A (3:29)

Oh, very good. Yeah. I may have, may have been at that same workshop as, as you were at. I was in San Francisco about that same time. So who knows, maybe we came close to crossing paths or something like that.

B (3:44)

Maybe we did, but I've been a GTD practitioner since that time. That's. That's my system and my way of thinking. So as we've evolved tools over the years and we've gone from paper to electronic to groupware to now what we're using today, which is AI based, it's still always with the GTD thought process and structural thinking in mind.

A (4:12)

Very good. Then you brought up the magic two letters, A. And I. I know you have a lot to say about this, so I, I kind of want to just turn you loose. I do still have a few questions that, that, that we may get. If we don't get to them kind of organically in the way this unfolds, then I may make a point of asking them. But I also want to give you free reign to do whatever it is, go wherever it is that you want to with this around AI, gtd, maybe a starting point is what are you seeing in your clients and their use of AI?

B (4:51)

Well, it's kind of fun. It's really what's caused us to develop this more and more as part of our practice, because AI is becoming integrated in everything we do. I think it's important to note that there are several kinds of AI, so they do different things for you. There's generative AI, which lots of people are aware of. You ask ChatGPT to write something for you, where you go to perplexity to get it to write something for you or generate a table or generate a report or whatever. There is also what we call boundaried AI. Boundaried AI means that it's operating as it normally would, but it has boundaries around it. So we have a client, their legal department, their corporate legal department across the US is the client. So their system is boundaried and it means that they can search outside the system, but no one can search inside their system. So their system doesn't release any information, but they can go outside to search or grab information if they care to. But it allows them to assemble and repurpose their information so easily and so well and so fast. One of those clients this morning was telling me that function that used to take her four hours to piece together information from presentations, from documents, from court documents, client documents, whatever, now takes her about 10 minutes. So it's a really useful tool in terms of productivity for her.

A (6:36)

Yeah. It almost sounds like she would be in spending 10 minutes checking the results as opposed to spending four hours compiling all of that.

B (6:47)

Exactly, exactly. Well, you know, they're using it the way we. We really want our clients to use it as a collaborative teammate.

A (6:56)

Yeah.

B (6:57)

Not as a tool, not as something to be afraid of, but as a collaborative teammate. Like it's one more person on the team. They make a specific contribution. So.

A (7:08)

And that was boundary to AI after generative. And that's where we left off. And please pick up there.

B (7:16)

Okay. We have a genic AI, which the keyword there is transformational. So it's an agent designed to take something and make it into something else. So it will take an email from a customer that they sent as a response or a message they sent as a response to maybe advertising you put out, and it will research the customer and then it will figure out the best reply to that email and it will, you know, move it through the sales chain. So it's going to transform it from this particular incoming piece all the way down to a sale and manage it all the way through, or take a customer call that needs service and work it through the process until they are satisfied. And so AGENC AI is very transformative. It's designed to move it from here to there. And the agent does the repetitive work that some humans used to do. And then you have what we call embedded AI, meaning that it's kind of. It's subsurface, you're operating with the top of the ice cap, but underneath that is a whole big iceberg of stuff going on that is taking care of things. For the organizational system that we use right now, TANA is based on its own embedded. They built their own AI model, their own language, large language model, and a lot of the functionality is embedded in there. So as it executes, I don't have to think about it, I don't have to see it, but it's operating there sort of silently, behind the scenes.

A (9:03)

Well, that's a whole different thing than having to deal with one of the other large language models where you have to prompt it in a whole different way, because it doesn't have that to start with.

B (9:13)

Yeah. So I think when you look for, here's what I want to do. You have to think through very carefully what you want to do and. And then choose what partner you're going to pick. Is it a chat GPT thing? Is it a perplexity thing? Is it a time of thing? Is it a. And I'll fill in the blank thing?

A (9:33)

Okay.

B (9:37)

Which teammate am I going to choose?

A (9:39)

Yeah. And are your clients using all four of those or mostly one more than the other?

B (9:48)

No, I think the span that we see in terms of differences in our client is more. Our clients is more around adoption and culture. So you see everything from. We know we need to use this as a tool, but we really don't know how and we're not sure that we trust it all the way to. It's really integrated as a partner in our business.

A (10:13)

Ah, okay.

