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Chapter 1: What is the main topic discussed in this episode?
Today on this AI Operators bonus episode of the AI Daily Brief, we're talking about how to learn AI with AI. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, we are back with another unplanned AI Operators bonus episode.
For those of you who are new around here, these operator bonus episodes are not anywhere near our normal format. They're not about the news. They're not about a discourse. They're not about a big idea necessarily. They are instead much more practical and specifically for people who are trying to figure out how to use AI.
I toyed with the idea of actually spinning out a separate AI Operators podcast this year and decided at least for now to drop these bonus episodes in the feed sometimes when it made sense. And so I'm always interested in hearing your feedback on whether these things are valuable, whether you want more of them, whether you think they should be on their own feed or anything else.
Chapter 2: How has the paradigm of learning AI shifted?
And what we're trying to do here is talk about how to learn AI. Specifically, we're talking about how to learn AI with AI. But the genesis for this is that I think that the way that learning is going to happen has fundamentally shifted.
Instead of a paradigm of instructor-led tutorials, explainer videos, step-by-step guides, basically that entire former paradigm of education and particularly online education, instead now everything is going to be effectively the equivalent of pair learning with an AI build partner. AI, in other words, is going to be your companion for using AI to learn.
And it turns out there's a lot to figure out about how to do that well. Now, I want to give a little bit of specific context in why this is coming up right now. First and most important is that just after OpenAI announced 5.3 Codex, President Greg Brockman talked about how the company was endeavoring to work in a fundamentally different way.
He tweeted, By March 31st, we're aiming that for any technical task, the tool of first resort for humans is interacting with an agent rather than using an editor or terminal. In other words, agent first work by March 31st.
Chapter 3: What mindset shifts are essential for learning AI with AI?
Well, you might've noticed that has kind of a ring to it. And something I've been thinking about a lot recently anyways, is how to give people better resources for self-directed learning around what I see as this shifted paradigm of AI. Already, we weren't doing such a good job of helping people learn how to use AI.
And that was before this Code AGI moment that we've experienced over the last couple of months. Now, everything is shifting once again. And while the ceiling of what you can achieve has heightened dramatically, so too has the difficulty of using the tools to get there.
Now, I had already wanted to expand what we built for the AIDB New Year's Resolution Program into a broader, free, self-directed learning platform, but this just sped up the timeline. Okay, so we've got this idea of agent-first work by March 31st, but the other catalyst for this actually came from a discussion on a post by Tribe CEO Jacqueline Rice-Nelson.
Now to be clear, Jaclyn is great, Tribe does awesome work, and the broader point of her post, which is that the UI and the products around agents need to improve dramatically for them to be widely adopted, especially in a work or enterprise setting, I absolutely agree with.
Her post was about how a bunch of her non-technical team members and herself had used Cloud Cowork to do things that were impossible for them just a few months ago.
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Chapter 4: What practical tactics can enhance collaboration with AI?
But when they actually dug in, it was quite difficult. And in fact, many of the team had actually paired with engineers for hours to get the output that they eventually got. The line that got me was this one. What cowork really shows us is what many of us already knew. Cloud Code is incredible.
We're seeing a glimpse of the future where these capabilities will be available for everyone, but the future isn't here quite yet. The part that got me to Bristol was this part, the capabilities will be available for everyone. My contention is that for anyone who is high agency enough to take the time to work through these challenges, the capabilities are available right now.
What's more, the people who take the time to take advantage of these capabilities being available right now, difficult though they may be, are going to be the people who shape the next generation of work in the economy.
And in my mind, the foundational mindset shift for being able to take advantage of those new capabilities is to stop looking for tutorials or videos or explainers and fully embrace the idea of AI itself as your learning and build partner.
Chapter 5: How can handoff documents improve AI learning sessions?
Now, I've been living fully in this reality for a couple of months now. I have dozens of live projects on Lovable, a bunch of things that I'm building in Cloud Code, seven agents that are actively interacting with me via OpenClaw that I built over the past week. And I don't know how to code. I am completely and utterly non-technical.
What I have is Cloud to help me work through things step by step, figure them out, and persevere even through challenges that might otherwise have stopped me. But I realized that how to work with an AI learning partner like that is not self-evident.
