HeyJaredCode, Don't Fear AI
Helping developers master AI-powered coding, automation, and modern software development. Learn practical tools, build real projects, and stay ahead in the AI era. We believe AI is not here to replace developers—it's here to amplify them.
- Indexed videos, last 90 days
- 15
- Latest publication
- Aug 8, 2026
- Audience
- ~2 subscribers
- Earliest in this view
- Aug 3, 2026
Latest videos
Every developer has a dream (opens the original)
Agents Replace Chatbot Tax (opens the original)
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Are you tired of constantly hand-holding your AI to get a simple task done? That frustration has a name: The Chatbot Tax. It is the cognitive load and wasted time spent micromanaging, reprompting, and babysitting a standard Large Language Model. In this video, we break down why the industry is moving away from conversational chatbots and toward autonomous AI agents. Unlike a chatbot that requires constant user input, an agent takes a high-level goal, plans the execution, manages its own tools, a
The Only 3 AI Agents You Actually Need (Command, IDE, Browser) (opens the original)
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The AI space is overwhelming right now. There are thousands of new tools launching every week, but the truth is, you don't need all of them. To build a completely automated, agentic workflow, there are only three types of AI agents you actually need to master: The Command Line Agent, the IDE Agent, and the Browser Agent. In this video, we break down this essential AI stack. We look at how terminal agents manage your system, how IDE agents write and refactor your codebase, and how browser agents
How the AI Agent Loop Actually Does Your Work (opens the original)
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You no longer need to babysit your AI. By leveraging the AI agent loop, you can hand off high-level goals and let the system do the heavy lifting for you. In this video, we move from theory to practice and show exactly how the agent loop takes over your workload. We walk through real-world examples of an agent receiving a task, planning the steps, executing tools, and evaluating its own work without any human intervention. The foundation of a successful, hands-off loop is pristine information ma
Demystifying LLMs: How AI Actually Works (opens the original)
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Have you ever wondered what actually happens inside a Large Language Model when you submit a prompt? In this video, we are completely demystifying LLMs, breaking down the mechanics of tokens, transformers, attention mechanisms, and neural networks without the confusing math or heavy jargon. A huge part of making these models work for you is understanding their limitations, specifically around memory. Context engineering is the practice of deliberately designing and managing what information goes
Publishing over time
Last 90 days. Choose a month to open its work.
Recurring subjects
Named in the text we hold. One piece can cover several.
Audience
~2 subscribers
Measured Sep 19, 2026
Source's subscribers, not the number who saw an individual piece.
About this data
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