B (10:14)

So as an example, the other day I was working with one of our clients and she was looking for more ways to integrate AI into her routine. And I said, okay, I said, here's what I want you to answer for me. What tasks on your list make you say, oh, if I have to do one more of those, I'm gonna scream? And she said, well, great question, because it's not that I would scream. She said, but I review a lot of presentations. She heads up a sales and marketing team. She said, I review a lot of presentations. And she. She said. I said, so if you had a clone with your brain of brilliance attached to it, who could do those for you? You would give it to the brain, right? And she said, absolutely. And I said, well, I think that's a great thing for AI as long as you can teach it the criteria that you use. Because it doesn't work. Just like as a manager, it doesn't work to say, I'll know it when I see it and send people out with a half bake request are constantly guessing, did I get it right? Did I get it right that I get it right?

A (11:26)

Right. Until they come back and get a bunch of responses, they're really not learning anything. It's all just trial and error and.

B (11:34)

Pretty much frustration along with it. So I said, what we really have to do is go back and figure out that criteria. Because even though you know it when you see it, you weren't born knowing that. You weren't born knowing how to evaluate those presentations. So you form that judgment some way, and we can teach that to your AI partner as long as we can kind of capture what it is. And so that's really the thinking. I hear people say oh, it's going to make humans stop thinking? Oh, no, no, not at all. It's that much more crucial that you think through very carefully what you want or how it is you do something to be able to get the model to, to take that on. And then it frees her up to spend more one on one time with her folks, to spend more time strategically planning for their future. All the things that she wants to do at her level of work or above the model. Her AI partner can really start to pick up some of the stuff that really is below her thinking level. And she's capable of so much more if she could just get the time. So we look for those kinds of things.

A (12:57)

That's so helpful because what you're describing sounds like years ago I said to David something about how I was thinking about something, and he shook his head and said, oh, well, the trouble is most people aren't even aware that they're thinking. And so they're not even getting to the point of thinking about how they're thinking that much. And it sounds like what you're encouraging clients to do is think about how they think and then use AI to train, train AI to be able to think the way they think. But to get there, they have to know, oh, I am thinking in a certain way when I review presentations.

B (13:39)

Right. And actually, once we can give it that judgment criteria, it can think faster, it can think better, sometimes it can think more creatively, a little more out of the box. So I don't want to make, I don't want to train chat GP to think ex GP to think exactly like me.

A (14:01)

Yes.

B (14:01)

I'd like to give it that baseline and then say, you know, challenge and ask it questions about, you know, is there other criteria I've missed? Is there other criteria that you think should be in there and let, let that thinking partner, that AI thinking partner generate, you know, other things, if that's a better way to think about it. Yeah, but I, we work really on the cultural aspect of AI, on helping people adopt it, on designing the training and the sort of, the strategies of integrating it into an organization. We don't do the actual implementations, we don't do the programming, we don't do any of that. But it's all about helping people work better with this new teammate that they have.

A (14:56)

Yeah, yeah. Well, it seems as though you're teaching them more at a meta level about this instead of trying to say, all right, here's how you spell this word when you're writing this prompt for this LLM that's.

B (15:11)

Yeah, yeah, we, we actually, in terms of writing prompts and writing good prompts, we actually suggest that people take the Google Essentials course. It's pretty much free online and it's really good in terms of teaching you how to think through that and what to feed different models. And I think they've done a really nice job. So it's a great baseline course to take. The thing to remember is that our new work interface is the prompt window. That's how we're interfacing today. And years ago you used to collect information, you put it in a file folder, you would file it certain way or you'd put it in a digital folder and you name it a certain thing so you could find it later. Files and folders, that was king, you know, and we saw clients build these massively, you know, intricate systems of files and folders. Right. And we have them as well as productivity concerns. And I remember very clearly, I we decide we had a Windows server that crashed twice in the same year and if you're a productivity consultant and you can't see your own calendar, that's sort of, oops, not good.

A (16:34)

Nope.

B (16:35)

So I decided we would move everything to the cloud. And Genentech is a client of ours and was at that time as well and they had just standardized on Google and I thought, well, if they did it, I could do it. So we moved everything to the cloud and then I realized we didn't have to have Windows computers anymore. We could have whatever we wanted. And I'd always wanted to try a Mac. So I bought a Mac and I bought some one on one training and I said to my folks, we were 40 people at that. 44 people at that time. Today we're 84, but they're about half as big as we are now. And everybody, I'm going to try this and then if it works for, for our clients and our business, let me know if you want to switch. Not realizing there were 44 people wanted to switch, they were all like, oh, I can't wait.

A (17:29)

So they were already waiting for you to say, let's do this. And they were, okay, yeah, no persuasion required.