And so as I was working on these projects today, I actually asked Claude to extract some of the lessons that we had figured out as I had worked with it over the past couple of months. And the rest of this episode is about those tips. So we, the royal we, me and Claude, have broken it into two categories. The first is mindset shifts, and the second is specific tactics.
Chapter 6: What are the benefits of using screenshots when working with AI?
I'm gonna go through them kind of fast, but hopefully this provides a way to think about how to dive into using Claude or ChatGPT or Gemini or whatever your preferred LLM is as your learning and build partner. Okay, so mindset tips first. Number one, you gotta start with the vision, not the task. The watchword for AI in 2026 is, of course, context.
And when it comes to building like this, the context that the AI needs is the big idea of what you're trying to achieve. That means instead of saying, help me build a learning platform to help people launch their first agents, Instead, you start with your goals and your perception of what does or doesn't exist out there and what the challenges are.
It might feel slow, but I guarantee it's going to save time on the other end and get your AI partner way closer to what you're trying to actually achieve than just trying to describe the outcome alone. Now, in some cases, you might even not know exactly what you're trying to achieve or not fully, which brings us to tip two, which is thinking out loud even when it's messy.
One of the things that I realized earlier today is that I was actually building two things at once.
Chapter 7: How should one manage context when switching between AIs?
One was a set of self-directed skills projects that people could combine in whatever way that made sense. The second was a library of agent starter prompts that people could just download. I think the exact line to Claude was, okay, not to be insane, but am I building two things at once? Your AI partner has the capability to handle that sort of messiness.
It doesn't need perfectly formed thoughts to be useful. In fact, much of its utility is in helping you think through half-formed thoughts. Number three, and this one could be hard to get used to for some, you got to push back hard and often. AI doesn't have feelings in the way that your employees or colleagues do.
To the extent that it wants anything, it wants to help you achieve whatever it is that you're setting out to do. And one of the things that anyone who's used AI knows is that it says everything pretty confidently, which means you have to push back.
Chapter 8: What final tips can help optimize learning with AI?
Now, an inverse of this, which is a little bit better with current models, but which is still a little bit of a challenge, is you also want AI to push back on you. And sometimes that involves explicitly saying, I'm not sure about this. I want you to critique it from first principles or something like that.
The point is that the conversation can't be the AI just accepting your ideas as good or you accepting the AI's ideas as good. You got to push back on each other to make progress. Number four, and honestly, this probably could have gone with think out loud, is sort of a subset of messy thinking out loud, which is dump first, organize later.
Once again, you don't need to have everything perfectly structured. And in fact, a lot of what AI is good at is taking your messy, disorganized, and unstructured thoughts and structuring it in ways that can help you make progress. Number five, AI partner as mirror. Sometimes you need the AI to generate a net new idea.
A lot of times you need to speak an idea to it and have it play it back for you to make sure it makes sense. And the lesson here is that you know more than you think you do. You don't have to rely on the AI for all the new ideas. A lot of its job is to help you work through your own.
An example from our building earlier today, we were trying to talk through what the categories would be for the agents on the agent bench portal where you can download this starter template to start working through building your own agents.
And after the AI gave me a set of categories that it thought made sense, I fed it back the seven that I had built with OpenClaw this week to see how they would fit together. It ended up revealing a couple of gaps in the framework that I didn't consciously catch, but which something else that I had built actually revealed. Number six, I would summarize as get existential.
Every once in a while, especially the deeper into the weeds you go, it's really valuable to zoom all the way out and reground yourself in what you're actually trying to build. I can't even tell you how many different versions of AIDB training have existed in my head. And even as I've been designing this project, it's shifted.
It is very, very easy to get lost in the sauce and knee deep in the weeds. And to the extent that you can pull yourself out every once in a while, it's going to help you and your AI build partner reground yourself in what you're actually trying to accomplish. Number seven, this is another one that I think is sneakily difficult for people because it's not how we're used to thinking about things.
I think a lot of the first way that people used AI was they drafted stuff and then they had the AI comment. I think increasingly we're shifting in the other direction where the flow that makes the most sense is to let the AI draft and then to react. Take advantage of that near infinite output capacity to go wide first.
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