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Vibe_Coding_for_Beginners_with_Python_and_ChatGPT.jpg?v=1779351580"},{"product_id":"ai-systems-performance-engineering","title":"AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch by Chris Fregly","description":"\u003ch2\u003eAI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch by Chris Fregly\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003e\u003cspan class=\"\"\u003eThe core thesis of \u003c\/span\u003e\u003ci data-path-to-node=\"6\" data-index-in-node=\"19\" class=\"\"\u003eAI Systems Performance Engineering\u003c\/i\u003e\u003cspan class=\"\"\u003e is that \u003c\/span\u003e\u003cb data-path-to-node=\"6\" data-index-in-node=\"62\" class=\"\"\u003ethrowing more hardware at an AI workload is an expensive, fundamentally flawed strategy; true scalability requires hardware, software, and algorithmic co-design.\u003c\/b\u003e\u003cspan class=\"\"\u003e Fregly targets a widespread,\u003c\/span\u003e\u003cspan class=\"\"\u003e costly failure pattern across modern tech companies:\u003c\/span\u003e\u003cspan class=\"\"\u003e software teams regularly deploy massive generative or neural network models using standard,\u003c\/span\u003e\u003cspan class=\"\"\u003e high-level code configurations without considering memory bandwidth limits,\u003c\/span\u003e\u003cspan class=\"\"\u003e compute bound states,\u003c\/span\u003e\u003cspan class=\"\"\u003e or data communication delays.\u003c\/span\u003e\u003cspan class=\"\"\u003e This results in massive server bills,\u003c\/span\u003e\u003cspan class=\"\"\u003e low hardware utility,\u003c\/span\u003e\u003cspan class=\"\"\u003e and major inference delays.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"\"\u003eInstead of presenting high-level conceptual summaries or basic cloud service guides,\u003c\/span\u003e\u003cspan class=\"\"\u003e Fregly digs deeply into low-level infrastructure tuning.\u003c\/span\u003e\u003cspan class=\"\"\u003e He teaches engineers to look past standard processing speeds to analyze \"goodput\"—the actual volume of useful data a system processes per unit of time.\u003c\/span\u003e\u003cspan class=\"\"\u003e The book provides intensive coverage on diagnosing bottlenecks using advanced diagnostic tools like NVIDIA Nsight Systems and PyTorch Profiler.\u003c\/span\u003e\u003cspan class=\"\"\u003e It shows readers how to bypass restrictive C++ boilerplate using modern compiler stacks like \u003c\/span\u003e\u003cb data-path-to-node=\"7\" data-index-in-node=\"531\" class=\"\"\u003eOpenAI Triton\u003c\/b\u003e\u003cspan class=\"\"\u003e to write high-impact custom GPU kernels,\u003c\/span\u003e\u003cspan class=\"\"\u003e handle memory layouts effectively,\u003c\/span\u003e\u003cspan class=\"\"\u003e and remove processing delays across large-scale distributed computing systems.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c!----\u003e\u003c\/p\u003e\n\u003cdiv class=\"code-block ng-tns-c3878679226-132 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjP-v3438mUAxUAAAAAHQAAAAAQggQ\"\u003e\n\u003c!----\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3878679226-132\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3878679226-132\"\u003e\n\u003c!----\u003e\n\u003cpre class=\"ng-tns-c3878679226-132\"\u003e\u003ccode role=\"text\" data-test-id=\"code-content\" class=\"code-container formatted ng-tns-c3878679226-132 no-decoration-radius\"\u003e\u003c\/code\u003e\u003cbr\u003e\u003c\/pre\u003e\n\u003cp data-path-to-node=\"29\"\u003e\u003cspan class=\"\"\u003eAs regional software infrastructure houses,\u003c\/span\u003e\u003cspan class=\"\"\u003e dedicated AI cloud startups,\u003c\/span\u003e\u003cspan class=\"\"\u003e and offshore engineering operations aggressively spin up large-scale machine learning systems,\u003c\/span\u003e\u003cspan class=\"\"\u003e engineering groups are hitting an intense financial and operational barrier.\u003c\/span\u003e\u003cspan class=\"\"\u003e While developers can effortlessly build and test models on small,\u003c\/span\u003e\u003cspan class=\"\"\u003e localized training data,\u003c\/span\u003e\u003cspan class=\"\"\u003e moving those systems into production clusters frequently results in massive cloud expenses,\u003c\/span\u003e\u003cspan class=\"\"\u003e slow system responsiveness,\u003c\/span\u003e\u003cspan class=\"\"\u003e and broken data flows due to underlying code bottlenecks.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\" class=\"\"\u003eAI Systems Performance Engineering\u003c\/i\u003e\u003cspan class=\"\"\u003e delivers the definitive,\u003c\/span\u003e\u003cspan class=\"\"\u003e low-level cure our industry requires.\u003c\/span\u003e\u003cspan class=\"\"\u003e Chris Fregly merges his unmatched history at tech giants like Netflix and AWS with incredibly detailed code implementations.