Geek Capsule

arxiv.org/abs/2507.13334v200:00:00 Introduction00:02:46 Related Work00:05:43 Why Context Engineering?00:09:36 Foundational Components00:17:52 System Implementations00:29:05 Evaluation00:35:30 Future Directions and Open Challenges00:41:13 Conclusion

Source: RESTful Web APIs by Leonard Richardson and Mike Amundsen with a Foreword by Sam Ruby00:00:00 Introduction00:01:57 Surfing the Web00:05:11 A Simple API00:08:46 Resources and Representations00:13:07 Hypermedia00:17:40 Domain-Specific Designs00:22:28 The Collection Pattern00:27:04 Pure-Hypermedia Designs00:31:48 Profiles00:36:34 The Design Procedure00:41:39 The Hypermedia Zoo00:46:18 HTTP for APIs00:50:51 Resource Description and Linked Data00:55:42 CoAP: REST for Embedded Systems01:00:19 Appendix C: An API Designer's Guide to the Fielding Dissertation01:04:43 Glossary

00:00:00 Introducing Knowledge Graphs00:03:15 Organizing Principles for Building Knowledge Graphs00:07:37 Graph Databases00:11:58 Loading Knowledge Graph Data00:16:08 Integrating Knowledge Graphs with Information Systems00:20:43 Enriching Knowledge Graphs with Data Science00:24:49 Graph-Native Machine Learning00:28:54 Mapping Data with Metadata Knowledge Graphs00:32:41 Identity Knowledge Graphs00:36:35 Pattern Detection Knowledge Graphs00:40:53 Dependency Knowledge Graphs00:45:31 Semantic Search and Similarity00:49:51 Talking to Your Knowledge Graph00:54:16 From Knowledge Graphs to Knowledge Lakes

00:00:00 Introduction00:05:40 Chapter 1 - Hierarchy is Everything00:13:41 Chapter 2 - Layout and Spacing00:20:30 Chapter 3 - Designing Text00:28:49 Chapter 4 - Working with Color00:36:34 Chapter 5 - Creating Depth00:42:04 Chapter 6 - Working with Images00:47:23 Chapter 7 - Finishing Touches00:54:09 Chapter 8 - Leveling Up

00:00:00 Introduction00:01:02 Discrete-Time Signals and Systems00:02:20 The z-Transform and Its Application to the Analysis of LTI Systems00:03:32 Frequency Analysis of Signals and Systems00:04:41 The Discrete Fourier Transform: Its Properties and Applications00:05:49 Efficient Computation of the DFT: Fast Fourier Algorithms00:06:50 Implementation of Discrete-Time Systems

00:00:00 Domain-Driven Design00:01:14 Concept and Principles00:03:56 Building Blocks of DDD00:07:06 Implementation of DDD00:10:13 Implementation of DDD00:10:33 DDD and Other Software Development Approaches00:12:05 Criticism and Limitations of DDD

00:00:00 What is AI Ethics?00:02:48 An Ethical Platform for a Responsible AI Project00:04:37 The SUM Values00:06:23 The FAST Track Principles00:13:59 Securing Responsible Delivery Through Human-Centered Implementation00:16:00 Conclusion

00:00:00 Introduction00:00:54 Definitions and Examples00:01:58 Paths and Cycles00:03:42 Trees00:05:00 Planarity00:06:23 Colouring Graphs00:07:29 Matching, Marriage, and Menger's Theorem00:08:51 Matroids

00:00:00 Elasticsearch00:02:58 Life Inside a Cluster00:05:51 Data In, Data Out00:09:35 Distributed Document Store00:11:56 Searching—The Basic Tools00:14:05 Mapping and Analysis00:16:51 Full-Body Search00:19:01 Sorting and Relevance00:21:19 Distributed Search Execution00:23:10 Index Management00:25:41 Inside a Shard

