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In-depth courses on web scraping, anti-bot evasion, cybersecurity, and networking, plus learning roadmaps for engineers who value their time. No fluff, just the implementation.

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Web Scraping Cybersecurity Networking Anti-Bot Evasion

206 lessons 39 roadmaps 1767 articles

coursesall topics ->
Web Scraping 67 lessons A complete web scraping course: HTTP and parsing fundamentals, crawling, browsers, anti-bot engineering, and production-scale data pipelines. 01 HTTP Basics for Scraping02 Understanding HTML Structure03 requests, Sessions & Headers 67 lessons · free start ->
Cybersecurity 42 lessons Concepts, attack vectors, and defensive techniques for application and network security. 01 Encryption02 Hashing vs Encoding03 Digital Signatures and PKI 42 lessons · free start ->
Networking 35 lessons How data moves. Protocols, layers, and the infrastructure of the internet. 01 TCP/IP Model02 Ethernet, ARP & Switching03 IPv4 and IPv6 Addressing 35 lessons · free start ->
Anti-Bot Evasion 62 lessons Learn techniques for bypassing modern anti-bot protections, CAPTCHAs, and advanced fingerprinting. 01 How Anti-Bots Work02 The Defender's Signal Inventory03 IP Reputation & Proxies 62 lessons · free start ->
featured roadmapsall 39 roadmaps ->
Web Scraping 15 topics 01 Foundations02 Core Concepts03 Intermediate Skills 4 stages open roadmap -> Cybersecurity 16 topics 01 Foundations02 Offensive03 Defensive 4 stages open roadmap -> Frontend Development 40 topics 01 Internet Fundamentals02 HTML Fundamentals03 CSS Styling 8 stages open roadmap -> Backend Development 38 topics 01 Internet & OS02 Languages03 Relational Databases 8 stages open roadmap -> Python 116 topics 01 Foundations02 Core Concepts03 Intermediate Skills 29 stages open roadmap -> Machine Learning & AI 17 topics 01 Mathematics02 Fundamentals03 Deep Learning 4 stages open roadmap ->
latest newsall news ->
ai Oct 28 Why We Should Work on AI Instead of Fearing It: A Philosophical Perspective The Hacker News post by almustakim47 surfaces a question many developers whisper about: should we fear artificial intelligence, or should we work on it? A colleague once told the author, "You will enjoy exploiting nature and takin... ai Oct 28 Analysts followed AI agent's randomly assigned preferences in 86% of decisions When experts hand work to an AI agent, the default assumption is that they remain the final decision-maker. The agent writes code, runs numbers, offers advice, but the human chooses what to measures, how to interpret ambiguous evi... ai Oct 28 AI Agents Flooded RubyGems to Get Code Execution on Its Docs Builder The GemStuffer campaign that unfolded between May and June 2026 demonstrated how AI agents can turn a package registry into an execution engine. During that window, autonomous agents submitted thousands of new gems to RubyGems. Ea...
latest papersall papers ->
arXiv:2610.37658 Oct 28 When Should We Intervene at Test Time? Jiahao Zhang, Xinling Wen, Liangliang Li, Yu Chen Test-time adaptation helps a deployed model respond to changing inputs, but the same intervention can rescue one batch and damage the next. Updating normalization statistics, for example, can improve corrupted images yet fail when... arXiv:2610.32978 Oct 26 SceneBloom: Agentic Indoor Environment Generation with Realistic and Diverse Layouts for Embodied Learning Yuhan Liu, Lianhui Qin, Shenji Wan Training generalizable embodied agents requires realistic, varied, and simulation-ready indoor environments. Language-guided methods support semantic planning and object-level reasoning, but precise spatial grounding remains diffi... arXiv:2610.32971 Oct 26 RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms Xi Chen, Fengqi Hu, Zeyu Shen, Jiaying Xu, Shangding Gu, Pan Xu, Anqi Liu, Laixi Shi Robust reinforcement learning (RL) is critical for reliable real-world deployment, where policies may fail under diverse shifts and disruptions. Existing robust RL algorithms offer a rich set of mechanisms and principles, yet two...