\u003c\/span\u003e\u003cspan class=\"\"\u003e By packing over a thousand pages with clear,\u003c\/span\u003e\u003cspan class=\"\"\u003e actionable PyTorch,\u003c\/span\u003e\u003cspan class=\"\"\u003e CUDA C++,\u003c\/span\u003e\u003cspan class=\"\"\u003e and Triton engineering examples—and providing an elite,\u003c\/span\u003e\u003cspan class=\"\"\u003e 200+ point optimization checklist—this book arms machine learning platform engineers,\u003c\/span\u003e\u003cspan class=\"\"\u003e infrastructure leads,\u003c\/span\u003e\u003cspan class=\"\"\u003e and systems architects with the precise skills needed to run massive models at maximum speed and lowest cost.\u003c\/span\u003e\u003cspan class=\"\"\u003e It is an indispensable desk reference for serious tech teams.\u003c\/span\u003e\u003c\/p\u003e\n\u003csection id=\"shopify-section-template--21334189375673__main\" class=\"shopify-section section\"\u003e\n\u003cdiv class=\"page-width\"\u003e\n\u003cdiv class=\"product product--medium product--left product--thumbnail product--mobile-hide grid grid--1-col grid--2-col-tablet\"\u003e\n\u003cdiv class=\"product__info-wrapper grid__item scroll-trigger animate--slide-in\"\u003e\n\u003csection id=\"ProductInfo-template--21334189375673__main\" class=\"product__info-container product__column-sticky\"\u003e\n\u003cdiv class=\"product__description rte quick-add-hidden\"\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-108 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQ-gI\"\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-108\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-108\"\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: \u003cb data-path-to-node=\"32,1,0\" data-index-in-node=\"0\"\u003e:\u003c\/b\u003e\u003cspan class=\"citation-296 citation-end-296\"\u003e High-Performance Computing.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv id=\"shopify-block-AbTNjUDdGSldnOEd5a__judge_me_reviews_preview_badge_MPK6Xc\" class=\"shopify-block shopify-app-block\"\u003e\u003c\/div\u003e\n\u003c\/section\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/section\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47231951405241,"sku":null,"price":650.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/231191100.jpg?v=1779351764"},{"product_id":"the-vibe-coding-playbook","title":"The Vibe Coding Playbook: Building Your Tech Business with AI by Siraj Raval","description":"\u003ch2 data-path-to-node=\"6\"\u003eThe Vibe Coding Playbook: Building Your Tech Business with AI by Siraj Raval\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003e\u003cspan class=\"\"\u003eThe core philosophy of \u003ci data-path-to-node=\"6\" data-index-in-node=\"23\"\u003eThe Vibe Coding Playbook\u003c\/i\u003e is that \u003cb data-path-to-node=\"6\" data-index-in-node=\"56\"\u003ein the age of autonomous code agents, speed and distribution beat deep syntax expertise every single time.\u003c\/b\u003eRaval addresses a monumental shift in the startup landscape:\u003c\/span\u003e\u003cspan class=\"\"\u003e the traditional bottleneck of launching a tech company—hiring expensive software engineering agencies or spending years mastering frameworks—has vanished.\u003c\/span\u003e\u003cspan class=\"\"\u003e Instead,\u003c\/span\u003e\u003cspan class=\"\"\u003e Raval teaches readers to view advanced AI code assistants (like Cursor,\u003c\/span\u003e\u003cspan class=\"\"\u003e GitHub Copilot,\u003c\/span\u003e\u003cspan class=\"\"\u003e or specialized LLMs) not merely as auto-complete plug-ins,\u003c\/span\u003e\u003cspan class=\"\"\u003e but as virtual \"technical co-founders.\u003c\/span\u003e\u003cspan class=\"\"\u003e\"\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003e\u003cspan class=\"\"\u003eRather than bogging readers down with abstract computer science history,\u003c\/span\u003e\u003cspan class=\"\"\u003e Raval jumps straight into the economics of the \"AI Gold Rush.\u003c\/span\u003e\u003cspan class=\"\"\u003e\" The book outlines how to find highly profitable niche problems,\u003c\/span\u003e\u003cspan class=\"\"\u003e generate functional MVPs (Minimum Viable Products) within 24 hours,\u003c\/span\u003e\u003cspan class=\"\"\u003e and set up rapid validation loops using digital waitlists and pre-orders.\u003c\/span\u003e\u003cspan class=\"\"\u003e By providing clear frameworks for automated data collection,\u003c\/span\u003e\u003cspan class=\"\"\u003e simple MLOps pipelines for non-coders,\u003c\/span\u003e\u003cspan class=\"\"\u003e and weekly continuous deployment routines,\u003c\/span\u003e\u003cspan class=\"\"\u003e Raval shows how to manage and scale a technology business while keeping your organization small,\u003c\/span\u003e\u003cspan class=\"\"\u003e fast,\u003c\/span\u003e\u003cspan class=\"\"\u003e and highly profitable.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our regional software innovation hubs, tech incubator centers, and ambitious business graduates look to launch products for the international market, they frequently encounter an expensive roadblock. While local founders possess brilliant business strategies and clear market insights, they are routinely held back by a lack of deep software engineering skills or the heavy capital needed to hire tech teams, leaving great ideas trapped on drawing boards.