00:00:00 Introduction to Docker00:02:06 Installing Docker00:03:17 Getting Started with Docker00:04:42 Working with Docker Images and Repositories00:06:29 Testing with Docker00:08:02 Building Services with Docker00:09:36 Docker Orchestration and Service Discovery00:11:22 Using the Docker API00:12:21 Getting Help and Extending Docker

00:00:00 What Is Observability?00:01:30 How Debugging Practices Differ Between Observability and Monitoring00:02:47 Lessons from Scaling Without Observability00:04:20 How Observability Relates to DevOps, SRE, and Cloud Native00:05:49 Structured Events Are the Building Blocks of Observability00:07:21 Stitching Events into Traces00:08:48 Instrumentation with OpenTelemetry00:10:31 Analyzing Events to Achieve Observability00:12:24 How Observability and Monitoring Come Together00:14:00 Applying Observability Practices in Your Team00:15:45 Observability-Driven Development00:17:07 Using Service-Level Objectives for Reliability00:18:45 Acting on and Debugging SLO-Based Alerts00:20:20 Observability and the Software Supply Chain00:21:55 Build Versus Buy and Return on Investment00:23:50 Efficient Data Storage00:26:26 Cheap and Accurate Enough: Sampling00:28:38 Telemetry Management with Pipelines00:30:25 The Business Case for Observability00:32:06 Observability's Stakeholders and Allies00:33:51 An Observability Maturity Model00:35:42 Where to Go from Here

00:00:00 What is GraphQL00:02:09 Graph Theory00:03:53 The GraphQL Query Language00:06:01 Designing a Schema00:08:08 Creating a GraphQL API00:10:05 GraphQL Clients00:12:11 GraphQL in the Real World

00:00:00 Introduction00:00:30 It All Starts with a Good Question00:02:41 Tidying Up Your Data00:03:40 Giving Your Data a Quick Health Check00:04:21 Exploring Your Data00:05:19 Making Sense of It All with Models00:06:26 Predicting the Future with Machine Learning00:07:44 The Quest for Causality00:08:40 Writing Up Your Analysis00:09:15 Creating Great Figures00:10:11 Presenting Your Data00:10:56 Making Your Work Reproducible

00:00:00 Introduction00:00:55 Commodity Hardware Today00:05:39 CPU Caches00:10:46 Virtual Memory00:13:02 NUMA Support00:14:31 What Programmers Can Do00:19:26 Memory Performance Tools00:21:05 Upcoming Technology

00:00:00 Introduction00:00:57 How much compute is used for frontier reasoning training?00:03:04 What does reasoning compute scale mean for AI progress?00:03:58 Can reasoning actually scale?Josh You (2025), "How far can reasoning models scale?". Published online at epoch.ai. Retrieved from: 'https://epoch.ai/gradient-updates/how-far-can-reasoning-models-scale' [online resource]

00:00:00 Chapter 1.1: What is MCP?00:01:41 Chapter 1.2: Why Was MCP Created?00:03:42 Chapter 1.3: MCP Architecture Overview00:05:26 Chapter 1.4: Tools, Resources, and Prompts00:07:33 Section 2: MCP Projects00:07:50 Project 1: 100% Local and Private MCP Client00:08:29 Project 2: MCP-powered Agentic RAG00:09:16 Project 3: MCP-powered Financial Analyst00:10:09 Project 4: MCP-powered Voice Agent00:11:11 Project 5: A Unified MCP Server with MindsDB00:11:55 Project 6: MCP-powered Shared Memory for Claude and Cursor00:12:41 Project 7: MCP-powered RAG over Complex Documents00:13:19 Project 8: MCP-powered Synthetic Data Generator00:14:12 Project 9: MCP-powered Deep Researcher00:15:07 Project 10: MCP-powered RAG over Videos00:15:58 Project 11: MCP-powered Audio Analysis ToolkitSource: MCP illustrated guidebook by Daily Dose of Data Science

