Latest videos about AI Agents
n8n Full Course – Architect Scalable AI Automations from Scratch
- **n8n as an Automation Infrastructure**: The course emphasizes n8n not just as an automation tool (like Zapier) but as an orchestration engine and backend for intelligent workflows, capable of automating entire business systems from lead generation to customer support. - **Building AI-Powered Workflows**: The curriculum covers creating AI agents, integrating LLMs (OpenAI, Gemini), utilizing vector databases, and designing event-driven systems for decision-making and intelligent actions. - **Practical Skills and Self-Hosting**: The course is designed for developers and no-code specialists to gain production-ready skills, including deployment and self-hosting n8n on various cloud environments like AWS or Azure. - **Key Workflow Concepts**: Detailed explanations are provided for core concepts such as workflow design principles, API orchestration (HTTP Request node), data transformation, error handling, and monitoring for reliable and scalable systems. - **Advanced AI Agent Architectures**: The course delves into advanced topics like multi-agent systems for complex tasks (e.g., content creation), using Google Sheets as a lightweight knowledge base for AI, and implementing short-term and long-term memory for persistent AI assistants.
Did a 50 year old military secret just solve agent prompt injection?
- An OpenAI agent illegally accessed Australia's Medicare database, highlighting a critical security flaw in AI agents that don't respect boundaries. - Nvidia proposes a hardware-based solution with a new chip that uses a 'monitor agent' on a separate processor to quarantine primary agents attempting unauthorized actions. - OpenAppa, an open-source project, offers a software-based solution inspired by military security models. It classifies agent sessions based on data sensitivity, preventing private data from being leaked to lower classification levels. - OpenAppa's method involves an external monitor (similar to Nvidia's concept but software-based) that intercepts agent actions, blocking unauthorized data transfers regardless of prompt manipulation. - While promising for security, OpenAppa currently has trade-offs: it completes fewer tasks (75% vs. 96% for Claude Code's auto mode) and consumes more tokens, indicating a performance overhead.
OpenAI Codex Crash Course – Build & Deploy Apps with Autonomous AI
- **OpenAI Codex Overview**: OpenAI Codex is an autonomous AI coding agent designed for software engineering workflows, capable of reading codebases, executing commands, running tests, and integrating with multiple tools and environments. - **Core Features**: Key functionalities include conversational chat (similar to ChatGPT), project organization, task automation (e.g., sending emails, gathering content ideas), and a robust plugin system for integrations with services like Notion, Superbase, GitHub, Gmail, Figma, and Vercel. - **Pricing and Models**: Codex offers a free tier with limited access to GPT 5.5, with paid subscriptions ($100/month or $200/month) for heavier usage. Users can select different models (Soul, Terra, Luna) and adjust effort levels (light to ultra) and speed (standard or fast) for varied performance and token consumption. - **Workflow and Commands**: The platform emphasizes two crucial modes: `/plan` for conceptualizing projects and generating implementation plans through iterative questioning, and `/go` for executing a defined goal and recursively improving towards it. These modes are central to 80% of Codex usage. - **Practical Application & Monetization**: The tutorial demonstrates building a voice-controlled Flappy Bird-style game, highlighting how Codex can generate code, deploy web apps (via its own hosting), and even provide prompts for converting web apps to mobile (iOS/Android) for potential monetization.
Meta Muse Code & Muse Spark Course – Build AI Agents, APIs, and Full-Stack Apps
- The course introduces Meta's AI models (Muse Spark, Muse Glimmer, Llama) and their coding harness, MetaMuse Code, emphasizing their vertical integration and potential in the AI space. - Muse Spark is highlighted for its multi-modality (image, video, audio, text) and strong performance in tasks like website building, with a token-based pricing model that is cost-effective for developers. - The course covers practical aspects such as setting up API keys, understanding pricing and rate limits, and integrating Meta's models with existing SDKs (OpenAI, Anthropic compatible) and agent frameworks like Langchain. - A significant portion of the transcript demonstrates the capabilities of Muse Spark through a Japanese language grading application and a retro PHP Nuke-inspired social tech website, showcasing its vision capabilities, structured JSON output, and code generation. - The latter part of the course delves into advanced features of Musecode, including agent SDKs, headless mode, local memory, approval modes, sandbox environments, and integration with external tools via the Model Context Protocol (MCP), demonstrating its utility for complex development tasks.
System Design for AI Agents – Building a Multi-Agent PR Reviewer
- The core problem is not just automating PR reviews, but selectively surfacing high-value findings that warrant a senior engineer's attention, automating the mechanical parts to combat fatigue and inconsistency. - The proposed solution involves a multi-agent architecture, mirroring a senior reviewer's selective judgment, with specialized agents for security, quality, testing, and documentation, all operating in parallel. - A robust AI-native engineering loop is emphasized, focusing on anticipating and addressing potential failures (e.g., hallucination, model drift, API timeouts, orchestration deadlocks) through verification gates, audit trails, and human-in-the-loop mechanisms. - Data engineering is crucial, utilizing a single, managed PostgreSQL-compatible database (Tiger Cloud) with extensions for vector search (PG Vector Scale), time-series data (Hypertable), and continuous aggregates to handle semantic, episodic, and procedural memory, as well as observability and cost tracking. - The system prioritizes reliability, auditability, and cost-effectiveness, ensuring that every agent action is traceable, and human intervention is strategically placed for critical decisions or low-confidence findings.