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eThe Vibe Coding Playbook\u003c\/i\u003e provides the ultimate, high-speed solution our entrepreneurial community needs. Siraj Raval strips away traditional development intimidation to deliver a beautifully clear, accessible, and deeply inspiring roadmap. By loading every chapter with practical prompt templates, explicit software stack decision trees, and real-world case studies of non-technical founders building major digital platforms, this book arms upcoming creators, product designers, and small business owners with the exact execution frameworks required to build real software wealth. It is a vital playbook for our modern digital storefronts.\u003c\/p\u003e\n\u003csection id=\"shopify-section-template--21334189375673__main\" class=\"shopify-section section\"\u003e\n\u003cdiv class=\"page-width\"\u003e\n\u003cdiv class=\"product product--medium product--left product--thumbnail product--mobile-hide grid grid--1-col grid--2-col-tablet\"\u003e\n\u003cdiv class=\"product__info-wrapper grid__item scroll-trigger animate--slide-in\"\u003e\n\u003csection id=\"ProductInfo-template--21334189375673__main\" class=\"product__info-container product__column-sticky\"\u003e\n\u003cdiv class=\"product__description rte quick-add-hidden\"\u003e\n\u003cdiv class=\"code-block ng-tns-c3299913081-108 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahgKEwjvvJOByMeUAxUAAAAAHQAAAAAQ-gI\"\u003e\n\u003cdiv class=\"formatted-code-block-internal-container ng-tns-c3299913081-108\"\u003e\n\u003cdiv class=\"animated-opacity ng-tns-c3299913081-108\"\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: AI Startup Strategy.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv id=\"shopify-block-AbTNjUDdGSldnOEd5a__judge_me_reviews_preview_badge_MPK6Xc\" class=\"shopify-block shopify-app-block\"\u003e\u003c\/div\u003e\n\u003c\/section\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/section\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47232237928633,"sku":null,"price":300.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/The_Vibe_Coding_Playbook.jpg?v=1779352209"},{"product_id":"vibe-coding-with-cursor-windsurf-and-lovable","title":"Vibe Coding with Cursor, Windsurf, and Lovable: Build Apps Fast with AI-Assisted Software Development and Testing by Greg Lim","description":"\u003ch2\u003eVibe Coding with Cursor, Windsurf, and Lovable: Build Apps Fast with AI-Assisted Software Development and Testing by Greg Lim\u003c\/h2\u003e\n\u003cp data-path-to-node=\"6\"\u003eThe central thesis of the book is that \u003cb data-path-to-node=\"6\" data-index-in-node=\"39\"\u003ethe technical mechanics of writing code have been fully abstracted away by AI agents; success now depends entirely on system orchestration, logical clear prompting, and architectural direction.\u003c\/b\u003e Lim addresses a massive shift occurring across the software engineering landscape. While previous tools merely offered basic autocomplete text generation, modern platforms act as autonomous engineering partners capable of editing multiple files, managing local environments, reading error terminals, and deploying complete web apps on their own.\u003c\/p\u003e\n\u003cp data-path-to-node=\"7\"\u003eInstead of bogging readers down with abstract computer science theory or legacy language rules, Lim provides a structured, multi-tool sandbox. He breaks down the unique strengths of the three industry-leading platforms: using \u003cb data-path-to-node=\"7\" data-index-in-node=\"226\"\u003eCursor\u003c\/b\u003e for elite repository-wide contextual intelligence, \u003cb data-path-to-node=\"7\" data-index-in-node=\"284\"\u003eWindsurf\u003c\/b\u003e for its deeply integrated, multi-file agent flows, and \u003cb data-path-to-node=\"7\" data-index-in-node=\"348\"\u003eLovable\u003c\/b\u003e for spinning up beautiful, production-ready full-stack frontends in seconds. By building real, functional projects—including automated dashboards, API integrators, and dynamic web apps—Lim teaches readers how to confidently direct an AI workforce from concept to live deployment.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\"\u003eAs regional software innovation hubs, tech startups, and freelance creators look to build products for the international market, they frequently run into major time and resource limits. Traditional software development pipelines can take months to produce a basic working prototype, leaving small teams struggling to keep up with fast-changing market demands.