B (17:37)

We really weren't able to do 44 all at the same time. We had to kind of roll it in. But we did. Anyway, I went and I took my class with my wonderful trainer from Apple and I said, okay, the first thing I need to know is how to build files and folders and build this structure on a Mac. And he looks at me and he goes, why do you think that's your job? And I said, I don't understand. And he said, I said, I.

A (18:04)

Why are you asking me with that? Of course it's my job.

B (18:08)

I have to know where I put stuff. He goes, no. He said, you're supposed to apply your brilliance to creating the document. He said, then the computer is supposed to take it from you, park it someplace safe, and bring it back when you need it. And I said, doesn't that sound fun? And I don't trust that as far as I could throw a grand piano. He said, okay, I'm going to make you a deal. He said, I'll teach you how to build files and folders, and you can file things that way. He said, but I need you to promise me your end of this bargain is for the next two months, you're only going to retrieve things using Search. And I said, okay, I'll make that deal.

A (18:47)

Wow. So he let you have the files and folders because that would make you comfortable, but he seemed pretty confident that you wouldn't even bother using them eventually.

B (18:56)

Oh, it took, like, a couple weeks of seeing how wonderful search was, and it brought back to me stuff that even I didn't realize I had written. It saved me so much time and rework of stuff that had already been parked there. The previous files we moved over. And before I knew it, I was just using Search for everything. And I thought, this is absolutely brilliant. So we started teaching that as a tool and technique to help people adopt the fact that search beat files and folders every day of the week. And twice, you know, we went from that big structure in our email to one folder that's referenced, one folder that was actionable. That was pretty much it. And it was referenced for that particular year. And at the end of the year, we'd just open a new reference folder. So it had some kind of boundary around it, but really didn't need it at that time. The search function.

A (19:54)

You keep going. I'll remember my comment.

B (19:57)

The search functions on Mac were better than on Windows today. They're exactly the same. They're using the same technology. Sorry.

A (20:05)

Yeah. This takes me back to what you were saying a few minutes ago about prompts for AI. And I'm thinking, well, if, if, if search is how you find things instead of building a file structure, then search prompts are going to be very important, too. And the example you used. I. It occurred to me that it's possible that sometimes when I think I have to retrieve it using a search prompt, I may be limiting the Search too much because of how I write the prompt. And search might give me even better results if I prompted it better without trying to tell it where to look in a way.

B (20:50)

Well, I think if you're using AI as a search tool, it's kind of like using a Stradivarius to teach your 5 year old how to play violin. You know, it's kind of using a massively brilliant, wonderful tool for something that's way too small. Context is what beats search today.

A (21:13)

Yeah.

B (21:14)

So giving it really good context. Here's the situation, here's what I'm looking for, here's the outcome that I want to generate. But also here's, you know, we have a whole, we use text expander, which is an, you know, an auto typer. You put in a little abbreviation, it gives you the whole thing you put in. And so we have a text expander abbreviation for us as consultants, each one of us has one. So it will say, I'm, this is who I am. This is my expertise, this is my client base. This is the kind of work I do. This is to give it the whole background because context is the thing that's going to beat out search. If I, it's, you know, I wish I could give credit to the right person for this. And I, I don't know their name because I met them at a cocktail party at a conference and you know how your ID tag gets slipped around and you know, I don't, I don't know who they were. It's not my brilliance, it's their brilliance. But he was saying to me, it's the difference between lazy and leveraged AI. Oh, lazy AI, you're an eighth grader and you have an assignment to write a report about Abraham Lincoln. So you go to AI and you say, write my report. Write me a report on Abraham Lincoln. End of story. Lazy AI, right?

A (22:40)

And the teacher reads it and says, sorry, you're flunked. You didn't write this because you didn't write this. There was no context. I'm an 8th grader writing report, or I'm an 8th grader in the bottom half of my class writing a report. And AI could write a respectable report based on that context.

B (23:03)

So if you were using leveraged AI, you would say, I'm an 8th grader, I have an assignment to write a report on Abe Lincoln. What kinds of things should I be asking for in this report or asking about in this report? And AI might come back and say, well, you might want to. How he became president. You might want to know about moral conflicts he had during the Civil War. You might want to know the timeline. You might want to. So you're having a conversation, asking it questions. Tell me more about the moral dilemmas and it would tell you more about that. Or could you lay out the timeline for me and tell me about that? Or so you. You really have that conversation. That iteration with AI, it's where the value is. And it's not just, you know, write me a report, write me a job description, write me a. Analyze this thing. But it's the context and the back and forth conversation that you have with it that really gets you the value of these tools and really helps you pull it out.