Based on MATTHEW FOWLER: Python Concurrency with asyncio00:00:00 Python concurrency with asyncio00:02:17 asyncio basics00:04:47 A first asyncio application00:06:35 Concurrent web requests00:08:16 Non-blocking database drivers00:09:48 Handling CPU-bound work00:11:48 Handling blocking work with threads00:13:31 Streams00:14:48 Web applications00:16:32 Microservices00:18:29 Synchronization00:20:25 Asynchronous queues00:22:19 Managing subprocesses00:23:55 Advanced asyncio

Based on Neil Madden: API Security in Action00:00:00 What is API security?00:02:41 Secure API development00:04:38 Securing the Natter API00:06:08 Session cookie authentication00:07:56 Modern token-based authentication00:09:56 Self-contained tokens and JWTs00:11:46 OAuth2 and OpenID Connect00:13:43 Identity-based access control00:15:07 Capability-based security and macaroons00:16:43 Microservice APIs in Kubernetes00:18:20 Securing service-to-service APIs00:19:38 Securing IoT communications00:21:15 Securing IoT APIs

Source: Innovation Endeavors 00:00:00 The Big Picture in 202500:01:26 How We Got Here00:03:33 A Deep Dive into the Models00:06:52 A Deep Dive into the Models00:07:37 Use Cases & Applications - Where AI is Making an Impact00:09:52 Building AI Products - From Models to Systems00:12:59 Market Structure & Dynamics00:15:14 What's Next? The AI-Native Organization00:17:37 What We're Excited to See Built

00:00:00 Caching00:00:37 Chaptertransition00:00:37 What is Distributed Caching, Anyway?00:01:40 Chaptertransition00:01:41 How to Do Caching the Right Way: Key Strategies00:04:02 Chaptertransition00:04:03 Choosing the Right Tool for the Job00:05:26 Chaptertransition00:05:27 The Performance Payoff: Cache vs. Database00:06:32 Chaptertransition00:06:33 In Conclusion: Caching is a Must-Have00:07:03 OutroSource: Designing Resilient Systems: A Guide to Distributed Caching for Modern Applications

00:00:00 Database Design00:02:15 Normalization00:04:49 Numeric Data in SQL00:07:09 Temporal Data Types in SQL00:09:30 Character Data Types in SQL00:11:31 NULLs: Missing Data in SQL00:13:38 Multiple Column Data Elements00:15:34 Table Operations00:18:09 Comparison or Theta Operators00:19:39 Valued Predicates00:21:18 CASE Expressions00:23:33 LIKE Predicate00:25:06 BETWEEN and OVERLAPS Predicates00:26:35 The [NOT] IN() Predicate00:28:14 EXISTS() Predicate00:29:54 Quantified Subquery Predicates00:31:50 The SELECT Statement00:33:37 VIEWs, Derived Tables, Materialized Tables, and Temporary Tables00:35:41 Partitioning Data in Queries00:37:09 Grouping Operations00:38:29 Aggregate Functions00:40:18 Auxiliary Tables00:41:52 Statistics in SQL00:43:31 Regions, Runs, Gaps, Sequences, and Series00:44:55 Arrays in SQL00:46:32 Set Operations00:47:59 Subsets00:49:17 Trees and Hierarchies in SQL00:51:09 Temporal Queries00:52:54 Graphs in SQL00:54:18 OLAP in SQL00:56:26 Transactions and Concurrency Control00:58:29 Optimizing SQLBased on Joe Celko's SQL for Smarties: Advanced SQL Programming.