\u003c\/p\u003e\n\u003cp data-path-to-node=\"32\"\u003e\u003ci data-path-to-node=\"32\" data-index-in-node=\"0\"\u003eVibe Coding with Cursor, Windsurf, and Lovable\u003c\/i\u003e provides the high-velocity solution today's digital landscape demands. Greg Lim skips the dry, academic theories to deliver an incredibly direct, project-focused guide. By packing each chapter with clear prompt blueprints, real-world troubleshooting workflows, and step-by-step application exercises, this book equips absolute beginners, product managers, and experienced engineers alike with the skills to build apps at the speed of thought. It is an indispensable playbook for the modern developer's desk.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: AI-Assisted Programming.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47233613168825,"sku":null,"price":300.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Vibe_Coding_with_Cursor_Windsurf_and_Lovable.jpg?v=1779356233"},{"product_id":"vibe-coding-with-github-copilot","title":"Vibe Coding with GitHub Copilot: Enhancing Productivity by Leveraging GitHub Copilot Inside Visual Studio by Naga Santhosh Reddy Vootukuri","description":"\u003ch2\u003eVibe Coding with GitHub Copilot: Enhancing Productivity by Leveraging GitHub Copilot Inside Visual Studio by Naga Santhosh Reddy Vootukuri\u003c\/h2\u003e\n\u003cp\u003eThe core thesis of \u003ci data-path-to-node=\"6\" data-index-in-node=\"19\"\u003eVibe Coding with GitHub Copilot\u003c\/i\u003e is that \u003cb data-path-to-node=\"6\" data-index-in-node=\"59\"\u003eas natural language becomes the dominant syntax for building applications, a developer's success depends entirely on intent precision, context management, and architectural guardrails.\u003c\/b\u003e \u003cspan class=\"citation-389 citation-end-389\"\u003eVootukuri addresses an ironclad reality facing modern engineering teams: \"vibe coding\" (directing AI to write and modify complete files based on conversational prompts) dramatically accelerates initial prototyping but can lead to structural decay if executed blindly.\u003csup class=\"superscript\" data-turn-source-index=\"2\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e Without disciplined constraints, AI-generated systems can quickly accumulate massive hidden technical debt.\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"citation-388 citation-end-388\"\u003eRather than approaching AI pair programming as a casual hobbyist trick, Vootukuri views it through the lens of strict corporate software standards.\u003csup class=\"superscript\" data-turn-source-index=\"3\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-387\"\u003eCentered around the \u003c\/span\u003e\u003cb data-path-to-node=\"7\" data-index-in-node=\"168\"\u003e\u003cspan class=\"citation-387\"\u003eC# and .NET ecosystem\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-387 citation-end-387\"\u003e, the book details how to properly configure GitHub Copilot inside heavy-duty IDEs like Visual Studio and VS Code.\u003csup class=\"superscript\" data-turn-source-index=\"4\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e It provides step-by-step tutorials on leveraging Copilot's groundbreaking \u003cb data-path-to-node=\"7\" data-index-in-node=\"378\"\u003eAgent Mode\u003c\/b\u003e and the \u003cb data-path-to-node=\"7\" data-index-in-node=\"397\"\u003eModel Context Protocol (MCP)\u003c\/b\u003e, demonstrating how to securely feed domain-specific repositories into the model to generate enterprise-ready, well-tested, and maintainable software architectures.\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs regional software firms, local IT consulting operations, and financial tech systems groups aggressively adopt conversational artificial intelligence to speed up product lifecycles, dev teams are hitting a major architectural wall. While engineers can easily generate detached code snippets using basic chatbots, blending these quick shortcuts into deep enterprise applications frequently leads to broken data links, fragile systems, and hours spent debugging structural code.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003e\u003ci data-path-to-node=\"30\" data-index-in-node=\"0\"\u003eVibe Coding with GitHub Copilot\u003c\/i\u003e delivers the precise, production-ready solution today's professional developers require. Naga Santhosh Reddy Vootukuri expertly merges his unique experience inside Microsoft's Azure SQL engineering labs with highly practical, project-focused C# instruction. By packing every single section with explicit prompting formulas, detailed Agent Mode workflows, and actionable data-maintenance strategies, this Apress publication gives software engineers, technical leads, and solutions architects the exact blueprints needed to build clean, self-correcting, and hyper-efficient applications. It is a vital textbook for the modern developer's desk.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: AI-Assisted Programming.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47233616543929,"sku":null,"price":280.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Vibe_Coding_with_GitHub_Copilot.jpg?v=1779356457"}],"url":"https:\/\/royalbooksbd.com\/collections\/ai-technology.oembed?page=3","provider":"Royal Books BD","version":"1.0","type":"link"}