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Turbo Mode: Optimizing Productivity with AI Tools and Agents

Multiply your output with AI agents and automation

13 lessons2 hr 22 minFree Oct 10 to 11
Agents and context
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What you'll gain

Push your AI-assisted development to the next level. Learn advanced techniques for orchestrating AI agents, automating repetitive tasks, and building custom toolchains that multiply your output.

Syllabus

13 lessons · 2 hr 22 min

Where to Start?

1 lesson
1
Understanding AI Productivity
Can you actually be more efficient when using AI in your software development? Well, that's exactly what we're going to be talking about in this course. And spoiler alert, the answer is a resounding yes. But stick around because used poorly, AI can actually slow you down. Of course, the purpose of this course is to do the complete opposite.
2 minFree

Getting into the Details

11 lessons
2
Keeping up with the AI landscape
If you've been in tech for any length of time, you know things move fast. But in AI, it's like somebody hit the turbo button. Models drop weekly, tools pop up daily, and techniques that feel revolutionary one month are abandoned the next. Ever feel like you're trying to drink from a fire hose? Yeah, me too. But don't worry.
15 min
3
Choosing the Right AI Code Editor
To max out your productivity, you need two main tools in your arsenal. First is an AI-enhanced code editor that handles everything from smart completions to quick edits right in your workflow. That's what we're diving into now. The AI code editor falls into level two of the AI assistance spectrum we discussed in the first course.
5 min
4
Choosing the Right AI Agent: AI Meets CLI
You've seen how an AI-powered editor nails the little stuff, but now we're talking level three and four autonomous agents. Agents that can take on missions, the bigger jobs where you'd normally brief a teammate, grab a coffee, come back to a pull request. Well, that teammate is now an AI agent. In-editor agents are so convenient.
11 min
5
Rule files
Now let's teach that agent to remember your coding preferences and rules so you don't have to repeat yourself every morning. Remember from course one, large language models are completely stateless. Rule files act like memory. At their simplest, they are essentially just a readme, just for AI.
21 min
6
Using MCP to Extend Your Agents
Last lesson, we taught our agent to remember, but now we hand it an extension marketplace. MCP turns a plain vanilla agent into a Swiss army knife by letting it load tools on demand. First, what is MCP? Think of it like adding extensions to your code editor, except you're adding them to your agent's abilities.
8 min
7
Building a Custom MCP Server Intro (Customer Support Example)
Okay, so far we've been working with pre-built MCP servers, connecting them into our agent workflows. But the real power of MCP isn't just what's available out of the box. It's what you can build on your own. In this lesson, we're going to build a custom MCP server for a real business use case, customer support.
7 min
8
Building a Custom MCP Server (Walkthrough)
So let's get to building that customer support MCP server. Now, of course, MCP is really just glue between some existing system and these agents and chat interfaces. So to get started with, I've already created a demo CRM for us. Let's take a look.
23 min
9
Sub-Agent
So giving agents new tools through MCP is one way to boost efficiency. But what happens when that one agent starts running out of its context? Or when you want specialization that takes a lot of prompting to get right, like a dedicated reviewer, a documentation writer, a technical specialist. This is where sub-agents come in.
11 min
10
Parallel Agents
By now you've seen how a single agent can crank through focus tasks and even how sub-agents can help manage the context window of our single agent. But the reality is coding like this leads to a bunch of sitting around. You're essentially babysitting the agent while it does its work.
13 min
11
AI Development Best Practices
We've learned a lot about tools and agents already, but are there ways we can optimize our interaction with AI to ensure we're maximally productive? And maybe even more important, that we stay productive as our project grows and develops over time. In this lesson, we'll cover nine best practices for seamless collaboration with your AI agents.
16 min
12
Agent Workflow
Look, I love a good IDE, but I also love shipping fast. And this lesson is a practical, distilled example of my own workflow for coding with AI. It's a simple, repeatable flow that works with one or many parallel agents. And having a methodology to tame that unpredictable wildness of AI is actually really important.
8 min

To Finish Off

1 lesson
13
Conclusion
All right. Congratulations. You made it through course two. We covered a lot of ground together from quick inline edits all the way to powerful MCP integrations, writing crystal clear prompts, managing fleets of agents in parallel. You've seen how AI isn't just a one-off feature. It's a skill, actually. It's a muscle that you build over time.
2 min

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Access

Yes, Oct 10 to 11. It's one of the 6 courses free to watch with a free account that weekend (opens Fri 22:00 CEST).

Every lesson of every course, the full notes, transcripts and prompts, and the Unlearn-built tools. Blog posts and the tools that are public on GitHub stay free for everyone.

Yes. Finish all 13 lessons to earn the Turbo Mode: Optimizing Productivity with AI Tools and Agents certificate.

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