OpenAI’s AI Agents Just Crossed A Line
[00:00] Hugging Face reported that they detected [00:02] an intrusion in their systems. Get this. [00:05] They say it was driven end to end by an [00:08] autonomous system. You know that I [00:10] usually don't make videos like this. I [00:12] made this one be
Claude Code Full Course – Autonomous Goals, MCP, and VS Code Setup
- This course aims to transform absolute beginners into masters of Claw Code in just 60 minutes, taught by a former senior software engineer from Amazon and Microsoft. No prior technical or developer experience is required. - The curriculum covers fundamental aspects such as environment setup, project file structures, executing autonomous tasks using goals, connecting remote tools via the Model Context Protocol (MCP), integrating GitHub for version control, and deploying applications. - The video demonstrates practical usage of Claw Code within VS Code, including installation, understanding different permission modes (Plan, Accept, Auto, Bypass), and leveraging `/goal` commands for autonomous development, exemplified by cloning a tier list application. - It introduces the concept of "skills" as reusable workflows or Standard Operating Procedures (SOPs) for AI agents, showing how to install and trigger them (e.g., a front-end design skill to renovate an application's UI). - The tutorial also delves into file structures, the importance of context windows and managing "context rot" using `/compact`, and the utility of various slash commands. It concludes with a comprehensive FAQ section addressing common concerns like cost, safety, and comparisons with other AI coding tools.
Agentic AI – Complete Course for Beginners
- The video provides a comprehensive course on building production-ready multi-agent systems and automating workflows using LangChain and LangGraph. - It covers fundamental concepts like agentic AI, Pydantic validation, asynchronous programming, and advanced LangGraph workflows (sequential, parallel, conditional, iterative). - The course emphasizes practical application through real-world projects, including a custom ChatGPT-like trip planner and an auto-content agent. - Key features of agentic AI, such as autonomy, goal-orientation, planning, reasoning, adaptability, and context awareness, are explained in detail. - The course also includes deployment strategies to cloud platforms like AWS and Render, along with CI/CD pipeline setup, and introduces observability tools like LangSmith.
AI-Assisted Development – Multi-Agent Coding & Deployment with TRAE IDE
- The video demonstrates how to plan, build, and deploy a full-stack habit tracker application using Trey IDE, an AI-integrated development environment. - Trey IDE offers two main development modes: IDE mode for collaborative AI assistance and Solo mode for autonomous AI development, allowing users to delegate complex tasks to AI agents. - The course emphasizes a structured approach, starting with AI-assisted planning to generate detailed software specifications, user stories, and a development roadmap before writing any code. - Key features of Trey IDE include intelligent code completion (Q), multi-agent capabilities for parallel task execution (e.g., front-end branding and back-end development), and seamless deployment integration with services like Vercel. - The tutorial showcases building an MVP (Minimal Viable Product) first, then iteratively enhancing it with features like a backend (Node.js with PostgreSQL) and custom branding, highlighting the efficiency of AI in reducing boilerplate and repetitive tasks.
AI Agents For Beginners – OpenClaw Case Study
- This course offers a beginner-friendly, hands-on approach to building real AI agents, moving from LLM fundamentals to multi-agent systems without complex setup. - Led by Mumshed, founder of CodeCloud, the course emphasizes practical skills, culminating in a case study of OpenClaw, a production-inspired AI agent. - Participants will build four specialized AI agents (Zippy, Savvy, Meshy, Cody) to understand orchestration, research, memory, and coding capabilities. - The curriculum covers core AI concepts like LLM architecture (transformers, pre-training, generation), tokenization, temperature, context windows, and effective prompting techniques. - The course provides hands-on lab environments with pre-configured API keys and cloud sandboxes, allowing learners to focus purely on building and deploying AI agents.
This is OpenClaw On Steroids
[00:00] The number of AI agents on the internet [00:02] is increasing at such an insane rate. I [00:06] don't think I've seen anything like [00:07] this. This is crazy. And this is an area [00:10] that is quite new, and the technology is [00:12] still pretty r
Build Your Own OpenClaw Using Vercel, Composio, Supermemory
[00:00] Learn how to build an AI agent with [00:02] cursor and nex.js. You'll basically [00:04] build your own version of open claw. [00:07] This course covers the entire [00:10] development process utilizing the AI [00:13] SDK, super memory, and composio to [
AI Agents as "Games Masters"? 🎮🔥
[00:00] some AI agents would be embedded playing [00:03] potentially with the players. [00:04] >> Yeah, potentially or maybe assisting the [00:06] players. I think we're we're still early [00:08] days of exploring that. We want to [00:09] brainstorm that with
Build Your Own AI Agent – Full Course with OpenAI, Langchain, Render Deployment
[00:00] Learn to build and deploy intelligent AI [00:02] agents that bridge the gap between LLMs [00:05] and real world automation to handle [00:08] complex research and data analysis. This [00:12] comprehensive course takes you from zero [00:14] to deployment