Based on Evidence from Millions of Claude Conversations https://arxiv.org/pdf/2503.04761.00:00:00 Abstract00:01:36 Introduction: Understanding AI's Real-World Role00:02:28 How the Analysis Was Conducted00:03:42 Key Research Findings00:05:54 Discussion and Limitations00:06:51 Conclusion: A Starting Point for Tracking AI's Impact00:07:41 Outro

Based on Anthropic:On the Biology of a Large Language Model00:00:00 Unlocking the AI Black Box: How Does an AI Actually "Think"?00:01:49 How to Peek Inside the AI's Brain: A New Toolkit00:03:23 What They Found: Amazing Examples of AI Thinking00:11:39 The Big Picture and What's Next

00:00:00 Getting Started00:01:55 Git Basics00:04:00 Git Branching00:06:13 Git on the Server00:07:43 Distributed Git00:09:23 GitHub00:11:01 Git Tools00:13:07 Customizing Git00:14:38 Git and Other Systems00:15:39 Git Internals00:17:40 Outro

Google White Paper: Agents Companion00:00:00 Introduction00:01:44 AgentOps00:03:08 Agent Success Metrics00:04:44 Agent Evaluation00:05:51 Assessing Agent Capabilities00:06:55 Evaluating Trajectory and Tool Use00:08:33 Evaluating the Final Response00:09:26 Human-in-the-Loop Evaluation00:10:42 More about Agent Evaluation00:11:41 Multiple Agents & Their Evaluation00:13:00 Multi-Agent Design Patterns and Their Business Impact00:14:02 Important components of Agents00:15:30 Challenges in Multi-Agent systems00:16:25 Multi-Agent Evaluation00:17:38 Agentic RAG: A Critical Evolution in Retrieval-Augmented Generation00:18:54 Better Search, Better RAG00:20:16 Agents in the enterprise00:21:15 Google Agentspace, NotebookLM Enterprise, Google AgentSpace Enterprise00:22:34 From agents to contractors00:23:47 Automotive Al: Real World Use of Multi-Agent Architecture00:25:44 Key Takeaways for Developers00:27:17 Future Directions00:27:52 Outro

Paper: 2506.06576v2 Future of Work with AI Agents00:00:00 Abstract00:02:07 Introduction00:03:00 A New Auditing Framework00:04:38 The WORKBank Database00:05:23 Key Findings00:08:03 Conclusion00:09:11 Outro

00:00:00 Introduction00:02:06 Relational Operators00:04:30 More Operations on Relations00:06:54 Functional Dependencies00:08:16 Covers for Functional Dependencies00:10:29 Databases and Normal Forms00:13:15 Multivalued Dependencies, Join Dependencies and Further Normal Forms00:16:08 Project-Join Mappings, Tableaux, and the Chase00:18:24 Representation Theory00:20:44 Query Systems00:23:05 Query Modification00:25:51 Null Values, Partial Information and Database Semantics00:28:37 Acyclic Database Schemes00:31:35 Assorted Topics00:34:19 Relational Query Languages

00:00:00 Part I: Fundamental Cloud Computing00:10:07 Part II: Cloud Computing Mechanisms00:14:55 Part III: Cloud Computing Architecture00:18:33 Part IV: Working with Clouds00:21:41 Outro

00:00:00 Building Abstractions with Procedures00:02:21 Building Abstractions with Data00:05:12 Modularity, Objects, and State00:08:14 Metalinguistic Abstraction00:11:09 Computing with Register Machines00:13:53 Outro

00:00:00 Data and Sampling Distributions00:03:56 Statistical Experiments and Significance Testing00:08:17 Regression and Prediction00:12:26 Classification00:16:49 Statistical Machine Learning00:20:31 Unsupervised Learning

00:00:00 Introduction00:01:38 Basic Structures: Sets, Functions, Sequences, Sums, and Matrices00:02:56 Algorithms00:04:04 Number Theory and Cryptography00:05:02 Induction and Recursion00:06:01 Counting00:06:57 Discrete Probability00:08:02 Advanced Counting Techniques00:09:04 Relations00:10:01 Graphs00:11:01 Trees00:11:58 Boolean Algebra00:12:57 Modeling Computation00:13:58 Outro