A (24:16)

And that's actually a great tip for having a conversation with anyone. Just to say, let's start out and say, what questions should I ask you about you? And most people will say, well, here's what's interesting about me. Ask me about this. Ask me about this.

B (24:34)

Yeah, there's. There's a gentleman named Jeff Woods. Woods, I believe is his last name. Boy, I should have looked that up before I started with you. I think it is Woods. And Jeff is spelled G E, O F F. And he wrote a book, the AI Driven Leader. And in it he talks about his. His prompt structure is one that I just really like. I think it's amazing. It's context. So C is the context. C, R, I, T is the. Is the set of initials. So context. And then the R is the role that you want AI to take in this. The I is interview stands for interview. And in essence, what you're saying is, ask me three to five questions, or however many questions you want. We usually use three to five. Yeah, that will help you, one by one, individually. That will help you understand the context and the role better. So you're literally inviting the large language model to ask you questions so that it can get more of what it needs in context and role. And then the T is the actual task that you want.

A (26:00)

I like that a lot.

B (26:01)

Or whatever. It's a wonderful structure. He's absolutely brilliant in. In understanding how to help people have that conversation and really use it as partner, as opposed to just a tool.

A (26:16)

That makes all the difference in the world. Reminds me of, say, an actor going up. And the director doesn't just say, all right, read your lines. The director says, all right, here's the thing. Here's your context. You know, the role you're playing. Here's. Here's your name in the role. Here are your lines. Here's the Context, you've just come in from awful weather outside and somebody's just giving you bad news and then this happens. And it's a whole different thing than just saying, read these lines exactly right, exactly right.

B (26:47)

And if you give it the right information to start with, if you approach it in the right way as a partner, it can move you from first level thinking, second level thinking, sometimes third level thinking, so quickly, so much more quickly than you could have made it on your own. And I think that sometimes the thing that frightens people because they say, well, I'm gonna be out of a job. Well, if you only do first level thinking and you're not capable of second level thinking, then that is a possibility. You haven't been able to grow in your work and be able to move up. But there are a lot of folks who I think, and you know, I'm thinking of the population, like first level managers. And sometimes second level managers get so caught in the focus of today and solving problems that happened yesterday that they really don't have the time to be doing what they're supposed to be doing, which is coaching folks strategic planning for their future, trying to figure out what their folks are going to need in six months so they can start implementing those things. Now they go, I haven't time got time for that. I got 12, you know, crises to solve.

A (28:03)

Yeah.

B (28:04)

And I spend my whole day fighting fires. So I think for folks who get caught in that AI is going to either fight or eliminate a lot of those and they have to be ready to jump to that second and third level thinking. Yeah, yeah. Or they may, they may get lost in the shuffle. And you hate to see that. You want it to be a tool that really augments and helps, helps people build skills and, and perform at higher levels rather than eliminating their opportunity to do what they do.

A (28:37)

Right, right. Well, you're very clear there about, here's how you would keep it from eliminating by, by doing higher level thinking and ask it to help you with that.

B (28:49)

Yeah, yeah. Sometimes we'll ask clients to, to, you know, within a job, lay out their ideal day. I don't mean go sit on the beach and I mean, what would be the ideal work day? You know, you would, you would come in, you would get a chance to meet with your people. You might have a chance to interface really face to face with some clients. You might, you know, be able to research this. I might have time to plan this. I might, you know, so lay out your ideal day. And then let's, let's look at all the stuff that bumps that off course and see how we can get you some help from this partner.

A (29:29)

And that reminds me of the, the GTD model about the threefold nature of work. Most of the people, when they lay out their ideal day, it's going to have much more to do with planning their work and doing pre planned work instead of responding to latest and loudest unplanned work. But in reality, I bet a lot of them are saying I spend most of my time in reactive mode instead of proactive.

B (29:57)

Exactly, exactly. So I think there's so much leverage to be had there if we can help people set up on the right track. Yeah.

A (30:08)

Well on that then how do you see GTD applying to AI that way? GTD thought process applied to different tools?

B (30:22)

I think it's absolutely first of all the thought process being very, very clear about the outcome you're looking for. Because if you can't be clear about that, then you'll just get something that's kind of good. It's good enough.

A (30:41)

It's better than keeping it in your head. But it doesn't necessarily relieve all of your stress.

