Network:2 667
Subscribers
No data24 hours
+37 days
+130 days
Data loading in progress...
Similar Channels
Tags Cloud
Incoming and Outgoing Mentions
---
---
---
---
---
---
Attracting Subscribers
October '26Oct '26
October '26
+3
in 0 channels
September '26
+13
in 0 channels
Get PRO
August '26
+16
in 1 channels
Get PRO
July '26
+156
in 1 channels
Get PRO
June '26
+14
in 0 channels
Get PRO
May '26
+14
in 0 channels
Get PRO
April '26
+12
in 0 channels
Get PRO
March '26
+13
in 0 channels
Get PRO
February '26
+5
in 0 channels
Get PRO
January '26
+9
in 0 channels
Get PRO
December '25
+13
in 0 channels
Get PRO
November '25
+17
in 0 channels
Get PRO
October '25
+11
in 0 channels
Get PRO
September '25
+9
in 0 channels
Get PRO
August '25
+13
in 0 channels
Get PRO
July '25
+6
in 0 channels
Get PRO
June '25
+165
in 1 channels
Get PRO
May '25
+7
in 0 channels
Get PRO
April '25
+13
in 0 channels
Get PRO
March '25
+19
in 0 channels
Get PRO
February '25
+16
in 1 channels
Get PRO
January '25
+22
in 0 channels
Get PRO
December '24
+46
in 1 channels
Get PRO
November '24
+48
in 1 channels
Get PRO
October '24
+45
in 0 channels
Get PRO
September '24
+52
in 0 channels
Get PRO
August '24
+68
in 0 channels
Get PRO
July '24
+89
in 0 channels
Get PRO
June '24
+68
in 0 channels
Get PRO
May '24
+72
in 0 channels
Get PRO
April '24
+88
in 0 channels
Get PRO
March '24
+135
in 0 channels
Get PRO
February '24
+461
in 1 channels
Get PRO
January '24
+335
in 1 channels
Get PRO
December '23
+101
in 0 channels
Get PRO
November '23
+19
in 0 channels
Get PRO
October '23
+21
in 0 channels
Get PRO
September '23
+33
in 0 channels
Get PRO
August '23
+12
in 0 channels
Get PRO
July '23
+24
in 0 channels
Get PRO
June '23
+24
in 0 channels
Get PRO
May '23
+36
in 0 channels
Get PRO
April '23
+186
in 0 channels
Get PRO
March '23
+19
in 0 channels
Get PRO
February '23
+208
in 0 channels
Get PRO
January '23
+19
in 0 channels
Get PRO
December '22
+38
in 0 channels
Get PRO
November '22
+22
in 0 channels
Get PRO
October '22
+43
in 0 channels
Get PRO
September '22
+48
in 0 channels
Get PRO
August '22
+385
in 0 channels
Get PRO
July '22
+33
in 0 channels
Get PRO
June '22
+42
in 0 channels
Get PRO
May '22
+760
in 0 channels
| Date | Subscriber Growth | Mentions | Channels | |
| 05 October | 0 | |||
| 04 October | 0 | |||
| 03 October | 0 | |||
| 02 October | +1 | |||
| 01 October | +2 |
Channel Posts
| 2 |  Digital Image Processing: Theory, Practice, and AI Applications 2027
Автор: Mahmood R. Azimi-Sadjadi
Integrate Machine Learning and AI-based approaches into practical image processing with Python.
Engineers and researchers implementing image processing systems need methods that bridge classical techniques with modern Machine Learning approaches. This book delivers both traditional and modern AI-based methods and algorithms in image enhancement, restoration, segmentation, compression, and analysis. Written by an educator and researcher with more than 40 years’ experience in signal/image processing and Machine Learning, this reference provides theoretical and practical tools using the Python platform for a wide range of applications. | 400 |
| 3 | Building Distributed Applications that Work_True.pdf | 657 |
| 4 |  Building Distributed Applications that Work: Design, debug, test, observe, secure, and deploy (Final Release) 2027
Fiodar Sazanavets
"Connects the developer experience to the real problems of distributed applications: configuration, dependencies, debugging, testing, and deployment.” - Scott Hanselman, Microsoft
Systems that are split into independent, network-connected services, whatever their benefits, can be difficult to design, deploy, and maintain. In Building Distributed Applications that Work, author Fiodar Sazanavets shows you how to conquer the inevitable communication failures, complex startup dependencies, and fragmented diagnostic data using industry-grade architectural patterns. Written for busy builders and software shippers, this book sticks to the techniques you’ll actually need—all demonstrated with examples using the .NET Aspire framework. Practical from the very start, this book gives you the tools and techniques to have a real project up and running by the end of the first chapter! | 595 |
| 5 | OpenAI Codex CLI in Practice.pdf | 740 |
| 6 |  OpenAI Codex CLI in Practice: Build, Debug, Test, and Ship Real Software with AI Coding Agents, AGENTS.md, MCP, Skills, and Agentic Workflows 2026
Автор: Marcus T. Calder
Who this book is for:
Designed for developers who understand basic programming, the terminal, Git, and ordinary software workflows—but want to move beyond autocomplete and uncontrolled experimentation. It is especially useful for software engineers, DevOps practitioners, technical leads, architects, and engineering teams integrating AI coding agents into serious workflows. No previous mastery of Codex, MCP, skills, or sandboxing is required. | 686 |
| 7 | Prompt Engineering in Practice_True.pdf | 839 |
| 8 |  Prompt Engineering in Practice: Design, test, and improve AI prompts (Final Release) 2027
Автор: Richard Davies
"A thoughtful, practical guide to prompt engineering as a real discipline.” - Che Gamble, Davies Group
Sometimes your LLMs return brilliant responses. Other times, not so much. Do you know why? Prompt Engineering in Practice shows you how to move from accidental AI results to reliable, production-grade systems you can deploy with confidence. Written by AI veterans Richard Davies and Rafael Fischer, this book introduces a unique approach: treat prompts as engineered, self-contained interfaces that you can compose, evaluate, and refine. This shift reframes model interactions as a | 745 |
| 9 | Foundations of Cybersecurity_2Ed_True.pdf | 807 |
| 10 |  Foundations of Cybersecurity: A Straightforward Introduction, 2nd Edition 2026
Автор: Jason Andress
This comprehensive introduction to the information security field covers the industry’s essential concepts, using real-world security breaches to illustrate key lessons.