00:00:00 Introduction00:02:47 Part 2: Managing Processes00:04:59 Part 3: Keeping Processes in Sync00:06:52 Part 4: Managing Memory00:09:10 Part 5: Managing Storage00:10:58 Part 6: The File System00:13:04 Part 7: Security and Protection00:15:39 Part 8: Advanced and Modern Topics00:17:18 Part 9: Real-World Case Studies00:18:35 Outro

00:00:00 Introduction00:01:25 Entropy, Relative Entropy, and Mutual Information00:03:16 Asymptotic Equipartition Property (AEP)00:04:22 Entropy Rates of a Stochastic Process00:05:37 Data Compression00:07:09 Gambling and Data Compression00:08:50 Channel Capacity00:10:44 Differential Entropy00:12:13 Gaussian Channel00:13:37 Rate Distortion Theory00:15:08 Information Theory and Statistics00:16:38 Maximum Entropy00:17:54 Universal Source Coding00:19:14 Kolmogorov Complexity00:20:42 Network Information Theory00:22:19 Information Theory and Portfolio Theory00:23:43 Inequalities in Information Theory00:25:03 Outro

This episode is based on Introduction to the Theory of Computation, Third Edition Michael Sipser.00:00:00 Introduction00:00:21 Regular Languages00:02:20 Context-Free Languages00:03:53 The Church-Turing Thesis00:05:28 Decidability00:07:05 Reducibility00:08:37 Advanced Topics in Computability00:10:17 Time Complexity00:12:25 Space Complexity00:14:06 Intractability00:15:19 Advanced Topics in Complexity Theory00:17:12 Outro

Computer Networking: A Top-Down ApproachAuthored by the renowned computer scientists James Kurose and Keith Ross, is one of the most popular and influential textbooks for learning about computer networking . Its main goal is to provide a modern and comprehensive introduction to the field, explaining not just the individual components of the Internet, but how they all work together .What makes this book unique is its celebrated **top-down approach** . Instead of starting with the low-level details of physical wires and data bits, it begins with the application layer—the part of the network we interact with every day, like web browsers and e-mail clients. From there, it moves step-by-step down the protocol stack. This method helps you understand *why* each layer exists, as you see the services it must provide to the layer above it .

Part I: FoundationsChapter 1: The Role of Algorithms in ComputingChapter 2: Getting StartedChapter 3: Characterizing Running TimesChapter 4: Divide-and-ConquerPart II: Sorting and Order StatisticsIntroduction to SortingChapter 6: HeapsortChapter 7: QuicksortChapter 8: Sorting in Linear TimeChapter 9: Medians and Order StatisticsPart III: Data Structures1Introduction to Dynamic SetsChapter 10: Elementary Data StructuresChapter 11: Hash TablesChapter 12: Binary Search TreesChapter 13: Red-Black TreesPart IV: Advanced Design and Analysis TechniquesIntroduction to Advanced TechniquesChapter 14: Dynamic ProgrammingChapter 15: Greedy AlgorithmsChapter 16: Amortized AnalysisPart V: Advanced Data StructuresIntroduction to Advanced Data StructuresChapter 17: Augmenting Data StructuresChapter 18: B-TreesChapter 19: Data Structures for Disjoint SetsPart VI: Graph AlgorithmsIntroduction to Graph ProblemsChapter 20: Elementary Graph AlgorithmsChapter 21: Minimum Spanning TreesChapter 22: Single-Source Shortest PathsChapter 23: All-Pairs Shortest PathsChapter 24: Maximum FlowChapter 25: Matchings in Bipartite GraphsPart VII: Selected TopicsIntroduction to Selected TopicsChapter 26: Parallel AlgorithmsChapter 27: Online AlgorithmsChapter 28: Matrix OperationsChapter 29: Linear ProgrammingChapter 30: Polynomials and the FFTChapter 31: Number-Theoretic AlgorithmsChapter 32: String MatchingChapter2 33: Machine-Learning AlgorithmsChapter 34: NP-CompletenessChapter 35: Approximation Algorithms