B (30:47)

No, not at all. It's interesting to me that some people look at it as, it's just, you know, instead of a blank piece of paper, I got something. So that's good enough. Instead of having a very clear idea about what a really outstanding outcome would look like, you know, what would this be if it were just outrageously successful and moving through that thought process, whether it's a natural planning model for a project or whether it's the outcome outcome of a phone call you need to make this morning or a meeting that you're setting up and you want to have. I'm always stunned at people who pick up the phone, go to type an email, go into a meeting and no clue about the outcome they wanted to generate. It's just, you know, what's going to happen is going to happen. No, you go in by design. And so I think outcome thinking is one of the most valuable things that there is. And the natural planning model is a wonderful way to think 360 degrees around something. So it's very useful as a way to think through even how you might approach something through AI. I ran an experiment knowing that I was going to get the chance to do this interview with you. I went to one of our AI tools and I said, you are David Allen and you are using the natural planning model. And then I laid out information about a project. I said, would you write, go through all that thinking lay it out for me and write the plan. It did an excellent job. So it pretended to be Dave and Allen. And it's interesting. There's a guy named Jensen Huang who is the CEO of Intuit, I think. Is that right? No, that's not right. He's the CEO of Nvidia. Nvidia, yes.

A (32:52)

He rings a bell now.

B (32:52)

Yeah, yeah. And he gave a speech where he talked about AI as being the great equalizer, because he said years ago, if you wanted a computer to do something for you, you had to know programming language. He said, now you can just ask it in your natural language. And there you go, we'll do it. He said, but you have to be nice. He said, so start with flattery. He said, you're the world's expert on whatever you are, David Allen, using the natural planning model, you are whatever. He said, give it some compliments, use some flattery, and then ask it for what you want. And then when it gives you back stuff, you still have to be nice. So tell it. It did great. Could I have a little more of this? Could I have a little less that? Could you focus it more like this? If I don't understand what that criteria is, if I don't understand what I want or less of, if I don't understand, what's the next iteration? I haven't done the thinking. So just like when computers came on the scene, it was garbage in, garbage out. It's going to be the same here. It's garbage in, minimal output out. You'll get something, but it isn't what you really could, you know, it isn't a super duper result with the highest level of return for your investment, if you will.

A (34:21)

That's what I've been finding with some people when they start trying to get AI to do gtd, thinking for them is they, they don't really understand it to begin with, or they may not be asking for a clear outcome, or they don't know the methodology well enough to ask a question that gets them a good result. So they may get a lot of results back, but the quality, not there.

B (34:47)

No. And you know, when we first start with clients, we suggest to them that they think of AI as a really, really scary, smart intern who's been with you for two weeks. They don't know your business, they don't have any context around what you're asking them to do, but they're really smart and really want to please you. More and more we're seeing it die down. But when AI first came on the scene, there was the problem with what they called hallucinations. Right, Right.

A (35:20)

I want to please you so much that I'm going to give you something that isn't even real.

B (35:24)

Yeah, I made it up. There's an attorney somewhere back east, I don't remember where, who had chatgpt write his brief, which he submitted to the court. And the court went back to him and said, you know, these cases that you've cited don't exist. And he goes, oh, that's my bad. I had AI write it for me. And they said, yeah, that is your bad, and you won't have a law license anymore. And he evidently lost his law license, so it's reducing. But I think that's because in part they recognized that was a problem, but also in part because people are learning how to use it. Yeah, much in a much better way than they did before.

A (36:10)

Well, I remember when it first, when it appeared, chat, GPT and a few other of the models appeared early on, the big concern for, say, academics was, oh, my students are going to be writing fake papers, and I won't be able to tell the difference. But what you're describing is, in a way, even, even worse. My students are writing fake papers and citing fake sources instead of writing fake papers, citing real sources.

B (36:39)

Well, you're. You risk triggering my bugaboo about education, which is we should be teaching people how to think.

A (36:47)

Oh, really? That's. That's radical.