Cybersecurity is a huge field, and breaking in can feel overwhelming. Where do you start when the territory spans everything from cryptography to cloud security to social engineering?
In Foundations of Cybersecurity, you’ll learn how security professionals actually think about protecting systems. You’ll start with core principles like authentication, authorization, and access control, then build outward into network defense, operating system hardening, application security, and security operations.
Each chapter introduces concepts in context, showing how they connect to real decisions you’ll face on the job. | 779 |
| 11 | Intelligent Cybersecurity in the AI-IoT Era.pdf | 835 |
| 12 |  Intelligent Cybersecurity in the AI-IoT Era: Architectures, Threats, and Directions 2027
Автор: Shalli Rani
This book explores cybersecurity in the age of AI and IoT, delving into next-generation threats and intelligent defense mechanisms. Bridging foundational knowledge with real-world applications, it provides a comprehensive roadmap for researchers, academicians, and industry professionals striving to secure evolving digital landscapes. | 828 |
| 13 | Recursive_Filtering_of_Networked_Systems_with_Communication_Prot.pdf | 839 |
| 14 |  Recursive Filtering of Networked Systems with Communication Protocol Scheduling 2027
Автор: Shuai Liu
Recursive Filtering of Networked Systems with Communication Protocol Scheduling explores protocol-based state estimation for complex networked systems including Kalman filtering for nonlinear systems under Round-Robin (RR) and MEF-TOD protocols, finite-horizon robust state estimation under FlexRay protocol, robust filtering for multi-rate systems under RR and stochastic communication protocols, and state estimation under event-triggering protocol and redundant channel for neural networks. This book provides theoretical frameworks using techniques like backward Riccati equations, Kalman filtering theory, Unscented transform, and ellipsoid estimation theory. | 781 |
| 15 | Learning Cybersecurity Fundamentals_Final.pdf | 833 |
| 16 |  Learning Cybersecurity Fundamentals: A Practical Guide to Essential Cybersecurity Concepts (Final Release) 2026
Автор: Nicole Dove
Cybersecurity is now a business-critical function at the core of every organization. Learning Cybersecurity Fundamentals introduces readers to this vital field through the lens of the NIST 2.0 Cybersecurity Framework. Written by Nicole Dove, CIA, CRMA, this guide demystifies cybersecurity team structures and explains the core principles that harden, govern, and protect the systems we rely on. Whether you're a student, an early-career professional, or considering a career change, this book helps you align your skills with in-demand roles. | 742 |
| 17 | MCP For Dummies.pdf | 956 |
| 18 |  MCP For Dummies 2027
Автор: Wei-Meng Lee
MCP For Dummies is the essential guide to getting started with the open-source standard that’s transforming how businesses deploy AI. With Model Context Protocol (MCP), you can connect AI to real-world data, for practical, secure tools that deliver real value. This beginner-friendly book shows you why major tech companies like OpenAI, Google DeepMind, and Microsoft are already using MCP servers every day. It also gives you step-by-step guidance on how to deploy secure AI workflows that connect to databases, APIs, and enterprise systems. Customer service automation, business intelligence dashboards, logistics optimization—with MCP, you can harness the power of AI, just about anywhere. This Dummies guide shows you how. | 873 |
| 19 | Sutskevers List_modern AI_True.pdf | 905 |
| 20 |  Sutskever's List: Foundational ideas of modern AI (Final Release) 2026
Автор: Richard Heimann
"A perspective the field has needed. Sutskever’s List delivers it with care and historical accuracy.” - Yanping Huang, Google
Sutskever’s List is a guided intellectual journey through the ideas that made modern AI suddenly possible. Each chapter is anchored in specific papers, books, or other sources from Sutskever’s list. The papers themselves are not the focus. Instead, the author uses them as entry points into the larger breakthroughs, arguments, interconnections, and shifts in thinking that transformed the field.
It begins with AlexNet, where data, GPUs, and training craft made neural networks impossible to dismiss, then moves to ResNet, where depth becomes a superpower rather than a liability. From there, the story accelerates through sequence models, speech systems, attention, Transformers, and hyperscale, showing how AI escaped older bottlenecks and became built to grow. | 798 |
