Udemy - AI Operating System Bootcamp - OpenClaw + Claude + Clawdbot

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Udemy - AI Operating System Bootcamp - OpenClaw + Claude + Clawdbot (Size: 1.6 GB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~Get Your Files Here !
  1 - Foundations of AI Operating Systems
  1. What is an AI Operating System.mp4 79.3 MB
  10 - Wrap-Up & Next Steps
  19. From Builder to AI Architect.mp4 71.3 MB
  2 - Building Intelligent Agents (Claude)
  3 - Multi-Agent Systems & Orchestration
  4 - Memory Systems with ClawDB
  5 - Execution Layer with OpenClaw
  10. Workflow Automation Systems.mp4 83.6 MB
  6 - Interfaces & Integrations
  11. Building AI Interfaces.mp4 111.9 MB
  12. External Integrations.mp4 87.1 MB
  7 - Long-Running & Autonomous Systems
  13. Continuous AI Systems.mp4 107.6 MB
  14. Feedback Loops & Self-Improvement.mp4 97 MB
  8 - Debugging, Safety & Optimization
  15. Debugging AI Systems.mp4 99.3 MB
  16. Performance & Cost Optimization.mp4 114.2 MB
  9 - Capstone Projects (HIGH VALUE)
  17. Project 1 Personal AI Operating System.html 14.7 KB
  18. Project 2 AI Business Automation System.html 12.3 KB
  9. Tool Calling & Function Execution.mp4 99.5 MB
  7. Designing Memory in AI Systems.mp4 88.2 MB
  8. Retrieval & Context Injection.mp4 122.5 MB
  5. From Single Agent → Multi-Agent.mp4 87 MB
  6. Supervisor & Worker Architectures.mp4 80.7 MB
  3. Designing Agent Behavior.mp4 113.3 MB
  4. Multi-Step Agent Thinking.mp4 92.6 MB
  20. Career & Business Paths.mp4 65 MB
  2. Core Components of AI Systems.mp4 78.7 MB

Description


AI Operating System Bootcamp: OpenClaw + Claude + Clawdbot
https://WebToolTip.com
Published 3/2026

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English | Duration: 4h 9m | Size: 1.64 GB
Build a full AI OS using Claude, multi-agent systems, memory, and automation with real-world projects
What you'll learn

Build a complete AI Operating System by combining agents, memory, and automation into one unified system

Design and deploy intelligent AI agents using structured prompts, reasoning, and multi-step workflows

Create and manage multi-agent systems with supervisor-worker architectures and task orchestration

Implement memory-driven AI systems using vector databases, embeddings, and context injection techniques

Develop tool-calling and automation pipelines to connect AI with real-world APIs, workflows, and data systems

Build long-running autonomous AI systems with event-driven execution, scheduling, and feedback loops

Optimize AI systems for performance, cost, and scalability including token efficiency and latency reduction

Apply skills through real-world projects like personal AI assistants, business automation systems, and research agents
Requirements

Basic understanding of programming concepts (Python or JavaScript is helpful but not mandatory)

Familiarity with using web applications and APIs is a plus, but everything will be explained step-by-step

No prior experience in AI or machine learning is required — this course starts from fundamentals

A computer or laptop with a stable internet connection

Willingness to learn by building real-world projects and experimenting with AI systems

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