B (36:50)

I really. I tell you, when I went to school, which was like 150 years ago, so keep that in mind. But when I went to school as a kid, the school that I went to was very unusual because all the other kids around me were learning things by rote. They were learning multiplication tables by rote. They were memorizing all this stuff. Where I went to school, they taught you how to think and search for stuff, and curiosity was kind of the key. And it turned out to be. I didn't love the school the whole time I was there. I was there from Pre kindergarten to 12th grade. So I sort of was there the whole time time with a lot of kids that were way, way, way smarter than I was. I have no idea how I got in, and I have no idea how I lasted through, but I did, and it turned out to be a great education because they taught you how to think and so you could literally conquer anything. You knew how to analyze, you knew how to think, you knew how to approach a problem from all different angles. But we never did the memorization kind of stuff. And I Think it was a wonderful thing. And today when I look at folks who are saying, oh, my students are going to use AI. Yeah, let's teach them to use it. Well, exactly. And teach them the thought process that they need. Because if you teach them how to think, the results they're going to generate are great, but it's going to be a tool. Yeah, you know, it's. It's like when the calculator came out and people say, oh, we can't use that. That's cheating, for crying out loud. I don't know anybody who, you know, hand writes an Excel spreadsheet today. It doesn't happen. Let's give them the tools they need to use the tools they're going to have available to them in the future.

A (38:42)

Exactly. Yeah. I think that was probably the first computer program I learned was a spreadsheet program and I could plug in formulas. And I went like, wow, this is. This is world changing to be able to just put a formula in there and not have to redo all the calculations.

B (39:01)

I know because I remember in my very first job, it was a sales job, we did sales forecasting. He brought the whole team together for day. It's the only time I have ever crushed a Coca Cola can on my forehead out of frustration. But we came in and everybody had a different market, right? So he had to make this all add up to his number. So he gave everybody a percentage of growth, and we were doing spreadsheets by hand. Excel hadn't been invented yet. I'm that old. And so he gave me 6%. I'll never forget. So I do the whole thing. Takes like an hour and a half. I blow the whole thing out. I take it back, and here's what we came out with, 6%. And he goes, oh, that's not gonna fit. Do 9%. I said, okay. So I go back and I do 9% that didn't work. Do 12%. Okay. Then I go back, do 8%. It was like back in and out and in and out. And finally at the end of the day, it was like 4:30 in the afternoon. And I go in with the last iteration, whatever number that was, and he goes, that won't work. 6%. 6%'s what worked will work. Go. Do 6%. And I said, okay. I walked out, there's an empty soda can there. I smashed it on my forehead. I went for a walk for an hour and then went back in and gave him the first one that I'd done. And he goes, oh, that's perfect.

C (40:45)

I'D like to give a short message to those of you who've been participating and playing with GTD Connect for a while and sort of remind you that all of us with this GTD methodology and this set of practices go through cycles. You know, I still go through cycles myself initially. There's. There's kind of the inspiration and there's a lot of material to ingest and to get familiar with. And so people oftentimes, when they first come onto Connect, are just potentially overwhelmed by how much information there is. In a way, it's just a huge library where we've been able to archive so much different information from so many different perspectives and people and points of view, and so understood that it's like walking into a library going, gee, where do I start? So that's oftentimes the initial phase of this. And many people, after a year or two, you know, probably get on some level or some plateau where they go, well, I kind of got it now. I've got my system set up and everything's fine and I'm fine tuning. And you may find yourself at that point also finding yourself saying, gee, I'm now becoming a resource of this methodology for people around me, you know, people asking me for assistance and help in this. And we've seen in the forums a number of people now sharing ideas about how to got your teams more involved or families more involved with this information. So some of that information is in there as well. But I think you'll find yourself going through cycles of this and you may find that much like if you've ever read a software manual. I remember when I read, when I learned Microsoft Word to begin with, for instance, I read the manual, wow, this is really cool. And I started to use the tool and didn't need the manual anymore. As a matter of fact, a good example of that right here, the manual for this camera that that's taking this picture right now. Initially I read this, got it all set up. That's really cool. And that's really fine. And so pretty much everything was onto cruise control. I didn't need to go back to my library to make this really work. And then of course, as I started to get more sophisticated in terms of the stuff I wanted to do, got more inspired about some things I saw other people are doing. I go, how do I do that? Went back to the manual. I went, oh, God, I didn't realize I could do that. I didn't realize I could do that. I remember at least two or three iterations of going back to Microsoft Word back in the days when there actually was a manual for that, as opposed to just all online and realizing, oh, my God, I didn't realize that, oh, I could do that now. I could do that now. And I think that's what you might find with Connect, too, is that it's a gold mine of stuff. Well, many people have read Getting things Done more than three or four times, and every time they read it, they get something new out of it. So I think you may find Connect the same way, and probably easier. Even easier. Because, hey, it doesn't take much to just click on, surf around, see what might be new or what might be of interest to you, and pay attention. You know, there's more than meets the eye in there.